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Listing 1124 available manuscripts in the database


Application Centric Virtual Machine Placements to Minimize Bandwidth Utilization in Datacenters

by Muhammad Abdullah, Saad Khana, Mamdouh Alenezi, Khaled Almustafa, Waheed Iqbal
Abstract

An efficient placement of virtual machines (VMs) in a cloud data center is important to maximize the utilization of infrastructure. Most of the existing work maximizes the number of VMs to place on a minimum number of physical machines (PMs) to reduce energy consumption. Recently, big data applications become popular which are mostly hosted on cloud data centers. Big data applications are deployed on multiple VMs and considered data and communication-intensive applications. These applications can consume most of the datacenter bandwidth if VMs do not place close to each other. In this paper, we investigate the use of different VM placement methods to decrease the usage of bandwidth in different sizes of data centers. We implemented and evaluated five different VM placement algorithms. Our comprehensive set of experiments show a significant decrease in bandwidth ranging from 65% to 78% can be achieved using the extended implementations of the knapsack and first fit VM placement methods.

Online Article

BDI Agent and QPSO-based Parameter Optimization for a Marine Generator Excitation Controller

by Wei Zhang, Weifeng Shi, Bing Sun
Abstract

An intelligent optimization algorithm for a marine generator excitation controller is proposed to improve dynamic performance of shipboard power systems. This algorithm combines a belief-desire-intention agent with a quantum-behaved particle swarm optimization (QPSO) algorithm to optimize a marine generator excitation controller. The shipboard zonal power system is simulated under disturbance due to load change or severe fault. The results show that the proposed optimization algorithm can improve marine generator stability compared with conventional excitation controllers under various operating conditions. Moreover, the proposed intelligent algorithm is highly robust because its performance is insensitive to the accuracy of system parameters.

Online Article

Hybrid Architecture for Autonomous Load Balancing in Distributed Systems based on Smooth Fuzzy Function

by Moazam Ali, Susmit Bagchi
Abstract

Due to the rapid advancements and developments in wide area networks and powerful computational resources, the load balancing mechanisms in distributed systems have gained pervasive applications covering wired as well as mobile distributed systems. In large-scale distributed systems, sharing of distributed resources is required for enhancing overall resource utilization. This paper presents a comprehensive study and detailed comparative analysis of different load balancing algorithms employing fuzzy logic and mobile agents. We have proposed a hybrid architecture for integrated load balancing and monitoring in distributed computing systems employing fuzzy logic and autonomous mobile agents. Furthermore, we have proposed a smooth and composite fuzzy membership function in order to model fine grained load information in a system. The simulation study and a detailed qualitative, as well as quantitative analysis of algorithmic performances, are presented. Lastly, a deployment environment is described.

Online Article

Improved Teaching Learning Based Optimization and Its Application in Parameter Estimation of Solar Cell Models

by Qinqin Fan, Yilian Zhang, Zhihuan Wang
Abstract

Weak global exploration capability is one of the primary drawbacks in teaching learning based optimization (TLBO). To enhance the search capability of TLBO, an improved TLBO (ITLBO) is introduced in this study. In ITLBO, a uniform random number is replaced by a normal random number, and a weighted average position of the current population is chosen as the other teacher. The performance of ITLBO is compared with that of five meta-heuristic algorithms on a well-known test suite. Results demonstrate that the average performance of ITLBO is superior to that of the compared algorithms. Finally, ITLBO is employed to estimate parameters of two solar cell models. Experiments verify that ITLBO can provide competitive results.

Online Article

Adaptive Image Enhancement using Hybrid Particle Swarm Optimization and Watershed Segmentation

by N. Mohanapriya, B. Kalaavathi
Abstract

Medical images are obtained straight from the medical acquisition devices so that, the image quality becomes poor and may contain noises. Low contrast and poor quality are the major issues in the production of medical images. Medical imaging enhancement technology gives way to solve these issues; it helps the doctors to see the interior portions of the body for early diagnosis, also it improves the features the visual aspects of an image for a right diagnosis. This paper proposes a new blend of Particle Swarm Optimization (PSO) and Accelerated Particle Swarm Optimization (APSO) called Hybrid Partial Swarm Optimization (HPSO) to enhance medical images and also gives optimal results. The work starts with (i) watershed segmentation followed by (ii) HPSO enhancement algorithm. The watershed segmentation is a morphological gradient-based transformation technique. The gradient map of an image has different gradient values corresponds to different heights. It extracts the continuous boundaries of each region to give solid results and intuitively provides better performance on noisy images. After segmentation, the HPSO algorithm is applied to improve the quality of Computed Tomography (CT) images by calculating the local and global information. The transformation function uses the calculated information to optimize the medical image. The algorithm is tested on a real-time data set of CT images, which were collected from MIT-BIH dataset and the performance is analyzed and compared with existing Region Merging (RM), Fuzzy C Means (FCM), Histogram Thresholding, Discrete Wavelet Transformation (DWT), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Histogram Equalization (HE), Contrast Stretching and Adaptive Filtering based on PSNR, SSIM, CII, MSE, RMSE, BER and Execution time parameters. The experimental result shows that the proposed medical image enhancement algorithm achieves 96.7% accuracy and defeat the over segmentation problem of existing systems.

Online Article

Intrusion Detection and Anticipation System (IDAS) for IEEE 802.15.4 Devices

by Usman Tariq
Abstract

Wireless Sensor Networks (WSNs) empower the reflection of the environment with an extraordinary resolve. These systems are a combination of several minuscule squat-cost, and stumpy-power on-chip transceiver sensing motes. Characteristically, a sensing device comprises of four key gears: an identifying element for data attainment, a microcontroller for native data dispensation, a message component to permit the broadcast/response of data to/from additional associated hardware, and lastly, a trivial energy source. Near field frequency series and inadequate bandwidth of transceiver device drags to multi-stage data transactions at minimum achievable requirements. State of art, and prevailing operating systems, such as TinyOS (Levis, et.al. 2005), Contiki (Dunkels, et.al. 2004), (MANTIS) (Bhatti, et.al. 2005) and Nano-RK (Eswaran, et.al. 2005) have the amenities which they can provide to convey novel prospects to aggressors toward conceding the hardware and the facts kept on it. This is laterally through the upsurge of portable malware which is projected to contain a somber risk in the adjacent times. Consequently, the researchers are regularly looking for explanations to handle these afresh-familiarized threats. Therefore, a necessity for smart and useful defence panels, such as Intrusion Detection and Anticipation Systems (IDAS) is a compulsory consideration. Nevertheless, at the same time as considerable exertion has been fervent to moveable intrusion detection system, study on variance-oriented or performance-oriented IDS has been imperfect parting some glitches unresolved. Reviewed IDS method is projected and assessed in the framework of the contemporary literature which is proficient in sensing innovative but undocumented malware or illicit practice of amenities. This is accomplished by offering constant validation to guarantee genuine practice of the hardware and avoid risks via smart upright-validation and nonrepudiation rejoinder method. This is validated by the tentative outcomes that confirm the effectiveness of the projected methodology.

Online Article

Dynamic Task Assignment for Multi-AUV Cooperative Hunting

by Xiang Cao, Haichun Yu, Hongbing Sun
Abstract

For cooperative hunting by a multi-AUV (multiple autonomous underwater vehicles) team, not only basic problems such as path planning and collision avoidance should be considered but also task assignments in a dynamic way. In this paper, an integrated algorithm is proposed by combining the self-organizing map (SOM) neural network and the Glasius Bio-Inspired Neural Network (GBNN) approach to improve the efficiency of multi-AUV cooperative hunting. With this integrated algorithm, the SOM neural network is adopted for dynamic allocation, while the GBNN is employed for path planning. It deals with various situations for single/multiple target(s) hunting in underwater environments with obstacles. The simulation results show that the proposed algorithm is capable of a cooperative hunting task with efficiency and adaptability.

Online Article

MTN Optimal Control of SISO Nonlinear Time-varying Discrete-time Systems for Tracking by Output Feedback

by Hong-Sen Yan, Jiao-Jun Zhang, Qi-Ming Sun
Abstract

MTN optimal control scheme of SISO nonlinear time-varying discrete-time systems based on multi-dimensional Taylor network (MTN) is proposed to achieve the real-time output tracking control for a given reference signal. Firstly, an ideal output signal is selected and Pontryagin minimum principle adopted to obtain the numerical solution of the optimal control law for the system relative to the ideal output signal, with the corresponding optimal output termed as desired output signal. Then, MTN optimal controller (MTNC) is generated automatically to fit the optimal control law, and the conjugate gradient (CG) method is employed to train the weight parameters of MTNC offline to acquire the initial weight parameters of MTNC for online training that guarantees the stability of closed-loop system. Finally, a four-term back propagation (BP) algorithm with a second order momentum term and error term is proposed to adjust the weight parameters of MTNC adaptively to implement the output tracking control of the systems in real time; the convergence conditions for the four-term BP algorithm are determined and proved. Simulation results show that the proposed MTN optimal control scheme is valid; the system's actual output response is capable of tracking the given reference signal in real time.

Online Article

Short-term Forecasting of Air Passengers based on Hybrid Rough Set and Double Exponential Smoothing Models

by Haresh Sharma, Kriti Kumari, Samajit Kar
Abstract

This article focuses on the use of rough set theory in the modeling of time series forecasting. In this paper, we have used double exponential smoothing (DES) model for forecasting. The classical DES model has been improved by using the rough set technique. The improved double exponential smoothing (IDES) method can be used for the time series data without any statistical assumptions. The proposed method is applied on tourism demand of air transportation passenger data set in Australia and the results are compared with classical DES model. It has been observed that he forecasting accuracy of the proposed model is better than that of the classical model.

Online Article

State-Space based Linear Modeling for Human Activity Recognition in Smart Space

by M. Kabir, Keshav Thapa, Jae-Young Yang, Sung-Hyun Yang
Abstract

Recognition of human activity is a key element for building intelligent and pervasive environments. Inhabitants interact with several objects and devices while performing any activity. Interactive objects and devices convey information that can be essential factors for activity recognition. Using embedded sensors with devices or objects, it is possible to get object-use sequencing data. This approach does not create discomfort to the user than wearable sensors and has no impact or issue in terms of user privacy than image sensors. In this paper, we propose a linear model for activity recognition based on the state-space method. The activities and sensor data are considered as states and inputs respectively for linear modeling. The relationship between the states and inputs are defined by a coefficient matrix. This model is flexible in terms of control because all the elements are represented by matrix elements. Three real datasets are used to compare the recognition accuracy of the proposed method to those of other well-known activity recognition model to validate the proposed model. The results indicate that the proposed model achieves a significantly better recognition performance than other models.

Online Article

Surgical Outcome Prediction in Total Knee Arthroplasty using Machine Learning

by Belayat Hossain, Takatoshi Morooka, Makiko Okuno, Manabu Nii, Shinichi Yoshiya, Syoji Kobashi
Abstract

This work aimed to predict postoperative knee functions of a new patient prior to total knee arthroplasty (TKA) surgery using machine learning, because such prediction is essential for surgical planning and for patients to better understand the TKA outcome. However, the main difficulty is to determine the relationships among individual varieties of preoperative and postoperative knee kinematics. The problem was solved by constructing predictive models from the knee kinematics data of 35 osteoarthritis patients, operated by posterior stabilized implant, based on generalized linear regression (GLR) analysis. Two prediction methods (without and with principal component analysis followed by GLR) along with their sub-classes were proposed, and they were finally evaluated by a leave-one-out cross-validation procedure. The best method can predict the postoperative outcome of a new patient with a Pearson's correlation coefficient (cc) of 0.84 ± 0.15 (mean±SD) and a root-mean-squared-error (RMSE) of 3.27±1.42 mm for anterior-posterior vs. flexion/extension (A-P pattern), and a cc of 0.89±0.15 and RMSE of 4.25±1.92° for valgus-varus vs. flexion/extension (v-v pattern). Although these were validated for one type of prosthesis, they could be applicable to other implants, because the definition of knee kinematics, measured by a navigation system, is appropriate for other implants.

Online Article

EDITORIAL Special Issue on Machine Learning and Data Mining for Cyber-Physical Systems

by Zhiguo Yan, Zheng Xu
Abstract

Online Article

An Accelerated Convergent Particle Swarm Optimizer (ACPSO) of Multimodal Functions

by Yasir Mehmood, Waseem Shahzad
Abstract

Particle swarm optimization (PSO) algorithm is a global optimization technique that is used to find the optimal solution in multimodal problems. However, one of the limitation of PSO is its slow convergence rate along with a local trapping dilemma in complex multimodal problems. To address this issue, this paper provides an alternative technique known as ACPSO algorithm, which enables to adopt a new simplified velocity update rule to enhance the performance of PSO. As a result, the efficiency of convergence speed and solution accuracy can be maximized. The experimental results show that the ACPSO outperforms most of the compared PSO variants on a diverse set of problems.

Online Article

Development of Available Transfer Capability Enhancement Using Intelligent Genetic Algorithm for IEEE Bus System

by R. Rohini, Dasari Rao, S. Ravi, V. Kumar
Abstract

Improving Available Transfer Capability is an important issue in the deregulated power systems. The Available Transfer Capability of a transmission network is the transfer capabilities of a transmission network for the transfer of power for further commercial activity, over and above already committed usage. It is a proven fact that Flexible Alternating Current Transmission System technology can control voltage magnitude, phase angle, and circuit reactance. Therefore, it is important to investigate the impact of Flexible Alternating Current Transmission System controllers on the available transfer capability. This paper is focuses on the evaluation impact of Thyristor controlled switched capacitors and static var compensators in the network. A real-coded genetic algorithm tested on IEEE 14 Bus System, and IEEE 24 bus system is used to analyze the transfer capability and enhancement in two modes, one with line outage and another without line outage on the flexible alternating current transmission network to obtain optimum results. In a competitive (deregulated) power market, the location of these devices and their control can significantly affect the operation of the system.

Online Article

An Efficient Adaptive Network-based Fuzzy Inference System with Mosquito Host-Seeking for Facial Expression Recognition

by M. Sobia, A. Abudhahir
Abstract

In this paper, an efficient facial expression recognition system using ANFIS-MHS (Adaptive Network-based Fuzzy Inference System with Mosquito Host-Seeking) has been proposed. The features were extracted using MLDA (Modified Linear Discriminant Analysis) and then the optimized parameters are computed by using mGSO (modified Glow-worm Swarm Optimization).The proposed system recognizes the facial expressions using ANFIS-MHS. The experimental results demonstrate that the proposed technique is performed better than existing classification schemes like HAKELM (Hybridization of Adaptive Kernel based Extreme Learning Machine), Support Vector Machine (SVM) and Principal Component Analysis (PCA). The proposed approach is implemented in MATLAB.

Online Article

Simulation of Real-Time Path Planning for Large-Scale Transportation Network Using Parallel Computation

by Jiping Liu, Xiaochen Kang, Chun Dong, Fuhao Zhang
Abstract

To guarantee both the efficiency and accuracy of the transportation system, the real-time status should be analyzed to provide a reasonable plan for the near future. This paper proposes a model for simulating the real-world transportation networks by representing the irregular road networks with static and dynamic attributes, and the vehicles as moving agents constrained by the road networks. The all pairs shortest paths (APSP) for the networks are calculated in a real-time manner, and the ever-changing paths can be used for navigating the moving vehicles with real-time positioning devices. In addition, parallel computation is used to accelerate the shortest path searching and vehicle navigation. The testing results suggest that considerable time reduction can be realized in comparison with the non-real-time computations. This finding demonstrates that the proposed model is useful in improving the efficiency of a large-scale transportation system.

Online Article

Formal Modelling of Real-Time Self-Adaptive Multi-Agent Systems

by Awais Qasim, Syed Raza Kazim
Abstract

The paradigm of multi-agent systems is very expressive to model distributed real-time systems. These real-time multi-agent systems by their working nature have temporal constraints as they need to operate in pervasive, dynamic and unpredictable environments. To achieve better fault-tolerance, they need to have the ability of self-adaptivity making them adaptable to the failures. Presently there is a lack of vocabulary for the formal modelling of real-time multi-agent systems with self-adaptive ability. In this research we proposed a framework named SMARTS for the formal modelling of self-adaptive real-time multi-agent systems. Our framework integrates MAPE-K interfaces, reflection perspective and unification with distribution perspective into the SIMBA agent architecture. For a precise semantic description of the constructs of our framework, we have used Timed Communicating Object-Z language.

Online Article

A Distributed Heterogeneous Inspection System for High Performance In-line Surface Defect Detection

by Yu-Cheng Chou, Wei-Chieh Liao, Yan-Liang Chen, Ming Chang, Po Lin
Abstract

This paper presents the Distributed Heterogeneous Inspection System (DHIS), which comprises two CUDA workstations and is equipped with CPU distributed computing, CPU concurrent computing, and GPU concurrent computing functions. Thirty-two grayscale images, each with 5,000✕712,288 pixels and simulated defect patterns, were created to evaluate the performances of three system configurations: (1) DHIS; (2) two CUDA workstations with CPU distributed computing and GPU concurrent computing; (3) one CUDA workstation with GPU concurrent computing. Experimental results indicated that: (1) only DHIS can satisfy the time limit, and the average turnaround time of DHIS is 37.65% of the time limit; (2) a good linear relationship exists between the processing speed ratio and the instruction sequence quantity ratio.

Online Article

Image Classification using Optimized MKL for sSPM

by Lu Wu, Quan Liu, Ping Lou
Abstract

The scheme of spatial pyramid matching (SPM) causes feature ambiguity near dividing lines because it divides an image into different scales in a fixed manner. A new method called soft SPM (sSPM) is proposed in this paper to reduce feature ambiguity. First, an auxiliary area rotating around a dividing line in four orientations is used to correlate the feature relativity. Second, sSPM is performed to combine these four orientations to describe the image. Finally, an optimized multiple kernel learning (MKL) algorithm with three basic kernels for the support vector machine is applied. Specifically, for each level, a suitable kernel is selected to map the data that fall within the corresponding neighbourhood. In addition, a mixed-norm regularization formulation is optimized using MKL to solve the classification problem. The method proposed in this paper performs well when applied to the Caltech 101 and Scene 15 datasets. Experimental results are collected under various conditions. The results of sSPM are improved by nearly 4% compared with the existing experimental results.

Online Article

Adaptive Hybrid Control Scheme for Controlling the Position of Coaxial Tri-rotor UAS

by Rana Masood, DaoBo Wang, Zain Ali, Muhammad Anwar
Abstract

In this article, adaptive hybrid control scheme is proposed for controlling the position of a coaxial tri-rotor unmanned aerial system (UAS) in the presence of input saturation and external wind disturbance. The adaptive hybrid controller consists of model reference adaptive control with integral feedback (MRACI) and proportional integral derivative (PID) controller. The adaptive controller deals with the flight dynamics uncertainties and PID controller is used for tuning the gains of MRACI whereas the stability of system is verified by Lyapunov stability criterion. The integrator improves the order of the system thereby improving the convergence rate by rejecting the noise and eliminating steady state errors. Moreover, anti-windup Compensator (AWC) is used to handle the saturation problem. The designed algorithm is applied to a six degree of freedom (6-DOF) nonlinear model of coaxial tri-rotor UAS. Simulations are carried out to validate the reference path of UAS and are compared with MRAC. In this article the wind disturbance test is also performed to check the robustness of the designed controller. It is observed that the proposed algorithm exhibits, quick error convergence, zero steady state error and robustness in the presence of input saturation and external wind disturbance.

Online Article

Optimal Tuning for Load Frequency Control using Ant Lion Algorithm in Multi-Area Interconnected Power System

by Nour Kouba, Mohamed Menaa, Kambriz Tehrani, Mohamed Boudour
Abstract

This paper presents the use of a novel nature inspired meta-heuristic algorithm namely Ant Lion Optimizer (ALO), which is inspired from the ant lions hunting mechanism to enhance the frequency regulation and optimize the load frequency control (LFC) loop parameters. The frequency regulation issue was formulated as an optimal load frequency control problem (OLFC). The proposed ALO algorithm was applied to reach the best combination of the PID controller parameters in each control area to achieve both frequency and tie-line power flow exchange deviations minimization. The control strategy has been tested firstly with the standard two-area power system, followed by the IEEE three-area Western System Coordinating Council (WSCC) and, lastly, with the large three-area South-Western part of the Mediterranean interconnected power system (SWM): Tunisia, Algeria and Morocco. The dynamic performances of the test systems are compared to other approaches available in literature. The simulation results of this research show that ALO algorithm is able to solve LFC problem and achieve less frequency and tie-line power flow deviations than those determined by other methods used in this paper.

Online Article

A Method for Decision Making Problems by using Graph Representation of Soft Set Relations

by Nazan Polat, Gözde Yaylali, Bekir Tanay
Abstract

Soft set theory, which was defined by D. Molodtsov, has a rich potential for applications in several fields of life. One of the successful application of the soft set theory is to construct new methods for Decision Making problems. In this study, we are introducing a method using graph representation of soft set relations to solve Decision Making problems. We have successfully applied this method to various examples.

Online Article

Hybrid ABF-APSO Algorithm with Application to Tuning of Fuzzy PID Controller for Wireless HART Networked Control System

by Sabo Hassan, Rosdiazli Ibrahim, Nordin Saad, Vijanth Asirvadam, Kishore Bingi, Tran Chung
Abstract

In a WirelessHART networked control system (WHNCS), transmitters always introduce stochastic delays which are often difficult to handle by conventional PID controllers. This is due to the gain range limitation of the PID. This necessitated the need for intelligent controllers that are capable of handling the stochastic delay in the network. Therefore, this paper proposes the use of Fuzzy PID controller for WHNCS. Additionally, the paper utilizes the potentials of Adaptive Bacterial Foraging Algorithm (ABFA), Particle Swarm Optimisation (PSO) and Accelerated Particle Swarm Optimisation (APSO) to propose Hybrid ABF-APSO Algorithm for tuning the proposed controller parameters. Validation results with benchmark functions showed that the proposed algorithm outperformed the compared algorithms in terms of function approximation. Simulation results for WHNCS shows that the fuzzy PID controller optimised with the proposed algorithm outperformed its performance when optimised with PSO, APSO, ABFA and ABF-PSO in terms of settling times, overshoot and recovery from effect of disturbance.

Online Article

An Improved k-nearest Neighbor Algorithm using Tree Structure and Pruning Technology

by Juan Li
Abstract

K-Nearest Neighbor algorithm (KNN) is a simple and mature classification method. However there are susceptible factors influencing the classification performance, such as k value determination, the overlarge search space, unbalanced and multi-class patterns, etc. To deal with the above problems, a new classification algorithm that absorbs tree structure, tree pruning and adaptive k value method was proposed. The proposed algorithm can overcome the shortcoming of KNN, improve the performance of multi-class and unbalanced classification, reduce the scale of dataset maintaining the comparable classification accuracy. The simulations are conducted and the proposed algorithm is compared with several existing algorithms. The results indicate that the proposed algorithm can obtain higher classification efficiency and smaller search reference set on UCI datasets. Furthermore, the proposed algorithm can overcome the shortcoming of KNN and improve the performance of multi-class and unbalanced classification. This illustrates that the proposed algorithm is an expedient method in design nearest neighbour classifiers.

Online Article

Developing a Holistic Model for Assessing the ICT Impact on Organizations: A Managerial Perspective

by Farrukh Saleem, Naomie Salim, Abdulrahman Altalhi, Abdullah Al-Malaise Al-Ghamdi, Zahid Ullah, Noor ul Qayyum
Abstract

Organizations are currently more dependent on Information and Communication Technology (ICT) resources. The main purpose of this research is to help the organization in order to maintain the quality of their ICT project based on evaluation criteria presented in this research. This paper followed several steps to support the methodology section. Firstly, an experimental investigation conducted to explore the values assessment criterion, an organization may realize from ICT project such as information systems, enterprise systems and IT infrastructure. Secondly, the investigation is further based on empirical data collected and analyzed from the respondents of six case studies using questionnaire based on the findings of literature review. Finally this paper propose the development of a holistic model for assessing business values of ICT from the managerial point of view based on measured factors. The study has contributed in this field practically and theoretically, as the literature has not shown a holistic approach of used eight distinct dimensions for assessing ICT impact over business values. It has combined the previous researches in a manner to extend the dimensions of measuring ICT business values. The model has shown its significance for managers and ICT decision makers to align between business strategies and ICT strategies. The findings suggest that ICT positively support business processes and several other business values dimensions. The proposed holistic model and identified factors can be useful for managers to measure the impact of emerging ICT on business and organizational values.

Online Article

Balanced GHM Mutiwavelet Transform based Contrast Enhancement Technique for Dark Images using Dynamic Stochastic Resonance

by S. Deivalakshmi, P. Palanisamy, X. Gao
Abstract

The main aim of this paper is to propose a new technique for enhancing the contrast of dark images using Dynamic Stochastic Resonance (DSR) and Multi Wavelet Transform (MWT), which is computationally more efficient than the conventional methods. In the work, for enhancing the contrast of dark images, the intrinsic noise (darkness) of dark images has been used. The proposed MWT-based DSR scheme (MWT-DSR) can yield better performances in terms of visual information and color preservation than already reported techniques. The desired output response is validated by the Relative Contrast Enhancement Factor (F), Perceptual Quality Measures (PQM) and Color Enhancement Factor (CEF).

Online Article

Accurate Location Prediction of Social-users using mHMM

by Ahsan Hussain, Bettahally Keshavamurthy, Ravi Jagannath
Abstract

Prediction space of distinct check-in locations in Location-Based Social Networks is a challenge. In this paper, a thorough analysis of Foursquare Check-ins is done. Based on previous check-in sequences, next location of social-users is accurately predicted using multinomial-Hidden Markov Model (mHMM) with Steady-State probabilities. This information benefits security-agencies in tracking suspects and restaurant-owners to predict their customers' arrivals at different venues on given days. Higher accuracy and Steady-State venue-popularities obtained for location-prediction using the proposed method, outperform various other baseline methods.

Online Article

Design and implementation of an intelligent ultrasonic cleaning device

by Fecir Duran, Mustafa Teke
Abstract

Ultrasonic cleaners are devices that perform ultrasonic cleaning by using ultrasonic converters. Ultrasonic cleaners have been employed to clean dirty and rusty materials such as optic, jewelers, automotive and dental prosthesis sectors. Due to non-identified correctly cleaning time, cavitation erosion has been occurred at some materials, which desire for cleaning. In this study, an intelligent cleaning device that runs autonomously identified cleaning time, saves energy, and makes the cleaning process safely has been designed and implemented. An ultrasonic cleaning time has been adjusted automatically by monitoring of turbidity and conductivity values of liquid that is put in to the cleaning tank. Thus, the process cleaning has been achieved without cavitation erosion by the developed device. In addition, energy and time consumptions have been lowered by the intelligent algorithm defining the cleaning time.

Online Article

An Efficient Optimized Handover in Cognitive Radio Networks using Cooperative Spectrum Sensing

by H. Anandakumar, K. Umamaheswari
Abstract

Cognitive radio systems necessitate the incorporation of cooperative spectrum sensing among cognitive users to increase the reliability of detection. We have found that cooperative spectrum sensing is not only advantageous, but is also essential to avoid interference with any primary users. Interference by licensed users becomes a chief concern and issue, which affects primary as well as secondary users leading to restrictions in spectrum sensing in cognitive radios. When the number of cognitive users increases, the overheads of the systems, which are meant to report the sensing results to the common receiver, which becomes massive. When the spectrum, which is in use becomes unavailable or when the licensed user takes the allocated band, these networks have the capability of changing their operating frequencies. In addition, cognitive radio networks are seen to have the unique capability of sensing the spectrum range and detecting any spectrum, which has been left underutilized. Further this capability of recognizing the spectrum range based on the dimensions detected, allows for determination of the band, which may be utilized. The main objective of this paper is to analyze the cognitive radio's spectrum sensing ability and evolving a self-configured system with dynamic intelligence networks without causing any interference to the primary user. The paper also brings focus to the quantitative analysis of the two spectrum sensing techniques namely; Energy Detection and Band Limited White Noise Detection. The estimation technique for detecting spectrum noise is based on the detection of probability and probability of false alarms at different Signal-to-Noise Ratio (SNR) levels using Additive White Gaussian Noise signal (AWGN). The efficiency of the proposed Cooperative CUSUM spectrum sensing algorithm performs better than existing optimal rules based on a single observation spectrum sensing techniques under cooperative networks.

Online Article

System Integration For Cognitive Model Of A Robot Partner

by Jinseok Woo, Janos Botzheim, Naoyuki Kubota
Abstract

This paper introduces the integrated system of a smart-device-based cognitive robot partner called iPhonoid-C. Interaction with a robot partner requires many elements, including verbal communication, nonverbal communication, and embodiment as well. A robot partner should be able to understand human sentences, as well as nonverbal information such as human gestures. In the proposed system, the robot has an emotional model connecting the input information from the human with the robot’s behavior. Since emotions are involved in human natural communication, and emotion has a significant impact on humans’ actions, it is important to develop an emotional model for the robot partner to enhance human robot interaction. In our proposed system, human sentences and gestures influence the robot’s emotional state, and then the robot will perform gestural and facial expressions and generate sentences according to its emotional state. The proposed cognitive method is validated using a real robot partner.

Online Article

Friends classification of Ego Network based on combined Features

by Jing Jia, Tinghuai Ma, Fan Xing, William Farah, Donghai Guan
Abstract

Ego networks consist of a user and his/her friends and depending on the number of friends a user has, makes them cumbersome to deal with. Social Networks allow users to manually categorize their “circle of friends”, but in today’s social networks due to the unlimited number of friends a user has, it is imperative to find a suitable method to automatically administrate these friends. Manually categorizing friends means that the user has to regularly check and update his circle of friends whenever the friends list grows. This may be time consuming for users and the results may not be accurate enough. In this paper, to solve this problem, we present a method, which combining user attributes, network structure and contact frequent three aspects. Efficiently using the profile of users, we first identify the relationship between them and then we attempt to solve the problem of community identification when a user’s profile is missing or inaccessible by use of ego network structural features. Lastly, to obtain more accurate results and realize updates automatically, we attempt to find those friends who have frequent contacts with the user. We compare the performance of the proposed algorithm with other methods, and the results show that our method has significant advantages to them.

Online Article

A Novel Cardholder Behavior Model for Detecting Credit Card Fraud

by Yiğit Kültür, Mehmet Ufuk Çağlayan
Abstract

Because credit card fraud costs the banking sector billions of dollars every year, decreasing the losses incurred from credit card fraud is an important driver for the sector and end-users. In this paper, we focus on analyzing cardholder spending behavior and propose a novel cardholder behavior model for detecting credit card fraud. The model is called the Cardholder Behavior Model (CBM). Two focus points are proposed and evaluated for CBMs. The first focus point is building the behavior model using single-card transactions versus multi-card transactions. As the second focus point, we introduce holiday seasons as spending periods that are different from the rest of the year. The CBM is fine-tuned by using a real credit card transaction data-set from a leading bank in Turkey, and the credit card fraud detection accuracy is evaluated with respect to the abovementioned two focus points.

Online Article

Quad-Rotor Directional Steering System Controller Design Using Gravitational Search Optimization

by M. A. Kamel, M. Abido, Moustafa Elshafei
Abstract

Directional Steering System (DSS) has been established for well drilling in the oilfield in order to accomplish high reservoir productivity and to improve accessibility of oil reservoirs in complex locations. In this paper, a novel feedback linearization controller to cancel the nonlinear dynamics of a DSS is proposed. The proposed controller design problem is formulated as an optimization problem for optimal settings of the controller feedback gains. Gravitational Search Algorithm (GSA) is developed to search for optimal settings of the proposed controller. The objective function considered is to minimize the tracking error and drilling efforts. In this study, the DSS considered has 4 downhole motors. The robustness of the proposed GSA-based approach for the controller design is demonstrated. The simulation results of the considered 4-rotor DSS is presented and the effectiveness of the proposed controller is confirmed.

Online Article

Feature Selection for Activity Recognition from Smartphone Accelerometer Data

by Juan C. Quiroz, Amit Banerjee, Sergiu M. Dascalu, Sian Lun Lau
Abstract

We use the public Human Activity Recognition Using Smartphones (HARUS) data-set to investigate and identify the most informative features for determining the physical activity performed by a user based on smartphone accelerometer and gyroscope data. The HARUS data-set includes 561 time domain and frequency domain features extracted from sensor readings collected from a smartphone carried by 30 users while performing specific activities. We compare the performance of a decision tree, support vector machines, Naive Bayes, multilayer perceptron, and bagging. We report the various classification performances of these algorithms for subject independent cases. Our results show that bagging and the multilayer perceptron achieve the highest classification accuracies across all feature sets. In addition, the signal from gravity contains the most information for classification of activities in the HARUS data-set.

Online Article

Improving Performance Prediction On Education Data With Noise And Class Imbalance

by Akram M. Radwan, Zehra Cataltepe
Abstract

This paper proposes to apply machine learning techniques to predict students’ performance on two real-world educational data-sets. The first data-set is used to predict the response of students with autism while they learn a specific task, whereas the second one is used to predict students’ failure at a secondary school. The two data-sets suffer from two major problems that can negatively impact the ability of classification models to predict the correct label; class imbalance and class noise. A series of experiments have been carried out to improve the quality of training data, and hence improve prediction results. In this paper, we propose two noise filter methods to eliminate the noisy instances from the majority class located inside the borderline area. Our methods combine the over-sampling SMOTE technique with the thresholding technique to balance the training data and choose the best boundary between classes. Then we apply a noise detection approach to identify the noisy instances. We have used the two data-sets to assess the efficacy of class-imbalance approaches as well as both proposed methods. Results for different classifiers show that, the AUC scores significantly improved when the two proposed methods combined with existing class-imbalance techniques.

Online Article

Output Consensus of Heterogeneous Multi-agent Systems under Directed Topologies via Dynamic Feedback

by Xiaofeng Liu, Siqi An, Dongxu Zhang
Abstract

This paper discusses the problem of dynamic output consensus for heterogeneous multi-agent systems (MAS) with fixed topologies. All the agents possess unique linear dynamics, and only the output information of each agent is delivered throughout the communication digraphs. A series of conditions and protocols are set for reaching the consensus. With the proper feedback controllers, the output consensus of the overall system is guaranteed. An application illustrates the theorems.

Online Article

Performance Analyses of Nature-inspired Algorithms on the Traveling Salesman’s Problems for Strategic Management

by Julius Beneoluchi Odili, Mohd Nizam Mohmad Kahar, A. Noraziah, M. Zarina, Riaz Ul Haq
Abstract

This paper carries out a performance analysis of major Nature-inspired Algorithms in solving the benchmark symmetric and asymmetric Traveling Salesman’s Problems (TSP). Knowledge of the workings of the TSP is very useful in strategic management as it provides useful guidance to planners. After critical assessments of the performances of eleven algorithms consisting of two heuristics (Randomized Insertion Algorithm and the Honey Bee Mating Optimization for the Travelling Salesman’s Problem), two trajectory algorithms (Simulated Annealing and Evolutionary Simulated Annealing) and seven population-based optimization algorithms (Genetic Algorithm, Artificial Bee Colony, African Buffalo Optimization, Bat Algorithm, Particle Swarm Optimization, Ant Colony Optimization and Firefly Algorithm) in solving the 60 popular and complex benchmark symmetric Travelling Salesman’s optimization problems out of the total 118 as well as all the 18 asymmetric Travelling Salesman’s Problems test cases available in TSPLIB91. The study reveals that the African Buffalo Optimization and the Ant Colony Optimization are the best in solving the symmetric TSP, which is similar to intelligence gathering channel in the strategic management of big organizations, while the Randomized Insertion Algorithm holds the best promise in asymmetric TSP instances akin to strategic information exchange channels in strategic management.

Online Article

Analysis of Collaborative Brain Computer Interface (BCI) based personalized GUI for differently abled

by M. Uma, T. Sheela
Abstract

Brain-Computer Interfaces (BCI) use Electroencephalography (EEG) signals recorded from the brain scalp, which enable a communication between the human and the outside world. The present study helps the patients who are people locked-in to manage their needs such as accessing of web url’s, sending/receiving sms to/from mobile device, personalized music player, personalized movie player, wheelchair control and home appliances control. In the proposed system, the user needs are designed as a button in the form of a matrix, in which the main panel of rows and columns button is flashed in 3 sec intervals. Subjects were asked to choose the desired task/need from the main panel of the GUI by blinking their eyes twice. The double eye blink signals extracted by using the bio-sensor of NeuroSky’s mind wave device with portable EEG sensors are used as the command signal. Each task is designed and implemented using a Matlab tool. The developed Personalized GUI application collaborated with the EEG device accesses the user’s need. Once the system identifies the desired option through the input control signal, the appropriate algorithm is called and performed. The users can also locate the next required option within the matrix. Therefore, users can easily navigate through the GUI Model. A list of personalized music, movies, books and web URL’s are preloaded in the database. Hence, it could be suitable to assist disabled people to improve their quality of life. Analysis of variance (ANOVA) is also carried out to find out the significant signals influencing a user’s need in order to improve the motion characteristics of the brain computer interface based system.

Online Article

Hierarchical Optimization of Network Resource for Heterogeneous Service in Cloud Scenarios

by Dong Huang, Yong Bai, Jingcheng Liu, Hongtao Chen, Jinghua Lin, Jingjing Wu
Abstract

With limited homogeneous and heterogeneous resources in a cloud computing system, it is not feasible to successively expand network infrastructure to adequately support the rapid growth in the cloud service. In this paper, an approach for optimal transmission of hierarchical network for heterogeneous service in Cloud Scenarios was presented. Initially, the theoretical optimal transmission model of a common network was transformed into the hierarchical network with the upper and lower optimization transmission model. Furthermore, the computation simplification and engineering transformation were presented for an approximation method at the low cost of computational complexity. In the final section, the average delay in the engineering method shows its influence on the capability of access for common nodes.

Online Article

Portrait Vision Fusion for Augmented Reality

by Li-Hong Juang, Ming-Ni Wu, Feng-Mao Tsou
Abstract

Video communication is a common way to communicate via interactive technology, especially using webcams for remote interaction and for each participant to see each other’s characteristics from the screen display. In this paper, the main goal is to augment some dynamic interactive virtual environments. Towards this goal , a method using superimposing a segmented human portrait on a panoramic background is proposed, then the limb interactive element is added into these videos involved with a dynamic portrait segmentation method meanwhile using a Kinect (+openCV) device to extract a portrait for amendment, finally acquires a full portrait of information. Because the face is the most important identification region in a portrait, a head skeleton tracking method is also used to strengthen the remedy for its head segmentation, further uses the edge transparent processing to synthesize them into the video. The approach leds the users can verbally and physically communicate through these video interactive much more vibrantly.

Online Article

A novel strategy for mining highly imbalanced data in credit card transactions

by Masoumeh Zareapoor, Jie Yang
Abstract

The design of an efficient credit card fraud detection technique is, however, particularly challenging, due to the most striking characteristics which are; imbalancedness and non-stationary environment of the data. These issues in credit card datasets limit the machine learning algorithm to show a good performance in detecting the frauds. The research in the area of credit card fraud detection focused on detection the fraudulent transaction by analysis of normality and abnormality concepts. Balancing strategy which is designed in this paper can facilitate classification and retrieval problems in this domain. In this paper, we consider the classification problem in supervised learning scenario by creating a contrast vector for each customer based on its historical behaviors. The performance evaluation of proposed model is made possible by a real credit card data-set provided by FICO, and it is found that the proposed model has significant performance than other state-of-the-art classifiers.

Online Article

A Multi Criterion Fuzzy based Energy Efficient Routing Protocol for Ad hoc Networks

by Geetha N., Sankar A.
Abstract

The routing protocol for an ad hoc network should be efficient in utilizing the available resources to prolong the network lifetime. A Multi Criterion Fuzzy based Energy Efficient Routing Protocol (MCFEER) for Ad hoc Networks selects the path on constraints like bandwidth, battery life, hop count and buffer occupancy. In the route discovery phase, fuzzy system is applied for optimal route selection by destination node leading to successful data transmission. Multiple stable paths are preserved in route cache for usage during the route maintenance phase. The results are competitive when compared with Power aware Energy Efficient Routing (PEER) protocol using standard metrics, which ensures better network quality and efficiency.

Online Article

An Intelligent Incremental Filtering Feature Selection and Clustering Algorithm for Effective Classification

by U. Kanimozhi, D. Manjula
Abstract

We are witnessing the era of big data computing where computing the resources is becoming the main bottleneck to deal with those large datasets. In the case of high-dimensional data where each view of data is of high dimensionality, feature selection is necessary for further improving the clustering and classification results. In this paper, we propose a new feature selection method, Incremental Filtering Feature Selection (IF 2 S) algorithm, and a new clustering algorithm, Temporal Interval based Fuzzy Minimal Clustering (TIFMC) algorithm that employs the Fuzzy Rough Set for selecting optimal subset of features and for effective grouping of large volumes of data, respectively. An extensive experimental comparison of the proposed method and other methods are done using four different classifiers. The performance of the proposed algorithms yields promising results on the feature selection, clustering and classification accuracy in the field of biomedical data mining.

Online Article

A Fuzzy Multi-Criteria Decision Analysis Approach For The Evaluation Of The Network Service Providers In Turkey

by Serkan Ballı, Mustafa Tuker
Abstract

Heterogeneous networks are environments where networks having different topologies and technologies can be connected. In an environment including more than one heterogeneous access network, selection of a bad network may lead to emergence of negative results such as high cost and poor service experience for the users. Ensuring the use of the most effective access network for the personal needs of individuals is a complex decision-making process. In the present study, a multi-criteria decision-making system employing fuzzy logic was developed to evaluate and select network service providers in Turkey. Fuzzy logic was used for the criteria containing uncertain and unclear information. Parameter values of the candidate networks obtained from the real world were evaluated by using Fuzzy Analytic Hierarchy Process method and then results were discussed.

Online Article

Automatic Fibex Generation For Migration From Can Message Description Format To Flexray Fibex Format

by Young Hun Song, Suk Lee, Kyoung Nam Ha, Kyung-Chang Lee
Abstract

Recently, FlexRay was developed to replace the controller area network (CAN) protocol in the chassis network systems to provide high-speed data transmission as well as hardware redundancy for safety. However, FlexRay network design is more complicated than with CAN protocol, which has been an in-vehicle network (IVN) standard for car manufacturers for decades, because the FlexRay has many parameters such as the base cycle or slot lengths. To simplify the FlexRay network design and assist vehicle network designers in configuring a FlexRay network, this paper presents an automatic field bus exchange format (FIBEX) generation method for migration from the CAN message description format such as the DBC format to the FlexRay FIBEX format. The automatic FIBEX generation method is examined by simulating a chassis networking system using a DBC benchmark tool, which demonstrates the feasibility of the system and the reduction in workload for network designers.

Online Article

On the use of genetic algorithm for solving re-entrant flowshop scheduling with sum-of-processing-times-based learning effect to minimize the total tardiness

by Win-Chin Lin, Chin-Chia Wu, Kejian Yu, Yong-Han Zhuang, Shang-Chia Liu
Abstract

Most research studies on scheduling problems assume that a job visits certain machines only one time. However, this assumption is invalid in some real-life situations. For example, a job may be processed by the same machine more than once in semiconductor wafer manufacturing or in a printed circuit board manufacturing machine. Such a setting is known as the “re-entrant flowshop”. On the other hand, the importance of learning effect present in many practical situations such as machine shop, in different branches of industry and for a variety of corporate activities, in shortening life cycles, and in an increasing diversity of products in the manufacturing environment. Inspired by these observations, this paper addresses a re-entrant m-machine flowshop scheduling problems with time-dependent learning effect to minimize the total tardiness. The complexity of the proposed problem is very difficult. Therefore, in this paper we first present four heuristic algorithms, which are modified from existing algorithms to solve the problem. Then, we use the solutions as four initials to a genetic algorithm. Finally, we report experimental performances of all the proposed methods for the small and big numbers of jobs, respectively.

Online Article

Active Detecting DDoS Attack Approach based on Entropy Measurement for the Next Generation Instant Messaging App on Smartphones

by Hsing-Chung Chen, Shyi-Shiun Kuo
Abstract

Nowadays, more and more smartphones communicate to each otheru2019s by using some popular Next Generation Instant Messaging (NGIM) applications (Apps) which are based on the blockchain (BC) technologies, such as XChat, via IPv4/IPv6 dual stack network environments. Owing to XChat addresses are soon to be implemented as stealth addresses, any DoS attack activated form malicious XChat node will be treated as a kind of DDoS attack. Therefore, the huge NGIM usages with stealth addresses in IPv4/IPv6 dual stack mobile networks, mobile devices will suffer the Distributed Denial of Service (DDoS) attack from Internet. The probing method is deployed in this paper by using the active ICMP (Internet Control Message Protocol) protocol. Thus, the aim of this paper is to provide the active approach based on the integrated entropy calculations for the NGIM traffics, the numbers of IPv4 and IPv6 addresses of the abnormal events found and counted after active inquiring ICMP procedure. However, many DDoS attacks in Internet were found to paralyze NGIM Apps on smartphones. It is a lightweight approach could be applied in mobile device

Online Article

Visual Object Detection and Tracking using Analytical Learning Approach of Validity Level

by Yong-Hwan Lee, Hyochang Ahn, Hyo-Beom Ahn, Sun-Young Lee
Abstract

Object tracking plays an important role in many vision applications. This paper proposes a novel and robust object detection and tracking method to localize and track a visual object in video stream. The proposed method is consisted of three modules; object detection, tracking and learning. Detection module finds and localizes all apparent objects, corrects the tracker if necessary. Tracking module follows the interest object by every frame of sequences. Learning module estimates a detecting error, and updates its value of credibility level. With a validity level where the tracking is failed on tracing the learned object, detection module finds again the desired object. The experimental results show that the proposed approach is more robust in appearance changes, viewpoint changes, and rotation of the object, compared to the traditional method. The proposed method can track the interest object accurately in various environments.

Online Article

User Authentication System based on Baseline-corrected ECG for Biometrics

by Gyu Choi, Jae Jung, Hae Moon, Youn Kim, Sung Pan
Abstract

Recently, ECG-based user authentication technology, which is strong against forgery and falsification, has been actively studied compared to fingerprint and face authentication. It is impossible to measure the open ECG DB measured with expensive medical equipment in daily living, and the ECG measured with the developed device for easy ECG measurement has much noise. In this paper, we developed a device that easily measures the ECG for user authentication in everyday life, measured the ECG through the development equipment, adjusted the baseline correction of the measured ECG, extracted it from the adjusted ECG do. The proposed system includes the steps of obtaining ECGs, pre-processing ECG signals, segmenting the signals and extracting features thereof to evaluate user authentication performance. The ECG lead-u2160 was obtained from 100 subjects for 120 seconds while they were comfortably positioned. Although the maximum user authentication performance of the existing algorithm was EER of 1.82%, it was observed the maximum performance of the proposed user authentication system was 0.71% higher than the existing algorithm.

Online Article

Cracking of WPA & WPA2 Using GPUs and Rule-based Method

by Tien-Ho Chang, Chia-Mei Chen, Han-Wei Hsiao, Gu-Hsin Lai
Abstract

Wi-Fi Protected Access (WPA) and Wi-Fi Protected Access II (WPA2) are two security protocols developed by the Wi-Fi Alliance to secure wireless computer networks. The prevailing usage of GPUs improves the brute force attacks and cryptanalysis on access points of the wireless networks. It is time-consuming for the cryptanalysis with the huge total combinations of 9563 max. Now, it is the turning point that the leap progress of GPUs makes the Wi-Fi cryptanalysis much more efficient than before. In this research, we proposed a rule-based password cracking scheme without dictionary files which improves the efficiency of cracking WPA/WPA2 protected access points. The experiment was performed on the real environment and the results demonstrate that the proposed scheme. The proposed scheme improves the efficiency from 2088000 PMKs/min to 16200000 PMKs/min.

Online Article

A Novel Privacy-preserving Multi-attribute Reverse Auction Scheme with Bidder Anonymity using Multi-server Homomorphic Computation

by Wenbo Shi, Jiaqi Wang, Jinxiu Zhu, YuPeng Wang, Dongmin Choi
Abstract

With the further development of Internet, the decision-making ability of the smart service is getting stronger and stronger, and the electronic auction is paid attention to as one of the ways of decision system. In this paper, a secure multi-attribute reverse auction protocol without the trusted third party is proposed. It uses the Paillier public key cryptosystem with homomorphism and combines with oblivious transfer and anonymization techniques. A single auction server easily collides with a bidder, in order to solve this problem, a single auction server is replaced with multiple auction servers. The proposed scheme uses multiple auction servers to calculate the attributes under encryption protection and obtains the linear additive score function value finally. Since the attribute is calculated under the protection of encryption, the proposed scheme achieves privacy-preserving winner determination with bid privacy. Furthermore, the proposed scheme uses oblivious transfer and anonymization techniques to achieve bidder anonymity. In accordance with the security analysis, major properties, bidder anonymity and somewhat reducing collusion possibilities, are provided under the semi-honest model. According to a comparison of computation, the proposalu2019s computation cost is reasonable.

Online Article

Trust Provision in the Internet of Things using Transversal Blockchain Networks

by Borja Bordel, Ramon Alcarria, Diego Martín, Álvaro Sánchez-Picot
Abstract

The Internet-of-Things (IoT) paradigm faces new and genuine challenges and problems associated, mainly, with the ubiquitous access to the Internet, the huge number of devices involved and the heterogeneity of the components making up this new global network. In this context, protecting these systems against cyberattacks and cybercrimes has turn into a basic issue. In relation to this topic, most proposed solutions in the literature are focused on security; however other aspects have to be considered (such as privacy or trust). Therefore, in this paper we define a theoretical framework for trust in IoT scenarios, including a mathematical formalization and a discussion about the requirements which should fulfill a solution for trust provision. An analysis of these requirements shows that blockchain technology meets them perfectly, so a first trust provision system based on blockchain networks is also provided. An experimental validation is also proposed and performed in order to evaluate the described solution.

Online Article

Protecting Android Applications with Multiple DEX Files against Static Reverse Engineering Attacks

by Kyeonghwan Lim, Nak Kim, Younsik Jeong, Seong-je Cho, Sangchul Han, Minkyu Park
Abstract

The Android application package (APK) uses the DEX format as an executable file format. Since DEX files are in Java bytecode format, you can easily get Java source code using static reverse engineering tools. This feature makes it easy to steal Android applications. Tools such as ijiami, liapp, alibaba, etc. can be used to protect applications from static reverse engineering attacks. These tools typically save encrypted classes.dex in the APK file, and then decrypt and load dynamically when the application starts. However, these tools do not protect multidex Android applications. A multidex Android application is an APK that contains multiple DEX files, mostly used in a large-scale application. We propose a method to protect multidex Android applications from static reverse engineering attacks. The proposed method encrypts multiple DEX files and stores them in an APK file. When an APK is launched, encrypted DEX files are decrypted and loaded dynamically. Experiment results show that the proposed method can effectively protect multidex APKs.

Online Article

The Design and Implementation of a Multidimensional and Hierarchical Web Anomaly Detection System

by Jianfeng Guan, Jiawei Li, Zhongbai Jiang
Abstract

The traditional web anomaly detection systems face the challenges derived from the constantly evolving of the web malicious attacks, which therefore result in high false positive rate, poor adaptability, easy over-fitting, and high time complexity. Due to these limitations, we need a new anomaly detection system to satisfy the requirements of enterprise-level anomaly detection. There are lots of anomaly detection systems designed for different application domains. However, as for web anomaly detection, it has to describe the network accessing behaviours characters from as many dimensions as possible to improve the performance. In this paper we design and implement a Multidimensional and Hierarchical Web Anomaly Detection System (MHWADS) with the objectives to provide high performance, low latency, multi-dimension and adaptability. MHWADS calculates the statistical characteristics, and constructs the corresponding statistical model, detects the behaviour characteristics to generate the multidimensional correlation eigenvectors, and adopts several classifications to build an ensemble model. The system performance is evaluated based on realistic dataset, and the experimental results show that MHWADS yields substantial improvements than the previous single model. More important, by using 2-fold Stacking as the ensemble architecture, the detection precision and recall are 0.99988 and 0.99647, respectively.

Online Article

Cyber-security risk assessment framework for critical infrastructures

by Zubair Baig, Sherali Zeadally
Abstract

A critical infrastructure provides essential services to a nationu2019s population. Interruptions in its smooth operations are highly undesirable because they will cause significant and devastating consequences on all stakeholders in the society. In order to provide sustained protection to a nationu2019s critical infrastructure, we must continually assess and evaluate the risks thereof. We propose a risk assessment framework that can evaluate the risks posed to the security of a critical infrastructure from threat agents, with a special emphasis on the smart grid communications infrastructure. The framework defines fine-grained risk identification to help quantify and assess exploitable vulnerabilities within a critical infrastructure.

Online Article

Guest Editorial: Advances In Security and Privacy Technologies for Forthcoming Smart Systems, Services, Computing, and Networks

by Llsun You, Chang Choi, Vishal Sharma, Isaac Woungang, Bharat Bhargava
Abstract

Online Article

Multi-phase Oil Tank Recognition for High Resolution Remote Sensing Images

by Changjiang Liu, Xuling Wu, Bing Mo, Yi Zhang
Abstract

With continuing commercialization of remote sensing satellites, high resolution remote sensing image has been increasingly used in various fields of our life. However, processing technology of high resolution remote sensing images is still a tough problem. How to extract useful information from the massive information in high resolution remote sensing images is significant to the subsequent process. A multi-phase oil tank recognition of remote sensing images, namely coarse detection and artificial neural network (ANN) recognition, is proposed. The experimental results of algorithms presented in this paper show that the proposed processing technology is reliable and effective.

Volume: 24, Issue: 3

Association Link Network based Concept Learning in Patent Corpus

by Wei Qin, Xiangfeng Luo
Abstract

Concept learning has attracted considerable attention as a means to tackle problem of representation and learning corpus knowledge. In this paper, we investigate a challenging problem to automatically construct patent concept learning model. Our model consist of two main processes, which are the acquisition of the initial concept graph and refine process for initial concept graph. The learning algorithm of patent concept graph is designed based on Association Link Network (ALN). A concept is usually described by multiple document, enable ALN to be used in concept learning, we propose mixture-ALN, which add links between document and lexical level, compared with ALN. Then, a heuristic algorithm is proposed to refine the concept graph which could learn a more concise and simpler knowledge for concept. The heuristic algorithm consists of four phases, firstly, for simplifying bag of words for concept in patent corpus, we start to select core node from initial concept graph. Secondly, for learning the association rule for concept, we search important association rules around core node in our rules collection. Thirdly, ensure coherent semantics of the concept, we select corresponding documents based on the selected association rules and words. Finally, for enriching semantics of refined concept, we iteratively select core nodes based on corresponding documents and restart our heuristic algorithm. In the experiments, our model shows effectiveness and improvement in prediction accuracy in retrieve task of patent.

Volume: 24, Issue: 3

The Data Analyses of Vertical Storage Tank using Finite Element Soft Computing

by Lin Gao, Mingzhen Wang
Abstract

With the rapid development of petrochemical industry, the number of large-scale oil storage tanks has increased significantly, and many storage tanks are located in potential seismic regions. It is very necessary to analyze seismic response of oil storage tanks since their damage in an earthquake can lead to seriously disasters and losses. In this paper, three models of vertical cylindrical oil storage tank in different sizes which are commonly used in practical engineering are established. The dynamic characteristics, sloshing wave height and hydrodynamic pressure of oil tank considering liquid-structure coupling effect are analyzed by using ADINA finite element software, which are compared with the result of standard method. The close numerical values of both results have verified the correctness and reliability of finite element model. The analytic results show that liquid sloshing wave height is basically in direct proportion to ground motion peak acceleration, the standard method of portion sloshing wave height calculation is not conservative. The hydrodynamic pressure generated by liquid sloshing caused by ground motion is not negligible compared with the hydrostatic pressure. The tank radius and oil height have a significant effect on the numerical value of hydrodynamic pressure. The ratio of hydrodynamic pressure and hydrostatic pressure, which is named hydraulic pressure increase coefficients, is related to the height, which given by the GB 50341-2014 code in China have a high reliability. The seismic performances of tank wall near the bottom need to be enhanced and improved in the seismic design of oil tank.

Volume: 24, Issue: 3

NARX Network based Driver Behavior Analysis and Prediction using Time-series Modeling

by Ling Wu, Haoxue Liu, Tong Zhu, Yueqi Hu
Abstract

The objective of the current study was to examine how experienced and inexperienced driver behaviour changed (including heart rate and longitudinal speeds) when approaching and exiting highway tunnels. Simultaneously, the NARX neural network was used to predict real-time speed with the heart rate regarded as the input variable. The results indicated that familiarity with the experimental route did decrease drivers' mental stress but resulted in higher speed. The proposed NARX model could predict synchronous speed with high accuracy. These results of the present study concern how to establish the automated driver model in the simulation environment.

Volume: 24, Issue: 3

Intelligent Control for Integrated Guidance and Control based on Intelligent Characteristic Model

by Jun Zhou, Zhenzhen Ge
Abstract

In this paper, an adaptive integrated guidance and control (IGC) scheme for the homing missile is proposed based on the novel continuous characteristic model and the dynamic surface control technique. The novel continuous characteristic model is firstly proposed in the presence of unknown model coefficients and uncertainties. Then, the dynamic surface control technique is applied to the continuous characteristic model. The proposed IGC scheme guarantees the line-of-sight angular rates converge to an arbitrarily small neighbourhood of zero and all the closed-loop signals to be semi-globally uniformly ultimately bounded, which is proved using the Lyapunov stability theory. Finally, the effectiveness of the adaptive IGC scheme is demonstrated using nonlinear numerical simulations for the maneuvering target.

Volume: 24, Issue: 3

Simulation and Data Analysis of Energy Recovery Sensing on Parallel Hydraulic Hybrid Crane

by Youquan Chen, Xinhui Liu, Xin Wang, Jinshi Chen
Abstract

In order to study the braking energy regeneration characteristics of the Front-mounted Parallel Hydraulic Hybrid Crane (FPHHC), the AMESim simulation models are established and analyzed by establishing vehicle dynamics model and referencing to the actual data of the crane and physical hydraulic components, the simulation results are verified by road tests on the experimental prototype. The experiment results basically match with the simulation results. In the vehicle braking process, the hydraulic hybrid system of the experimental prototype can effectively recycle the vehicle braking energy, the energy recovery rate is up to 50.84%, and the energy-saving effect is obvious. The performance in terms of the vehicle acceleration and braking has obviously been improved than the prototype crane, which can show that this system has a certain practical significance in saving energy.

Volume: 24, Issue: 3

The Lateral Conflict Risk Assessment for Low-altitude Training Airspace using Weakly Supervised Learning Method

by Kaijun Xu, Xueting Chen, Yusheng Yao, Shanshan Li
Abstract

The lateral conflict risk assessment of low-altitude training airspace strategic planning which is based on the TSE errors has always been a difficult task for training flight research. In order to effectively evaluate the safety interval and lateral collision risk in training airspace, in this paper, TSE error performance using weakly supervised learning method was modelled. Firstly, the lateral probability density function of TSE is given by using multidimensional random variable covariance matrix, and the risk model of training flight lateral collision based on TSE error is established, and the lateral conflict risk in specific training airspace is analyzed, and then the lateral collision model is built. Through the quantification of the risk probability of lateral collisions, the security level of specific airspace is evaluated. The analysis of the examples shows that for normal training flight in a variety of 4D flight track data, the lateral collision risk in specific training airspace is , the conflict risk meets the requirement of safety target level of international civil aviation organization.

Volume: 24, Issue: 3

The SLAM Algorithm for Multiple Robots based on Parameter Estimation

by MengYuan Chen
Abstract

With the increasing number of the feature points of the map, the dimension of systematic observation is added gradually, which leads to the deviation of the volume points from the desired trajectory and significant errors on the state estimation. An Iterative Squared-Root Cubature Kalman Filter (ISR-CKF) algorithm proposed is aimed at improving SR-CKF algorithm on simultaneous localization and mapping (SLAM). By introducing the method of iterative updating, the sample points are re-determined by the estie re-determined by the estimated value and the square root factor, which keeps the distortion small in the highly nonlinear environment and improves the precision further. A robust tracking Square Root Cubature Kalman Filter algorithm (STF-SRCKF-SLAM) is proposed to solve the problem of reduced accuracy in the condition of state change on SLAM. The algorithm is predicted according to the kinematic model and observation model of the mobile robot at first, and then the algorithm updates itself by spreading the square root of the error covariance matrix directly, which greatly reduces the computational complexity. At the same time, the time-varying fading factor is introduced in the process of forecasting and updating, and the corresponding weight of the data is adjusted in real time to improve the accuracy of multi-robot localization. The results of simulation shows that the algorithm can improve the accuracy of multi-robot pose effectively.

Volume: 24, Issue: 3

The Virtual Prototype Model Simulation on the Steady-state Machine Performance

by Huanyu Zhao, Guoqiang Wang, Shuai Wang, Ruipeng Yang, He Tian, Qiushi Bi
Abstract

Articulated tracked vehicles have high mobility and steering performance. The unique structure of articulated tracked vehicles can avoid the subsidence of tracks caused by high traction from instantaneous braking and steering. In order to improve the accuracy of the steady-state steering of the articulated tracked vehicle, the velocity of both sides of the track and the deflection angle of the articulated point need to match better, to achieve the purpose of steering accurately and reducing energy consumption and wear of components. In this study, a virtual prototype model of the articulated tracked vehicle is established based on the multi-body dynamic software RecurDyn. The trend of the driving torque and power of each track changes as the velocity difference of two sides of the tracks and the traveling trajectory of the mass center of the front vehicle change in a specific condition are obtained by the experiment. The experimental results are compared and verified with the results obtained from the virtual prototype simulation. The change law of driving power in the steady-state steering process on the horizontal firm ground as changing the velocity difference of two sides of the tracks, the theoretical steering radius, and the ground friction is obtained by the virtual prototype model simulation analysis. The steering inaccuracy and track slip rate are used as indexes in evaluating the steady-state steering performance of the articulated tracked vehicle. The research provides references for the study of steady-state steering performance of articulated tracked vehicles.

Volume: 24, Issue: 3

Kinematic Calibration of Parallel Manipulator for Semi-physical Simulation System

by Dayong Yu
Abstract

In the application of semi-physical simulation system of space docking mechanism, the simulation precision is determined by pose accuracy of the parallel manipulator. In order to improve pose accuracy, an effective kinematic calibration method is presented to enable the full set of kinematic parameter errors to be estimated by measuring the docking mechanism's poses. A new calibration model that takes into account geometrical parameter errors and coordinate transformation errors is derived by using differential geometry method. Based on the calibration model, an iterative least square algorithm is utilized to calculate the above errors. Simulation and experimental results show the calibration method can obviously improve pose accuracy.

Volume: 24, Issue: 3

Optimal Learning Slip Ratio Control for Tractor-semitrailer Braking in a Turn based on Fuzzy Logic

by Jinsong Dong, Hongwei Zhang, Ronghui Zhang, Xiaohong Jin, Fang Chen
Abstract

The research on braking performance of tractor-semitrailer is a hard and difficult point in the field of vehicle reliability and safety technology. In this paper, the tire braking model and the dynamic characteristic model of the brake torque with the variable of the controlling air pressure were established. And we also established a nonlinear kinematic model of the tractor-semitrailer when it brakes on a curve. The parameters and variables of the model were measured and determined by the road experiment test. The optimal control strategy for the tractor-semitrailer based on the optimal slipping ratio was proposed. Then, the PID controller and the fuzzy controller were designed respectively. Simulation results show that the reasonable control strategy can significantly improve the braking directional stability when a tractor-semitrailer runs on a curving road. The research results provide technical references for improving the lateral stability when a tractor-semitrailer brakes on a curve, and it also provides a technical reference for the road traffic safety.

Volume: 24, Issue: 3

A Computable General Equilibrium Model based Simulation on Water Conservancy Investment

by Jun Wang
Abstract

The water conservancy industry is one of the oldest fundamental industries in human histories with highly attention all the time, which is a vital factor to national well-being and the people's livelihood. The "Five Water Governance" is one of important strategies as a breakthrough to force transforming and upgrading for ecological water conservancy and sustainable development. A regional dynamic CGE model was constructed to simulate and analyze the short-term and long-term influence of water conservancy investment to water conservancy industry itself, other national economy sectors and macro economy, so as to provide scientific proof for policy-making of water conservancy, and coordinated and sustainable development police-making of the whole society.

Volume: 24, Issue: 3

Delay-dependent Stability of Recurrent Neural Networks with Time-varying Delay

by Guobao Zhang, Jing-Jing Xiong, Yongming Huang, Yong Liu, Ling Wang
Abstract

This paper investigates the delay-dependent stability problem of recurrent neural networks with time-varying delay. A new and less conservative stability criterion is derived through constructing a new augmented Lyapunov-Krasovskii functional (LKF) and employing linear matrix inequality method. A new augmented LKF that considers more information of the slope of neuron activation functions is developed for further reducing the conservatism of stability results. To deal with the derivative of the LKF, several commonly used techniques, including the integral inequality, reciprocally convex combination, and free-weighting matrix method, are applied. Moreover, it is found that the obtained stability criterion has lower computational burden than some recent existing ones. Finally, two numerical examples are considered to demonstrate the effectiveness of the presented stability results.

Volume: 24, Issue: 3

The Machine Learning Based Finite Element Analysis on Road Engineering of Built-in Carbon Fiber Heating Wire

by Yuhua Peng, Dingyue Chen, Lihao Chen, Jiayu Yu, Mengjie Bao
Abstract

For the study of the effect of deicing with carbon fiber heating wire in the bridge pavement structure, through built-in carbon fiber heating wire in the bridge pavement structure, experimental studies were carried out indoor on the effect of thermal conductivity in different embedding position, layout spacing and the installs power of carbon fiber heating wire; With indoor laboratory test data as the basic parameters, using ABAQUS finite element software simulation, an analysis was carried out of the degree that the surface temperature of heating wire, the thermal physical parameters of asphalt concrete, and environmental conditions have influence on the melting effect; With a 50 m bridge between the left Shanggaoqiao tunnel exports with 2# Dabaozhai tunnel entrance in Ma Zhao Highway as the test section, this paper introduces the carbon fiber heating wire grooving laying process and construction methods.

Volume: 24, Issue: 3

A user authentication protocol combined with trust model, biometrics and ECC for wireless sensor networks

by Tao Liu, Gan Huang
Abstract

In this article, a new user authentication protocol using trust model, elliptic curve cryptography and biometrics for WSNs is submitted. The result of the trust model analysis indicates that the model can improve the model's ability of withstanding attacks from the malicious nodes. The results of safety analysis and performance analysis for our proposed user authentication protocol demonstrate that this protocol can be flexible to all sorts of common known attacks and performs similarly or better compared with some active user authentication protocols. It is suitable for WSNs which have a prominent request for the security and the performance.

Volume: 24, Issue: 3

Synthesis Optimization of Piezo Driven Four Bar Mechanism using Genetic Algorithm

by Laith Sawaqed, Khaled Hatamleh, Mohammad Jaradat, Qais Khasawneh
Abstract

Over the past few years, there has been a growing demand to develop efficient precision mechanisms for fine moving applications. Therefore, several piezoelectric driven mechanisms have been proposed for such applications. In this work an optimal synthesis of a four-bar mechanism with three PEAs is proposed. Two evolutionary multi-objective Genetic Algorithms (GAs) are formulated and applied; A Genetic Algorithm Synthesis method (GAS) is first used to obtain a synthesis solution for the mechanism regardless of power consumption. Then another Genetic Algorithm Minimum Power Synthesis method (GAMPS) is used to obtain the synthesis solution of minimum power consumption. For that purpose, the study performs simulation investigation of the aforementioned algorithms for each point along sinusoidal and kidney-shaped paths of motion. Results show the capability of both methods in obtaining a synthesis solution. However, GAMPS outperformed GAS in terms of driving power consumption as it is minimized by 99% ratio.

Volume: 24, Issue: 3

Highly Accurate Recognition of Handwritten Arabic Decimal Numbers Based on a Self-Organizing Maps Approach

by Amin Alqudah, Hussein Al-Zoubi, Mahmood Al-Khassaweneh, Mohammed Al-Qodah
Abstract

Handwritten numeral recognition is one of the most popular fields of research in automation because it is used in many applications. Indeed, automation has continually received substantial attention from researchers. Therefore, great efforts have been made to devise accurate recognition methods with high recognition ratios. In this paper, we propose a method for integrating the correlation coefficient with a Self-Organizing Maps (SOM)-based technique to recognize offline handwritten Arabic decimal digits. The simulation results show very high recognition rates compared with the rates achieved by other existing methods.

Volume: 24, Issue: 3

Application of Multi Agent Systems in Automation of Distributed Energy Management in Micro-grid using MACSimJX

by Leo Raju, R.S Milton, Senthilkumaran Mahadevan
Abstract

The objective of this paper is to monitor and control a micro-grid model developed in MATLAB-Simulink through Multi Agent System (MAS) for autonomous and distributed energy management. Since MATLAB/Simulink is not compatible with parallel operations of MAS, MAS operating in Java Agent Development Environment (JADE) is linked with MATLAB/Simulink through Multi Agent Control using Simulink with Jade extension (MACSimJX). This allows the micro-grid system designed with Simulink to be controlled by MAS for realizing the advantages of MAS in distributed and decentralized micro-grid systems. JADE agents receive environmental information through Simulink and they coordinate to take best possible action, which is reflected in MATLAB/Simulink simulations. After validation and performance evaluation through dynamic simulations, the operations of the agents at various scenarios are practically verified by using the Arduino microcontroller. These validation and verification moves MAS closer to Smartgrid applications and takes micro-grid automation to a new level.

Volume: 24, Issue: 3

Comparison of Local Descriptors For Humanoid Robots Localization Using a Visual Bag of Words Approach

by Noé G. Aldana-Murillo, Jean-Bernard Hayet, HECTOR BECERRA
Abstract

In this paper, we address the problem of the appearance-based localization of a humanoid robot, in the context of robot navigation. We only use information obtained by a single sensor, in this case the camera mounted on the robot. We aim at determining the most similar image within a previously acquired set of key images (also referred to as a visual memory) to the current view of the monocular camera carried by the robot. The robot is initially kidnapped and the current image has to be compared with the visual memory. To solve this problem, we rely on a hierarchical visual bag-of-words approach. The contribution of this paper is twofold: (1) we compare binary, floating-point and color descriptors, which feed the representation in bag-of-words using images captured by a humanoid robot; (2) a specific visual vocabulary is proposed to deal with the typical issues generated by the humanoid locomotion.

Volume: 24, Issue: 3

Structure from Motion Using Bio-Inspired Intelligence Algorithm and Conformal Geometric Algebra

by Nancy Arana-Daniel, Carlos Villaseñor, Carlos López-Franco, Alma Alanis, Roberto Valencia-Murillo
Abstract

Structure from Motion algorithms offer good advantages, such as extract 3D information in monocular systems and structures estimation as shown in Hartley Zisserman for numerous applications, for instance; augmented reality, autonomous navigation, motion capture, remote sensing and object recognition among others. Nevertheless, this algorithm suffers some weaknesses in precision. In the present work, we extent the proposal in Arana-Daniel, Villaseñor, López-Franco, Alanís that presents a new strategy using bio-inspired intelligence algorithm and Conformal Geometric Algebra, based in the object mapping paradigm, to overcome the accuracy problem in two-view Structure form motion algorithms. For this instance, we include two new experiments and the inclusion of the circle entity; the circle carries stronger information about its motion than other geometric entities, as we will show.

Volume: 24, Issue: 3

Active Control of a Piezoelectric Actuated Four-Bar Mechanism Deployed in Robotics Applications

by Qais Khasawneh, Mohammad Jaradat, Mohammad Al-Shabi, Hala Khalaf
Abstract

This work presents a new micro-positioning system that is implemented in an inchworm robot to move into desired locations. The system consists of four-bar mechanism; one link is fixed, and each one of the remaining links carries a piezoelectric actuator (PZT). PZTs are specifically chosen since they provide fast response and small displacements; up to ±30 µm for ±100 Volts. The system's mathematical model is derived and is numerically simulated by MATLAB. Three fuzzy PI controllers, which are tuned automatically by genetic algorithm, are designed to control the system. Results indicate an error of less than 1% although disturbances present.

Volume: 24, Issue: 2

Robot Pose Estimation Based on Visual Information and Particle Swarm Optimization

by Carlos Lopez-Franco, Javier Gomez-Avila, Nancy Arana-Daniel, Alma Alanis
Abstract

This paper presents a method for 3D pose estimation using visual information and a soft-computing algorithm. The algorithm uses quaternions to represent rotations, and Particle Swarm Optimization to estimate such quaternion. The rotation estimation problem is cast as a minimization problem, which finds the best quaternion for the given data using the PSO algorithm. With this technique, the algorithm always returns a valid quaternion, and therefore a valid rotation. During the estimation process, the algorithm is able to detect and reject outliers. The simulations and experimental results show the robustness of algorithm against noise and outliers.

Volume: 24, Issue: 2

A Clustering-based Approach for Balancing and Scheduling Bicycle-sharing Systems

by Imed Kacem, Ahmed Kadri, Pierre Laroche
Abstract

This paper addresses an inventory regulation problem in bicycle sharing-systems. The problem is to balance a network consisting of a set of stations by using a single vehicle, with the aim of minimizing the weighted sum of the waiting times during which some stations remain imbalanced. Motivated by the complexity of this problem, we propose a two-stage procedure based on decomposition. First, the network is divided into multiple zones by using two different clustering strategies. Then, the balancing problem is solved in each zone. Finally, the order in which the zones must be visited is defined. To solve these problems, different algorithms based on approximate, greedy and exact methods are developed. The numerical results show the effectiveness of the proposed regulation methodology.

Volume: 24, Issue: 2

Big Data based Self-Optimization Networking: A Novel Approach Beyond Cognition

by Amin Mohajer, Morteza Barari, Houman Zarrabi
Abstract

It is essential to satisfy class-specific QoS constraints to provide broadband services for new generation wireless networks. A self-optimization technique is introduced as the only viable solution for controlling and managing this type of huge data networks. This technique allows control of resources and key performance indicators without human intervention, based solely on the network intelligence. The present study proposes a big data based self optimization networking (BD-SON) model for wireless networks in which the KPI parameters affecting the QoS are assumed to be controlled through a multi-dimensional decision-making process. Also, Resource Management Center (RMC) was used to allocate the required resources to each part of the network based on made decision in SON engine, which can satisfy QoS constraints of a multicast session in which satisfying interference constraints is the main challenge. A load-balanced gradient power allocation (L-GPA) scheme was also applied for the QoS-aware multicast model to accommodate the effect of transmission power level based on link capacity requirements. Experimental results confirm that the proposed power allocation techniques considerably increase the chances of finding an optimal solution. Also, results confirm that proposed model achieves significant gain in terms of quality of service and capacity along with low complexity and load balancing optimality in the network.

Volume: 24, Issue: 2

Modeling of a fuzzy expert system for choosing an appropriate supply chain collaboration strategy

by Kazim Sari
Abstract

Nowadays, there has been a great interest for business enterprises to work together or collaborate in the supply chain. It is thus possible for them to gain a competitive advantage in the marketplace. However, determining the right collaboration strategy is not an easy task. Namely, there are several factors that need to be considered at the same time. In this regard, an expert system based on fuzzy rules is proposed to choose an appropriate collaboration strategy for a given supply chain. To this end, firstly, the factors that are significant for supply chain collaboration are extracted via an extensive review of literature. Then, a simulation model of a supply chain is constructed to reveal the performance of collaborative practices under various scenarios. Thereby, it is made possible to establish fuzzy rules for the expert system. Finally, feasibility and practicability of our proposed model is verified with an illustrative case.

Volume: 24, Issue: 2

An algorithm for fast mining top-rank-k frequent patterns based on Node-list data structure

by Qian Wang, Jiadong Ren, Darryl N Davis, Yongqiang Cheng
Abstract

Frequent pattern mining usually requires much run time and memory usage. In some applications, only the patterns with top frequency rank are needed. Because of the limited pattern numbers, quality of the results is even more important than time and memory consumption. A Frequent Pattern algorithm for mining Top-rank-K patterns, FP_TopK, is proposed. It is based on a Node-list data structure extracted from FTPP-tree. Each node is with one or more triple sets, which contain supports, preorder and post-order transversal orders for candidate pattern generation and top-rank-k frequent pattern mining. FP_TopK uses the minimal support threshold for pruning strategy to guarantee that each pattern in the top-rank-k table is really frequent and this further improves the efficiency. Experiments are conducted to compare FP_TopK with iNTK and BTK on four datasets. The results show that FP_TopK achieves better performance.

Volume: 24, Issue: 2

Forest Above Ground Biomass Estimation from Remotely Sensed Imagery in the Mount Tai Area Using the RBF ANN Algorithm

by Liang Wang, jiping liu, Shenghua Xu, Jinjin Dong, Yi Yang
Abstract

Forest biomass is a significant indicator for substance accumulation and forest succession, and can provide valuable information for forest management and scientific planning. Accurate estimations of forest biomass at a fine resolution are important for a better understanding of the forest productivity and carbon cycling dynamics. In this study, considering the low efficiency and accuracy of the existing biomass estimation models for remote sensing data, Landsat 8 OLI imagery and field data cooperated with the radial basis function artificial neural network (RBF ANN) approach is used to estimate the forest Above Ground Biomass (AGB) in the Mount Tai area, Shandong Province of East China. The experimental results show that the RBF model produces a relatively accurate biomass estimation compared with multivariate linear regression (MLR), k-Nearest Neighbor (KNN), and backpropagation artificial neural network (BP ANN) models.

Volume: 24, Issue: 2

Random Controlled Pool base Differential Evolution algorithm (RCPDE)

by Qamar Abbas, Jamil Ahmad, Hajira Jabeen
Abstract

This paper presents a novel random controlled pool base differential evolution algorithm (RCPDE) where powerful mutation strategy and control parameter pools have been used. The mutation strategy pool contains mutations strategies having diverse parameter values, whereas the control parameter pool contains varying nature pairs of control parameter values. It has also been observed that with the addition of rarely used control parameter values in these pools are highly beneficial to enhance the performance of the DE algorithm. The proposed mutation strategy and control parameter pools improve the solution quality and the convergence speed of DE algorithm. The simulation results of the proposed RCPDE algorithm shows significant performance as compared to other algorithms when tested over a set of multi-dimensional benchmark functions.

Volume: 24, Issue: 2

A Multi-Objective Metaheuristics Study on Solving Constrained Relay Node Deployment Problem in WSNS

by Wenjie Yu, Xunbo Li, Hang Yang, Bo Huang
Abstract

This paper studies how to deploy relay nodes into traditional wireless sensor networks with constraint aiming to simultaneously optimize two important factors; average energy consumption and average network reliability. We consider tackling this multi-objective (MO) optimization problem with three metaheuristics, which employ greatly different evolutional strategies, and aim at an in-depth analysis of different performances of these metaheuristics to our problem. For this purpose, a statistical procedure is employed to analyse the results for confidence, in consideration of two MO quality metrics; hypervolume and coverage of two sets. After comprehensive analysis of the results, it is concluded that NSGA-II provides the best performance.

Volume: 24, Issue: 2

Comparative study of prey predator algorithm and firefly algorithm

by Hong Choon Ong, Surafel Luleseged Tilahun, Wai Soon Lee, Jean Meadard T Ngnotchouye
Abstract

Metaheuristic algorithms are found to be promising for difficult and high dimensional problems. Most of these algorithms are inspired by different natural phenomena. Currently, there are hundreds of these metaheuristic algorithms introduced and used. The introduction of new algorithm has been one of the issues researchers focused in the past fifteen years. However, there is a critic that some of the new algorithms are not in fact new in terms of their search behavior. Hence, a comparative study in between existing algorithms to highlight their differences and similarity needs to be studied. Apart from knowing the similarity and difference in search mechanisms of these algorithms it will also help to set criteria on when to use these algorithms. In this paper a comparative study of prey predator algorithm and firefly algorithm will be discussed. The discussion will also be supported by simulation results on selected twenty benchmark problems with different properties. A statistical analysis called Mann—Whitney U 2 test is used to compare the algorithms. The theoretical as well as simulation results support that prey predator algorithm is a more generalized search algorithm, whereas firefly algorithm falls as a special case of prey predator algorithm by fixing some of the parameters of prey predator algorithm to certain values.

Volume: 24, Issue: 2

Hyperspectral Reflectance Imaging for Detecting Typical Defects of Durum Kernel Surface

by Feng-Nong Chen, Pu-Lan Chen, Kai Fan, Fang Cheng
Abstract

In recent years, foodstuff quality has triggered tremendous interest and attention in our society as a series of food safety problems. The hyperspectral imaging techniques have been widely applied for foodstuff quality. In this study, we were undertaken to explore the possibility of unsound kernel detecting (Triticum durum Desf ), which were defined as black germ kernels, moldy kernels and broken kernels, by selecting the best band in hyperspectral imaging system. The system possessed a wavelength in the range of 400 to 1,000 nm with neighboring bands 2.73 nm apart, acquiring images of bulk wheat samples from different wheat varieties. A series of technologies of hyperspectral imaging processing and spectral analysis were used to separate unsound kernels from sound kernels, including the Principal Component Analysis (PCA), the band ratio, the band difference and the best band. According to the selected bands, the best accuracy was 95.6, 96.7 and 98.5% for 710 black germ kernels, 627 break kernels and 1,169 healthy kernels,respectively. The result shows that the method based on the band selection was feasible.

Volume: 24, Issue: 2

Multi-objective optimization of slow moving inventory system using Cuckoo Search

by Achin Srivastav, Sunil Agrawal
Abstract

This paper focuses on the development of a multi-objective lot size–reorder point backorder inventory model for a slow moving item. The three objectives are the minimization of (1) the total annual relevant cost, (2) the expected number of stocked out units incurred annually and (3) the expected frequency of stockout occasions annually. Laplace distribution is used to model the variability of lead time demand. The multi-objective Cuckoo Search (MOCS) algorithm is proposed to solve the model. Pareto curves are generated between cost and service levels for decision-makers. A numerical problem is considered on a slow moving item to illustrate the results. Furthermore, the performance of the MOCS algorithm is evaluated in comparison to multi-objective particle swarm optimization (MOPSO) using metrics, such as error ratio, maximum spread and spacing.

Volume: 24, Issue: 2

Particle Swarm Optimization with Chaos-based Initialization for Numerical Optimization

by Dongping Tian
Abstract

Particle swarm optimization (PSO) is a population based swarm intelligence algorithm that has been deeply studied and widely applied to a variety of problems. However, it is easily trapped into the local optima and premature convergence appears when solving complex multimodal problems. To address these issues, we present a new particle swarm optimization by introducing chaotic maps (Tent and Logistic) and Gaussian mutation mechanism as well as a local re-initialization strategy into the standard PSO algorithm. On one hand, the chaotic map is utilized to generate uniformly distributed particles to improve the quality of the initial population. On the other hand, Gaussian mutation as well as the local re-initialization strategy based on the maximal focus distance is exploited to help the algorithm escape from the local optima and make the particles proceed with searching in other regions of the solution space. In addition, an auxiliary velocity-position update strategy is exclusively used for the global best particle, which can effectively guarantee the convergence of the proposed particle swarm optimization. Extensive experiments on eight well-known benchmark functions with different dimensions demonstrate that the proposed PSO is superior or highly competitive to several state-of-the-art PSO variants in dealing with complex multimodal problems.

Volume: 24, Issue: 2

The challenge of the Paris Agreement to contain climate change

by E. Grigoroudis, F. Kanellos, V. S. Kouikoglou, Y. A. Phillis
Abstract

Climate change due to anthropogenic CO 2 and other greenhouse gas emissions has had and will continue to have widespread negative impacts on human society and natural ecosystems. Drastic and concerted actions should be undertaken immediately if such impacts are to be prevented. The Paris Agreement on climate change aims to limit global mean temperature below 2 °C compared to the pre-industrial level. Using simulation and optimization tools and the most recent data, this paper investigates optimal emissions policies satisfying certain temperature constraints. The results show that only if we consider negative emissions coupled with drastic emissions reductions, temperature could be stabilized at about 2.5 °C, otherwise higher temperatures could possibly occur. To this end, two scenarios are developed based on the national emissions reduction plan of China and the USA. According to the simulation results, the objective of keeping temperature rise under 2 °C cannot be met. Clearly, negative emissions are needed if the Paris targets are to be given a chance for success. However, the feasibility of negative emissions mainly depends on technologies not yet developed. Reliance on future technological breakthroughs could very well prove unfounded and provide excuses for continued carbon releases with possible severe and irreversible climate repercussions. Thus, the Paris Agreement needs immediate amendments that will lead to stronger mitigation and adaptation commitments if it is to stay close to its goals.

Volume: 24, Issue: 2

Middleware for Internet of Things: Survey and Challenges

by Samia Allaoua Chelloug, Mohamed A. El-Zawawy
Abstract

The Internet of things (IoT) applications span many potential fields. Furthermore, smart homes, smart cities, smart vehicular networks, and healthcare are very attractive and intelligent applications. In most of these applications, the system consists of smart objects that are equipped by sensors and Radio Frequency Identification (RFID) and may rely on other technological computing and paradigm solutions such as M2 M (machine to machine) computing, Wifi, Wimax, LTE, cloud computing, etc. Thus, the IoT vision foresees that we can shift from traditional sensor networks to pervasive systems, which deliver intelligent automation by running services on objects. Actually, a significant attention has been given to designing a middleware that supports many features; heterogeneity, mobility, scalability, multiplicity, and security. This papers reviews the-state-of-the-art techniques for IoT middleware systems and reveals an interesting classification for these systems into service and agent-oriented systems. Therefore two visions have emerged to provide the IoT middleware systems: Via designing the middleware for IoT system as an eco-system of services or as an eco-system of agents. The most common feature of the two approaches is the ability to overcome heterogeneity issues. However, the agent approach provides context awareness and intelligent elements. The review presented in this paper includes a detailed comparison between the IoT middleware approaches. The paper also explores challenges that form directions for future research on IoT middleware systems. Some of the challenges arise, because some crucial features are not provided (or at most partially provided) by the existing middleware systems, while others have not been yet tackled by current research in IoT.

Volume: 24, Issue: 2

A Hybrid Modular Context-Aware Services Adaptation For A Smart Living Room

by Moeiz Miraoui, Sherif El-etriby, Chakib Tadj, Abdulbasit Zaid Abid
Abstract

Smart spaces have attracted considerable amount of interest over the past few years. The introduction of sensor networks, powerful electronics and communication infrastructures have helped a lot in the realization of smart homes. The main objective of smart homes is the automation of tasks that might be complex or tedious for inhabitants by distracting them from concentrating on setting and configuring home appliances. Such automation could improve comfort, energy savings, security, and tremendous benefits for elderly persons living alone or persons with disabilities. Context awareness is a key enabling feature for development of smart homes. It allows the automation task to be done pro-actively according to the inhabitant’s current context and in an unobtrusive and seamlessly manner. Although there are several works conducted for the development of smart homes with various technologies, in most cases, robust. However, the context-awareness aspect of services adaptation was not based on clear steps for context elements extraction (resp. clear definition of context). In this paper, we use the divide and conquer approach to master the complexity of automation task by proposing a hybrid modular system for context-aware services adaptation in a smart living room. We propose to use for the context-aware adaptation three techniques of machine learning, namely Naïve Bayes, fuzzy logic and case-based reasoning techniques according to their convenience.

Volume: 24, Issue: 2

Recent Advances in Mobile Grid and Cloud Computing

by Sayed Shah
Abstract

Grid and cloud computing systems have been extensively used to solve large and complex problems in science and engineering fields. These systems include powerful computing resources that are connected through high-speed networks. Due to the recent advances in mobile computing and networking technologies, it has become feasible to integrate various mobile devices, such as robots, aerial vehicles, sensors, and smart phones, with grid and cloud computing systems. This integration enables the design and development of the next generation of applications by sharing of resources in mobile environments and introduces several challenges due to a dynamic and unpredictable network. This paper discusses applications, research challenges involved in the design and development of mobile grid and cloud computing systems, and recent advances in the field.

Volume: 24, Issue: 2

A LIGHTWEIGHT APPROACH TO ACCESS TO WIRELESS NETWORK WITHOUT OPERATING SYSTEM SUPPORT

by Yonghua Xiong, Jinhua She, Keyuan Jiang
Abstract

Wireless network is crucial for the Mobile Transparent Computing (MTC), in which a mobile device without any Operating System (OS) support needs to load the demanded OSes and applications through accessing the wireless network connection. In this paper, a lightweight approach based on the Boot Management System (BMS) was proposed to ensure the wireless network connection before booting OS. In BMS, the Virtual File System (VFS) technology was used to drive the wireless network card and establish a stable network connection. A prototype of the BMS was tested on ARM11 hardware platform and the results demonstrate the validity of the BMS.

Volume: 24, Issue: 2

Soft Computing Techniques for Classification of Voiced/Unvoiced Phonemes

by Mohammed Algabri, Mohamed Bencherif, Mansour Alsulaiman, Ghulam Muhammad, Mohamed Amine Mekhtiche
Abstract

A method that uses fuzzy logic to classify two simple speech features for the automatic classification of voiced and unvoiced phonemes is proposed. In addition, two variants, in which soft computing techniques are used to enhance the performance of fuzzy logic by tuning the parameters of the membership functions, are also presented. The three methods, manually constructed fuzzy logic (VUFL), fuzzy logic optimized with genetic algorithm (VUFL-GA), and fuzzy logic with optimized particle swarm optimization (VUFL-PSO), are implemented and then evaluated using the TIMIT speech corpus. Performance is evaluated using the TIMIT database in both clean and noisy environments. Four different noise types from the AURORA database—babble, white, restaurant, and car noise—at six different signal-to-noise ratios (SNRs) are used. In all cases, the optimized fuzzy logic methods (VUFL-GA and VUFL-PSO) outperformed manual fuzzy logic (VUFL). The proposed method and variants are suitable for applications featuring the presence of highly noisy environments. In addition, classification accuracy by gender is also studied.

Volume: 24, Issue: 2

TUMOR CLASSIFICATION USING AUTOMATIC MULTI-THRESHOLDING

by Li-Hong Juang, Ming-Ni Wu
Abstract

In this paper we explore these math approaches for medical image applications. The application of the proposed method for detection tumor will be able to distinguish exactly tumor size and region. In this research, some major design and experimental results of tumor objects detection method for medical brain images is developed to utilize an automatic multi-thresholding method to handle this problem by combining the histogram analysis and the Otsu clustering. The histogram evaluations can decide the superior number of clusters firstly. The Otsu classification algorithm solves the given medical image by continuously separating the input gray-level image by multi-thresholding until reaching optimal smooth rate. The method solves exactly the problem of the uncertain contoured objects in medical image by using the Otsu clustering classification with automatic multi-thresholding operation.

Volume: 24, Issue: 2

Gender recognition based on computer vision system

by Li-Hong Juang, Ming-Ni Wu, Shin-An Lin
Abstract

Detecting human gender from complex background, illumination variations and objects under computer vision system is very difficult but important for an adaptive information service. In this paper, a preliminary design and some experimental results of gender recognition will be presented from the walking movement that utilizes the gait-energy image (GEI) with denoised energy image (DEI) pre-processing as a machine learning support vector machine (SVM) classifier to train and extract its characteristics. The results show that the proposed method can adopt some characteristic values and the accuracy can reach up to 100% gender recognition rate under combining the horizontal added vertical feature and using a normal image size and test data when people are walking at a fixed angle. Meanwhile, it will be able to achieve over 80% rate within some allowed fault-tolerant angle range.

Volume: 24, Issue: 2

Mobile Robots Navigation Modeling in Known 2d Environment Based on Petri Nets

by S. Bartkevicius, O. Fiodorova, A. Knys, A. Derviniene, G. Dervinis, V. Raudonis, A. Lipnickas, V. Baranauskas, K. Sarkauskas, L. Balasevicius
Abstract

The paper deals with supervised robot navigation in known environments. The navigation task is divided into two parts, where one part of the navigation is done by the supervisor system i.e. the system sets the vector marks on the salient edges of the virtual environment map and guides the robot to reach these marks. Mobile robots have to perform a specific task according to the given paths and solve the local obstacles avoidance individually. The salient point’s detection, vector mark estimation and optimal path calculation are done on the supervisor computer using colored Petri nets. The proposed approach was extended to simulate a flexible manufacturing system consisting of swarm of 17 robots, 17 - warehouses and 17 - manufacturing places. Our experimental investigation showed that simulated mobile robots with proposed supervision system were efficiently moving on the planned path.

Volume: 24, Issue: 2

Improved Geometric Anisotropic Diffusion Filter for Radiography Image Enhancement

by Mohamed Ben Gharsallah, Issam Ben Mhammed, Ezzedine Ben Braiek
Abstract

In radiography imaging, contrast, sharpness and noise there are three fundamental factors that determine the image quality. Removing noise while preserving and sharpening image contours is a complicated task particularly for images with low contrast like radiography. This paper proposes a new anisotropic diffusion method for radiography image enhancement. The proposed method is based on the integration of geometric parameters derived from the local pixel intensity distribution in a nonlinear diffusion formulation that can concurrently perform the smoothing and the sharpening operations. The main novelty of the proposed anisotropic diffusion model is the ability to combine in one process noise reduction, edge preserving and sharpening. Experimental results using both synthetic and real welding radiography images prove the efficiency of the proposed method in comparison with other anisotropic diffusion methods.

Volume: 24, Issue: 2

An Improved Lung Sound Denoising Method By Wavelet Packet Transform With Pso-Based Threshold Selection

by Qing-Hua HE, Bin YU, Xin HONG, Bo LV, Tao LIU, Jian RAN, Yu-Tian BI
Abstract

Lung abnormalities and respiratory diseases increase with the development of urban life. Lung sound analysis provides vital information of the present condition of the pulmonary. But lung sounds are easily interfered by noises in the transmission and record process, then it cannot be used for diagnosis of diseases. So the noised sound should be processed to reduce noises and to enhance the quality of signals received. On the basis of analyzing wavelet packet transform theory and the characteristics of traditional wavelet threshold de-noising method, we proposed a modified threshold selection method based on Particle Swarm Optimization (PSO) and support vector machine (SVM) to improve the quality of the signal, which has been polluted by noises. Experimental results show that the recognition accuracy of de-noised lung sounds by the improved de-noising method is 90.03%, which is much higher than by the other traditional de-noising methods. Meanwhile, the lung sound processed by the proposed method sounds better than by other methods. All results make it clear the modified threshold selection can obtain a better threshold vector and improve the quality of lung sounds.

Volume: 24, Issue: 2

New multi-layer method for Z-number ranking using Hyperbolic Tangent function and convex combination

by Somayeh Ezadi, Tofigh Allahviranloo
Abstract

Many practical applications, under the definitive evolutionary state of the nature, the consequences of the decisions, mental states of a decision maker are required. Thus, the need is for a new concept in the analysis of decision-making. Zadeh has introduced this concept as the Z-number. Because the concept is relatively new, Z-number in fuzzy sets, hence, its basic theoretical aspects are yet undetermined. This paper presents a method for ranking Z-numbers. Hence, we propose a new method for ranking fuzzy numbers based on that of hyperbolic tangent function and convex combination. Then, using the same technique we propose a method for ranking Z-numbers.

Volume: 24, Issue: 1

Introduction to U-Number Calculus

by R.A. Aliev
Abstract

Commonsense reasoning plays a pivotal role in the development of intelligent systems for decision-making, system analysis, control and other applications. As Prof. L. Zadeh mentions a kernel of the theory of commonsense is the concept of usuality. Zadeh suggested main principles of the theory of usuality, unfortunately up to present day; a fundamental and systemic approach to reasoning with usual knowledge is not developed. In this study, we develop a new approach to calculus of usual numbers (U-numbers). We consider a U-number as a Z-number, where the second component is “usually”. Validity of the suggested approach is verified by examples.

Volume: 24, Issue: 1

Z-Numbers and Type-2 Fuzzy Sets: A Representation Result

by R.A. Aliev, Vladik Kreinovich
Abstract

Traditional [0; 1] based fuzzy sets were originally invented to describe expert knowledge expressed in terms of imprecise “fuzzy” words from the natural language. To make this description more adequate, several generalizations of the traditional [0; 1] based fuzzy sets have been proposed, among them type-2 fuzzy sets and Z-numbers. The main objective of this paper is to study the relation between these two generalizations. As a result of this study, we show that if we apply data processing to Z-numbers, then we get type-2 sets of special type —that we call monotonic. We also prove that every monotonic type-2 fuzzy set can be represented as a result of applying an appropriate data processing algorithm to some Z-numbers.

Volume: 24, Issue: 1

Numerical solution of linear regression based on Z-numbers by improved neural network

by Somayeh Ezadi, Tofigh Allahviranloo
Abstract

In this article, the researcher at first focuses on introducing a linear regression based on the Z-number. In this regression, observations are real, but the coefficients and results of observations are unknown and in the form of Z-rating. Therefore, to estimate this type of regression, we have three distinct ways depending on different conditions dominating the problem. The three methods are a combination of artificial neural networks and fuzzy generalized improvements of the technique. Moreover the method of calculating the weights of the Z-number neural network has been mentioned and the stability of neural network weights is considered. In some examples, the answer is estimated compared with the original answer.

Volume: 24, Issue: 1

A Z-Number Valued Regression Model and its Application

by Lala Zeinalova, O. Huseynov, P. Sharghi
Abstract

Regression analysis is widely used for modeling of real-world processes in various fields. It should be noted that information relevant to real-world processes is characterized by imprecision and partial reliability. This involves combination of fuzzy and probabilistic uncertainties. Prof.. L. Zadeh introduced the concept of a Z-number as a formal construct for dealing with such information. The present state-of-the-art of regression analysis under Z-number valued information is very scarce. In this paper we consider a Z-number valued multiple regression analysis and its application to a real-world decision-making problem. The obtained results show applicability of the proposed approach.

Volume: 24, Issue: 1

Zet Theory

by Mark Wierman
Abstract

The theory of Zets is presented and the standard techniques of set theory allows for the development of a rich algebra of Zets. It shows that Zets and fuzzy sets are essentially interchangeable. However, the fundamental manipulations, techniques, and definitions of Zets are simple and more amenable to analyze. For example, the extension principle is easy to define.

Volume: 24, Issue: 1

Modeling of Consumer Buying Behaviour Using Z-Number Concept

by Gunay Sadikoglu
Abstract

Consumer behaviour has always been of a great interest in marketing research. The consumer buying behaviour has become an integral part of strategic market planning and includes mental, emotional and physical activities. The consumer behaviour and decision-making process are usually subject to uncertainties related to influences of socio-cultural, psychological and personal factors. In this paper, the Z-number concept is applied for handling uncertainties in analysing the consumer buying behaviour.

Volume: 24, Issue: 1

Failure Mode and Effects Analysis based on Z-numbers

by Wen Jiang, Chunhe Xie, Boya Wei, Yongchuan Tang
Abstract

The main objective of this paper is to propose a new method for failure mode and effects analysis (FMEA) based on Z-numbers. In the proposed method, firstly, Z-numbers are used to perform the valuations (Z-valuation) of the risk factors like occurrence (O), severity (S) and detection (D). Secondly, the Z-valuations of the risk factors are integrated by fuzzy weighted mean method. A new risk priority number named as ZRPN is calculated to prioritize failure modes based on a modified method of ranking fuzzy numbers. Finally, a case study for the rotor blades of an aircraft turbine is performed to demonstrate the feasibility of the proposed method.

Volume: 24, Issue: 1

The Identification of Job Satisfaction under Z-Information

by S. Eyupoglu, K. Jabbarova, K. Aliyeva
Abstract

Complexities in organizational and economical environments have lead psychologists, management scholars, and economists to investigate the multi-dimensional essence of job satisfaction. Unfortunately, existing studies are based on exact data, whereas relevant information is imperfect. To deal with imprecise and partially reliable information, Zadeh proposed the concept of a Z-number. In this paper we consider the Z-number valued rule based model to represent the relationship between job satisfaction and the facets/factors influencing job satisfaction. A real-world job satisfaction index evaluation problem is used to illustrate the suggested approach.

Volume: 24, Issue: 1

Numerical Solution of Fuzzy Equations with Z-numbers using Neural Networks

by Raheleh Jafari, Wen Yu, Xiaoou Li
Abstract

In this paper, the uncertainty property is represented by the Z-number as the coefficients of the fuzzy equation. This modification for the fuzzy equation is suitable for nonlinear system modeling with uncertain parameters. We also extend the fuzzy equation into dual type, which is natural for linear-in-parameter nonlinear systems. The solutions of these fuzzy equations are the controllers when the desired references are regarded as the outputs. The existence conditions of the solutions (controllability) are proposed. Two types of neural networks are implemented to approximate solutions of the fuzzy equations with Z-number coefficients.

Volume: 24, Issue: 1

On an optimization method based on Z-numbers and the multi-objective evolutionary algorithm

by Dong Qiu, Rongwen Dong, Shuqiao Chen, Andi Li
Abstract

In this paper, we research the optimization problems with multiple Z-number valued objectives. First, we convert Z-numbers to classical fuzzy numbers to simplify the calculation. A new dominance relationship of two fuzzy numbers based on the lower limit of the possibility degree is proposed. Then according to this dominance relationship, we present a multi-objective evolutionary algorithm to solve the optimization problems. Finally, a simple example is used to demonstrate the validity of the suggested algorithm.

Volume: 24, Issue: 1

The Research Of Address Message Of An Unknown Single Protocol Data Frame

by Zheng Jie, Li Jianping
Abstract

Network protocols are sets of standards for certain network communications. The identification and analysis of network protocol are of significance to network management and security. There are various technologies of protocol identification, but in the process of identification protocols, in order to simplify the identification process and improve the efficiency of protocol identification, unknown mixed multi-protocol needs to be separated into single protocol so as to make further identification. This paper presents an efficient method to determine the single protocol address message based on the previous research of separating unknown mixed data frame into single protocol. By this way, the data frames of single protocol are split into point-to-point data frame according to the address; consequently, the final identification of unknown protocol can be realized. Moreover, the method was evaluated by analysis of the ARP and TCP data; this method is able to find the more than 2/3 of address information.

Volume: 24, Issue: 1

The Study on Evaluation Method of Urban Network Security in the Big Data Era

by Qingyuan Zhou, Jianjian Luo
Abstract

Big data is an emerging paradigm applied to datasets whose size is beyond the ability of commonly used software tools to capture, manage, and process the data within a tolerable elapsed time. In a Smarter City, available resources are harnessed safely, sustainably and efficiently to achieve positive, measurable economic and societal outcomes. Most of the challenges of Big Data in Smart Cities are multi-dimensional and can be addressed from different multidisciplinary perspectives. Based on the above considerations, this paper combined the PSR method, the fuzzy logic model and the entropy weight method in an empirical study for feasible urban public security evaluation modeling. The PSR method was used to establish an evaluation index system regarding the essence of public security. The Entropy method was used in the weighing assignment process to verify the objectivity of this modeling. The fuzzy method was used for the quantitative analysis to determine the fuzziness of urban public security.

Volume: 24, Issue: 1

The Big Data Analysis On The Camera-Based Face Image In Surveillance Cameras

by Zhiguo Yan, Zheng Xu, Jie Dai
Abstract

In the Big-Data era, currently how to automatically realize acquisition, refining and fast retrieval of the target information in a surveillance video has become an urgent demand in the public security video surveillance field. This paper proposes a new gun-dome camera cooperative system, which solves the above problem partly. The system adopts a master-slave static panorama-variable view dual-camera cooperative video-monitoring system. In this dual-camera system the gun camera static camera) with a wide viewing -angle lenses is in charge of the pedestrian detection and the dome camera can maneuver its focus and cradle orientation to get the clear and enlarged face images. In the proposed architecture, Deformable Part Model (DPM) method realizes real-time detection of pedestrians. The look-up table method is proved feasible in a dual-camera cooperative calibration procedure, while the depth information of the moving target changes slightly. As respect to the face detection, the deep learning architecture is exploited and proves its effectiveness. Moreover, we utilize the Haar-Like feature and LQV classifier to execute the frontal face image capture. The experimental results show the effectiveness and efficiency of the dual-camera system in close-up face image acquisition.

Volume: 24, Issue: 1

Research On Application Of Location Technology In 3d Virtual Environment Modelling System For Substation Switch Indicator

by Lijuan Qin, Ting Wang, Chen Yao
Abstract

Substation inspection work plays a very important role in ensuring the normal production and the safe operation of a transformer substation. Use of a substation inspection robot can effectively solve the omission problems of a manual inspection. It can further improve the unmanned and automation of substation and improve security of station. A substation inspection system requires the establishment of a spatial position relation between the robot and the inspection object in the monitoring station. Key technology is the spatial location of the object being detected in the substation. The position of inspection object is unknown. This paper gives technical route of monocular vision positioning system based on switch indicator sign cooperative target. The knife switch indicator is one of the most important objects in the substation inspection. Usually the switch indicator lies in the middle or top of the substation equipment and conventional method can’t measure. This paper presents a substation label measurement method based on monocular vision. In this method, the label frame of knife switch indicator is as a cooperative target. The 3D coordinates and attitude of the label frame can be calculated in a camera coordinates system. At the same time, this paper introduces an extraction process of knife brake indicator label with multi-features under complex background. At last, we do simulation experiment for positioning technology of 3D virtual environment modeling system for substation switch indicator. Simulation results show that the method introduced in this paper can realize positioning of substation switch indicator label.

Volume: 24, Issue: 1

Enhancing Knowledge Management and Decision-Making Capability of China’s Emergency Operations Center Using Big Data

by Yefeng Ma, Hui Zhang
Abstract

Emerging communication and computing technologies such as social media, Internet of Things and big data provide great opportunities to improve information management systems for emergency operations. This paper studies the issues of information management at China’s Emergency Operations Center (EOC), and proposes a data-driven knowledge management system (KMS) to support decision-making, coordination, and collaboration within EOCs and with the public. In the proposed KMS, big data analytics is employed to gather and analyze information from different knowledge domains and track how a crisis evolves in physical world and in cyber space. The proposed system aims at improving situation awareness of public opinions and regulating human behaviors in regards to an emergency. A case study is presented to explain how the proposed system is applied to improve decision-making during emergency.

Volume: 24, Issue: 1

A Hot Event Influence Scope Assessment Method in Cyber-Physical Space For Big Data Application

by Yunlan Xue, Lingyu Xu, Jie Yu, Gaowei Zhang
Abstract

The increase of scale and complexity of Internet big data presents unprecedented opportunities on Cyber-Physical Systems (CPS). The incompleteness and incredibility of Internet big data are challenging issues for confirming the event influence scope. To solve the above problem, we propose Cyber-Physical Space Event Model (CPSEM) to analyze event influence in multi-viewer, which maps real data into Cyber Space (CS) and Physical Space (PS). In addition, we propose Event Influence Scope Detection Algorithm (EISDA) to detect the scope of a hot event in Cyber Space and Physical Space.

Volume: 24, Issue: 1

Public Health Emergency Management and Multi-Source Data Technology in China

by Xinzhi Wang, Yi Liu, H. Zhang, Qiuju Ma, Zhidong Cao
Abstract

Public health emergency is governed by the physical rules, which are related to the propagation of diseases in the physical space, and the social rules, which are related to government structures, social behaviors, and social media in cyber space. Effective preparedness and response of public health emergency is strongly related to interoperation, collaboration and cooperation among different levels of government agencies and among different regions, as well as information flow and data mining between agencies and general public. In this paper, we review the technologies using multi-source data for public health emergency in China. At the micro-level, risk analysis and operation plans are developed based on data analysis. At the macro-level, decision-making and strategical plans are realized based on the scenario-response method. As the results, public health emergencies can be detected and responded at the very early stage. As an example, Ebola monitoring and scenario response in China are presented.

Volume: 24, Issue: 1

Electro-Mechanical Impedance Based Position Identification of Bolt Loosening Using LibSVM

by Yuxiang Zhang, Xin Zhang, Jiazhao Chen, Jianhai Yang
Abstract

Bolt loosening is a common structural failure, which received extensive attention from many industrial departments. Because the uneven stress on different directions of a bolt is the common reason that the bolt becomes loose, it is quite important to carry out the research about sensitive detection of bolt loosening. Using Agilent 4924A instrument, this paper precedes the loosening test of bolts based on Electro-Mechanical Impedance (EMI), on an aluminum plate instead of flange plate for simplification. And the electromechanical admittance is given according to the fundamental equation of Piezo-Material Lead Zirconate Titanate (PZT). Specifically, the paper first studies the detecting sensitivity of EMI on bolt loosening; then, it shows that RMSD can be seen as a good damage index to identify damage; at last, our experiment result shows that by using LibSVM to process big data, the position of a loose bolt can be correctly identified from 12 possible bolt positions. The method mentioned in this paper shows the great potential to be used for the damage monitoring of bolted structure.

Volume: 24, Issue: 1

Analyzing and Assessing Reviews on Jd.com

by Jie Liu, Xiaodong Fu, Jin Liu, Yunchuan Sun
Abstract

Reviews are contents written by users to express opinions on products or services. The information contained in reviews is valuable to users who are going to make decisions on products or services. However, there are numbers of reviews for popular products, and the quality of reviews is not always good. It’s necessary to pick out reviews, which are in high quality from numbers of reviews to assist user in making decision. In this paper, we collected 21,501 reviews flagged as good from 499,253 products on JD.com. We observed the level of users is an important factor affects the quality of reviews, and users prefer to post short reviews containing the description of the quality and price of the product. We proposed a system to assess the quality of reviews automatically in this paper. We achieved that by applying SVM classification based on two kinds of features; reviews and reviewers that would help users find out high quality reviews and useful information from massive reviews. We evaluated our system on JD.com. The accuracy of our experiments for reviews quality assessing reached to 87.5 percent.

Volume: 24, Issue: 1

Building Ontology for Different Emotional Contexts and Multilingual Environment in Opinion Mining

by Wan Tao, Tao Liu
Abstract

With the explosive growth of various social media applications, individuals and organizations are increasingly using their contents (e.g. reviews, forum discussions, blogs, micro-blogs, comments, and postings in social network sites) for decision-making. These contents are typical big data. Opinion mining or sentiment analysis focuses on how to extract emotional semantics from these big data to help users to get a better decision. That is not an easy task, because it faces many problems, such as different context may make the meaning of the same word change variously, at the same time multilingual environment restricts the full use of the analysis results. Ontology provides knowledge about specific domains that are understandable by both the computers and developers. Building ontology is mainly a useful first step in providing and formalizing the semantics of information representation. We proposed an ontology DEMLOnto based on six basic emotions to help users to share existed information. The ontology DEMLOnto would help in identifying the opinion features associated with the contextual environment, which may change along with applications. We built the ontology according to ontology engineering. It was developed on the platform Protégé by using OWL2.

Volume: 24, Issue: 1

Design of a System to Generate a Four Quadrant Signal at High-Frequency

by Yichi Zhang, Hongbin Wang
Abstract

In order to research biological cells, a well-established physical method can be used, that is electrorotation. To achieve electrorotation system, a signal oscillator or a signal generator is always needed. The signal generator is used for generating signals with phase shift and transfers it to the electro chamber. The frequency range of the output signal is generally between 20 Hz to 100 MHz for the signal generator, but for high frequency range, like 100 MHz to 1 GHz, the signal generator is hard to control and the linear properties for the output signal is not good enough to do the electrorotation. So design a signal generator to generate a signal with high frequency range is indispensable, and this is good for researching the biological cells in high frequency environment. The project finished by doing research for some available signal generators, like Phase-Locked Loop (PLL) and Direct Digital Synthesis (DDS), and the final system with high frequency output signal has been designed after the research. The frequency range of the output signal is between 100 MHz to 1.35 GHz, and the phase shift is 90 degree for the four output signals. The system finally designed is based on analogue circuit, all of the system blocks are designed in Cadence virtuoso software and the CMOS technology is 0.35um. It will affect the big data collection, processing and storing the result of the formation of the entire process

Volume: 24, Issue: 1

Meteorological correction model of IBIS-L System in the Slope Deformation Monitoring

by Xiaoqing Zuo, Hongchu Yu, Chenbo Zi, Xiaokun Xu, Liqi Wang, Haibo Liu
Abstract

Micro deformation monitoring system (IBIS-L) using high frequency microwave as signal for transmission, is easily affected by meteorology. How to eliminate the meteorological influence effectively, and extract useful information from the big data becomes a key to monitor the slope deformation with high precision by the IBIS-L system. Evaluation of the optimum meteorological correction mode for Slope Deformation Monitoring to ensure the accuracy of measurement is considered. This objective was realized by model construction technology, which uses calculation formula of Microwave Refraction rate, and the radial distance from the target point to the IBIS-L system to estimate the irreal displacement by meteorological influence. In this paper we examine feasibility and accuracy of the meteorological correction model via experiment analysis. This experiment takes the Nuozhadu hydropower station slope monitoring for example. Firstly, the temperature, humidity, air pressure and other meteorological parameters were measured simultaneously with IBIS-L system monitoring. Secondly, the measured meteorological parameters were taken into calculation formula of Microwave Refraction rate. Thirdly, combined with the radial distance from the target point to the IBIS-L system, the meteorological correction model in using IBIS-L system for slope deformation monitoring was established.

Volume: 24, Issue: 1

Verifiable Outsourcing of High-Degree Polynomials and its Application in Keyword Search

by Jun Ye, Xianlin Zhou, Zheng Xu, Yong Ding
Abstract

In big data era, people cannot afford more and more complex computation work due to the constrained computation resources. The high reliability, strong processing capacity, large storage space of cloud computing makes the resource-constrained clients remotely operate the heavy computation task with the help of cloud server. In this paper, a new algorithm for secure outsourcing of high degree polynomials is proposed. We introduce a camouflage technique, which the real polynomial will be disguised to the untrusted cloud server. In addition, the input and output will not be revealed in the computation process and the clients can easily verify the returned result. The application of the secure outsourcing algorithm in keyword search system is also studied. A verification technique for keyword search is generated based on the outsourcing algorithm. The client can easily verify whether the server faithfully implement the search work in the whole ciphertext space. If the server does not implement the search work and returns the client “null” to indicate there is no files with the query keyword, the client can easily verify whether there are some related files in the ciphertext database.

Volume: 24, Issue: 1

A Complex Networked Method of Sorting Negotiation Demand Based on Answer Set Programs

by Hui Wang, Liang Li, Long-yun Gao, Wu Chen
Abstract

With the development of big data science, handling intensive knowledge in the complex network becomes more and more important. Knowledge representation of multi-agent negotiation in the complex network plays an important role in big data science. As a modern approach to declarative programming, answer set programming is widely applied in representing the multi-agent negotiation knowledge in recent years. But almost all the relevant negotiation models are based on complete rational agents, which make the negotiation process complex and low efficient. Sorting negotiation demands is the most key step in creating an efficient negotiation model to improve the negotiation ability of agents. Traditional sorting method is not suitable for the negotiation in the complex network. In this paper, we propose a complex networked negotiation, which can show the relationships among demands, and then a sorting method of negotiation demands is proposed based on demand relationships. What’s more, we use the betweenness of literals and the boundary co-efficient of rules to evaluate the importance of demands and rules.

Volume: 24, Issue: 1

Study for Multi-Resources Spatial Data Fusion Methods in Big Data Environment

by Zhiquan Huang, Yu Fu, Fuchu Dai
Abstract

The rapid development and extensive application of geographic information system (GIS) and the advent of the age of big data bring about the generation of multi-resources spatial data, which makes data integration and fusion share more difficult due to the differences on data source, data accuracy and data modal. Meanwhile, study for multi-resources spatial data fusion methods has an important practical significance for reducing the production cost of geographic data, accelerating the updating speed of existing geographical information and improving the quality of GIS big data. To expound the formation and developing trends of multi-resources spatial data fusion methods systematically, and on the basis of referring to lots of related technical documents both at home and abroad, this paper makes a conclusion and discussion about multi-resources spatial data fusion methods, and foresees the prospects of data fusion in big data environment, which has certain reference value for the related research work.

Volume: 24, Issue: 1

Sox compliance with OEE, enterprise modeling and temporal-abc

by K. Donald Tham, Asad M. Madni
Abstract

The Sarbanes-Oxley (SOX) Act 2002 resulted from the mounting accounting and corporate scandals in the late 1990s and early 2000s. Since the passage of the SOX Act, companies are facing even greater challenges to meet raised expectations to provide accurate, visible, and timely information for SOX compliance. This research puts forth a systems design framework to achieve a real time, accurate, consistently traceable and easily verifiable SOX compliant technology. Our multidisciplinary and integrative systems design incorporates Overall Equipment Effectiveness (OEE) to ensure effective business performance within a knowledge represented company modeled as Enveloped Activity Based Enterprise Model (EABEM) that facilitates Temporal-Activity Based Costing (ABC) so as to effectively lead to accurate, traceable and verifiable operational cost transparencies necessary for SOX compliance.

Volume: 24, Issue: 1

An efficient hybrid algorithm for a bi-objectives hybrid flow shop scheduling

by S.M. Mousavi, M. Zandieh
Abstract

This paper considers the problem of scheduling n independent jobs in g-stage hybrid flow shop environment. To address the realistic assumptions of the proposed problem, two additional traits were added to the scheduling problem. These include setup times, and the consideration of maximum completion time together with total tardiness as objective function. The problem is to determine a schedule that minimizes a convex combination of objectives. A procedure based on hybrid the simulated annealing; genetic algorithm and local search so-called HSA-GA-LS are proposed to handle this problem approximately. The performance of the proposed algorithm is compared with a genetic algorithm proposed in the literature on a set of test problems. Several performance measures are applied to evaluate the effectiveness and efficiency of the proposed algorithm in finding a good quality schedule. From the results obtained, it can be seen that the proposed method is efficient and effective.

Volume: 24, Issue: 1

Fault diagnoses of hydraulic turbine using the dimension root similarity measure of single-valued neutrosophic sets

by Jun Ye
Abstract

This paper proposes a dimension root distance and its similarity measure of single-valued neutrosophic sets (SVNSs), and then develops the fault diagnosis method of hydraulic turbine by using the dimension root similarity measure of SVNSs. By the similarity measures between the fault diagnosis patterns and a testing sample with single-valued neutrosophic information and the relation indices, we can determine the main fault type and the ranking order of various vibration faults for predicting some possible fault trend. Then, the comparison of the fault diagnoses of hydraulic turbine based of the proposed dimension root similarity measure and the existing cotangent similarity measure of SVNSs is provided to demonstrate the effectiveness and rationality of the proposed fault diagnosis method. The fault diagnosis results of hydraulic turbine show that the proposed fault diagnosis method not only gives the main fault types of hydraulic turbine, but also provides useful information for multi-fault analyses and future possible fault trends. The developed fault diagnosis method is effective and reasonable in the fault diagnosis of hydraulic turbine under single-valued neutrosophic environment.

Volume: 24, Issue: 1

Adaptive Intelligent Single Particle Optimizer Based Image De-noising in Shearlet Domain

by Jia Zhao, Tanghuai Fan, Li Lü, Hui Sun, Jun Wang
Abstract

Adaptive intelligent single particle optimizer is proposed based on analyzing the deficiency of intelligent single particle optimizer, learning characteristics of particle swarm optimization, and introducing the Cauchy mutation. In the evolution of the algorithm, the particles not only learn from themselves, and can learn from their own historical experience, and finally can decide their velocity and position. Image edge blur is obtained by using the traditional nonlinear diffusion image de-noising method; Shearlet is a new-style multi-scale geometry analysis tool. It creates Shearlet functions, which have different characteristics through zooming, shearing translating and other affine transforming methods and enables its capable of optimally sparse representation. The paper proposed discusses adaptive intelligent single particle optimizer based image de-noising in Shearlet domain. Experimental results show that the method can effectively filter out image noise and better retain edge information, especially to the images containing abundant texture. Meanwhile, the de-noised images have higher Peak Signal to Noise Ratio.

Volume: 23, Issue: 4

Object Motion Detection and Data Processing in Large-Scale Particle Image Velocimetry

by Mengxi Xu, Quansen Sun, Chenrong Huang, Jianqiang Shi
Abstract

Large-Scale Particle Image Velocimetry (LSPIV) is a non-intrusive imaging measurement method of the river surface flow velocity. However, in natural environments with the impact of the shadow and strong light, small objects on the water surface can only be seen as tiny points with little pixels, which degrade the performance of existing PIV methods in the lab in the view of the requirements of continuous tracer measurement and tracking. We propose an algorithm for tracer particles detection and image data processing of complex water surfaces. A combination of Top-Hat transform and adaptive threshold segmentation is utilized to detect the floating small objects first followed by a pre-matching based on the shape features of a single particle. Finally, a fine matching is carried out based on the similarity among the particles and motion distance without error vectors. The experimental results show that the proposed method has a higher detection rate for the small target detection in the river environment and can further improve the estimation accuracy of the tracer particle motion vector. The proposed method can solve the problem of the detection and estimation of motion vector of tiny targets under complex surface optical environment.

Volume: 23, Issue: 4

A Novel Solution to the Cognitive Radio Decision Engine Based on Improved Multi-Objective Artificial Bee Colony Algorithm and Fuzzy Reasoning

by Xiaojian You, Xiaohai He, Xuemei Han
Abstract

Targeting at the parameters reconfiguration of a cognitive radio system, a novel cognitive decision engine (CDE), based on an improved multi-objective artificial bee colony (IMOABC) algorithm and fuzzy reasoning, is proposed. First, a group of Pareto optimal solutions were obtained by applying IMOABC to solve CDE, and an optimal solution that meets user needs was selected by fuzzy reasoning. Such IMOABC algorithm was integrated into society cognitive strategies. New production and preservation mechanisms of individuals and parallel hybrid coding and multi-dimensional evolution strategies are evaluated. The proposed algorithm was evaluated on a set of standard test functions. Combined with the multi-carrier communication system, the simulation experiments are performed for reconfiguring of the physical layer parameters, and the results are able to satisfy user needs.

Volume: 23, Issue: 4

Research on the Learning Method Based on PCA-ELM

by Y. Z. Miao, X. P. Ma, S. P. Bu
Abstract

The Single-hidden Layer Feed-forward Neural Network has been widely applied in the fields such as pattern recognition, automatic control and data mining. However, the speed of the traditional learning method, since it is far from enough to satisfy the actual demand has become the main bottleneck, which restricts its development. As one of the new learning methods, the extreme learning machine (ELM) has its own remarkable characteristics, but the fact that ELM is based on the Empirical Risk Minimization may lead to over fitting. In addition, ELM does not consider the weight of error, so its performance will be severely affected when there are outliers in data integration. To solve the above problems, this paper referred to the two algorithms including PCA (Principal Component Analysis) and ELM, and put forward a learning method and prediction model, which combined PCA and ELM. From the results of simulation analysis, as combining advantages of PCA and ELM algorithms, the network structure can be simplified to improve the learning ability and its prediction precision.

Volume: 23, Issue: 4

Production State Trend Prediction and Control for Industry Data by LS-Ann

by Junlin Qiu, Chenming Li, Lei Qiu, Hui Liu, Lizhong Xu
Abstract

The modern industry data is characterized by large volume, large variety, low density value and high processing velocity. Hence, it is difficult to use industry big data for effectively analyzing the trend and the production state by traditional methods. Aiming to solve a problem, a technology platform and data processing framework are established, and the LS-ANN (least square-artificial neural network) method is applied to process the industry big data by analyzing the corresponding technological process and the working principle. By efficiently processing the time series data, this method gives the industry production process with the ability of self-adaptation and fault tolerance. The effectiveness of the proposed method is demonstrated by experimental simulations.

Volume: 23, Issue: 4

An SP-Tree-Based Web Service Matching Algorithm Considering Data Provenance

by Guoyan Xu, Jianxiang Luo, Xin Lv, Li Yang, Ming Tang
Abstract

Currently the semantic-based Web service matching has improved the precision ratio of service discovery. But it can't distinguish between different data on the different needs of the service, due to rarely considering the input data provenance of Web services. That is an important contribution of high precision ratio. Therefore, in this paper, for further improving the accuracy of Web service matching, the data provenance is considered as a constraint attribute of Web service matching. Firstly, an SP-tree-based Web service matching algorithm considering data provenance (SMDP) is proposed, consisting of three aspects: The general algorithm thoughts, the data How provenance matching calculation method, (which is mainly introduced for it is the hardest calculation part), and the experiments verifying precision ratio of SMDP is 10% higher than the traditional semantic-based matching. Secondly, a Web service matching model based on SMDP is designed and a prototype system for water resources application is implemented, which confirms the feasibility and effectiveness of SMDP, especially, the importance of data How provenance in service matching.

Volume: 23, Issue: 4

Chinese WeChat and Blog Hot Words Detection Method Based on Chinese Semantic Clustering

by Yu Wang, Sixin Song, Fanfan Zhou, Xiaoying Zheng
Abstract

This paper proposes a hot topic detection method based on Chinese semantic clustering. The method is aimed at high-dimensional Chinese WeChat and fragmentation of information. In order to analysis the sparse and content fragmentation features of Chinese WeChat and Blog data, we combine multiple strategies that repeated string computation, context adjacency analysis and linguistic rule filtering to abstract meaningful sentences, which can express independent and complete semantics. Then we construct the model of Chinese WeChat data in a relatively small and meaningful string space, and generate candidates2019 topics via feature clustering and pick up the hot topics according to the heat sorting. The experimental result on the WeChat data and Blog data shows that the method can reduce the dimension of high-dimension sparse space of the blog in a way, which is effective and feasible to the WeChat hot topic detection method.

Volume: 23, Issue: 4

A New Frequent Pattern Mining Algorithm with Weighted Multiple Minimum Supports

by Haoran Zhang, Jianwu Zhang, Xuyang Wei, Xueyan Zhang, Tengfei Zou, Guocai Yang
Abstract

Association rules mining is one of the momentous areas in data mining. Frequent patterns mining plays an important role in association rules mining. The effects of traditional frequent patterns mining with same minimum support are highly affected by the value of minimum support. But, for many real datasets, it2019s hard to choose the value of minimum support. Too small values of minimum support may cause rules explosion, and too large values may cause rare item dilemma. In this paper we propose an improved approach to extract frequent patterns, which are more interesting to users. Because of the different characteristics of each item, we assign a multiple minimum support and weight based on item support and users2019 interests for each item. In order to define the minimum supports of itemsets, we suggest a novel method, which exploits the minimum constraint and maximum constraint to deal with the rare item dilemma and rules explosion problem. The combination of minimum constraint and maximum constraint is based on the weight of the itemset. In this way, we extend the support confidence framework. Experimental results show that the proposed approach is more efficient than other comparing methods.

Volume: 23, Issue: 4

Implementation of Web Mining Algorithm Based on Cloud Computing

by Wei Wu, Yanming Chen, Dewen Seng
Abstract

The rapid growth of the Internet exceeds all expectations. The analysis and mining of huge amounts of web data is facing a bottleneck in computing power and storage space. Through the use of cloud computing technology, we can facilitate the network access to powerful computing power, storage capacity and infrastructure. Cloud computing can effectively solve the problems by providing a data processing storage center of high reliability and scalability, which will improve the ability to process web data and reduce the requirements of the terminal devices. This paper studies web mining algorithms in a cloud computing environment. The web data mining algorithm and the MapReduce programming model are combined. We study the web mining techniques, especially the K-centers clustering algorithm, explore the combination of web mining algorithms and cloud computing technology and improve the data mining algorithms to adapt to the analysis and processing of mass web data based on cloud computing platforms. Our study constructs a distributed cloud environment using a Hadoop framework. In the experimental environment, we analyze the impact on computational performance by setting different block size parameters. Here, the block size determines the number that the pending data file is split, and the corresponding scale and amount of parallel calculation.

Volume: 23, Issue: 4

Evaluation of Search Engine Weight by Considering Repeated Web Page Contents

by Hui Zhou, Chao Li, Yimin Wang
Abstract

The ranking of search results largely determines the quality of service (QoS) of a meta-search engine (MSE). To address the demand of big data applications, this paper proposes a new method considering factors such as network bandwidth, client and limit server resources. In this method, Web pages with the same contents (but with different URLs) are identified by calculating similarity among contents of pages traversed by the user and those of pages not yet traversed. Hence, deviation of statistics about the user2019s intent for traversing caused by factors such as ranking differences in the orders of traversing and repeated contents of Web pages can be eliminated. While a search service is being provided, each component search engine (CSE) weight can be given dynamically before returned results receive a second rotary ranking in combination with initial ranking information. Experimental results and statistics show that (1) the numbers of traversals and downloads can be decreased; (2) the ratio of the number of pages clicked by the user to that of pages navigated can also be decreased; (3) the matching degree between searches/traversals and returned results can be increased; and (4) the stability of a search engine can be improved by taking into account the factor of repeated contents of Web pages.

Volume: 23, Issue: 4

Two-phase PT-Top

by Yingchi Mao, Haishi Zhong, Hao Chen, Xiaofang Li
Abstract

Uncertain data has become ubiquitous due to the development of Internet of Things (IOT) for collecting data in an imprecise way, such as in the dam safety monitoring applications. Efficient Top-k processing of uncertain data is an important requirement in the field of dam safety monitoring. In order to reduce energy consumption and query response time in the applications of IOTs, an uncertain data PT-Topk query processing scheme was studied in a hierarchical structural sensor network. Based on the x-tuple Rule of uncertain data, adopting intra-cluster and inter-cluster two phases query processing, a distributed Two-Phase PT-Topk Query Processing approximation algorithm (TPQP) was proposed. In the intra-cluster phase and inter-cluster phase, the local and global pruning upper bounds can be computed respectively. The data ranked lower than the two bounds cannot be forwarded to the sink node. Therefore, the proposed TPQP algorithm can reduce the transmission cost and shorten the query response time. The extensive experiment results demonstrate that TPQP can significantly reduce the transmission cost against the centralized algorithm by 87.51%, and shorten the query response time by 6%-31% and 35%-54% compared to BB and SSB, respectively. Meanwhile, TPQP can obtain the error rate below 5.5% in the different probability p and ranking number k.

Volume: 23, Issue: 4

by Jian Guo, Lijuan Sun, Chong Han, Ling Liu
Abstract

Ubiquitous sensing enabled by wireless sensor network results in increasingly large amounts of sensor data. Effective data storage and query are effective means of dealing with this issue, and distributed storage technology is a focus in the current research. This paper focuses on the issue of storage node selection, and discusses how to select k nodes as storage nodes when the data generating speed of nodes are different. This problem is formulated as a k-storage-node problem in this paper and proven to be NP-hard, then three distributed storage schemes are proposed; random strategy based data storage scheme (RDS), reverse greedy strategy based data storage scheme (GDS), and SQGA (small world model based quantum genetic algorithm) based data storage scheme (SDS). Simulation results showed that GDS and SDS had better performance than RDS in the network lifecycle, energy consumption, storage delay and query delay. Furthermore, taking the balance of node energy consumption into account, SDS performed better.

Volume: 23, Issue: 4

A Cooperative Dynamic Cluster in Multitasking Mobile Networks

by SHUFANG XU, Dazhuan Xu, Yingchi Mao, Huibin Wang
Abstract

Cluster as a classical paradigm has been often used in a multitasking mobile network and evolved into a dynamic cluster, which is motivated by the tasks with dynamic scale and nodal locations. An important question is how to synthesize the power of multiply members in a dynamic cluster and enhance the overall performance of the network. The almost universal setting is that most clusters are based on the fully connected network and the same level of tasks. To solve these questions, it should evaluate and develop the cooperation between nodes in a dynamic cluster and further propose a corresponding cooperation model. In this paper, the theoretical analysis and the basic models of cluster and dynamic cluster are introduced at first. Then an improved cooperation model of a dynamic cluster is proposed in multitasking mobile networks, which is named, cooperative dynamic cluster. As the main elements of the proposed model, the network connectivity and task priorities are also considered and discussed. Finally, a cooperative message forwarding mechanism is established to settle the cluster, the scales of which should be bounded at a reasonable level in view of a time cost. Simulation results demonstrate the efficiency of our proposed cooperative model was higher than the general dynamic cluster and normal method without cluster, respectively about 14% and 66% higher.

Volume: 23, Issue: 4

PROXZONE: One Cloud Computing System for Support PaaS in Energy Power Applications

by Yanhong Sun, Xiaofang Li, Yingchi Mao, Weihua Fang
Abstract

As a core of the cloud computing, PaaS (Platform as a Service) provides a computing and software service platform to support large-scale cloud services. This paper presents one cloud computing system, PROXZONE and introduces its architecture, service interface, user and service administration. We focus on its working ways of cloud scheduling service, cloud uniform authorization service and cloud message service. PROXZONE has been used in state grid management.

Volume: 23, Issue: 4

A Load Balancing Method For Massive Data Processing Under Cloud Computing Environment

by Jianhua Peng, Ming Tang, Ming Li, Zhiqin Zha
Abstract

High processing efficiency and equalization are needed when a cloud computing system is used to deal with massive data. Better load balancing methods can further improve the data processing ability of the cloud computing system. In this paper, we first defined the data process efficiency (DPE) and relatively free rate (RFR). Then based on the DPE and RFR, we proposed a load balancing method for massive data (LBMM). And we further described the flow of the LBMM method. Finally, we compared the LBMM method and the consistent hashing method through experiments. The experimental results showed that the LBMM method had better data processing equalization and higher data processing efficiency.

Volume: 23, Issue: 4

Oral health promotion program for fostering self-management of the elderly living in communities

by Reiko Sakashita, Misao Hamada, Takuichi Sato, Yuki Abiko, Miho Takami
Abstract

Objectives: A program fostering self-management for the elderly was implemented and the effects of the program and their continuities were assessed. Methods: Subjects consisted of 19 males and 131 females (average age, 73.100A000B100A07.4; range, 6020139400A0years). The intervention program consisted of the collective experience learning and private consultation. The collective experience learnings included; (1) monitoring the oral condition and practicing oral self-care, (2) monitoring the oral function and practicing oral exercises, and (3) group discussion on continuing oral self-care. Outcomes were evaluated at the beginning and the end of the intervention program, and three months after the investigation by the scores in; (1) oral self-care (2) oral condition, i.e., decayed teeth, community periodontal index (CPI), deposits of plaque and dental calculus, (3) oral function such as RSST, oral diadochokinesis, (4) QOL (SF-8 v22122, and GOHAI), and (5) cognitive function (MMSE-J). Informed consent was obtained from all subjects, and this study was approved by the Research Ethics Committee of the University of Hyogo. Results and Discussion: At three months after intervention, 124 subjects continued participating and 88 subjects (71%) completed all data. On the oral self-care, subjects cleaned their teeth more often and longer than before (P lt 0.001). The use of dental floss and interdental brushing significantly increased in number (P lt 0.001), and 67 participants (54%) visited the dentist during the program. CPI and deposits of plaque were significantly reduced after intervention (P lt 0.001). The scores of oral function also significantly improved (P lt 0.00120130.05). The scores of QOL (physical health), oral QOL and cognitive function significantly improved (P lt 0.00120130.05). These results suggest that this program not only promotes oral self-care, resulting in good oral health conditions, but also improves cognitive functions of the elderly.

Volume: 23, Issue: 3

An intensive study on rule acquisition in formal decision contexts based on minimal closed label concept lattices

by Jinhai Li, Chenchen Huang, Changlin Mei, Yunqiang Yin
Abstract

Rule acquisition is one of the main purposes in the analysis of formal decision contexts. Up to now, there have been several types of rules in formal decision contexts. However, these existing rules were investigated independently and are, to some extent, unsatisfactory for making better decision analysis since some of them (e.g. decision implications) are computationally expensive and others (e.g. decision rules and granular rules) are short of compactness. Motivated by these problems, minimal closed label concept lattice is defined to present limitary decision implications, which not only are easier to be extracted than decision implications, but also have more concise premises than decision rules and granular rules. Moreover, we discuss the pairwise inclusion and inference relationships among limitary decision implications, decision implications, decision rules and granular rules. Finally, some numerical experiments are conducted to demonstrate that the proposed limitary decision implications are easier to be extracted than decision implications.

Volume: 23, Issue: 3

Activity recognition method based on weighted LDA data fusion

by JunHuai Li, Yang An, Rong Fei, HUAIJUN WANG, QISONG YAN
Abstract

Human Activity Recognition (HAR) has a positive impact on people2019s well-being and it can help decrease the occurrence of chronic diseases in the senior population. The main purpose of this paper is to present a novel activity recognition method based on missing data processing and multi-sensor data fusion that can be applied to identify Activities of Daily Living (ADLs). Hereinto, missing data processing based on the temporal correlation is presented first to estimate the missing data, which utilizes the neighboring non-missing values to construct a linear spline model. Then, considering that sensors on different body positions may play as 201Cexperts201D on different activity classes, a multi-sensor fusion method based on weighted Linear Discriminant Analysis (LDA) to learn activity-specific sensor weights is presented. Successively, an activity recognition method based on missing data processing and weighted LDA data fusion is proposed, which can further enhance data quality and the recognition accuracy. Experimental results show that the proposed method is more effective and robust, and its performance is competitive against other state-of-the-art methods.

Volume: 23, Issue: 3

Region based find and spray scheme for co-operative data communication in vehicular cyber-physical systems

by Poongodi Chinnasamy, Premalatha J, Lalitha K, Vijay Anand D
Abstract

Smart transportation in Vehicular Cyber Physical Systems (VCPS) are addressing a range of problems including reducing traffic accidents, congestion, fuel consumption, time spent on traffic jams, and to improve transportation safety. Smart transportation VCPS is expected to contribute an important role in the design and development of intelligent transportation systems. Intermittently Connected Vehicular Networks (ICVNs) are a kind of intelligent transportation systems, where due to mobility of vehicles there may be disconnections among the source and destination vehicles. A multi-hop scenario is used here, where a stationary road side unit exchanges data packets with a destination outside its communication range, using passing-by vehicles as information carriers. In order to deal with these ICVNs, an efficient routing scheme is necessary to withstand the maximum delay. We propose a routing scheme, which makes use of the movement history among the regions for routing a message. The proposed scheme tries to use the single copy routing wherever possible and then do fuzzy controlled multiple replications. Results show that the proposed scheme outperforms existing methods in terms of packet delivery ratio, packet delay and protocol overhead.

Volume: 23, Issue: 3

Design of an improved PSO algorithm for workflow scheduling in cloud computing environment

by N SADHASIVAM, P THANGARAJ
Abstract

Workflows have been used to represent a variety of applications involving high processing and storage demands. As a solution to supply this necessity, the cloud computing paradigm has emerged as an on demand resource provider. Cloud computing environments facilitate applications by providing virtualized resources that can be provisioned dynamically. User applications may incur large data retrieval and execution costs when they are scheduled taking into account of 2018execution time2019 only. In this work, proposed is an Improved Particle Swarm Optimization (IPSO) to schedule applications in cloud resources. The IPSO is used to minimize the total cost of placement of tasks on available resources. Total cost values are obtained by varying the communication cost between the resources, task dependency cost values, and the execution cost of compute resources. Compared with standard PSO, the results show that the improved algorithm is efficient.

Volume: 23, Issue: 3

Availability modeling for multi-tier cloud environment

by G Nalinipriya, K G Maheswari, Balamurugan Balusamy, K Kotteswari, Arun Kumar Sangaiah
Abstract

Performance modeling forms an essential process for evaluation of cloud quality. Cloud performance appraisal process and methods widely differ from that of other proven performance related methodologies being used for domains like computer networks, distributed computing and operations systems. Multi-tier Cloud is a scalable system in which many services or tiers can be constructed for all types of applications. The quality of service of a Multi-tier cloud environment is closely associated with several factors like dependability, availability, reliability, security, perform-ability and each of the performance entities directly or indirectly influences the overall functioning of the cloud. There are many models to evaluate cloud performance and quality, but these traditional models are not efficient enough and consider only certain primary parameters in their evaluation. In this paper, a high-level performance analysis model is proposed that can predict the availability of a Multi-tier cloud environment. As Multi-tier cloud deals with multiple services according to their application, the prediction of availability is essential one for performance modeling. The various prominent parameters that influence the performance of the cloud system are also considered in the proposed model. The proposed algorithm is experimentally verified by means of SHARPE tool.

Volume: 23, Issue: 3

Broker based trust architecture for federated healthcare cloud system

by K. Mohan, M Aramudhan
Abstract

Cloud healthcare is the challenging, growing exponentially and heterogeneous technical environment involving various roles such as medical practitioners, pharmacists, patients and IT professionals to access the information through different technologies and types of devices. In this paper, federated healthcare cloud broker architecture is proposed where the independent health service providers can be integrated together to form a large healthcare federation, fulfill the requirements of users by sharing the available resources at different locations with affordable prices and provides Quality of Service (QoS) to all end users. Different trust mechanisms such as Policy based trust; SLA verification trust and reputation based trust are computed to ensure the security and privacy of the users, who accessing the services in the proposed architecture. Service Measurement Index (SMI) attributes suggested by the Cloud Service Index Measurement Consortium (CSIMC) used to calculate trust values of the health provider, based on the calculated trust value, the selection is proposed for the specific request that helps to improve the reliability, security and privacy. To resolve the strict treatment of the differentiated module, a new mechanism is suggested as Patient Turned Queuing Scheduling (PTQS) to resolve the possibility of starvation happening in the existing differentiated modules. Simulation results show that the performance of the proposed mechanism is better than the existing random provider and feedback based selection mechanism in the related works.

Volume: 23, Issue: 3

The application of 3D fruit fly optimization algorithm to the keywords analysis of Macau2019s international relations

by Shianghau WU
Abstract

In order to comprehend the new policy directions regarding Macau2019s participation in international organizations, this paper made the depth interviews and applied the text mining analysis to the interviewer2019s responses. Through applying the 3D fruit fly optimization algorithm (3D FOA) and our modified 3D FOA model to make the classification analysis of interviewees2019 keywords. The study concluded the modified 3D FOA model has the better performance in the optimization process, and the key points for our interviewees regarding Macau2019s participation in international organizations include the Basic Law2019s regularization and Macau2019s participation in international economic organization.

Volume: 23, Issue: 3

Fuzzy TOPSIS evaluation approach for business process management software acquisition

by Saeed Rouhani, Ahad Zare Ravasan
Abstract

At the present time, Business Process Management software (BPMS) implementation is on focus of organizations. To implement a BPMS in a firm successfully, selecting a suitable BPMS is critical. Evaluation and selection of the BPMS software is complex and a time consuming decision-making process. This paper propounds an approach for dealing with such a decision. This approach introduces functional, non-functional and fuzzy evaluation method for BPMS evaluation. The presented BPM lifecycle based approach breaks down BPMS evaluation and selection criteria into two broad categories namely functional and non-functional requirements including totally 48 selection criteria. A facile Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) is customized for BPMS selection based on identified criteria. The provided approach is applied to a port and maritime organization in order to evaluate and acquire a BPMS and the provided numerical example illustrates the applicability of the approach. The approach can assist practitioners to evaluate BPMSs more effective.

Volume: 23, Issue: 3

Modeling and generating native code for cross-platform mobile applications using DSL

by Mohamed Lachgar, Abdelmounaïm Abdali
Abstract

A few years ago, mobile development technology has been quickly growing, and it has also been an emerging trend. Furthermore, different smartphones use diverse operating systems, which support different programming languages. Therefore, developing native applications individually for each platform turns out to be an arduous and expensive effort to undertake. The concept of 201Cwrite once, deploy everywhere201D, will massively reduce the cost of creating, maintaining and versioning mobile applications. In this paper, we suggest the automatic MDA (Model Driven Architecture) transformations to develop embedded heterogeneous software. Then we present our works in terms of defining a new target platform independent model (PIM), and the transformation rules for generating native code for such applications. Thus, we singled out domain specific language (DSL), in order to increase the productivity of software engineers, in terms of abstracting low-level boilerplate code.

Volume: 23, Issue: 3

RLCF: A collaborative filtering approach based on reinforcement learning with sequential ratings

by Jungkyu Lee, Byonghwa Oh, Jihoon Yang, Unsang Park
Abstract

We present a novel approach for collaborative filtering, RLCF, that considers the dynamics of user ratings. RLCF is based on reinforcement learning applied to the sequence of ratings. First, we formalize the collaborative filtering problem as a Markov Decision Process. Then, we learn the connection between the temporal sequences of user ratings using Q-learning. Experiments demonstrate the feasibility of our approach and a tight relationship between the past and the current ratings. We also suggest an ensemble learning in RLCF and demonstrate its improved performance.

Volume: 23, Issue: 3

Motor current signatures and their envelopes as tools for fault diagnosis

by Abitha Memala W., V. Rajini
Abstract

Induction motor inter-turn short circuit stator fault is identified using motor current signals with the help of Discrete Wavelet Transform along with Power Spectral Density. This methodology is adopted for motor current signals and their envelopes in order to identify a better tool for fault diagnosis. First, the analysis is performed using the original stator current signal, which has the fundamental frequency components along with the slip frequency and other harmonic/fault components. The fundamental component in the stator current can be eliminated by using the envelope of the signal. The transient in the wavelet decomposition can be suppressed by modifying the current envelope. This is done by pre-multiplying the original envelope with the Tukey window. The Power Spectral Density is calculated for both the signatures. Comparison of these two methods show that the proposed analysis based on modified envelope using Discrete Wavelet Transform along with Power Spectral Density offers better results compared to the original signal analysis.

Volume: 23, Issue: 3

Detection of heart disorders using an advanced intelligent swarm algorithm

by Sara Moein, Rajasvaran Logeswaran, Mohammad Faizal bin Ahmad Fauzi
Abstract

Electrocardiogram (ECG) is a well-known diagnostic tool, which is applied by cardiologists to diagnose cardiac disorders. Despite the simple shape of the ECG, various informative measures are included in each recording, which causes complexity for cardiac specialists to recognize the heart problem. Recent studies have concentrated on designing automatic decision-making systems to assist physicians in ECG interpretation and detecting the disorders using ECG signals. This paper applies one optimization algorithm known as Kinetic Gas Molecule Optimization (KGMO) that is based on swarm behavior of gas molecules to train a feedforward neural network for classification of ECG signals. Five types of ECG signals are used in this work including normal, supraventricular, brunch bundle block, anterior myocardial infarction (Anterior MI), and interior myocardial infarction (Interior MI). The classification performance of the proposed KGMO neural network (KGMONN) was evaluated on the Physiobank database and compared against conventional algorithms. The obtained results show the proposed neural network outperformed the Particle Swarm Optimization (PSO) and back propagation (BP) neural networks, with the accuracy of 0.85 and a Mean Square Error (MSE) of less than 20% for the training and test sets. The swarm based KGMONN provides a successful approach for detection of heart disorders with efficient performance.

Volume: 23, Issue: 3

A smart home system based on embedded technology and face recognition technology

by Hong Ai, Tongliang Li
Abstract

A smart home module program is designed based on Linux, in the ARM11 embedded development environment, and by using the UP-Magic 6410 development board. A touch-screen graphical interface for human-computer interaction is designed using QT. The sensor module is integrated into the underlying QT program, and the actuator module program performs external calls via QT, thus a complete set of smart home terminal is constituted. The function of remote video monitoring and the function of displaying module statuses on webpage are realized through connecting video cameras to the development board as well as constructing GoAhead web server and Spcaserv video server. A face recognition program is designed using MATLAB, a database for storing the face information for different users is designed, and they are connected to the smart home terminal. Based on face recognition, the identity information for the current user and the desired temperature are displayed, and the speed of the DC motor is changed. This system can also realize sound and light alarm. Alarm for harmful gas can be raised under the help of smoke sensor. The photosensitive sound switch module is used for simulating the voice control device so as to facilitate the user2019s control of the DC motor switch. Infrared irradiation sensor is used for simulating the identity-card identification equipment, so as to limit the system users. Through the coordination of the above parts, a complete smart home system which is composed of a computer, embedded development environment, sensors, actuators and ancillary devices is finally established.

Volume: 23, Issue: 3

Function approximation based energy detection in cognitive radio using radial basis function network

by Barnali Dey, A. Hossain, A. Bhattacharjee, Rajeeb Dey, R. Bera
Abstract

In this paper an attempt has been made to evolve a computationally intelligent energy detection method for spectrum sensing in Cognitive Radio (CR). The proposed method utilizes the function approximation capability of radial basis function (RBF) network to learn the threshold function for a pre-determined range of probability of false alarm and number of samples. The receiver operating characteristic (ROC) results obtained by the proposed method have been compared with the conventional energy detection scheme. It is validated from the results that, the proposed method provides enhanced probability of detection in some cases compared to the conventional one due to its inherent shortcoming in terms of computational intelligence.

Volume: 23, Issue: 3

Deep learning control for complex and large scale cloud systems

by Mehdi Roopaei, Paul Rad, Prof Mo Jamshidi
Abstract

Deep learning attempts to model high level perceptions in data using deep graph representations and creating models to learn these representations from large-scale unlabeled signals. Efficient unsupervised feature learning is extracted by deep learning algorithms and with multiple processing layers, composed of multiple linear and non-linear transformations. Actual systems become more and more complex with huge numbers of state variables and control of such large and complex systems with chaotic behavior, which needs more information about systems. Deep learning control by discovering continoiusly almost all possible information seems to be a reasonable approach to model and control largescale and complex systems. Recent advancements in machine learning algorithms and platforms are leading to deep learning controllers in real-time applications. The goal of this paper is to describe the concept of deep learning control and explain how cloud fog computing and edge analytics could handle massive amount of real time data streams from Cyber Physical Systems (CPS).

Volume: 23, Issue: 3

Energy Efficient BEC Modified Carry Select Adder Based PTMAC Architecture for Biomedical Processors

by E. John Alex, M. Vijayaraj
Abstract

Power dissipation is considered as the critical objective in the design of integrated circuits. Concern has increased in the design of adders, which form the basic computational block of any circuit. Research work has been carried out towards the efficient design and performance analysis of Carry Select Adder. Carry Select Adder (CSLA) is used in many data processors to increase the speed of mathematical calculation. The main aim of this paper is to reduce the power by using Binary to Excess-1 Converter, which uses an uncomplicated and proficient gate-level adaptation to reduce the power and area of the Carry Select Adder to a tolerant level. Based on the analysis, the proposed design proves to be better than the Conventional CSLA, BEC Modified Carry Select Adder and the Regular Square Root CSLA. Moreover, the Programmable Truncated Multiplier and Accumulator Circuit architecture designed using the BEC modified CSLA can be used for low power biomedical instrumentation with a need for reticent digital signal processing like ECG fall detection, EEG filtering and others. The BEC modified CSLA has been proven efficient through its implementation in a DSP architecture with the combined benefit of fault tolerant.

Volume: 23, Issue: 2

Twitter Sentiment Classification Using Binary Shuffled Frog Algorithm

by N. Yuvaraj, A. Sabari
Abstract

Twitter is a popular social networking site allowing users to read/post messages (tweets). Among the topic varieties, people in Twitter express sentiments for brands, stars, merchandises, and social events. Hence, it draws attention to assess a crowd2019s sentiments in Twitter. Tweets classify a target2019s sentiments as positive, negative or neutral. Individuals comment on many entities (or targets) in a tweet, thereby affecting availabilities for current methods. This is beneficial for clients who explore products sentiment before acquisition, or corporations wanting to check public sentiment of their products. This work proposes a new Twitter Sentiment Classification algorithm using novel feature selection technique with ensemble classifier through a meta-heuristic algorithm. Feature vectors are represented using binary encoding and a novel transfer function to flip encoding bits using shuffled frog meta-heuristic algorithm is proposed. To evaluate the new algorithm, Twitter corpus from Stanford University is used.

Volume: 23, Issue: 2

Medical Development Platform Using ZyCAP-Based Partial Reconfiguration on ZynqSoC

by Iljung Yoon, Anand Paul, Jooheung Lee
Abstract

Analysis of medical image is important in many biomedical applications such as abnormality detection, diagnosis, and surgical planning. Especially, edge detection of medical images is essential for segmentation and automatic recognition of the human organs. However, performance of edge detection filter degrades significantly if medical images/videos are corrupted with noises. In this paper, Zynq SoC is used as a medical development platform to implement real-time edge detection of image/video sequences of up to 1080p-resolution. Dynamic partial reconfigurable capability is utilized so that adaptive reconfiguration of partial filter bitstreams is performed according to the detected noise density level in the image/video sequences. Moreover, open source controller, called ZyCAP, is implemented on the proposed platform to further increase the reconfiguration speed through DMA controller, which is connected to high performance port for efficient access to external DRAM memory. We demonstrate that the proposed reconfigurable platform increases the accuracy of edge detection results by adaptive partial reconfiguration and adopted ZyCAP controller enables about 2.2 times faster reconfiguration when compared to Processor Configuration Access Port (PCAP).

Volume: 23, Issue: 2

Tibia Fracture Healing Prediction Using Adaptive Neuro Fuzzy Inference System

by M. Sridevi, P. Prakasam, S. Kumaravel, P. Madhavsarma
Abstract

An artificial intelligent approach based human tibia fracture healing diagnosis using DC electrical stimulation, a technique to be used by orthopedists both for bone fracture treatment and also healing assessment, is described. Electrical data recorded across 20 different tibia fracture patients whose fracture site was stabilized using Teflon coated rings and a DC input voltage of 0.700A0V was applied via K-wires were used to train the networks. The novel element is the data processing, which incorporates neural network and Adaptive Neuro Fuzzy Inference System (ANFIS) for estimating the fracture reunion is demonstrated in 20 patients. The ANFIS model was developed using least square method and gradient descent method having 32 Gaussian membership functions. The performance of ANFIS model developed was evaluated in terms of training epochs, prediction accuracy and absolute error in healing prediction. ANFIS Relative Absolute Error (RAE) was Zero. The performance evaluation shows ANFIS us a better diagnostic to an orthopedic surgeon for the fracture reunion prediction.

Volume: 23, Issue: 2

Performance Improvement of Hardware/Software Architecture for Real-Time Bio Application Using MPSoC

by Raveendran Arun Prasath, Parasuraman Ganesh Kumar, Erulappan Sakthivel
Abstract

In biomedical applications, the awareness in chip architectures with high performance has extended a lot of attention in market and research. A remarkable problematic situation for biomedical engineers is to monitor and analyze heart diseases, which is considered as the main reason or Electrocardiography (ECG), which plays vital role in heart medicines. Since examination of ECG meets computational tasks particularly in real time, in order to analyze the 12 lead signals in parallel with increase in sampling frequencies. Additional contest is the examination of large amounts of data to develop the times of recordings. Currently, doctors use 12-lead ECG paper information for monitoring of the eyeball, which could extremely harm analysis accuracy. In conventional work, the researchers introduced the multi-processor system-on-chip architectures to focus on the parallelization of the ECG evaluation kernel. Similar to that of conventional work in this work Hardware-Software (HW/SW) Multi-Processor System-on-Chip (MPSoC) is introduced in this proposed work. The conventional architecture is complex, which results in the performance degradation. The major focus of this method initiates its design methodology from the specifications of 12-lead ECG application to the ultimate HW/SW architecture. In this proposed work the computational complexity is very much reduced, which results the performance improvement is achieved.

Volume: 23, Issue: 2

High Performance Multi-Operand Adder for Medical Images

by Bharathiraja Nallathambi, P Karthigaikumar, Anand Paul
Abstract

The Internet of Things (IoT) gives a new path for future Internet through which it had boosted the growth in number of devices ranging from home automation to medical devices. Security in IoT is a research topic, which involves security for low memory footprint devices. Hence light weighted cryptographic algorithms plays a major role in providing data integrity for IoT devices. The term light weightiness begins from gate level to system level. Multi-operand addition, which is widely used in block ciphers and hash functions. This work presents different approaches for realization of highly efficient multi-operand addition for medical IoT field devices. The use of conventional adders in the multipliers for summation of the intermediate results leads to area and delay overhead. Multi-operand addition and compressor trees can be used more efficiently for decimal operations, which will reduce the delay without much overhead in area. The proposed approach is experimented with different multipliers for intermediate product summation, which shows the considerable amount of delay without much compromising the area. The proposed work is implemented on Nexys 2 FPGA Development board (Xilinx Spartan 3E - 500 FG 320) along with MATLAB using FPGA in loop concept and the results are compared along with medical image application.

Volume: 23, Issue: 2

A Framework for Automated Content Based Medical Image Queries in Grid

by E. Saravana Kumar, B. Madhusudhanan, Xiao-Zhi Gao
Abstract

In the current work, the effect of implementing a medical image data-set query application on the grid is studied. Medical imaging is extremely data intensive, because of the size of medical image scans. Grids offer immense processing power as well as a possibility for great levels of coarse grain parallelism adequate for tackling queries on medical image datasets in a comparatively shorter time period. Apart from the security issues, which are common in the domain, the possible parallelism of grids is challenging to make use of. In the current study, the max2013min method, Genetic Algorithm (GA), wherein genetic material is substituted by strings of bits while natural selection is substituted by fitness functions, Particle Swarm Optimization (PSO), wherein all particles utilize their own memories and optimum solutions are discovered on the basis of the knowledge obtained by the swarm as a whole as well as a modified PSO (PSO with 2-opt algorithms) are suggested. The outcomes of experiments proved that modified PSO outperformed max2013min, GA as well as PSO.

Volume: 23, Issue: 2

Dimensionality Reduction for Hybrid Medical Information Opinion Mining

by T Gopalakrishnan, P Sengottuvelan, A Bharathi
Abstract

The web has changed how people collaborate, communicate and express opinions and sentiments. Opinion Mining (OM) is popular due to the quick growth of web users, increasing online discussion forums and social media sites. OM determines feelings/opinions of others about services, products, politics and policies. There are huge unstructured, free-text information about health care quality available on the net from social networks, blogs and health-care rating websites. When sentiment analysis is applied to health care, it reveals a new approach to analyse huge volumes of textual information about patient2019s experiences to locate patterns and understand data. This paper proposes an OM system dimensionality reduction technique to mine user generated health reviews. The new method classifies patient reviews from online forums as positive/negative automatically. Results show the new dimensionality reduction techniques efficiency in classifying.

Volume: 23, Issue: 2

MRI Brain Image Segmentation Using Enhanced Adaptive Fuzzy K-Means Algorithm

by M Ganesh, M Naresh, C Arvind
Abstract

Medical images are widely used to plan further treatment for the patient. However, the images sometimes are corrupted with a noise, which normally exists or occurs during storage or while transferring the image. Therefore, the need to enhance the image is crucial in order to improve the image quality. Segmentation techniques for Magnetic Resonance Imaging (MRI) of the brain are one of the methods used by radiographer to detect any abnormality that has happened specifically for the brain. The method is used to identify important regions in brain such as white matter (WM), grey matter (GM) and cerebrospinal fluid spaces (CSF). The clustering method known as Enhanced Adaptive Fuzzy K-means (EAFKM) is proposed to be used in this project as a tool to classify the three regions. The results are then compared with fuzzy C-means clustering (FCM) and adaptive fuzzy k-means (AFKM).The segmented image is analyzed both qualitative and quantitative. The proposed method provides better visual quality of the image and minimum Mean Square Error.

Volume: 23, Issue: 2

Fusion for Image Based Human Age Estimation

by Madhusudhanan Baskaran, R. Kalpana
Abstract

Human faces act as essential visual signal and reveal significant amounts of nonverbal information in human-to-human communication. In an automatic age estimation system, shape-based and texture-based features extracted from faces forms the basis of age estimation. Human age estimation with facial image analysis as an automated method houses many potential real2010world factors. This paper presents an automated age calculation framework with the help of Support Vector Regression (SVR) strategy and it highlights feature extraction with Gray Level Co-occurrence Matrix (GLCM), and Active Appearance Models (AAMs) to evaluate human age. A fused feature technique and a SVR based Cuckoo Search (CS) is proposed in order to reduce error in age estimation.

Volume: 23, Issue: 2

Local Multi Code Pattern Generation for Face Identification

by M Arunkumar, S Valarmathy
Abstract

The existing LTrP method shows the relationship between the reference pixel and its neighbor2019s, based on the four directional code that are computed using the first-order derivatives in both vertical and horizontal directions. It is apparent that the performance of LTrP can be improved by differentiating the edges in more than four directions. This observation has motivated us to propose a nine direction code referred to as Local Multi Code Pattern (LMCP). LMCP extracts supplementary directional information as compared with LTrP. The performance analysis of the aforementioned techniques were done with the help of ORL, Yale, Jaffee databases and also the recital analysis on the same significantly improves the recognition rate of the proposed method.

Volume: 23, Issue: 2

Self-Adaptive PCNN Based on the ACO Algorithm and its Application on Medical Image Segmentation

by Xinzheng Xu, Tianming Liang, Guanying Wang, Maxin Wang, Xuesong Wang
Abstract

Medical image segmentation plays a dominant role in medical image analysis and clinical research. As an effective method for image segmentation, pulse-coupled neural networks (PCNN) has its own limitation that the values of the parameters have a strong effect on its performance when it is used to segment the image. This paper proposed a new method for medical image segmentation using the self-adaptive PCNN model. In this method, we combined the searching capabilities of ant colony optimization (ACO) algorithm in the solution space with the biological characteristics of PCNN, to find the optimal values of PCNN2019s parameters for each input image. Moreover, the search process of the ACO algorithm was divided into the local searching and the global searching to accelerate the speed of the ASO2019s convergence. Based on the above work, a new automatic method for the image segmentation, namely ACO-PCNN, was presented. Lastly, four pairs of different MR medical images, including transaxial, sagittal, coronal sections and noisy medical images, were used to test and validate the performance of the proposed method. The experimental results illustrated that our method was accurate and effective to MRI medical images.

Volume: 23, Issue: 2

Comparing the Machine Ability to Recognize Hand-Written Hindu and Arabic Digits

by Khalil El Hindi, Muna Khayyat, Areej Abu Kar
Abstract

The main aim of this work is to compare Hindu and Arabic digits with respect to a machine2019s ability to recognize them. This comparison is done on the raw representation (images) of the digits and on their features extracted using two feature selection methods. Three learning algorithms with different inductive biases were used in the comparison performed using the raw representation; two of them were also used to compare the digits using their extracted features. All classifiers gave better results for Hindu digits in both cases; when raw representation was used and when the selected features where used. The experiments also show that Hindu digits can be classified with better accuracy, higher confidence and using fewer features than Arabic digits. These results indicate that hand-written Hindu digits are actually easier to recognize than hand-written Arabic digits. The machine learning methods used in this work are instance based learning (the kNN algorithm), Na00EFve Bayesian and neural networks. The feature extraction methods we used were Fourier transformation and histograms.

Volume: 23, Issue: 2

Multi-Objective Complete Fuzzy Clustering Approach

by Parastou Shahsamandi E., Soheil Sadi-nezhad, Abbas Saghaei
Abstract

The process of data clustering has mainly focused on optimizing a single objective function, and thus, some information is not used for clustering. Therefore, the aim of this study is to propose a multi-objective complete fuzzy clustering model (MoCFC) that simultaneously optimizes data compactness, separation, and connectedness. The model employs two optimization algorithms; AUGMECON and NSGA-II. Using some fuzzy datasets, the results show that AUGMECON has lower convergence and coverage than NSGA-II, but a higher success index. Moreover, in terms of various cluster validity indices, AUGMECON achieves better performance. However, NSGA-II is the better choice if execution time is critical.

Volume: 23, Issue: 2

Binary-State Bacterial Foraging Optimization Based on Network Topology and its Application

by Sun’an Wang, Shenli Wu, Xiaohu Li, Chenlong Kang
Abstract

Bacterial foraging optimization (BFO) inspired by the foraging behavior of E.coli has been used to solve optimization problems. This paper presents a novel binary-state bacterial foraging optimization based on network topology (BBFO-NT). In the proposed BBFO-NT, a binary-state bacterial foraging strategy, which makes the bacteria to have mutual learning mechanism, is introduced. The two behavioral states include an explorative state based on Von Neumann topology and an exploitative state based on small-world networks. The bacteria co-evolve during the optimization process under the two states. Experiments on a set of benchmark functions validate the effectiveness of the improved algorithm. BFO and some other intelligent optimization algorithms are employed for comparison. The simulations show that the proposed BBFO-NT offers significant improvements than BFO. On this basis, the new algorithm has been successfully applied to the docking control. The experiments indicate that the improved algorithm is effective in controller design.

Volume: 23, Issue: 2

The Effect of Neighborhood Selection on Collaborative Filtering and a Novel Hybrid Algorithm

by Musa Milli, Hasan Bulut
Abstract

Recommender systems are widely used in industry and are still active research areas in academia. For many businesses, they have become indispensable business tools. Producing accurate results for such systems is important for the operations of the businesses. For this reason, various algorithms and approaches have been developed for recommender systems to increase the prediction accuracy. Collaborative filtering is one of the most successful approaches. In collaborative filtering, in order to predict more accurately, it is recommended to determine user2019s active neighbors. k-nearest neighbor (k-NN) algorithm is one of the most widely used neighbor selection algorithms. However, k-NN algorithm uses a fixed k value that reduces the accuracy of the prediction. In this paper, we present two novel approaches to increase the prediction accuracy of recommender systems; k%-nearest neighbor (k%-NN) algorithm to determine the appropriate k value for a user and a hybrid algorithm that combines a collaborative filtering technique and content-based approach. Our test results demonstrate that k%-NN algorithm increases the average prediction accuracy compared to the traditional k-NN algorithm. Additionally, when the proposed hybrid algorithm is used with k%-NN, it produces more accurate results than the conventional collaborative filtering technique and content-based approach.

Volume: 23, Issue: 2

Class Attendance Management System Using NFC Mobile Devices

by Mohamed Mohandes
Abstract

Monitoring students2019 class attendance in any educational institution is an important process as it is directly linked to academic performance. Collecting the student attendance manually results in loss of precious time, and also delays in subsequent processing of the collected data. In order to help faculty members concentrate on teaching, a solution is proposed for automating, monitoring, and further processing attendance collection, This paper describes a prototype of Class Attendance Management System (CAMS) that has been developed and evaluated using an NFC enabled mobile device and an NFC (or RFID) tag/card. This system helps school/university faculty in taking attendance in a class using his/her mobile phone in a quick and simple way, thus saving precious time in a classroom. Faculty can monitor students2019 attendance throughout an academic term, issue warnings, and request withdrawal of a student due to poor attendance as per the policy of the institution. The application in the NFC enabled phone reads a student ID by simply tapping it against an NFC student ID card. The application depends on the NFC enabled Android devices to read the student campus card, and extract his ID number to be used as a student identifier in CAMS. The developed system has been evaluated at King Fahd University of Petroleum and Minerals, Saudi Arabia during two academic terms. Positive feedback has been obtained from faculty and administration.

Volume: 23, Issue: 2

A Data Mining Approach to the Analysis of a Catering Lean Service Project

by Wen-Tsao Pan, Yungho Leu, Wenzhong Zhu, Wei-Yuan Lin
Abstract

Applied quantile regression to explore different ways to improve the catering service so as to promote the customer2019s service satisfaction.A lean service project aims to reduce the cost of material, labor and time required in providing a service to a customer so as to promote the service satisfaction from the customer. This paper presents a data mining approach to analyze the effectiveness of a lean service project on a catering service provided by a university restaurant. We have designed three consecutive stages of service scenarios; each represents an improvement over its previous stage. In this study, we first applied the grey relational analysis to confirm the effectiveness of the lean service project. That is, stage two and three actually obtained higher service satisfaction from customers than their corresponding previous stages did. We have performed a quantile regression analysis to explore the effect of different factors on low and high quantiles of service satisfaction. The result of the quantile regression analysis provides different ways for the restaurant to improve its customer2019s service satisfaction. Finally, we have built several prediction models to forecast the service satisfaction (Poor or Good) of a service sample. The experimental result showed that among the eight prediction models, FOAGRNN is the best in terms of the sensitivity, specificity, AUC and Gini performance measures.

Volume: 23, Issue: 2

A hybrid Genetic-Ant Colony Optimization Algorithm for the Optimal Path Selection

by Jiping Liu, Shenghua Xu, Fuhao Zhang, Liang Wang
Abstract

The shortest path problem lies at the heart of network flows that seeks for the paths with minimum cost from source node to sink node in networks. This paper presents a hybrid genetic-ant colony optimization algorithmic approach to the optimal path selection problem. First, some existing solutions for the optimal path selection problem are analyzed, and some shortages and flaws are pointed out. Second, the data organization method for road network based on the graph theory is proposed. Furthermore, the optimal path selection algorithm integrated of sinusoidal probability transfer rules, pheromone update strategy and dual population is presented. Finally, the experimental results show that the proposed algorithm speeds up the convergence rate and improves the efficiency.

Volume: 23, Issue: 2

Training ANFIS Using the Enhanced Bees Algorithm and Least Squares Estimation

by Hosein Marzi, Ahmed Haj Darwish, Humam Helfawi
Abstract

This paper presents the result of research in developing a novel training model for Adaptive Neuro-Fuzzy Inference Systems (ANFIS). ANFIS integrates the learning ability of Artificial Neural Networks with the Takagi-Sugeno Fuzzy Inference System to approximate nonlinear functions. Therefore, it is considered as a Universal Estimator. The original algorithm used in ANFIS training process has a hybrid model that uses Steepest Decent Derivative; therefore, it inherits low convergence rate and local minima during training. In this study, a training algorithm is proposed that combines Bees Algorithm (BA) and Least Square Estimation (LSE) (BA-LSE). The local and global exploration of BA as integrates with the best-fit solution of the LSE improves current shortcomings of ANFIS training process. The proposed training algorithm is examined under three different scenarios of function approximation, time series prediction, and classification experiments in order to verify the promising improvements in the training process of ANFIS. The experimental results validate high generalization capabilities of the BA-LSE training algorithm in comparison to the original hybrid training model of ANFIS. The new training model also enhances local minima avoidance and has high convergence rate.

Volume: 23, Issue: 2

New Dv-distance Method Based on Path for Wireless Sensor Network

by De-gan Zhang, Shan Zhou, J. Chen
Abstract

Wireless location is one of the core technologies of Wireless Sensor Network. In many applications, the accuracy of the location is the precondition of the usefulness of data information the node collected. Under the premise of cost limits, improving the accuracy of wireless sensor node position has crucial significance. In this paper, after analyzing reasons of the location weakness of Dv-Distance method due to errors caused by different paths, we propose an estimation method that is based on the distance of different paths between node and the anchor node, thereby balancing the error of different paths to locate the node. This is an improved method, which is based on circle focal point positioning. The improved method is tested and its performance is analyzed in MATLAB. According to the test experiments, our method can improve the accuracy of positioning, and can reduce dependence on the number of nodes anchor.

Volume: 23, Issue: 2

Towards a Realistic Indoor World Reconstruction: Preliminary Results for an Object-Oriented 3D RGB-D Mapping

by ChangHyun Jun, Jaehyeon Kang, Suyong Yeon, Hyunga Choi, Tae-Young Chung, Nakju Lett Doh
Abstract

A real world reconstruction that generates cyberspace not from a computer graphics tool, but from the real world, has been one of the main issues in two different communities of robotics and computer vision under different names of Simultaneous Localization And Mapping (SLAM) and Structure from Motion (SfM). However, there have been few trials that actively integrate SLAM and SfM for possible synergy. This paper shows the real world reconstruction can be enabled through this integration. As a result, the preliminary map has been generated of which five subgoals are: Realistic view (RGB), accurate geometry (depth), applicability to multi-floor indoor building, initial classification of a possible set of objects, and full automation. To this end, an engineering framework of 201CAcquire-Build-Comprehend (ABC)201D is proposed, through which a sensor system acquires an RGB-Depth point cloud from the real world, builds a three-dimensional map, and comprehends this map to yield the possible set of objects. Its performance is demonstrated by building a map for three levels of indoor building of which volume is 1,40800A0m3.

Volume: 23, Issue: 2

Proposing a Novel Adaptive Learning Management System: An Application of Behavior Mining amp Intelligent Agents

by Rozita Jamili Oskouei, Nasroallah Moradi Kor
Abstract

Nowadays, the internet has become one of the main resources for distance education, learning or training purposes. Various learning management systems (LMSs) have been designed for providing various services to learners in all parts of the world. Each of these LMSs has some similarities or differences with others regarding to capabilities or services. One of the main problems in Iran is teaching foreign languages to people with different genders, experiences, jobs, educational levels, etc. People need to learn English for different purposes, but unfortunatly, all the learning matrials are similar for all the learners without considering their capabilities, age, actual usage, etc. This paper attempts to design a personalized LMS for teaching foreign language to people whose native language is not English. Our experimental results indicated that the proposed LMS is useful for teaching conversation or grammar of foreign languages such as English language even to illiterate people or those with low education. Furthermore, the proposed LMS decreased the time of learning process and increased the duration of remembering the words or grammar.

Volume: 23, Issue: 2

Detection of architectural distortion in mammograms using geometrical properties of thinned edge structures

by Rekha Lakshmanan, Shiji T.P., Suma Mariam Jacob, Thara Pratab, Chinchu Thomas, Vinu Thomas
Abstract

The proposed method detects the most commonly missed breast cancer symptom, Architectural Distortion. The basis of the method lies in the analysis of geometrical properties of abnormal patterns that correspond to Architectural Distortion in mammograms. Pre-processing methods are employed for the elimination of Pectoral Muscle (PM) region from the mammogram and to localize possible centers of Architectural Distortion. Regions that are candidates to contain centroids of Architectural Distortion are identified using a modification of the isotropic SUSAN filter. Edge features are computed in these regions using Phase Congruency, which are thinned using Gradient Magnitude Maximization. From these thinned edges, relevant edge structures are retained based on three geometric properties namely eccentricity to retain near linear structures, perpendicular distance from each such structure to the centroid of the edges and quadrant support membership of these edge structures. Features for classification are generated from these three properties; a feed-forward neural network, trained using a combination of backpropagation and a metaheuristic algorithm based on Cuckoo search, is employed for classifying the suspicious regions identified by the modified filter for Architectural Distortion, as normal or malignant. Experimental analyses were carried out on mammograms obtained from the standard databases MIAS and DDSM as well as on images obtained from Lakeshore Hospital in Kochi, India. The classification step yielded a sensitivity of 89%, 89.8.7% and 97.6% and specificity of 90.9, 85 and 96.7% on 60 images from MIAS, 100 images from DDSM database and 100 images from Lakeshore Hospital respectively

Volume: 23, Issue: 1

Event-related theta and alpha oscillations under emotional stimuli: an MEG study

by Takuto Hayashi, Yuko Mizuno-Matsumoto, Shimpei Kohri, Yoshinori Nitta, Mitsuo Tonoike
Abstract

The present study investigates phase-locked and non-phase-locked brain responses in the theta and alpha frequency bands. A 122-ch whole-head magnetoencephalogram (MEG) was recorded continuously during emotional audio-visual stimuli. Compared to the pre-stimulus, the phase-locked theta activity increased over the left centro-temporal cluster at 400 2212 50000A0ms in the pleasant stimuli and at 100 2212 50000A0ms in the unpleasant stimuli. These activities were stronger with pleasant and unpleasant stimuli compared to relaxed stimuli. These results indicate that an emotionally high arousal state might induce the phase-desynchronized brain activity in the theta frequency band soon after the onset of the stimuli.

Volume: 23, Issue: 1

Override ship maneuvering simulator using AR toolkit

by Tadatsugi Okazaki, Rei Takaseki
Abstract

This paper proposes an override ship maneuvering simulator using an actual training ship for young pilot trainees. In this new simulator, augmented reality toolkit was used to reproduce scenery from the bridge of a large vessel on the actual training ship. Moreover, the system reproduced the large vessel2019s maneuverability under wind disturbance on the training ship. The effectiveness of the developed system was indicated with results of an actual ship experiment.

Volume: 23, Issue: 1

A ship navigator2019s mental workload using salivary NO

by Kenichi Kitamura, Koji MURAI, Shin-ichi Wakida, Nobuo Mitomo, Keiichi Fukushi
Abstract

We challenge an evaluation of a ship bridge teammate2019s mental workload. If we are able to evaluate using a quantitative index for a veteran navigator, it means we get more essential skill for practical education, for example, the educators show students the detail suggestion of skill of ship maneuvering for simulator-based and real ship handling. We think that the both of performance and mental workload are important to evaluate the skill of ship maneuvering; however, the mental workload doesn2019t use yet. Regarding the index of mental workload, the heart rate variability (HRV) is popular for a lot of research fields. We usual use HRV, nasal temperature, and try saliva. The saliva is possible to evaluate the mental workload on the spot for ship maneuvering. We hope simple measurement and clear result for the index, and it is practical method in a classroom. The spot measurement is one of research target.In this study, we aim salivary NO32212 concentration for simulator-based experiment. We show the results of the response of salivary NO32212 for narrow passage and entering a port in which the navigator needs the judgement for safe navigation.

Volume: 23, Issue: 1

On estimating quality of elderly monitoring service based on sensor reliability

by Seiki Tokunaga, Sachio Saiki, Shinsuke Matsumoto, Masahide Nakamura
Abstract

Remote monitoring service for elderly people is an effective method to support elderly people with safe. In this paper, we tackle to reveal the relationships between quality of elderly monitoring service and sensor reliability in remote monitoring service. To achieve the above goal, we propose four step methods, generalization by three-actor model, designing the algorithms of the three-actor and simulation of RMS. Moreover, we construct the elderly model based on the statistics report. Our most important contribution shows that the guideline, which shows the references between sensor accuracy and reliability of RMS. This guideline will provide benefit for RMS service provider and end-user.

Volume: 23, Issue: 1

Developing a framework and algorithm for scalability to evaluate the performance and throughput of CRM systems

by Abdulrahman Altalhi, Abdullah AL-Malaise AL-Ghamdi, Zahid Ullah, Farrukh Saleem
Abstract

Scalability in hardware and/or software is an important factor for enhancing the performance of running processes as well as the throughput of the system of business organizations. This paper explores the need for scalability and issues related to extending the resources in order to ensure an improved and scaled-up Customer Relationship Management (CRM) architecture. The main contribution discussed in this paper is the proposal of a conceptual framework for measuring the process performance and throughput of the system beyond the selection of the type of scalability. Furthermore, this paper concerns the CRM system, as customer requests, their online transactions, and responses need a fast and efficient system. Taking into consideration all these factors, ultimately this paper proposed a customer-friendly framework for measuring the process performance and throughput of the system. Finally, the proposed framework2019s steps are shown in an algorithm calculating process performance and throughput of the system.

Volume: 23, Issue: 1

A data aggregation scheme for boundary detection and tracking of continuous objects in WSN

by Hyun-Jung Lee, Myat Thida Soe, Sajjad Hussain Chauhdary, Soyeon Rhee, Myong-Soon Park
Abstract

Efficient and accurate detection and tracking of continuous objects such as fire and hazardous bio-chemical material diffusion requires an extensive communication between nodes in wireless sensor networks. In this paper, we propose an efficient algorithm that monitors a moving object by selecting a subset of monitoring data of object boundary nodes. The proposed algorithm uses a Data Aggregation method to reduce the number of report messages and a piecewise Quadratic Polynomial Interpolation algorithm to find the boundary points precisely. Simulation results show that the proposed scheme significantly reduces the number of report messages to the sink node and also improves boundary accuracy.

Volume: 23, Issue: 1

Reactive max-min ant system with recursive local search and its application to TSP and QAP

by Rafid Sagban, Ku Ruhana Ku-Mahamud, Muhamad Shahbani Abu Bakar
Abstract

Ant colony optimization is a successful metaheuristic for solving combinatorial optimization problems. However, the drawback of premature exploitation arises in ant colony optimization when coupled with local searches, in which the neighborhood2019s structures of the search space are not completely traversed. This paper proposes two algorithmic components for solving the premature exploitation, i.e. the reactive heuristics and recursive local search technique. The resulting algorithm is tested on two well-known combinatorial optimization problems arising in the artificial intelligence problems field and compared experimentally to six (6) variants of ACO with local search. Results showed that the enhanced algorithm outperforms the six ACO variants.

Volume: 23, Issue: 1

Particle filter planar target tracking with a monocular camera for mobile robots

by Yu-Cheng Chou, Madoka Nakajima
Abstract

This paper presents an effective and simple target tracking approach called PSIPT (Particle filter Single Image based Planar Target tracking). Compared with other works, the uniqueness of PSIPT includes: (1) only a single color camera provides the images to be processed; (2) the particle filter does not perform data fusion calculations; (3) the distance evaluation carried out in the particle filter does not need the camera2019s intrinsic and extrinsic parameters. Meanwhile, this paper also reveals that, under different target shapes and cameras, a high degree of negative linear dependence remains between: (1) a target2019s pixel height and vertical distance; (2) a target2019s vertical distance and PWHD (Pixel-Width-to-Horizontal Distance) ratio. According to the experimental results, PSIPT performs better than its Kalman filter variant in both the L-shape and S-shape tracking experiments. In addition, PSIPT has moderate performance in the target missing surveillance experiment. Moreover, a hybrid and enhanced version of PSIPT, which is equipped with an AdaBoost classifier in this study, leads to good surveillance performance in the target missing experiment.

Volume: 23, Issue: 1

Soft sensor applications of RK-based nonlinear observers and experimental comparisons

by Meric Cetin, Selami Beyhan, Serdar Iplikci
Abstract

Soft sensing technology has still important industrial applications especially for chemical reactors, robotic applications, etc. Therefore, this paper introduces and applies novel Runge-Kutta (RK) discretization-based nonlinear observers for real-time sensing applications of unmeasurable quantities. The contribution of the paper is twofold. First, for reliability and accuracy, the stability of the RK based observers are proven. Second, they have been compared with well-known extended-Luenberger observer, extended-Kalman filter and sliding-mode observer on an experimental system. Experiments have shown that the RK model-based observers have a considerable place among conventional ones with respect to design issues and estimation performance for future soft sensing applications.

Volume: 23, Issue: 1

Study on cluster analysis characteristics and classification capabilities 2014 a case study of satisfaction regarding hotels and bed amp breakfasts of Chinese tourists in Taiwan

by Seng-Su Tsang, Wen-Cheng Wang, Hao-Hsiang Ku
Abstract

Cluster analysis is a multivariate statistical analysis method for the classification of samples based on the principle of 201Clike attracts like201D. It requires reasonable classification according to the characteristics in a reasonable manner, and without any mode for reference, in other words, classification is implemented without any prior knowledge. It has been applied in many aspects. In this paper, four cluster analysis methods are used to study the questionnaire data of Chinese tourists2019 satisfaction regarding Taiwan2019s hotels and Bed amp Breakfasts, (BampBs). First, this study applied principal component analysis in reducing questionnaire variables, and then gray relational analysis to assess the overall satisfaction performance. By sorting the overall satisfaction performance values, the performance values combined with the principle components were used as the testing sample data. Afterwards, the samples were categorized into three categories and four categories according to performance value. The four cluster analysis methods were used for clustering the principle components in order to observe their cluster performance and classification capabilities. The testing data testing results suggested that GK Cluster can obtain good cluster performance and good classification capabilities.

Volume: 23, Issue: 1

Selective surface normal estimation for volume rendering

by Ömer Cengiz Çelebi, Ulus Çevik
Abstract

Three-dimensional volumetric data is discrete by nature. It does not preserve continuous surface. Therefore, to render surfaces realistically, the normal vectors must be predicted before processing the image. During the rendering, if the normals are averaged directly, then the sharp edges are smoothed out as well. In this paper, we show how to avoid over-smoothing problems by analyzing a few neighbor voxels, and selectively averaging the normals to keep details, within a predefined kernel.

Volume: 23, Issue: 1

Fuzzy functions with function expansion model for nonlinear system identification

by Musa Alci, Selami Beyhan
Abstract

In this study, the structure of fuzzy functions is improved by function expansion. Unlike fuzzy conventional if-then rules, classical fuzzy function structure includes fuzzy bases and linear inputs. Membership functions of fuzzy bases are set using fuzzy C-means (FCM) algorithm, and the linear parameters are computed using the least-square estimation (LSE). This study has two main contributions. First, conventional 201Cfuzzy functions201D structure is powered by the expansion of orthogonal 201Ctrigonometric functions201D where the approximation capabilities of the fuzzy functions are increased. Second, the widths of the normalized membership functions determined for the fuzzy function model are optimized using the Nelder-Mead simplex algorithm that provides further enhancement on the identification performance. The advantages of the proposed model are shown via offline identification of a benchmark nonlinear system and online identification of two real-time nonlinear systems.

Volume: 23, Issue: 1

Improving efficiency of heterogeneous multi relational classification by choosing efficient classifiers using ratio of success rate and time

by Amit Thakkar, Y P Kosta
Abstract

Traditional data mining algorithms will not work efficiently for most of the real world applications where the data is stored in relational format. Useful patterns can certainly be extracted from multiple relations using an existing traditional learning algorithm of data mining, but it would involve a lot of complexity. So there is a need of a multi relational classification, which analyzes relational data and predicts unknown patterns automatically. Moreover the performances of existing relational classifiers are limited, because the existing algorithms are not able to use different classifiers based on characteristics of different relations. The goal of the proposed approach is to select appropriate classifiers based on characteristics of different relations in the relational database to improve the overall performance without affecting the running time. So multi criteria classifier selection function based on ratio of accuracy and running time is used to select the most efficient classifier using Meta Learning. In the proposed classifier selection function, accuracy is used as a measure of benefit and running time is used as a measure of cost and their ratio is taken to ensure that the efficient classifier is selected. The experimental results show that the performance of proposed relational classification is better in terms of efficiency when compared to all other existing algorithms available in the literature. We are able to achieve best results by selecting an efficient algorithm for every relation contributing in the relational classification.

Volume: 23, Issue: 1

Two adaptive control strategies for trajectory tracking of the inertia wheel pendulum: neural networks

by Javier Moreno-Valenzuela, Carlos Aguilar–Avelar, Sergio Puga–Guzm′an, Victor Santibañez
Abstract

The problem addressed in this paper is to achieve robust motion control of the inertia wheel pendulum (IWP). Specifically, trajectory tracking control of the pendulum of the IWP under the assumption of uncertain model is discussed. Two new robust algorithms are introduced whose design departs from a model-based input-output linearization controller. Then, the control problem is firstly solved by means of an adaptive neural network-based controller and secondly by an adaptive regressor-based controller. For both controllers, rigorous analysis of the respective closed-loop system is given, where Barbalat2019s lemma is used to conclude asymptotic convergence of the pendulum tracking error. In addition, the wheel velocity and adaptive extension signals are shown to be bounded. An extensive real-time experimental study validates the introduced theory, where the performance of a classical linear PID controller and the two new adaptive schemes are compared.

Volume: 23, Issue: 1

Fuzzy matching of edge and curvature based features from range images for 3D face recognition

by Suranjan Ganguly, Debotosh Bhattacharjee, Mita Nasipuri
Abstract

Automatic human face recognition is already in research from some decades due to its application in different fields. But there is no unique technique that is very much worthwhile for robust automatic human face recognition, suitable for all possible situations. In this paper, a new technique is proposed, which is a holistic approach, and it is based on 2018one to all2019 comparison method. Along with the edge, four different types of curvatures are computed from face image profile to capture both the local features and surface features from 3D face image. Then, a new feature space, EC (Edge_Curvature) image, is generated for feature estimation during final recognition purpose. The similarities among intra-class members are carried out using fuzzy rule derived from the computed distance vectors by Hausdorff, distance that is used to match the probe images for the classification purpose automatically. For the validation of the algorithm, the algorithm is experimented on Frav3D and GavabDB databases with two sets of investigations. One is synthesized data-set, consisting of frontal range images (i.e. expression, illumination and neutral) and registered range face images. The other set is the original range face images. It does not include the registered faces. These investigations highlight the robustness of the proposed methodology. The success rates of acceptance of the probe images from two synthesized datasets are 98.87% for Frav3D and 87.20% from GavabDB. On the other hand, classification rate from original data-set for GavabDB is 79.78% and 91.69% for Frav3D.

Volume: 23, Issue: 1

Comparison of different methods for reconstruction of instantaneous peak flow data

by Ali Fathzadeh, Azam Jaydari, Ruhollah Taghizadeh-Mehrjardi
Abstract

In arid and semi-arid regions, documentary data of past floods remain justly rare and highly fragmentary in most cases. Existence of many effective parameters on maximum flood discharge and the complex relationships between them is an important challenge in the reconstruction of these data and hence, it limited the application of traditional methods. In this paper, an alternative approach (i.e. artificial intelligence methods) has been evaluated to determine the interactive relations of them. To this end, flow data was collected from 29 gauging stations in the central part of Iran for the period 1965 to 2007. Following quality and homogeneity controls of the data, reconstruction of instantaneous peak flow time series were made using maximum daily data by four different methods; regression method (REG), artificial neural network (ANN), genetic algorithm (GA) and adaptive neuro-fuzzy inference system (ANFIS). Results showed that in all studied stations, ANFIS reconstructs instantaneous peak flow values with the highest accuracy among the four tested methods.

Volume: 23, Issue: 1

Multi-AUV task assignment and path planning with ocean current based on biological inspired self-organizing map and velocity synthesis algorithm

by Xiang Cao, Daqi Zhu
Abstract

An integrated multiple autonomous underwater vehicles (multi-AUV) dynamic task assignment and path planning algorithm is proposed for three-dimensional underwater workspace with ocean current. The proposed algorithm in this paper combines biological inspired self-organizing map (BISOM) and a velocity synthesis algorithm (VS). The goal is to control a team of AUVs to visit all targets, while guaranteeing AUV2019s motion can offset the impact of ocean currents. First, the SOM neural network is developed to assign a team of AUVs to achieve multiple target locations in underwater environments. Then to avoid obstacle autonomously for each AUV to visit the corresponding target, the biological inspired neurodynamics model (BINM) is used to update weights of the winner of SOM, and realize AUVs path planning autonomously. Lastly, the velocity synthesis algorithm is applied to optimize a path for each AUV to visit the corresponding target in dynamic environment with the ocean current. To demonstrate the effectiveness of the proposed algorithm, simulation results are given in this paper. Undoubtedly, the proposed algorithm is capable of dealing with task assignment and path planning in different environment. The path of the AUV is not affected by the effects of ocean currents and there are no great changes.

Volume: 23, Issue: 1

Classification of physical activities using wearable sensors

by Muhammad Arif, Ahmed Kattan, Iqbal Ahamed
Abstract

An effective measurement of physical activity gives an accurate indication of physical health. This information can be highly useful, particularly in rehabilitation development and personal weight management. In this paper, time domain features are selected for different types of physical activities to produce the best classification in the feature space. We have used wavelet transform based rotation forest classifier to recognize seventeen different types of physical activities. Furthermore, to improve the time and space complexity, we have compared three types of attribute or feature selection methods. Our proposed framework has produced higher classification accuracy of 98% with only 58 features selected by correlation based feature selection (CFS) using tabu search for seventeen different physical activities.

Volume: 23, Issue: 1

A fuzzy neural approach for vehicle guidance in real2013time

by Bo Mi, Dongyan Liu
Abstract

In recent years, neural network has allured much attention of transportation studies due to its competence of addressing traffic complexity. However, design and implementation of such system remains intractable in terms of its opaqueness. Instead, adopting a knowledge-based approach, which can automatically generate a set of expert rules to model the problems, could be a possible solution. To this extent, we devised a fuzzy neural network strategy to optimize the route decision on urban roads in this paper. Our scheme works on an evolutionarily weighted network model, whose resource requirements are adequately alleviated. We also introduced a GA (Genetic Algorithm)-based learning algorithm to obtain the weights of fuzzy system and validated its performance by agent-based modeling.

Volume: 23, Issue: 1

An enhanced representation of thermal faces for improving local appearance-based face recognition

by Gabriel Hermosilla, Javier Ruiz-del-Solar, Rodrigo Verschae
Abstract

This paper proposes a new methodology to improve appearance-based thermal face recognition methods by using an enhanced representation of the thermal face information. This new representation is obtained by combining the pixels of the thermal face image and the vascular network information that is extracted from the same thermal face image. The effect of using the enhanced representation is evaluated for 5 different face recognition methods (LBP, WLD, GABOR, SIFT, SURF) in two public thermal face databases (Equinox and UCHThermalFace). The experimental results show that the proposed enhanced representation improves the performance of most of the analyzed appearance-based methods. The largest improvements are obtained when this representation is used together with methods based on the Gabor Jet Descriptor (GJD), the Weber Linear Discriminant (WLD) and Speeded Up Robust Features (SURF). In general terms the improvement is larger in indoor setups than in outdoors.

Volume: 23, Issue: 1

A parallel and distributed algorithm for role discovery in large-scale social networks

by Yunpeng Xiao, Xingyu Lu, Yanbing Liu
Abstract

By analyzing large-scale number of human behavior data, we propose a new parallel and distributed algorithms for social role discovery based on dynamic and fine-grained human behavior attributes in social networks. We first mining and propose number of properties that on behalf of human behavior. After that, to deal with the large human behavior data, a simple, scalable and distributed parallel clustering algorithm based on grid and density is developed. The theoretical analysis and experimental results show that the algorithm has better efficiency and effectiveness, and algorithms reveals valuable discovery on the real-life datasets. Besides, the methodology in this paper for user role discovery also can be applied to social networks in general.

Volume: 22, Issue: 4

RH: An improved AMH aggregate query method

by ZhiXian Tang, Jun Feng, ZhongHua Zhu, YaQing Shi, JiaMin Lu
Abstract

As data stream grows exponentially, the aggregate query technique is widely used since it can rapidly obtain the summary information. Typical approximate aggregate query methods, like sliding-window, random sampling, wavelet, sketch index structure, histogram, etc., all evaluate the quality of the algorithms by the average size of query errors and ignore the maximum relative error, which determines the availability of the methods. Regarding this issue, this paper proposes the Reasonable Histogram (RH) method to improve the classic aggregate query method AMH. Based on the analysis of AMH errors2019 mathematical characteristics, we build an aggregate query mathematical model based on the Kalman filter, using the optimal estimate of the buckets2019 average frequency to calculate the aggregate values of the anomalous points, so as to restrain the maximum relative error.

Volume: 22, Issue: 4

Influence of removable devices on worm propagation under pulse quarantine strategy

by Yu Yao, Zhao Zhang, Wei Yang, Fuxiang Gao
Abstract

Internet worms, a great threat to network security can spread quickly via networks. Many worms can also easily propagate via removable devices, which have become a key method for such worms to stealthily invade those computers not connected to the Internet. Therefore, it is necessary to analyze the dynamic behavior and containment strategy of such worms. By theoretical analysis and experiments, we found that the traditional constant quarantine strategy has a quite high demand on initial immunization rate, which is difficult to achieve in a real network environments. Thus, a pulse quarantine strategy is proposed to make up the deficiency of constant quarantine. Pulse quarantine adopts a hybrid intrusion detection system (IDS) integrating both misuse and anomaly IDS. By analyzing the systems2019 stability at infection-free equilibrium, a basic reproduction number is determined. If basic reproduction number is less than one, system will be stable, which is beneficial for us to predict worm propagation and implement containment strategy; otherwise, the system will lose its stability and worm propagation is out of control. Numerical analysis is given to illustrate our theory. Finally, simulation experiments are presented to simulate the worm propagation; the results fully demonstrate the correctness of our theoretical analysis.

Volume: 22, Issue: 4

Multi agent system-based dynamic trust calculation model and credit management mechanism of online trading

by W. J. Jiang, Y. S. Xu, H. Guo, C. C. Wang, L. M. Zhang
Abstract

Now all kinds of malicious acts appear in C2C online auctions, particularly the phenomenon of trust lack and credit fraud is very outstanding. Therefore, how to build an effective trust model has become a burning problem. Based on analyzing limitations of the existing online trust transaction mechanism, and according to characteristics (such as dynamic, innominate and suppositional) of online transaction trust problem, the article proposes a dynamic trust calculation model and reputation management mechanism of online trading based on multi-Agent system. The model consists of three parts. The first part is the trust of user domain, to put importance on the influence on current trust by recent credibility status, to motivate users to adopt an agreed cooperative strategy. The second part is the weighted average of reputation feedback score. The weighted part mainly considers the trust from the reputation feedback score person (the credibility of the feedback score), the value of the transaction (to prevent the 201Ccredit squeeze201D), temporal discounted (201Cguard against the fluctuations of the credibility201D ) and other factors. The third part is to give a weighting on the community contribution, according to the action taken by a user to the other members of the community in a time domain, to increase or decrease the user2019s trust to isolate the feedback submission of the credibility and punish the fraud. The paper builds the fraud limitation mechanism, which combines the prevention beforehand, coordination in the event and punishment afterwards. The mechanism makes the online transaction safe. Theoretic proof and experimental verification indicate the following three problems can be solved effectively: 1) solving the problem, which is difficult to prevent and is that speculative user accumulates the little trusts and squeeze on the large trading; 2) preventing members from cheating by false trading or personation; 3) reducing the arbitration workload of the online business platform.

Volume: 22, Issue: 4

Dynamic trust evaluation in open networks

by Bin Zhao, Jingsha He, Yixuan Zhang, Gongzheng Liu, Peng Zhai, Na Huang, Ruohong Liu
Abstract

Trust has been widely used as an effective means of dealing with security issues in open networks. By taking into consideration of the requirement on dynamic adaptability and incorporating the factors of insufficient conglomerate and incentive mechanisms in trust evaluation, we propose in this paper a dynamic trust evaluation model based on multiple factors that is suitable for open networks. In the proposed model, trust evaluation takes into consideration of direct trust that includes historical information to improve accuracy and recommendation trust that includes a bonus-penalty factor and the reliability of evaluation to improve reliability and efficiency. The calculation of integrated trust relies on solving the problem of determining the weights between direct trust and recommendation trust, resulting in a balanced weight factor being introduced into trust evaluation. In the interactions between network entities, the balanced weight factor changes dynamically as the interactions continue between the entities to make trust evaluation dynamically adaptable. Experimental results show that the proposed method can resist attacks from hostile entities, lower the influence of inaccurate or false recommendations from hostile entities and improve the accuracy of trust evaluation.

Volume: 22, Issue: 4

A kind of dynamic software behavior trust model based on improved subjective logic

by Tian Junfeng, Jiao Hongqiang
Abstract

This paper proposed a kind of dynamic software behavior trust model, which is based on improved J00F8sang2019s Subjective Logic. It used to build the trust model through turning Basic Rate and Uncertainty Factors into dynamic ones and Trace of Software Behavior (TSB) is introduced to describe the feature of Software Behavior (SB), so as to provide characteristic information, which supports the trustiness of Software Behavior. Finally, the sensitivity and efficiency of the model are demonstrated by simulation experiments, and the model can better adapt to the dynamic evaluation situation.

Volume: 22, Issue: 4

Channel allocation for hot spot areas in HAPS communication based on the prediction of mobile user characteristics

by Mingxiang Guan, Le Wang, Liming Chen
Abstract

High altitude platform station (HAPS) communication employs multiple high altitude platforms with the same bandwidth in order to serve the same region. This working mode greatly increases user capacity and improves frequency spectrum utilization, thus endowing HAPS communication with its superiority in solving hot spot issues. In this study, a standard for deciding the minimum distance in mobile user access systems was derived. Using this standard, channel allocation approaches based on the prediction of user number change and call volume change were proposed. These proposed approaches could effectively remove the problem of insufficient or wasted channels caused by the lack of proactive cooperation in conventional channel allocation methods. These approaches are validated through simulation, in which the proposed channel allocation approaches demonstrated the reasonable allocation of channels and the avoidance of call blocking while improving the utilization of channels.

Volume: 22, Issue: 4

A call admission control scheme on the uplink of D2D communications underlaying cellular networks

by Xujie Li, Honglang Zhang, Wenna Zhang, Feng Yan
Abstract

In device-to-device (D2D) communications, call admission control (CAC) scheme is a crucial problem. In D2D communications, the cellular user equipment (CUE) and D2D user equipment (DUE) pairs share the same channel resource. Hence additional interference is introduced. Meanwhile, the interference has an important impact on the system performance. In this paper, a CAC scheme based on interference analysis is proposed. First, the interference is modeled and analyzed in detail. Then based on the interference analysis, a CAC scheme is proposed. Simulation results show that D2D communications with the proposed CAC scheme can effectively improve system performance in terms of new call blocking probability and capacity. This proposed scheme can be applied to the design and optimization of D2D communications.

Volume: 22, Issue: 4

An in-network data cleaning approach for wireless sensor networks

by Jianjun Lei, Haiyang Bi, Ying Xia, Jun Huang, Haeyoung Bae
Abstract

Wireless Sensor Networks (WSNs) are widely used for monitoring physical happenings of the environment. However, the data gathered by the WSNs may be inaccurate and unreliable due to power exhaustion, noise and other reasons. Unnecessary data such as erroneous data and redundant data transmission causes a lot of extra energy consumption. To improve the data reliability and reduce the energy consumption, we proposed an in-network processing architecture for data cleaning, which divides the task into four stages implemented in different nodes respectively. This strategy guaranteed the cleaning algorithms were computationally lightweight in local nodes and energy-efficient due to almost no communication overhead. In addition, we presented the detection algorithms for data faults and event outliers, which were conducted by utilizing the related attributes from the local sensor node and the cooperation with its relaying neighbor. Experiment results show that our proposed approach is accurate and energy-efficient.

Volume: 22, Issue: 4

A method of virtual machine placement for fault-tolerant cloud applications

by Xiao Chen, Jian-Hui Jiang
Abstract

Cloud applications are usually large scale and complicated. The placement of virtual machines (VMs) for highly reliable cloud applications is a challenging and critical research problem. To attack this challenge, a method of VM placement based on adaptive selection of fault-tolerant strategy for cloud applications is proposed. It involves two phase. In the first phase, the fault-tolerant strategies of cloud applications are sorted according to the constantly change of cloud applications constraint factors including the response time, failure rate and resource consumption. In the second phase, the VM placement plan based on adaptive selection of fault-tolerant strategy for cloud applications is solved. A prototype of VM placement framework based on adaptive selection of fault-tolerant strategy for cloud applications, named SelfAdaptionFTPlace, is implemented. Experimental results demonstrate that the proposed method shows up better00A0performance and VM placement plan according to the constant change of cloud applications constraint factors compared with the existing methods.

Volume: 22, Issue: 4

A multi-tenant hierarchical modeling for cloud computing workload

by Chunyan An, Jiantao Zhou, shuai Liu, Kurt geihs
Abstract

Diverse realistic workloads are urgently needed by SaaS providers and researchers to study the cloud environment. Workload model is an important way to specify and produce workloads. However, existing workload models are usually simplified. They cannot comprehensively describe the variations of the behaviors in user-level, application-level and service level in real environment. Moreover, existing workload models are aimed to only specific applications. They fail to support multi-users, multi-applications and multi-service-units in one workload model. To solve the problem, a hierarchical workload modeling approach is proposed in this paper. The approach constructs a cloud workload model on three layers: User, application, service-units. At each layer two key time-varying variations are captured; amount and composition. Then the workload model is the superposition of the three layers. The approach provides a unified model for different applications and various user behaviors. The experiments give the hierarchical workload models of three examples, which applications are Bag-of-Tasks, MapReduce and self-contained service-units. The experimental results show the flexibility of the novel workload modeling. Finally, architecture of a workload generator based on the model is given.

Volume: 22, Issue: 4

Characterizing performance interference for performance crisis mitigation in the virtualized environment

by Lei Yang, Yu Dai, Bin Zhang
Abstract

With the rapid development of the Web, cloud computing has received much attention. The key enabling factor for cloud computing is the virtualization technology by which multiple virtual machines can run in a shared physical machine and enable high utilization of hardware resources. However, the consolidation also introduces contention in shared resources, leading to degraded application performance. Then, one of challenges is to detect and mitigate the performance crisis where one or more virtualized machines2019 performance target is not achieved. We present a method for mitigating the performance crisis, which leverages the performance interference model to predict the possibility of the performance crisis caused by the consolidation and employs this model to learn the bad or good consolidation plan to identify a new placement strategy to mitigate the performance crisis. Our experiments show the better performance of the mitigating methods.

Volume: 22, Issue: 4

Trust-Driven and PSO-SFLA based job scheduling algorithm on Cloud

by Xiaolan Xie, Ruikun Liu, X Cheng, Xin Hu, Jinsheng Ni
Abstract

With the advent of big data era, Cloud Computing has drawn widespread interests from industrial and academia. Job scheduling algorithm plays a crucial role in the paradigm of Cloud Computing. The well-designed job scheduling algorithms can provide fast, high quality and safe services. However, the conventional job scheduling algorithms are focusing on the improvement of efficiency, these obscure the important issue of trustworthiness in Cloud. This paper proposes a job scheduling algorithm with the consideration of efficiency and trustworthiness in Cloud. The intuition of the proposed algorithm is based on Particle Swarm Optimization (PSO) and Shuffled Frog Leaping Algorithm (SFLA). In this way, the proposed algorithm can avoid to obtain local optimal results. Also, the trust model is introduced to improve the trust of resources. The comprehensive simulations have been conducted via CloudSim. The experimental results have demonstrated that the proposed algorithm improve the trustworthiness than that of two classical compared algorithms GA and TDMin-Min, respectively.

Volume: 22, Issue: 4

Towards consistency of virtual machine migration in Software-Defined Network

by Wenbo Hu, Tao Huang, Jian Ding, Jian Wang, Jiang Liu, Yunjie Liu
Abstract

Virtualization has been widely used in Data Centers, which are running the applications that process data operations or provide services. Migration of virtual machines happens regularly for system load balance, scheduled maintenance, disaster recovery or even in case of unscheduled server downtime. However, packets are still forwarded along the previous path when the migration has completed, which leads to a long time connection interruption and can be abstracted as a consistency problem. We define Connection Recovery Time (CRT) as the duration from the time of migration initiation to the time of reconstructing forwarding rules for migration. In this paper, we analyze the reasons of long CRT in different kinds of Layer-2 switching implementations and propose a novel proactive mechanism to reduce CRT in Software-Defined Network (SDN). Finally, we evaluate the performance of our mechanism, and the CRT in our mechanism can be greatly reduced to the same order of magnitude with general TCP roundtrip time and the occupied hardware resources are hugely reduced.

Volume: 22, Issue: 4

VLAN-reusing: A novel solution for efficient network virtualization

by Jiang Liu, Tao Huang, Yuanming Xin, Jiannan Zhang, F. Richard Yu, Yunjie Liu
Abstract

In network virtualization, slice isolation is a key issue since it is hard to realize in a distribute system in a flexible way. Recently, the rise of Software Defined Networking architecture provides a promising way to deal with this problem. Existing work leverage, and existing bits in the head field, such as VLAN ID, isolates the tenant traffic. However, we argue that more potential should be exploited in SDN architecture to isolate the network better. Following this consideration, we propose a new scheme called VLAN-Reusing. It enhances the isolation by reusing VLAN IDs with location information, while extending the VLAN quantity from 4096 to 4096*k (k refers to the number of links in the physical network) without changing the packet format. Simulation results show that the proposed scheme can shorten the latency between tenants and physical layers and enables greater capacity of tenants than traditional VLANs.

Volume: 22, Issue: 4

Group-based fast data packet attribute authentication in the reconfigurable networks

by Hongyong Jia, Yue Chen, Julong Lan, Wei Yue, Zhiwei Wang
Abstract

The reconfigurable networks in the Chinese national basic research project- Flexible Architecture of Reconfigurable Infrastructure can adjust its own structure to provide better services for applications according to their transactional attributes and is compatible with today2019s networks. Data packet authentication is necessary to guarantee that precious resources in the reconfigurable networks couldn2019t be abused. In this paper, we propose a novel fast data packet authentication scheme for the reconfigurable networks based on the attribute based signature and RSA accumulator. In our scheme, packets are organized and authenticated in groups. Every group only needs one signature to guarantee the authenticity of the first packet, and other packets in the group can be verified using the membership witness of RSA accumulator embedded in the packet. The proposed scheme can greatly reduce the generation and verification time of the packet authentication information. Simulation results demonstrate the practicality and efficiency of the proposed scheme.

Volume: 22, Issue: 4

An Integration Model of Semantic Annotation Based on Synergetic Neural Network

by Zhehuang Huang, Yidong Chen
Abstract

Correct and automatical semantic analysis has always been one of major goals in natural language understanding. However, due to the difficulties in deep semantic analysis, at present, the mainstream studies of semantic analysis are focused on semantic role labeling (SRL) and word sense disambiguation (WSD). Nowadays, these two issues are mostly considered as separate tasks. However, this approach ignores possible dependencies between them. In order to address the issue, an integrative semantic analysis model based on synergetic neural network (SNN) is proposed in this paper, which can easily express useful logic constraints between SRL and WSD. The semantic analysis process can be viewed as the competition process of semantic order parameters. The strongest order parameter will win by competition and desired semantic patterns will be recognized. There are three main innovations in this paper. First, an integrative semantic analysis model is proposed that jointly models word sense disambiguationand semantic role labeling. Second, integrative order parameter is reconstructed to reflect the relation among semantic patterns. Finally, integrative network parameters and integrative evolution equation are reconstructed, which can reflect the relationship of guiding and driving each other between word sense and semantic roles. The experiment results on OntoNotes 2.0 corpus shows the integrative method in this paper has a higher performance for semantic role labeling and word sense disambiguation, and provides a good practicability and a promising future for other natural language processing tasks.

Volume: 22, Issue: 3

Haptic Technology for Micro-robotic Cell Injection Training Systems2014A Review

by Syafizwan Faroque, Ben Horan, Husaini Adam, Mulyoto Pangestu, Matthew Joordens
Abstract

Currently, the micro-robotic cell injection procedure is performed manually by expert human bio-operators. In order to be proficient at the task, lengthy and expensive dedicated training is required. As such, effective specialized training systems for this procedure can prove highly beneficial. This paper presents a comprehensive review of haptic technology relevant to cell injection training and discusses the feasibility of developing such training systems, providing researchers with an inclusive resource enabling the application of the presented approaches, or extension and advancement of the work. A brief explanation of cell injection and the challenges associated with the procedure are first presented. Important skills, such as accuracy, trajectory, speed and applied force, which need to be mastered by the bio-operator in order to achieve successful injection, are then discussed. Then an overview of various types of haptic feedback, devices and approaches is presented. This is followed by discussion on the approaches to cell modeling Discussion of the application of haptics to skills training across various fields and haptically-enabled virtual training systems evaluation are then presented. Finally, given the findings of the review, this paper concludes that a haptically-enabled virtual cell injection training system is feasible and recommendations are made to developers of such systems.

Volume: 22, Issue: 3

Adaptive neural network control of chaos in permanent magnet synchronous motor

by Omar Aguilar, Rubén Tapia-Olvera, Antonio Valderrabano-González, Iván Rivas Cambero
Abstract

Permanent magnet synchronous motors have been used as variable-speed drives, especially for speed control and position, but it exhibits chaotic performance under certain parameter2019s changes. This work presents the use of a B-Spline neural network scheme to stabilize chaos and to adjust the rotor speed of synchronous motors. The B-spline neural network is an efficient tool to implement the adaptive speed control, with the possibility of carrying out this task on-line, taking into account the systems non-linearities. One of the main tasks is the adjustment of the proportional-integral parameters for rotor speed controller. In this work, a neural network algorithm is used to solve this problem. A nonlinear observer is designed for estimation of the rotor speed and load torque. The results of numerical simulations demonstrate that the permanent magnet synchronous motors with the B-Spline control scheme has a good dynamic performance and steady state accuracy.

Volume: 22, Issue: 3

Real-Time Implementation of a Discrete Reduced Order Neural Observer: Linear Induction Motors Application

by Alma Alanis
Abstract

This paper deals with a discrete-time neural observer for flux estimation of a linear induction motor, which is based on a Recurrent High Order Neural Network (RHONN). The RHONN weights are tuned on-line, with no off-line learning phase required, using an extended Kalman filter based algorithm. The observer stability and boundedness of the state estimation and NN weights are proven using the Lyapunov approach. Knowledge of the system model is not required. The applicability of this observer is illustrated by real-time implementation for flux estimation of a linear induction motor benchmark.

Volume: 22, Issue: 3

A Survey on Human Pose Estimation

by Hong-Bo Zhang, Qing Lei, Bi-Neng Zhong, Ji-Xiang Du, JiaLin Peng
Abstract

Human pose estimation has attracted widespread attention due to its important application value and theoretical significance. A systemic survey of human pose estimation would be very meaningful. However, relevant studies have significantly lagged behind in this respect. To this end, we discuss the difficulties of human pose estimation and give a data-driven overview of recent approaches to performing human pose estimation, including depth-based approaches and traditional image-based methods. While the focus of this study is on approaches using depth and RGB image data, we also discuss human pose estimation methods based on object detection and action recognition. Finally, we give some analysis advice to researchers.

Volume: 22, Issue: 3

A multi-criteria decision-making method based on triangular intuitionistic fuzzy preference information

by Chao-hui Wang, Jian-Qiang Wang
Abstract

The primary goal of this paper is to provide a new approach to multi-criteria decision-making to tackle situations where triangular intuitionistic fuzzy preference relations represent the evaluation values, and the criterion weight information is incompletely known. First, the triangular intuitionistic fuzzy aggregation operator is developed to aggregate criterion values. Subsequently, a mathematical programming model is employed to derive the optimal weights with incomplete weight information. Then, we present the corresponding triangular intuitionistic fuzzy decision-making method by combining the optimal weights. An illustrative example is given, and a comparative analysis is conducted between the proposed approach and other existing methods to demonstrate the effectiveness and feasibility of the developed approach.

Volume: 22, Issue: 3

Control of Pitch Angle of Wind Turbine by Fuzzy Pid Controller

by Zafer Civelek, Murat Lüy, Ertuğrul Çam, Necaattin Barışçı
Abstract

This article presents a study on set of PID parameters of blade pitch angle controller of wind turbine with fuzzy logic algorithm. Three individual control methods were used to control the wind turbine pitch angle. These control methods are conventional PI, fuzzy and fuzzy PID. With the use of these control methods, the system was protected from possible harms in high wind speed region and maintained changing of nominal output power. It was aimed to the control the wind turbine blade pitch angle in different wind speeds and to hold the output power stable in the set point by simulation of controllers with Matlab/Simulink Software. By evaluating the steady state time of output power received from the simulation results and steady state errors, the performances of the control systems have been measured and compared with one another. As a result of these simulation comparisons, it is clear that fuzzy PID controller performed better than PI and Fuzzy Controller.

Volume: 22, Issue: 3

A Review on Artificial Intelligence Methodologies for the Forecasting of Crude Oil Price

by Haruna Chiroma, Sameem Abdul-kareem, Ahmad Shukri Mohd Noor, Adamu Abubakar, Nader Sohrabi Safa, Liyana Shuib, Mukhtar Fatihu Hamza, Abdulsalam Yau Gital, Tutut Herawan
Abstract

When crude oil prices began to escalate in the 1970s, conventional methods were the predominant methods used in forecasting oil pricing. These methods can no longer be used to tackle the nonlinear, chaotic, non-stationary, volatile, and complex nature of crude oil prices, because of the methods2019 linearity. To address the methodological limitations, computational intelligence techniques and more recently, hybrid intelligent systems have been deployed. In this paper, we present an extensive review of the existing research that has been conducted on applications of computational intelligence algorithms to crude oil price forecasting. Analysis and synthesis of published research in this domain, limitations and strengths of existing studies are provided. This paper finds that conventional methods are still relevant in the domain of crude oil price forecasting and the integration of wavelet analysis and computational intelligence techniques is attracting unprecedented interest from scholars in the domain of crude oil price forecasting. We intend for researchers to use this review as a starting point for further advancement, as well as an exploration of other techniques that have received little or no attention from researchers. Energy demand and supply projection can effectively be tackled with accurate forecasting of crude oil price, which can create stability in the oil market.

Volume: 22, Issue: 3

Service Level Agreement Mapping and Provider Selection Using Annotated Semantic Information to WS-Agreement

by Nwe Nwe Htay Win, Ming-Cheng Qu, Gang Cui, Saif Rehman, Xiufeng Wang
Abstract

Due to the increasing number of service agreements between service providers and users and the lack of standard in agreement specifications in service computing environments, there is need for an efficient service mapping and provider selection approach that focuses on Service Level Agreement (SLA) documents. This paper proposes a novel SLA mapping and provider selection approach with annotated ontological semantic information to WS-Agreement and advanced mapping rules using semantic web rule language (SWRL) and web ontology language (OWL) ontologies. The experimental data shows that our proposed approach is superior to other proficient approaches, because it can properly exploit ontological semantic technology in service specification and matchmaking.

Volume: 22, Issue: 3

New AODV Routing Method for Mobile Wireless Mesh Network (MWMN)

by Zhen Ma, De-gan Zhang, Si Liu, Wen-bin Li
Abstract

Mobile Wireless Mesh Network (MWMN) consists of numerous wireless sensors, the energy and communication ability of which are limited, and they send the sensing data to the receivers through mutual cooperation. In the next generation of network, wireless mesh networks are expected to play a more important role in mobile applications. Multicast communication is an effective way to save energy, bandwidth, cost and other resources in MWMN, so we propose a new Extended Ad hoc On-Demand Distance Vector (EAODV) routing method of MWMN in this paper. The new EAODV routing method chooses the forwarding routes, which can connect more multicast receivers to solve routing optimization problems. The method proposes a dynamic strategy to change the multicast tree structure based on the characteristic of node mobility of wireless network. It tries to minimize the energy consumption to extend the lifetime of the network, while an energy-balanced routing method can help to make the network lifetime longer by using the energy in a balanced way. Simulation results demonstrate that our method effectively improves the efficiency of multicast routing, the advantage and novelty of the approach suggested is its good performance in many aspects of mobile applications.

Volume: 22, Issue: 3

Data Encryption Method Using Environmental Secret Key with Server Assistance

by Kun-Lin Tsai, Fang-Yie Leu, Shun-Hung Tsai
Abstract

Data encryption is an effective method for enterprises and government offices to protect their important information from being lost or leaked, especially when data is transmitted on the Internet. In this paper, we propose a novel data encryption/decryption method, named the Data Encryption Method using Environmental Secret Key with Server Assistance (DESK for short), which provides users with an environmental key of a trusted computer and a group key created for an authorized group to encrypt their data. In the DESK, the important parameters are separately stored and hidden in server and the trusted computer to avoid them from being cracked easily. The proposed method not only protects the important data from being loss, but is also able to resist replay attack and eavesdropping attack. The DESK has a very high security level, which is practically useful in homeland security.

Volume: 22, Issue: 3

A Non-Repudiated and Intelligent RFID Medication Safety Management System

by Chin-Ling Chen, Yeong-Lin Lai, Chih-Cheng Chen, Chun-Yi Zheng, Li-Chih Chang
Abstract

Radio frequency identification (RFID) technology is a significant solution for homeland and cyber defense. Patient and cyber safety issues have been regarded as the most important quality policy of hospital management. Enhancing patients2019 safety and reducing medication errors are priority issues of the World Health Organization (WHO). With the increasing number of inpatients, medication safety is a major concern for doctors, pharmacists, and nurses. In this paper, we employ the RFID, digital certificate, and digital signature to enhance inpatient medication safety management. The proposed scheme can promptly present the information on the inpatient medication and improve the management of the inpatient medication safety. It not only enhances the quality of medical treatment but also provides non-repudiated and intelligent medication safety management. In our scheme, the automated medication dispensing machine (AMDM) is equipped with RFID tags and readers to improve the medication safety. The comprehensive analysis of the key performance indicator (KPI) of our scheme shows a significant 50% reduction in total cost and increased satisfaction on the part of hospital nurses.

Volume: 22, Issue: 3

Advanced Risk Measurement Approach to Insider Threats in Cyberspace

by Inhyun Cho, Kyungho Lee
Abstract

Inside jobs have been a source of critical threats in cyberspace. To manage such insider threats, a proper measurement approach is required for effective risk-based decision-making. The measurement approach should include insider-related information (e.g. the significance of jobs, the position level, the required authority for data, and the type of employment) in order to better measure and analyze insider risks. In this paper, we suggest an approach that takes into account the insider-related information in calculating data leakage risk of insiders in the banking sector. We implement this approach by utilizing real-world data to calculate insider risks. We present an effective risk measurement approach, which we believe can enhance decision-making process for risk management for insider threats.

Volume: 22, Issue: 3

Efficient Attribute-Based Encryption Schemes for Secure Communications in Cyber Defense

by Yijun Mao, Yue Zhang, Min-Rong Chen, Yongbiao Li, Yiju Zhan
Abstract

How to securely transmit data over the cyber is an important problem in cyber defense. In this paper, we propose a ciphertext-policy attribute-based encryption (CP-ABE) scheme, in which the messages are encrypted together with access policy, while secret keys are associated with specified sets of attributes, and the secret keys can correctly decrypt the ciphertexts only when the attributes satisfy the associated access policy. Our proposed CP-ABE scheme is provably secure in the full model without random oracles, and has a tight security reduction. We stress that a tight security reduction implies a higher security or the requirement of smaller keys and ciphertext sizes to obtain the same security level, and thus make the scheme more efficient. Thus our scheme can be efficiently used to encrypt the transmitted data over cyber in a fine-grained manner.

Volume: 22, Issue: 3

An Access Authentication Scheme Based on Hierarchical IBS for Proxy Mobile IPV6 Network

by Tianhan Gao, Ling Tan, Peiyu Qiao, Kangbin Yim
Abstract

Proxy Mobile IPv6 (PMIPv6) enables local network-based mobility management for mobile node without being involved with any mobility-related signalling. However, the lack of access authentication makes PMIPv6 more vulnerable. The literature authentication schemes suffer from low efficiency and suitability. This paper presents a novel efficient authentication scheme for PMIPv6 based on a 2-level identity-based signature scheme. A mutual access authentication protocol is then achieved to eliminate the interactions between the home network and the access network for improving authentication efficiency and reducing communication cost. Moreover, the security and performance analysis demonstrate that the proposed scheme is robust and is able to provide better solution than existing ones.

Volume: 22, Issue: 3

Cyber threats to mobile messenger apps from identity cloning

by Suwan Park, Changho Seo, Jeong Yi
Abstract

People enjoy connecting to the Internet outside of their homes and offices, due to technological innovations and the convenience. With smartphones and other mobile devices giving us the ability to conduct everyday activities such as mobile banking, online shopping, and social networking, cyber criminals are constantly looking to take advantage of insecure wireless networks, third party applications, and texting to your personal information, steal your identity, or read personal e-mails and work documents. Such cyber threats become very high, in particular, with Android apps since they are structurally easy to rebuild, to modify or inject arbitrary code. When an adversary has victim2019s credential files in a local device, this vulnerability becomes more solemn. While the adversary can bypass all applied security techniques and forge an identity completely, he can do everything that victim can. If a mobile messenger app was selected as an attack model, the adversary is able to not only view chat history and timeline records of a specific user but to also receive or send messages in real time. In this paper, we analyze the security weak points of representative Android message apps and prove this is realistic threat in cyber defense. We then propose alternative solutions against this attack.

Volume: 22, Issue: 3

Malware Analysis and Classification Using Sequence Alignments

by In Kyeom Cho, Taeguen Kim, Yu Jin Shim, Minsoo Ryu, Eul Gyu Im
Abstract

With the increased uses of the Internet, the number of newly found malware keeps increasing every year. In addition, malware becomes more and more complex with various technologies, such as packing, anti-debugging, and so on. To defend against a large number of malware every day, the improvement of the analysis process is quite important. One way of expediting malware analysis processing is to classify unknown or new malware into known malware families. A malware family is a group of malware that share common modules and have similar malicious behaviors. This paper proposes a malware family classification framework using a sequence alignment method, which is widely used in the bioinformatics field. Our proposed framework can find common parts from invoked API sequences of malware, and these common API sequences can be used to find similar behaviors of malware variants. Since the sequence alignment methods usually have high performance overheads, our proposed framework used a couple of techniques to reduce the overheads. The proposed framework was tested with some malware families, and experimental results show that our mechanism can be used to classify malware families, because there are clear similarity differences between malware in the same family and malware in different families.

Volume: 22, Issue: 3

LinkA: A Link Layer Anonymization Method Based on Bloom Filter for Authenticated IoT Devices

by Minwoo Kim, Jihyun Bang, Taekyoung Kwon
Abstract

In point-to-point communication channels, an anonymization mechanism is necessary for a data link layer because link layer IDs such as MAC addresses can reveal further private information about communicating devices, which may threaten cyber security. Previous mechanisms based on heavy cryptographic operations are not suitable for link layer that needs to immediately accept or drop frames, particularly in resource-constrained IoT. In this paper, we study a link layer anonymization method that functions efficiently for the current existing systems using 48-bit or 64-bit MAC addresses. Our method called LinkA is based on the Bloom Filter and the use of pre-authenticated structure regarding MAC addresses. LinkA can efficiently frustrate passive adversaries to distinguish unicast from multicast frames. We also implement and analyze LinkA.

Volume: 22, Issue: 3

An Improved Square-always Exponentiation Resistant to Side-channel Attacks on RSA Implementation

by Yongje Choi, Dooho Choi, Hoonjae Lee, Jaecheol Ha
Abstract

Many cryptographic algorithms embedded in security devices have been used to strengthen home- land defense capability and protect critical information from cyber attacks. The RSA cryptosystem with the naive implementation of an exponentiation may reveal a secret key by two types of side-channel attacks, namely passive leakage information analysis and active fault injection attacks. Recently, a square-always exponentiation algorithm in which the multiplication is traded for squarings has been proposed. This novel algorithm for RSA implementation is faster than other regularity-based countermeasures and is resistant to SPA (simple power analysis) and fault injection attacks. This paper shows that the right-to-left version of square-always exponentiation algorithm is vulnerable to several side-channel attacks, namely collision distance-based doubling, chosen-message CPA (collision power analysis), and horizontal CPA-based combined attacks. Furthermore, an improved right-to-left square-always algorithm adopting the additive message blinding method and the intermediate message update technique is proposed to defeat previous and proposed side-channel attacks. The proposed exponentiation algorithm can be employed for secure CRT-RSA (RSA based on the Chinese remainder theorem) implementation resistant to the Bellcore attack. The paper presents some experimental results for the proposed power analysis attacks using an evaluation board.

Volume: 22, Issue: 3

Secure Access Control of E-Health System with Attribute-Based Encryption

by Hongyang Yan, Jin Li, Xuan Li, Gansen Zhao, Sun-Young Lee, Jian Shen
Abstract

Cloud computing is a new paradigm, which provides low-cost and effective outsourced data storage service. People are used to storing personal data in cloud server. E-Health system (EHS) privacy should be protected, because it is national security for citizen privacy. Personal Health Record (PHR) is the core of EHS that should be protected. Thus, how to efficiently store, access and share these data is critical. Attribute-based encryption (ABE) is a new public key encryption based on users2019 attributes. In this paper, we present a new method to realize a secure fine-grained access control to PHRs, which is based on emerging ABE primitives. In more details, PHR owners divide the PHR data in terms of the privacy levels. A key-policy ABE (KP-ABE) is utilized to provide a fine-grained access control storage system for outsourced sensitive data. It can also provide efficient user revocation by using the timestamp in the private key. Security analysis shows that our construction achieves the data confidentiality. That is, any unauthorized user is not allowed to access the data outsourced in the cloud.

Volume: 22, Issue: 3

Discussing the Initial Temperature Difference Correction Method for Vibrational Chord Strain Gauge in Bridge Construction Monitoring

by Jun Yang, Hong Zhang, Jianting Zhou, Bojian Xu, Aicheng Shan
Abstract

Strain monitoring is an important part of the bridge construction monitoring. The strain obtained from field measurements is usually affected by a variety of factors, an important one of which is temperature difference between the initial temperature and measured temperature. Based on the in-depth introduction of vibrational chord strain gauge2019s working principle, this part of error is analyzed. Through experiments, it is found that there is a significant linear relationship for initial temperature difference effect. On this basis the temperature difference correction fitting values are calculated and linear regression model is established for analysis. Finally, a field project application for a continuous rigid-frame bridge in Province Yunnan, China was conducted, and the results showed that using strain test fitting values to revise, the calculated stress is quite close to the theory one of structure. This correction method effectively eliminated this part of strain caused by temperature difference.

Volume: 22, Issue: 2

A Ranging Model Based on BP Neural Network

by Xiaohui Chen, Mengjiao Zhang, Kai Ruan, Canfeng Gong, Yinyin Zhang, Simon X. Yang
Abstract

The traditional Shadowing model relies too much on parameters and specific environment, so its application occasions in WSN are restricted. This paper studies the wireless signal propagation model and proposes the ranging model based on BP neural network model, it has the ability of autonomous learning according to different environments, and can improve the precision of measuring distance, then enhance the adaptability to the environment. The simulation results show that the ranging model based on BP neural network model can reduce the ranging error, which has the adaptability to the environment.

Volume: 22, Issue: 2

Path Navigation For Indoor Robot With Q-Learning

by Lvwen Huang, Dongjian He, Zhiyong Zhang, Peng Zhang
Abstract

A Q-learning based path navigation method is proposed and validated in this paper for solving the moving control along specified path of real indoor mobile robot. A grid and topological indoor corridor environment map is employed and characterized by a set of geometrical scale invariant key-points. The navigation strategy is composed of on-line and off-line stages with the same components redefinitions or definitions of Q-learning. During the off-line learning stage, the personal computer records the optimal path after computer learning simulation, and then the path is sent to the robot through wireless data radio with RS232. At the on-line navigation stage, the robot calculates the relative positions between the locations along this optimal path, and then navigates the environment autonomously. The experiments on computer simulation and an actual robot have been verified the superior effectiveness and applicability of the proposed strategy.

Volume: 22, Issue: 2

A New Simpler Third-Order Chaotic System and its Circuit Implementation

by Li Fei, Qin Aina
Abstract

This paper introduces a relatively simple three-dimensional chaotic system, which has only two equilibrium points. The system is theoretically demonstrated to be chaotic by calculation of the Lyapunov exponents and stability analysis of the equilibrium points. Then, the bifurcation diagram of the system is given to observe its period-doubling phenomenon. Compared with the R00F6ssler system, the structure of the new system is more simple, only one nonlinear product term and two parameters. Finally, an analog electronic circuit is designed to realize the system, the simulation result is achieved and basic dynamical behaviors are briefly described.

Volume: 22, Issue: 2

Video Recognition of Human Fall Based on Spatiotemporal Features

by Kai Wang, Youjin Zhao, Qingyu Xiong, Xiling Shen, Min Fan, Min Gao
Abstract

A systematic framework for recognizing human fall from video is presented in this work. For the foreground extraction, instead of remodeling background of every video frame, we directly extract cuboids that are composed of spatiotemporal interest points detected by separable linear filter from video sequences. We then represent these video patches as local image gradient descriptors with greatly reduced dimensions by principle component analysis (PCA). From labeled video patches, a supervised learning method based on Gaussian RBF kernel is proposed to determine the maximum margin between fall and normal activity, and then a novel video sequence can be categorize into fall or normal activity by an optimal hyperplane. We tested the above method on datasets set up based on the LPO-CV testing paradigm, which verified the proposed method and demonstrated its advantage over other state-of-the-art approaches for fall recognition.

Volume: 22, Issue: 2

Multi-Key Searchable Encryption with Designated Server

by Yousheng Zhou, Han Guo, Feng Wang, Wenjun Luo
Abstract

This paper presents a novel multi-key searchable encryption with designated server based on the RA Popa2019s multi-key searchable encryption and Rhee2019s PEKS. As far as we know, it is the first multi-key searchable encryption based on asymmetric cryptographic approach. One advantage of our proposed scheme is that it allows a user to provide a single trapdoor to the server, though it allows the designated server to search for that trapdoor2019s word in documents encrypted with different keys. Another advantage of our scheme is that only the designated server can identify whether a trapdoor matches the encrypted data or not.

Volume: 22, Issue: 2

Cloud Storage Access Control Scheme of Ciphertext Algorithm Based on Digital Envelope

by Anping Xiong, Chunxiang Xu
Abstract

Ciphertext Policy Attribute-Based Encryption (CP-ABE) has been widely studied in recent years because it is more suitable for access control of shared data under cloud storage environment. In view of problems of flexibility and efficiency of the existing encryption schemes under cloud storage environment, combined with digital envelopes technology, the paper puts forward an optimized scheme based on supporting fine-grained access control. The scheme has the following advantages: Adopting digital envelopes technology, reducing the computational overhead of Data Owner significantly on the basis of the ensuring data confidentiality, using proxy re-encryption technology to realize the support of user and attribute revocation flexibly, furthermore, the backward and forward secrecy also have been ensured. Adopting security protocols to distribute the shared keys between users and cloud storage server, cloud storage server can save the overhead in maintenance of a large user key2019s tree. Security and performance analysis show that Cloud storage access control scheme of ciphertext algorithm based on digital envelope can ensure the confidentiality of data, resist collusion attack with forward and backward security, and also reduce the calculation works of the user owners and accessing users.

Volume: 22, Issue: 2

Scheduling Parallel Soft Real-Time VM in Dynamic Workloads

by Xiaobo Ding, Zhong Ma, Xinfa Dai
Abstract

Virtual machine (VM) is widely used in many fields now. The CPUs of VM, different from those of physical machines, cannot be ensured to be on line at the same time, and the number of VMs will affect the performance of the parallel VM. So concurrent programs working on the VM is not efficient, and the soft real-time concurrent applications would be invalid especially in the worst case. Based on Xen Credit scheduler, this paper analyzes the factors affecting the performance of Parallel Soft Real-Time task VM (PSRTVM) and raises a Parallel Soft Real-Time Scheduling Algorithm (PSRTSA), which could ensure the performance of PSRTVM in dynamic workloads. In PSRTSA, the synchronized scheduling is proposed to meet the parallel workloads, and the opportunity of synchronized scheduling is set to meet the delay of the worst case. The percentage pre-allocation of CPU time is used to ensure the resource of PSRTVM in dynamic workloads of system. The evaluation shows the improvement of the PSRTSA compared to the default Credit scheduler and RT-Credit scheduler based on scheduling synchronization.

Volume: 22, Issue: 2

Time Efficient Virtual Network Embedding Algorithm

by Tao Huang, Ying Gu, Jiang Liu, Yunjie Liu
Abstract

The virtual network embedding problem is an essential problem in network virtualization. Researchers care about efficiency of the virtual network embedding algorithm. This issue has two procedures: node embedding and link embedding. However, previous studies mainly concentrate on the node embedding while neglecting the link embedding, which is the main cause for the bad running time. In this paper, we propose a new algorithm2014Deleting-first algorithm, which deletes incapable substrate links for the current virtual request before link mapping. The efficiency of virtual network embedding will be improved. The simulation results show that the average performance of the new algorithm is better at runtime.

Volume: 22, Issue: 2

An Application of SVM-Based Classification in Landslide Stability

by Tingyao Jiang, Peng Lei, Qin Qin
Abstract

The calculation method of landslide stability is a critical issue in landslide research. SVM-based multi-classification algorithm, which can structure multiple binary classifiers to accomplish the multi-classification task is used for landslide stability analysis. In this paper, the slope height, slope angle, capacity, internal friction angle and cohesion are selected as impact factors affecting the stability of landslide. Loop crossover method is used to verify the accuracy of the algorithm. Compared with the Mahalanobis distance and Bayes discriminant, the proposed algorithm has a better prediction result, but it also has the largest mis-judgment loss. The accuracy of Bayes discriminant is less than the SVM, but its mis-judgment loss is minimal.

Volume: 22, Issue: 2

A Method for Extending the Geostatistical Functions in Spatial Information Processing

by Kai Ma, Jihua Wang, Zehua Chen, Xiaobo Zhu, Ligang Pan
Abstract

Geostatistics is widely used in many fields. However, the geostatistical functions of many commercial geographic information platforms are not rich enough. The geostatistical interpolation and analysis is a typical spatial information processing. Aiming to overcome the deficiency of geostatistical functions in geographic information platform, this paper studies the spatial information processing model, and proposes a new method based on the model. R language package has various geostatistical interpolation interfaces and detailed parameter setting functions. By invoking R language package through Python scripts and PypeR, which can make full use of the R language analysis function and provide more interpolation algorithms and flexible parameter settings. The expansion method was applied in the soil heavy metal distribution system. The experimental results show that, it can be integrated seamlessly with commercial geographic information platform, without disturbing other modules. Compared with the original method, the extended method has similar performance in the realization of the same interpolation algorithm.

Volume: 22, Issue: 2

Monitoring and Forecasting Winter Wheat Freeze Injury and Yield from Multi-Temporal Remotely Sensed Data

by Huifang Wang, Zhiguo Huo, Guangsheng Zhou, Li Wu, Haikuan Feng
Abstract

Remote-sensing techniques provide crop growth information economically, rapidly, and objectively on a large scale. Remote sensing has been widely used to monitor crop growth and forecast yield. In this study, three Huanjing satellite (HJ) charge-coupled device (CCD) images of winter wheat at growth-wintering (December 2, 2009; pre-freeze injury), regreening (April 2, 2010; post-freeze injury), and jointing stages (April 23, 2010) were acquired for the Gaocheng area in Hebei Province. According to the change characteristics of the normalized difference vegetation index (NDVI) of field samplings of post-freeze injury, we built a multi-line-progress model between the NDVI difference (0394NDVI) and field samplings, which correspond with field investigation data. The damage levels (uninjured, mild, moderate, and serious) and the growth levels (better, good, bad, and worse) were also specified in the model. As a result, the coefficient of determination (R2) of this model reached 0.6001; 20 sampling points were used to validate the model and R2 reached 0.5255. This study demonstrates the feasibility of using early growth stage model to predict yield and provides a tentative prediction of the yield in the Hebei area using HJ-CCD images of China.

Volume: 22, Issue: 2

Clustering Detection Algorithm of Plant Leaf Relative Lesion Area Based on Improved GA

by Shun Ren, Haiye Yu, Linlin Wang, Lei Zhang
Abstract

In order to overcome the defects of traditional methods on measuring plant leaf diseases and achieve accurate detection of blade relative lesion area, clustering algorithm and the improved clustering algorithm are used to calculate the leaf relative lesion area with the knowledge of computer graphics technology. First, preprocess the image selectively by the image correction, color space conversion technology and so on, use the clustering algorithm to divide the target area. Finally calculate relative lesion area according to the partition determined by objective function. This paper proposed an improved genetic algorithm to improve the limitation problem of selecting the clustering algorithm initial value and enhance the searching capability and the robustness of the original algorithm by improving genetic operator of genetic algorithm. Considering the accuracy of image processing as the quota, the results show that the algorithm in this paper makes the lesion area calculated by the clustering center more accurate and lays more effective theoretical foundation for diagnosis of crop diseases and insects level.

Volume: 22, Issue: 2

Selecting the Optimal NDVI Time-Series Reconstruction Technique for Crop Phenology Detection

by Wei Wei, Wenbin Wu, Zhengguo Li, Peng Yang, Qingbo Zhou
Abstract

A new scored method has been proposed in this study to evaluate the performances of different NDVI time-series reconstruction techniques. By giving a synthetic score to each of the candidates techniques based on two quantified criteria the optimal one is selected for the purpose of phenology detection. Three widely used techniques including Asymmetric Gaussian function fitting (AG), Double Logistic function fitting (DL) and Savitzky-Golay filtering (SG) are compared using NDVI time-series products from Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra satellite over cropland of Northeast China. The results show that AG approach outperforms the two others in our study area. Cropland NDVI values have been improved obviously after the reconstruction by AG. Spatial patterns of the crop phenology detected from the AG reconstructed NDVI time-series are reasonable. The errors of the derived crop phenology metrics are within an acceptable limit.

Volume: 22, Issue: 2

Classification of Orange Growing Locations Based on the Near-infrared Spectroscopy Using Data Mining

by Songjian Dan, Simon X. Yang, Fengchun Tian, Lie Den
Abstract

The classification of growing locations is very important for quality control in the orange industries, which is also challenging work, because of its complex chemical composition and varies of taste and sizes. The traditional ways to classify them by human2019s sense are time consuming and at high cost. In this paper, a new general classification framework based on the Near-Infrared Reflection (NIR) spectroscopy using data mining technology was proposed. First, the raw NIR spectra data were reduced by the principal components analysis (PCA), and then an attribution selection method was applied to find the best feature subset. An evolution process was also introduced to test the performance of five classifiers (Decision Tree, KNN, Naive Bayesian, SVM and ANN) used in this paper. The proposed classification framework was verified on three NIR spectra datasets, which were collected from the different part of oranges (including two parts of fruit surface and juice) from 15 different places in china. The experimental results demonstrated that the juice NIR spectra is the most suitable data-set for identifying the orange growing locations, and the decision tree is the best and most stable classifier, which could achieve the highest average prediction rate of 96.66%.

Volume: 22, Issue: 2

Next Wave of Technology

by D Tesar
Abstract

A balanced electro-mechanical system must have the openness now represented by the electricals (computer chips, power boards, standardized interfaces, domain-relevant operational software) and the reconfigurability to not only meet the needs of ever-changing tasks, but also enable rapid assembly, repair, and refreshment on demand by means of highly-certified components made available by means of a competitive supply chain. This, then, requires a command/response in milli-sec for multi-input/multi-output (MIMO) systems that are normally perceived as highly non-linear and highly coupled (i.e., unsolvable by standard analytic tools).Criteria-based decisions in milli-sec essentially linearize these systems to make strictly algebraic processes viable with little or no uncertainty so long as the operational criteria have clear physical meanings. The human command (trains, surgery, aircraft, automobiles, etc.) in concert with semi-autonomous processes can be put into balance where each plays its relevant role. The missing ingredient in this accelerated development of the tech base for the mechanicals is the intelligent actuator (i.e., the action part of the full physical system) as the computer chip is to the electrical system.

Volume: 22, Issue: 2

Performance Analysis to Improve Quality of Service Using Cluster Based Hidden Node Detection Algorithm in Wireless Sensor Networks

by R. Rohini, R K Gnanamurthy
Abstract

This paper investigates the Hidden Node (HN) problem in wireless sensor networks. Hidden node occurs in the networks when nodes outside the carrier-sensing range of each other are nevertheless close enough to interfere with each other. As a result, the carrier sensing mechanism may fail to prevent packet collisions. Hidden node can cause many performance problems, including throughput and energy degradation and increases in delay. In this paper, a novel clustering mechanism based on energy consumption is proposed to detect the hidden nodes in the wireless sensor networks to improve the network performance. The performance of the network is analyzed using the parameters 2014 energy consumption, delay and throughput, with or without hidden nodes in the network.

Volume: 22, Issue: 2

A Novel Approach for Designing a Cognitive Sugarscape Cellular Society Using An Extended Moren Network

by Masumeh Maleki, Nasim Nourafza, Saeed Setayeshi
Abstract

Artificial life is a term used to describe man-made systems that have been designed to behave in ways that simulate the behavior of natural living systems. Agent-based modeling of social processes is called artificial society. The Sugarscape model is used to model, interpret, and organize social, political, and economic processes in an artificial society. In science, cognition refers to a whole series of mental processes, including attention, memory, language comprehension and production, and learning. The aim of this study was to develop a cognitive Sugarscape model. A Sugarscape model was designed, and the agents were placed in the environment randomly; the parameters of the model, such as sugar level and metabolism, were assigned to the agent randomly. In the present study, learning was applied in the Sugarscape model by the Moren algorithm and compared with the learner Sugarscape model that used the Boltzmann learning algorithm in previous studies. The results of the comparison showed that the number of agents in the sugar peaks in the convergent time of the cognitive Sugarscape model exceeded those in the learner Sugarscape model that used the Boltzmann learning algorithm.

Volume: 22, Issue: 2

An Improved Evolutionary Algorithm for Reducing the Number of Function Evaluations

by Erik Cuevas, Eduardo Santuario, Daniel Zaldivar, Marco Perez-Cisneros
Abstract

Many engineering applications can be approached as optimization problems whose solution commonly involves the execution of computational expensive objective functions. Recently, Evolutionary Algorithms (EAs) are gaining popularity for solving complex problems that are encountered in many disciplines, delivering a more robust and effective way to locate global optima in comparison to classical optimization methods. However, applying EA2019s to real-world problems demands a large number of function evaluations before delivering a satisfying result. Under such circumstances, several EAs have been adapted to reduce the number of function evaluations by using alternative models to substitute the original objective function. Despite such approaches employ a reduced number of function evaluations, the use of alternative models seriously affects their original EA search capacities and their solution accuracy. Recently, a new evolutionary method called the Adaptive Population with Reduced Evaluations (APRE) has been proposed to solve several image processing problems. APRE reduces the number of function evaluations through the use of two mechanisms: (1) The dynamic adaptation of the population and (2) the incorporation of a fitness calculation strategy, which decides when it is feasible to calculate or only estimate new generated individuals. As a result, the approach can substantially reduce the number of function evaluations, yet preserving the good search capabilities of an evolutionary approach. In this paper, the performance of APRE as a global optimization algorithm is presented. In order to illustrate the proficiency and robustness of APRE, it has been compared to other approaches that have been previously conceived to reduce the number of function evaluations. The comparison examines several standard benchmark functions, which are commonly considered within the EA field. Conducted simulations have confirmed that the proposed method achieves the best balance over its counterparts, in terms of the number of function evaluations and the solution accuracy.

Volume: 22, Issue: 2

Using an Enhanced Feed-Forward BP Network for Predictive Model Building from Students2019 Data

by Ajiboye Adeleke, Ruzaini Abdullah-Arshah, Hongwu Qin
Abstract

Feed-forward, Back Propagation (BP) Network is a network structure capable of modeling the class prediction as a nonlinear combination of the inputs. The network has proven its suitability in solving several complex tasks, most especially when trained with appropriate algorithms. This study presents an enhancement of this network with a view to boosting its prediction accuracy. The paper proposed a modification of the data partitioning function in the regular feed-forward network. A predictive model is constructed based on the proposed partition, while the second model is based on the partition of the existing network. Both models are trained and simulated with sets of untrained data. The mean absolute error is computed for both models and their error values are compared. Comparison of their results shows that the enhanced network consistently delivers higher accuracy and generalized better than the existing network in its regular structure; as there was a decrease in error from 0.261 to 0.016. The enhanced network has also shown its suitability in the fittings of models from students2019 data for prediction purposes.

Volume: 22, Issue: 2

Leveraging Clustering Techniques to Facilitate Metagenomic Analysis

by Damien Ennis, Sergiu Dascalu, Frederick C. Harris
Abstract

Machine learning clustering algorithms provide excellent methods for conducting metagenomic analysis with efficiency. This study uses two machine learning algorithms, the self-organizing map and the K-means algorithms, to cluster data from an environmental sample collected from a hot springs habitat and to provide a visual analysis of that data. A data processing pipeline is described that uses the clustering algorithms to identify which reference genomes should be included for further analysis in determining possible organisms that are present in a metagenomic sample. The clustering revealed probable candidates for additional analysis, including a thermophilic, anaerobic bacterium, which is likely to be found in a hot springs environment and serves to validate the functionality of these tools. The machine learning techniques discussed here can serve as a launching point for elucidating protein sequences that could serve as possible reference comparisons to a specific metagenomic sample and lead to further study.

Volume: 22, Issue: 1

An Approximation Method Of Optimal Scheduling For Multicommodity Flows In Cloud-Service Scenarios

by Dong Huang, Yong Yang, Lun Tang, Ju Zhang, Xiang Wang
Abstract

With limited physical and virtual resources in cloud computing systems, it is not feasible to successively expand network infrastructure to adequately support the rapid growth in service. So the pervasive boundary in communication networks is that of allocating bandwidth to satisfy a given collection of service requests is hard to reach. In situations where there is limited network capacity but plethory of requests, the current optimal methodology of the choice of which requests to satisfy, thereby resulting in congestion and low utilization rate.It is imperative to develop new efficient scheduling methods. In this paper, we demonstrate these concepts through a case test study of scheduling for multicommodity flows in cloud-service environment by successive heuristics algorithms, and the heuristic solutions are then compared with the related network performance metrics obtained using the presented model. The effectiveness of the new technique was evaluated by comparison to the differentiated cost utilization and the comparison indicates that substantial improvements are possible. Then the lower cost of deployment on the cloud-computing systems can be justified. Moreover, developing an approximation optimization model can be beneficial to the communication network planning.

Volume: 22, Issue: 1

Soft Computing Techniques for Reduced Order Modelling: Review and Application

by Othman Alsmadi, Zaer Abo-Hammour, Dia Abu-Al-Nadi, Saleh Saraireh
Abstract

As the mathematical procedure of system modelling often leads to a comprehensive description, which causes significant difficulty in both analysis and control synthesis, it is necessary to find lower order models, which maintain the dominant characteristics of the original system. In this paper, different soft computing (named as artificial intelligence (AI)) techniques are presented, applied, and analysed for model order reduction (MOR) of multi time scale systems with the objective of substructure preservation. In addition to that, we investigate the firefly optimization technique for MOR with substructure preservation. The analysis is concerned with the optimization approach and quality of method performance.

Volume: 22, Issue: 1

Adaptive Nonlinear Systems Identification via Discrete Multi-Time Scales Dynamic Neural Networks

by Wei-Dong Xie, Zhi-Jun Fu, Wen-Fang Xie
Abstract

In this paper, we extend our previous results on continuous multi-time scales dynamic neural networks identification to the discrete domain. A robust on-line identification algorithm is proposed for nonlinear systems identification via discrete multi-time scales dynamic neural networks. The main contribution of the paper is that the input-to-state stability (ISS) approach is used to tune the weights of the discrete multi-time scales dynamic neural networks in the sense of L1. The commonly used robustifying techniques, such as dead-zone or s-modification in the weight tuning, are not necessary for the proposed identification algorithm. The stability of the proposed identifier is proved by Lyapunov function and ISS theory. Two examples are given to demonstrate the correctness of the theoretical results.

Volume: 22, Issue: 1

Enhanced Particle Swarm Optimization With Self-Adaptation Based On Fitness-Weighted Acceleration Coefficients

by Hei-Chia Wang, Che-Tsung Yang
Abstract

Acceleration coefficients are the control parameters used to tune the movements of cognition and social components in the particle swarm optimization (PSO) algorithm. Because most of the PSO algorithms treat individual particles equally despite the different positions of distinct particles, the inhomogeneous spread and scattering of the data samples during evolution are ignored. In this regard, the proposed PSO-FWAC algorithm aims to enhance the adaptability of individual particles by introducing the diverse acceleration coefficients according to their corresponding fitness values. The experimental results show that the PSO-FWAC outperforms the static and time-varying approaches.

Volume: 22, Issue: 1

A Joint Optimal Hand-Off And Stability Methodology In Multi-Flow Priority-Based Heterogeneous Networks

by Dong Huang, Yong Yang, Lun Tang, Ju Zhang, Xiang Wang
Abstract

For traditional hand-off scheme oriented to multi-flow priority being a lack of effective channel utilization, a Joint Optimal hand-off and stability scheme was presented, while supporting the multiservice priority that is able to provide QoS guarantee. A novel stability determination method of channel allocation control was proposed in this paper, and shows that the average access delay of ongoing call or incoming call flow is much lower when using a proposed hand-off scheme than the diffserv/intserv mode in traditional hand-off schemes. According to the numerical simulations, the performances of mobility and channel utilization were improved compared to the traditional hand-off scheme.

Volume: 22, Issue: 1

Output Feedback Control For Trajectory Tracking Of Wheeled Mobile Robot

by Muhammad Asif, Attaullah Y. Memon, Muhammad Junaid Khan
Abstract

In this paper, output feedback control for trajectory tracking of a wheeled mobile robot (WMR) is proposed using a high gain observer. Kinematic and dynamic models of the WMR are described, and an output feedback controller is proposed using adaptive sliding mode controller. High gain observer is designed for velocity estimation of WMR trajectory tracking. It is shown that using high gain observer and a globally bounded state feedback stabilizing controller, the close-loop system performance can be recovered in the presence of un-modeled dynamics and disturbances. Stability analysis of the proposed observer is shown using Lyapunov methods. The effectiveness of the proposed system is shown using simulation.

Volume: 22, Issue: 1

Rail Mounted Gantry Crane Scheduling In Rail2013Truck Transshipment Terminal

by Li Wang, Xiaoning Zhu, Zhengyu Xie
Abstract

Rail mounted gantry crane (RMGC) scheduling is vital to handling efficiency in rail-truck transshipment terminals. In this paper, we propose an optimization model for RMGC scheduling problem based on a dual cycle mode. A genetic algorithm is designed to obtain the optimization handling sequence. Computational experiments on a specific rail-truck transshipment terminal show that the proposed method is effective to solve RMGC scheduling problems in rail-truck transshipment terminals.

Volume: 22, Issue: 1

Mobile Agent Based Distributed EM Algorithm For Data Clustering In Sensor Networks

by Behrouz Safarinejadian, Mohiyeddin Mozaffari
Abstract

In this paper, a mobile agent based distributed EM (Expectation Maximization) algorithm is developed for density estimation and data clustering in sensor networks. It has been assumed that sensor measurements can be statistically modeled by a common Gaussian mixture model. This algorithm not only executes the EM algorithm in a distributed manner, but reduces the number of iterations of the EM algorithm and increases its convergence rate. Convergence of the proposed method will also be studied analytically and will be shown that the estimated parameters will eventually converge to their true values. Finally, the proposed method will be applied to synthetic data sets in order to show its promising performance.

Volume: 22, Issue: 1

Design and Development of a Virtual Instrument for Hazardous Environment Monitoring and Control Using Lab VIEW

by A. Sureshkumar, S. Muruganand, P. Balakrishan
Abstract

The increased high performance of personal computers and their reduced cost has made it possible for the development of computer based monitoring and control systems. Industry needs several measurement systems that can measure safety risk parameters of the hazardous area. Although wireless sensor networks have been widely used, combining virtual instrument technology to achieve the purpose of safety measurement has several benefits. These systems are efficient and cost-effective for acquiring and analyzing sensor signals. Utilizing virtual instrumentation to achieve safety measurement will largely decrease the cost and increase the flexibility of the instruments. This work aims at designing a virtual instrument for acquiring and processing of hazardous parameter signal. The software platform in terms of virtual instruments is developed under Lab VIEW programming environment and integrated with computer controlled system.

Volume: 22, Issue: 1

Signal Processing for Noise and Online Force Modeling Detection for a Robot Hand Based on EtherCAT Communication

by Mingxin Hou, Li Jiang, Hong Liu, Zhaopeng Chen
Abstract

This study proposes three efficient algorithms for online computing of fingertip force modeling and noise involved in 1-D, 2-D and 3-D models. Fast and effective real-time detection for fingertip force modeling is extremely important for successful manipulation on an object by a robot hand in a real situation. Meanwhile, signal processing for a desired force signal is necessary if unwanted signals and noise cannot be ignored in practical applications. Butterworth low-pass filters designed in this study can be effectively used for filtering noise and obtaining flatter and smoother force modeling detection signals. An EtherCAT slave circuit is designed and force detection models using Beckhoff TwinCAT 3.1 provide feedback signals. The calculation is made during several real-time experiments.

Volume: 22, Issue: 1

Generic Evaluation Metrics for Hyperspectral Data Unmixing

by Ouiem Bchir, Mohamed Maher Ben Ismail
Abstract

We propose novel generic performance metric for hyperspectral unmixing techniques. This relative metric compares two abundance matrices. The first one represents the unmixing result. The second matrix can be either another unmixing result or the ground truth of the hyperspectral scene. This metric starts by computing coincidence matrices corresponding to the two abundance matrices, then the comparison is carried out by computing statistics of the number of pairs of data points that have high abundances with respect to the same endmember for the first unmixing approach, but have large abundance differences with respect to the same endmember for the second unmixing technique, or large differences in both. The main advantage of this metric approach is that there is no need to pair the endmembers of the two unmixing approaches. Rather, it assumes that the pixels, which are considered as different/same material by one unmixing approach should also be considered different/same material by the other. Our initial experiments on synthetic dataset have indicated the appropriateness of the proposed performance measures to assess unmixing techniques. Finally, the proposed metric are assessed using real dataset, and existing hyperspectral unmixing techniques.

Volume: 22, Issue: 1

Color Image Segmentation By Cuckoo Search

by Sudarshan Nandy, Xin-she Yang, Partha Pratim Sarkar, Achintya Das
Abstract

In this paper, a clustering based color image segmentation technique is proposed and the clustering technique is optimized by the cuckoo search method. The proposed approach consists of two phase segmentation processes. In the first phase, cluster centres are optimized by using the cuckoo search algorithm and in the second phase, empty and frequent clutters are removed and merged according to pre-defined rules. This cluster centre based clustering technique is then used to find the optimum centre within a cluster, while cuckoo search is applied to find the optimum cluster centre for each segment in the image. Comparison of the proposed method is performed with the genetic algorithm (GA), dynamic control particle swarm optimization (DCPSO) algorithm and firefly algorithm based color image segmentation methods over five benchmark color images. The parameters of the proposed method are tuned through empirical testing. Results demonstrated that the proposed method can be an effective tool for image segmentation.

Volume: 21, Issue: 4

Information Technology Education in a Digital Factory Learning Environment

by Gali Naveh, Adir Even, Lior Fink, Sigal Berman
Abstract

This paper presents the vision, construction, and implementation of an Integrated Manufacturing Technology (IMT) laboratory for higher education. The laboratory introduces integration of Information Technology (IT) in manufacturing processes and provides hands-on learning experience in an authentic, holistic, and automated production environment, not commonly found in academic teaching. The manufacturing process includes fully-automated, semi-automated, and manual stations and is supported by a heterogeneous IT infrastructure, including manufacturing databases, shop-floor control, data warehousing, business intelligence, and enterprise systems. The IMT laboratory is used to facilitate learning in a databases course and a business intelligence course, resulting in positive feedback from the students.

Volume: 21, Issue: 4

BIBO Stability Analysis of TSK Fuzzy PI/PD Control Systems

by Oh-Kyu Choi, Jinwook Kim, Jongkyoo Kim, Jin S. Lee
Abstract

In this paper, we determine the sufficient conditions in which nonlinear feedback systems using Takagi-Sugeno-Kang (TSK) fuzzy PI/PD controllers become bounded-input bounded-output (BIBO) stable. To determine BIBO stability conditions, we apply the small-gain theorem to the systems. Compared to previous results, BIBO stability conditions in this study are determined for controllers that are developed using more relaxed input fuzzy sets. In addition, the procedures used to determine sufficient conditions are simple because region-based input2013output relationship analysis is not required. Finally, the derived conditions are less conservative than those determined in previous studies. MATLAB simulations are performed to validate the results of this study.

Volume: 21, Issue: 4

A Model and Metaheuristic for Truck Scheduling in Multi-door Cross-dock Problems

by Medhi Yazdani, B. Naderi, M. Mousakhani
Abstract

The cross-docking is a warehousing strategy where trucks arrive from suppliers to unload, regroup, and reload their items onto trucks going to the customers. There is a common assumption that the receiving (shipping) dock has only one single door, but companies usually duplicate doors in parallel to expedite operations. This paper studies the problem of scheduling trucks in the cross-dock with multiple inbound and outbound doors with makespan minimization. The problem is first formulated by a new mixed integer programming model. Using the model, small instances are solved to optimality. The paper then proposes a hunting search metaheuristic inspired from group behavior of animals when searching and hunting for food. A set of experimental instances are carried out to evaluate the algorithm. The algorithm is carefully evaluated against the adaptation of two existing algorithms. The results show that the algorithm provides sound performance comparing with other algorithms.

Volume: 21, Issue: 4

Phone Number Extraction and Area Code Mining in Mobile Web Browsing for Smart Phones

by Taehwan Kim, Joongmin Choi, Jungsun Kim
Abstract

The phone numbers listed on a Web page displayed on a mobile phone can be useful information for instant reservations or general inquiries. Although current software technology for smart phones can recognize and highlight phone numbers on a page and auto-dial them when clicked, the extraction accuracy often drops significantly due to many cases of data that are falsely recognized as phone numbers. Furthermore, there are many situations in which the area code is missing, preventing the mobile phone from auto-dialing correctly. To resolve these problems, this paper proposes an intelligent phone number extraction and area code mining technique for smart phones in a mobile environment. A series of experiments performed using Android smart phones showed that our method greatly improved the accuracy of phone number mining and the speed of auto-dialing in a mobile Web environment.

Volume: 21, Issue: 4

Automatic Analysis of Dot Blot Images

by Cristina Caridade, A.R.S. Marcal, P. Albuquerque, M.V. Mendes, F. Tavares
Abstract

This paper presents a method for the automatic analysis of macroarray (dot blot) images. The system developed receives as input a dot blot image, corrects it for grid rotation, identifies the visible markers and provides an evaluation of the status of each marker (ON/OFF). Two experiments were carried out to evaluate the detection and classification stages. A total of 222 test images were produced from 6 original dot blot images, with various rotations, translations, contrast and noise level. Over 7500 markers were identified automatically and compared to manual reference. The RMS error in positioning the molecular marker center was between 1.1 and 3.8 pixels and the marker radius error less than 4%. The automatic classification of markers (ON/OFF) was compared to the classification by 3 human experts, using 10 test images. The overall accuracy evaluated on 5118 markers was 94.0%. For those markers that had the same evaluation by all 3 experts, the classification accuracies were 96.6% (ON) and 95.9% (OFF).

Volume: 21, Issue: 4

A Supervised Fine-Grained Sentiment Analysis System for Online Reviews

by Hanxiao Shi, Wenping Zhan, Xiaojun Li
Abstract

Sentiment analysis, as a heatedly-discussed research topic in the area of information extraction, has attracted more attention from the beginning of this century. With the rapid development of the Internet, especially the rising popularity of Web2.0 technology, network user has become not only the content maker, but also the receiver of information. Meanwhile, benefiting from the development and maturity of the technology in natural language processing and machine learning, we can widely employ sentiment analysis on subjective texts. In this paper, we propose a supervised learning method on fine-grained sentiment analysis to meet the new challenges by exploring new research ideas and methods to further improve the accuracy and practicability of sentiment analysis. First, this paper presents an improved strength computation method of sentiment word. Second, this paper introduces a sentiment information joint recognition model based on Conditional Random Fields and analyzes the related knowledge of the basic and semantic features. Finally, the experimental results show that our approach and a demo system are feasible and effective.

Volume: 21, Issue: 4

Dynamic Multiobjective Evolutionary Algorithm With Two Stages Evolution Operation

by Chun-an Liu, Huamin Jia
Abstract

Multiobjective optimization problems occur in many situations and aspects of the engineering optimization field. In reality, many of the multiobjective optimization problems are dynamic in nature, i.e. their Pareto fronts change with the time or environment parameter; these optimization problems most often are called dynamic multiobjective optimization problem (DMOP). The major problems in solving DMOP are how to track and predict the Pareto optimization solutions and how to get the uniformly distributed Pareto fronts, which change with the time parameter. In this paper, a new dynamic multi-objective optimization evolutionary algorithm with two stages evolution operation is proposed for solving the kind of dynamic multiobjective optimization problem in which the Pareto optimal solutions change with time parameter continuously and slowly. At the first stage, when the time parameter has been changed, we use a new core distribution estimation algorithm to generate the new evolution population in the next environment; at the second stage, when the environment of the optimization problem keeps unchanged, a new crossover operator and a mutation operator are used to search the Pareto optimal solutions in current environment. Moreover, three performance metric methods for DMOP based on the generation distance, the spacing and the error ratio are also given. The computer simulations are made on three dynamic multi-objective optimization problems, and the results indicate the proposed algorithm is effective for solving DMOP.

Volume: 21, Issue: 4

Simple and Computationally Efficient Movement Classification Approach for EMG-controlled Prosthetic Hand: ANFIS vs. Artificial Neural Network

by Hessam Jahani Fariman, Siti A. Ahmad, M. Hamiruce Marhaban, M. Ali Jan Ghasab, Paul H. Chappell
Abstract

The aim of this paper is to propose an exploratory study on simple, accurate and computationally efficient movement classification technique for prosthetic hand application. The surface myoelectric signals were acquired from 2 muscles2014Flexor Carpi Ulnaris and Extensor Carpi Radialis of 4 normal-limb subjects. These signals were segmented and the features extracted using a new combined time-domain method of feature extraction. The fuzzy C-mean clustering method and scatter plots were used to evaluate the performance of the proposed multi-feature versus other accurate multi-features. Finally, the movements were classified using the adaptive neuro-fuzzy inference system (ANFIS) and the artificial neural network. Comparison results indicate that ANFIS not only displays higher classification accuracy (88.90%) than the artificial neural network, but it also improves computation time significantly.

Volume: 21, Issue: 4

Authentication and Key Management Based on Kerberos for M2M Mobile Open IPTV Security

by Inshil Doh, Kijoon Chae, Jiyoung Lim, Min Young Chung
Abstract

Openness has been added to IPTV (Internet Protocol Television) services in order for users not only to receive, but also to provide them. Open IPTV is expected to bring about great changes in the IPTV service area. Recently, mobility has also been added. However, because of its openness and IP network characteristics, security is one of the major issues hindering its commercialization. For IPTV, several security mechanisms for user authentication and content protection have been proposed to make the service reliable. However, these traditional security mechanisms cannot be adopted in the mobile IPTV area, because the security and system performance are influenced by numerous factors in the many-to-many environments. In our work, we propose a mechanism for secure user authentication and key distribution based on Kerberos for mobile open IPTV. Our proposal provides an efficient authentication process and the secure transmission of content among users, and further decreases the authentication time compared with that of other mechanisms.

Volume: 21, Issue: 4

Leveraging The Data Gathering and Analysis Phases to Gain Situational Awareness

by Yaser Khamayseh, Wail Mardini, Hadeel Tbashate
Abstract

Situational Awareness (SA) is the process of collecting observations, understanding their meaning and projection of new possible updates. It is applied in several dynamic and complex environments like aviation, air traffic control, power plant operations, military command and control. Object classification in infrared images is a crucial component of smart surveillance systems. In this paper, a robust framework for person-vehicle classification is proposed, which classifies objects with 97.2% accuracy and successfully achieves its intended use in challenging real-world situations. Some of these challenges are: Random camera viewpoints and low resolution infrared images. The main concern of this framework is enhancing the preprocessing phase to achieve higher classification accuracy. This paper provides an improved classification process accompanied with interpolation process for extracting the concerned features (objects) in infrared images. This framework increases the classification accuracy and overcome some potential problems in the data analysis phase of Situational Awareness (SA).The proposed system is simulated using MATLAB environment and achieves accurate results by relying on powerful distinguishable features extraction, high productive preprocessing workflow, high quality interpolation and classification framework. Experimental results prove the effectiveness of the proposed framework. The obtained results are compared against the results obtained from other technique.

Volume: 21, Issue: 4

Hybrid support vector machine rule extraction method for discovering the preferences of stock market investors: Evidence from Montenegro

by Ljiljana Kašćelan, Vladimir Kašćelan, Miomir Jovanović
Abstract

In this study we developed a support vector machine (SVM) rule extraction method for discovering the effects of the features of investors and stock and corporate performance on stock trading preferences. We used this system to combine strengths of two approaches: SVM as an accurate classifier and a decision tree (DT) as a generator of interpretable models. The method is applied to Montenegro data in order to generate interpretable rules for stock market decision-makers. The results showed that this method, in terms of accuracy and interdependency of factors, outperformed the methods for detecting stock trading preferences from previous studies.

Volume: 21, Issue: 4

PSO based Automated Test Coverage Analysis of Event Driven Systems

by Abdul Rauf, Eisa al Eisa
Abstract

Graphical User Interface (GUI, pronounced sometimes as gooey as well) was first developed in 1981 and has become an essence for today0027s computing. A GUI contains graphical objects having certain distinct values which can be used to determine the state of the GUI at any time. Developing organizations always desire to thoroughly test the software to get maximum confidence about its quality, but this requires gigantic effort to test a GUI application due to complexity involve in such applications. This problem has led to automate GUI testing and different techniques have been proposed for automated GUI Testing. Event-flow graph is a fresh breach in the field of automated GUI testing. As control-flow graph, another GUI model represents all possible execution paths in a program; in the same way, event-flow model represents all promising progressions of events that can be executed on the GUI. Another challenging question in software testing is, how much testing is enough? There are few measures that can be used to provide guidance on the quality of an automatic test suite as development proceeds. Particle swarm optimization (PSO) algorithm searches for best possible test parameter combinations that are according to some predefined test criterion. Usually this test criterion corresponds a 201Ccoverage function201D that measures how much of the automatically generated optimization parameters satisfies the given test criterion. In this paper, we have tried to exploit event driven nature of GUI. Based on this nature, we have presented a GUI testing and coverage analysis technique based on PSO.

Volume: 21, Issue: 4

A Hybrid Evolutionary Algorithm for Numerical Optimization Problem

by Yu Xue, Suiming Zhong, Tinghuai Ma, Jie Cao
Abstract

The hybrid artificial bee colony (ABC) algorithm with differential evolution (DE) techniques (HABCwDE) is proposed for numerical optimization in this paper. The HABCwDE adopts multiple candidate solution generation strategies (CSGSes) from DE techniques to generate new solutions in the framework of the ABC algorithm. In the HABCwDE algorithm, three CSGSes and three groups of parameter settings are employed. The performance of HABCwDE and some other evolutionary algorithms are tested on 26 state-of-the-art benchmark functions. Experimental results demonstrate that HABCwDE is very competitive, and that it is an effective way to improve the performance of ABC algorithm by employing CSGSes from DE techniques.

Volume: 21, Issue: 4

A Green Time-Bounded Routing on Solar-Based Vehicular Ad-Hoc Networks

by YUH-SHYAN CHEN, YUN-WEI LIN, CHIH-HAO WANG
Abstract

A solar-based vehicular ad-hoc networking technique is a key issue to exploit the on-board sensing, computing, energy harvesting, and wireless communication capabilities in the intelligent transportation system. Most existing research results mostly paid attention to the energy savings, but lacking in consideration of the energy harvesting. An electric solar vehicle considered in this paper is equipped with a solar panel so that the vehicle battery can be periodically charged while keeping the high ECR, where ECR (energy consumption ratio) is the average packet arrival ratio divided by the energy consumption. This paper proposes a new green time-bounded routing protocol, whose goal is to deliver messages to the destination within user-defined delay and to minimize the usage of radio and power consumption, because the radio spectrum and the power are the limited resources. Simulation results justify the efficiency of the proposed protocol.

Volume: 21, Issue: 4

Mining Users Interest Navigation Patterns Using Improved Ant Colony Optimization

by Xuyang Wei, Yan Wang, Zhongliang Li, Tengfei Zou, Guocai Yang
Abstract

Web log mining is mainly to acquire users2019 interest navigation patterns from web logs and has been the subject of the web personalization research. In this paper, we define a new concept 201Cinterest pheromone201D and present a group users2019 navigation paths model. Then we propose a simple algorithm based on improved Ant Colony Optimization (ACO) to mine users2019 dynamic interest. In this algorithm, three factors relative browsing time, access frequency and operation time are considered to measure the 201Cinterest pheromone201D, which better reflects users2019 real interest. Finally, we conduct the simulation experiments to contrast the accuracy of navigation patterns mined by our approach and existing approaches. Experimental results illustrate that the proposed paradigm can truly capture users2019 browsing preference effectively.

Volume: 21, Issue: 3

Identity Based On-Line/Off-Line Signature With Designated Verifier

by Yi Jiang, Jianping Li, Anping Xiong
Abstract

It0027s necessary to equip WSNs with authentication mechanism accounts for that the nodes of WSNs are vulnerable to various attacks since they are open and resource-limited. However, the traditional authentication such as signature cannot be utilized to achieve this goal, because of its heavy computation. On-line/off-line signature can be considered as a practical solution for authentication WSNs, because the WSNs nodes only need to perform lightweight on-line sign, while the heavy computation can be performed off-sign phase using efficient computational devices, but it is not perfect for certain circumstances. For example, the signer unwilling to expose the message of the signatures, such as personal health records, to others. How to achieve confidence and efficiency simultaneously remains a problem for WSNs. In this paper, we present a strong designated on-line/off-line signature scheme for WSNs, which can ensure the confidence as well as security simultaneously. The proposed scheme is proven to be secure under the BDH assumption and its computation cost is acceptable for the nodes of WNSs.

Volume: 21, Issue: 3

Detection of Phosmet Residues on Navel Orange Skin by Surface-enhanced Raman Spectroscopy

by Yande Liu, Bingbing He, Yuxiang Zhang, Haiyang Wang, Bing Ye
Abstract

Residual pesticides such as phosmet in fruit have become a public concern in recent years. In this study, surface-enhanced Raman spectroscopy (SERS) with silver colloid and Klarite substrates was used to detect and characterize phosmet pesticides extracted from the navel orange surfaces. Enhanced Raman signals of phosmet over a concentration range of 5 to 3000A0mg/L were acquired with silver colloid. Partial least squares (PLS) regression combined with different data preprocessing methods was used to develop quantitative models. With the 2nd derivative data preprocessing, the best prediction model of phosmet pesticide was achieved with a correlation coefficient(r) of 0.852 and the root mean square error of prediction (RMSEP) of 5.17700A0mg/L. Enhanced Raman signals of phosmet over a concentration range of 10 to 8000A0mg/L were acquired with Klarite substrates. The PLS model was validated by leave-one-out cross validation. The results showed that the R

Volume: 21, Issue: 3

An Optical Detector for Determining Chlorophyll and Nitrogen Concentration Based on Photoreaction in Apple Tree Leaves

by Yao Zhang, Lihua Zheng, Hong Sun, Wei Yang
Abstract

It is a practical need for orchard production to detect chlorophyll and nitrogen concentration in apple tree leaves quickly. The interaction between apple tree leaves and sun light was analyzed through investigating the characteristics of spectral signatures of leaves, and a portable chlorophyll and nitrogen concentration detector was developed for apple tree leaves. Firstly 60 apple tree leaf samples were collected. The chlorophyll, nitrogen concentrations and reflectance were acquired in laboratories, then the spectral features of samples were analyzed, and light reaction characteristics were revealed accordingly. Therefore, the characteristic wavebands were obtained to predict the chlorophyll and nitrogen content in apple tree leaves effectively, and then the determination models for forecasting chlorophyll and nitrogen concentrations in apple leaves were established and validated. Finally a portable chlorophyll and nitrogen contents detector for apple leaves was developed based on the determination models. The performance test results showed that the detector could be used to detect the chlorophyll and nitrogen concentrations in apple leaves with high accuracy.

Volume: 21, Issue: 3

Different Algorithms for Detection of Malondialdehyde Content in Eggplant Leaves Stressed by Grey Mold Based on Hyperspectral Imaging Technique

by Chuanqi Xie, Hailong Wang, Yongni Shao, Yong He
Abstract

The feasibility of using hyperspectral imaging (HSI) technique to measure malondialdehyde (MDA) content in eggplant leaves stressed by grey mold was evaluated in this paper. Hyperspectral images of infected and healthy eggplant leaves were obtained in the spectral region of 380 to 103000A0nm, and their spectral reflectance of region of interest (ROI) was extracted by Environment for Visualizing Images (ENVI 4.7) software. Several pre-processing methods were adopted and partial least squares (PLS) models were established to estimate MDA content in eggplant leaves. In order to reduce high dimensionality of spectral data, competitive adaptive re-weighted sampling (CARS) and latent variables (LV) were carried out to identify the most effective wavebands. The result showed that PLS model based on baseline pre-processing had a good performance for prediction set. On the basis of the effective wavelengths suggested by CARS and LV, PLS and multiple linear regression (MLR) models were established, respectively. Among these models, LV-MLR performed best with the highest value of correlation coefficient (r) and lowest value of root mean square error of prediction (RMSEP) for prediction set. The overall results demonstrated the potentiality of HSI technique as an objective and non-destructive method to detect MDA content in eggplant leaves stressed by grey mold.

Volume: 21, Issue: 3

Hyperspectral Models for Estimating Chlorophyll Content of Young Apple Tree Leaves

by Zhuoyuan Wang, Xicun Zhu, Xianyi Fang, Yanan Wang
Abstract

A hyperspectral-based chlorophyll content estimating model for young apple tree leaves is proposed in this paper. It aims to have a contribution to modernized production and scientific management. The study takes the trees, which are two years old as the research objects. The young apple tree leaves are picked in autumn when they stop growing, and the spectral data and the chlorophyll content in the leaves of young apple trees are measured. First derivative (FD) is used to process the spectral data, and choose sensitive parameters. Hyperspectral models for estimating chlorophyll content in the leaves of young apple trees are established by a single variable (use one variable to establish models) and partial least square (PLS) methods. Four sensitive parameters are chosen to establish hyperspectral estimating models using partial least square. The model has the highest R2 (coefficient of determination), lower RMSE (root mean square error) and RE% (relative error). The partial least square model is more appropriate for estimating chlorophyll content in the leaves of young apple tree.

Volume: 21, Issue: 3

Research on Image Retrieval of Fruit Tree Plant-Diseases and Pests Based on Nprod

by wang zhijun, liu yuefeng, jiang meng, Cheng Shuhan, wang yucun
Abstract

Based on the normalized product correlation image matching algorithm, this paper uses wavelet transform for image pre-processing and conducts a content-based image retrieval applied research based on blue apple flea beetle. The experiment proves that this method can not only greatly shorten the matching time, but also improve the identification of Nprod coefficient. It makes image matching more accurate, and provides a new train of thoughts for the image retrieval of fruit tree diseases and insect pests based on content.

Volume: 21, Issue: 3

Optimization of Informative Spectral Regions in FT-NIR Spectroscopy for Measuring the Soluble Solids Content of Apple

by Jiahua Wang, J Cheng, Haiying Liu, Zhihui Tang, Donghai Han
Abstract

A novel potential method, linear combination weight PLS (LCW-PLS) model, was suggested for improving the performance of routine PLS model based on selected informative regions. Moving window partial least squares (MWPLS), genetic algorithms interval partial least squares (GAiPLS) and synergy interval partial least squares (SiPLS) were used to optimize informative spectral regions from FT-NIR spectra. A total of 660 apples harvested at 2006, 2007 and 2008, were divided into calibration and prediction sets by Kennard-Stone method. The best calibration model was obtained by LCW-PLS method based on informative spectral regions of 432820134787, 532320135512, 598220137135 and 71592013746300A0cm221200A01 selected by MWPLS procedure, and corresponding weights of 0.004, 0.070, 0.066 and 0.860, respectively. The LCW-MWPLS model was applied to predict samples, the prediction results were with R

Volume: 21, Issue: 3

A Vision System Based on TOF 3d Imaging Technology Applied to Robotic Citrus Harvesting

by Li Sun, Jian-Rong Cai, Jie-Wen Zhao
Abstract

This study was conducted to develop a fast machine vision system based on TOF (time of flight) three-dimensional (3D) imaging technology for automatic citrus recognition and location. Supported algorithms were specifically developed and programmed for image acquisition and processing. An adaptive filter coupled between partial differential equations filter and shock filter was presented to remove noise and sharpen edges efficiently. Image analysis algorithms integrated both range and amplitude information to generate regions of interest and parameters that are characteristic or very likely to belong to spherical objects. The three-dimensional position of the fruit and radius are obtained after the recognition stages. The total classification results showed that approximately 81.8% were detected and false detection occurred only once. On the whole, the process of imaging, recognition and location consumes less than 5000A0ms/fruit.

Volume: 21, Issue: 3

Development of Nitrogen Parameter Detector for Orchard Monitoring Based on Spectroscopy

by Lixuan Wu, Hong Sun, Minzan Li, Yao Wen, Yi Zhao
Abstract

In order to monitor nitrogen content of the jujube tree in the orchard, a spectroscopy-based nitrogen parameter detector was developed. It was designed with one control unit and several measuring sensor nodes. The sensor nodes were used to measure the canopy reflectance of jujube tree plant. Each sensor node contained four optical channels to measure the sunlight and canopy radiation at the wavebands of 550, 650, 766 and 85000A0nm respectively. The control unit was applied to receive, display and store all the data sent from several sensor nodes. The data was transferred based on ZigBee wireless network. The transmission quality between the sensors and control unit was evaluated. The result showed that the signals could be transmitted precisely without packet loss when the distance was less than 10000A0m. The calibration experiment indicated that the minimum correlation coefficient between illuminometer and each light channel of the sensors was 0.917. The NDVI (650, 766) was calculated from the developed sensor and used to monitor the nitrogen content of jujube tree in the orchard with r200A0003D00A00.638. It provides a new method to monitor the nitrogen parameter in the orchard non-destructively.

Volume: 21, Issue: 3

Technology Application of Smart Spray in Agriculture: A Review

by Yuanyuan Song, Hong Sun, Minzan Li, Qin Zhang
Abstract

A smart spraying system in agriculture is a targeted spraying system with efficient application of chemical and low cost for the environment. A smart sprayer generally includes a targeted detection system and spraying system, in which the targeted sensor is the foundation of the precision spraying management. The detection system of a smart sprayer is used to collect information in target areas and make spraying decisions. Varieties of sensing techniques are applied, such as Machine vision, spectral reflectance, remote sensing and so on. According to the detecting results of characteristics detection, species classification, disease symptom identification and damage severity evaluation, the spraying system controls the sprayer operation. The review of application of detection techniques, challenges and limitations are summarized, the developing trend is concluded based on the analysis.

Volume: 21, Issue: 3

Feasibility of SSC Prediction for Navel Orange Based on Origin Recognition Using NIR Spectroscopy

by Qiang Lyu, Qiuhong Liao, Yanli Liu, Yubin Lan
Abstract

Soluble solids content (SSC) is one of most important quality indicators of the navel orange. In order to explore the feasibility of SSC prediction for the navel orange from different origins using near infrared (NIR) spectroscopy, we collected seven groups Newhall navel orange (Citrus sinensis (L) Osb.) samples from seven origins (i.e. Beibei, Fengjie, Leibo, Linhai, Wusheng, Xinfeng, and Yizhang) in China, and all the samples were combined as the eighth group. The difference of the growing environments caused the variation of SSC of oranges from different origins. The partial least squares regression (PLS) models were applied to predict the SSC of its origin samples and other origin samples. The results predicted by origin-model were the best compared to cross-origin-prediction results. So it was necessary to recognize origin as the first steps of SSC prediction. Linear discriminant analysis (LDA) model with the top 18 principle components could recognize the origins of samples with 100% accuracy. The overall results demonstrated that it was feasible that SSC prediction for navel orange based on origin recognition using NIR spectroscopy.

Volume: 21, Issue: 3

Analysis of Phylogenetic Relationships of Main Citrus Germplasms Based on Ftir Spectra of Petals

by Xunlan Li, Shilai Yi, Yongqiang Zheng, Shaolan He
Abstract

To develop a quick, accurate and reliable technique for studying phylogenetic relationship of Citrus, FTIR (Fourier transform infrared spectroscopy) technique was used. The petals spectra of eighteen varieties of citrus germplasms were investigated by FTIR. Pretreatment methods of raw spectra (2000201350000A0cm00A0221200A01) were composed of baseline correction, normalize and first derivative (Savitzky-Golay). We used One-way ANOVA (analysis of variance) and Tukey2019s HSD (honestly significant difference) to extract effective wave bands, where the spectral absorbance values of different citrus germplasms were significantly different. The results showed that 2000223C183100A0cm00A0221200A01, 1763223C159500A0cm00A0221200A01, 1517223C109000A0cm00A0221200A01, 1035223C102400A0cm00A0221200A01, 950223C93500A0cm00A0221200A01, 861223C78400A0cm00A0221200A01, 744223C72100A0cm00A0221200A01 and 653223C60800A0cm00A0221200A01 were the effective wave bands. HCA (hierarchical cluster analysis) was adopted to classify citrus germplasms based on the above eight effective wave bands. It was found that eighteen citrus varieties were classified into six subgroups. The results of classification and citrus phylogenetic relationships between six subgroups were consistent of results from Morphology, Biochemistry, Cytology and Molecular Biology. The overall results demonstrated that fourier transform infrared spectroscopy technique with One-way ANOVA and Tukey2019s HSD and hierarchical cluster analysis model were promising for the rapid, accurate and reliable classification for citrus as well as studying citrus phylogenetic relationship.

Volume: 21, Issue: 3

WSN-based Control System of Co

by Y.Q. Jiang, T. Li, M. Zhang, S. Sha, Y.H. Ji
Abstract

Supplying proper CO

Volume: 21, Issue: 3

Diagnosis of CTV-Infected Leaves Using Hyperspectral Imaging

by Dongmei Guo, Rangjin Xie, Chun Qian, Fangyun Yang, Yan Zhou, Lie Deng
Abstract

Hyperspectral reflectance images of healthy and diseased leaves infected with different isolates of Citrus tristeza virus (CTV) including TRL514, CT30, CT32 and CT11A were collected in the visible and near-infrared region of 4002013100000A0nm. Average reflectance spectrum was generated from each hyperspectral image individually obtained from 60 healthy and 240 CTV-infected leaves. The spectra were transformed with 15-point Savitzky Golay second derivative. Then principal component analysis was performed on the transformed data in order to reduce the dimension of data. Comparative analysis was performed among supervised classification models, including back-propagation neural network (BPNN), linear discriminant analysis (LDA) and Mahalanobis distance (MD). When the second derivative spectra were analyzed, classifier models including BPNN, LDA and MD can discriminate the healthy and CTV-infected leaves with the highest classification accuracies of 100% in the spectral range of 4002013100000A0nm and 7602013100000A0nm. Nine optimal wavelengths (405, 424, 920, 947, 957, 972, 978, 980, and 99800A0nm) selected by stepwise regression resulted in 97.33% total classification accuracy for differentiation of healthy and CTV-infected leaves and showed great potential in CTV diagnosis. However, the overall classification accuracy of different CTV isolates infected leaves resulted in 70% based on the MD model using the selected optimal wavelengths. Further study is required to find out whether the method is suitable for CTV detection under field conditions.

Volume: 21, Issue: 3

Performance Evaluation of Node-mapping-based Flexray-CAN Gateway for in-vehicle Networking System

by Man-Ho Kim, Suk Lee, Kyung-Chang Lee
Abstract

As vehicles become more intelligent, in-vehicle networking (IVN) systems, such as the controller area network (CAN) and the FlexRay networks, are essential for the convenience and safety of drivers. To expand the applicability of IVN systems, attention is currently being focused on the communication between heterogeneous networks such as body or chassis networking systems. The message-mapping-based gateway was developed to improve communication between FlexRay and CAN networks concerning vehicle information. However, there are obstacles to the wide acceptance of the FlexRay-CAN gateway for a vehicle. First, when the message ID for the network is changed, the gateway must be reloaded with the revised message-mapping table. Second, if the number of messages exchanged is increased in the network, the complexity of the gateway software rapidly increases. In order to overcome these obstacles, this paper presents a FlexRay-CAN gateway using a node-mapping method. A gateway operation algorithm is described, and an experimental evaluation for ID change and software complexity is presented.

Volume: 21, Issue: 2

Security Completeness Problem in Wireless Sensor Networks

by Riaz Ahmed Shaikh, Sungyoung Lee, Aiiad Albeshri
Abstract

With the emergence of wireless sensor networks and its usage in sensitive monitoring and tracking applications, the need of ensuring complete security is gaining more importance than ever before. Complete security can only be ensured by adding privacy, cryptographic-based security and trust management aspects in a security solution. However, integration of all these three aspects in a single solution for resource constraints wireless sensor networks is not trivial. Current research intensively focuses on all these three aspects in an isolated manner. To the best of our knowledge, we have not found any work in the literature that comprehensively discusses: how these various privacy, security and trust solutions work together? In this work, we have made the first step towards this direction and to show how integration of various privacy, security and trust solutions can be performed in a single solution in step-by-step manner.

Volume: 21, Issue: 2

SignsWorld Facial Expression Recognition System (FERS)

by Samaa M. Shohieb, Hamdy K. Elminir
Abstract

Live facial expression recognition is an effective and essential research area in human computer interaction (HCI), and the automatic sign language recognition (ASLR) fields. This paper presents a fully automatic facial expression and direction of sight recognition system, that we called SignsWorld Facial Expression Recognition System (FERS). The SignsWorld FERS is divided into three main components: Face detection that is robust to occlusion, key facial features points extraction and facial expression with direction of sight recognition. We present a powerful multi-detector technique to localize the key facial feature points so that contours of the facial components such as the eyes, nostrils, chin, and mouth are sampled. Based on the extracted 66 facial features points, 20 geometric formulas (GFs), 15 ratios (Rs) are calculated, and the classifier based on rule-based reasoning approach are then formed for both of the gaze direction and the facial expression (Normal, Smiling, Sadness or Surprising). SignsWorld FERS is the person independent facial expression and achieved a recognition rate of 97%.

Volume: 21, Issue: 2

Integrating Preference by Means of Desirability Function with Evolutionary Multi-objective Optimization

by Zhenhua Li, Hai-Lin Liu
Abstract

Desirability function is a mathematically simple description of decision maker0027s preference. A desirability function transforms objective function to a scale-free desirability value, which actually measures the decision maker0027s satisfaction with the objective value. In this paper, we utilize desirability functions to express decision maker0027s preference to specific regions with an objective. These desirability functions are integrated into evolutionary multi-objective algorithms to generate a uniformly distributed set of Pareto solutions in desirability space. The corresponding images in objective space of this set of solutions are exactly the decision maker0027s preferred solutions. The experimental results show the effectiveness of this approach.

Volume: 21, Issue: 2

Perception-Based Software Release Planning

by Mubarak Alrashoud, Abdolreza Abhari
Abstract

Release planning is a cornerstone of incremental software development. This paper proposes a novel framework that performs the prioritization aspect of the software release-planning process. The aim of this framework is to help software product managers to select the most promising requirements that will be implemented in the next release. Many variables affect release planning, including: The importance of requirements as perceived by the different stakeholders; decision weights of the stakeholders; the risk associated with each requirement as estimated by the development team; the effort needed to implement each requirement; the release size (the effort allocated to implement and deliver a software release); and the dependencies among requirements. We assume that there are no ambiguities in defining the dependencies among requirements. Also it is assumed that the estimation of the available effort is accurate. Because of human perception, such variables as importance, risk, and required effort have a high degree of imprecision and uncertainty. Therefore, the strength and practicality of the Fuzzy Inference System (FIS) is employed to manipulate uncertainty in these three factors. In order to reflect the disagreements among the stakeholders on the FIS engine, the polling method is used to define the parameters of the membership functions of the importance variable. The effectiveness of the proposed framework is compared to genetic algorithm approach, which is applied in many works in the literature. The results of this comparison show that the proposed FIS-based approach achieves higher degree of stakeholders0027 satisfaction than genetic algorithm-based approach.

Volume: 21, Issue: 2

Modification of CFAR Algorithm for Oil Spill Detection from SAR Data

by Wang Siyuan, Xingyu Fu, Yan Zhao, Hui Wang
Abstract

It is very difficult to detect oil spills when the scattering intensity of background clutter is inhomogeneous in synthetic aperture radar (SAR) images. To improve the oil detection capability, we propose a modified constant false alarm rate (CFAR)-based method for the detection of oil spills in SAR images. This proposed method combines edge detection technique and CFAR detection theory to improve the accuracy of oil spills detection. First, we segment the image into the areas of interest (AOIs) by using ratio edge detection. Second, to get a more accurate detection result, an improved Weibull-CFAR detector is applied to these AOIs. Experimental results demonstrate that the modified CFAR algorithm can work more effectively than a global CFAR detector for oil spill detection, especially for the inhomogeneous intensity SAR images. This model can detect the target more effectively, and false alarms can be greatly diminished.

Volume: 21, Issue: 2

Mining Fuzzy Association Rules Based on Parallel Particle Swarm Optimization Algorithm

by Jin Gou, Fei Wang, Wei Luo
Abstract

The association rule extraction process often involves a large number of candidate item sets and multiple read operations on data sets. With the emergence of massive data, the sequential association rule extraction algorithm also suffers from large I/O overhead and insufficient memory. This paper presents a new multi-swarm parallel multi-mutation particle swarm optimization algorithm (MsP-MmPSO) to search several groups in parallel. Experimental results show that the MsP-MmPSO algorithm has an advantage in terms of execution time over traditional particle swarm optimization, especially when the amount or dimensions of the data increase. Experiments also verify that a good task allocation method can reduce the execution time of the parallel algorithm.

Volume: 21, Issue: 2

Internet Based Intelligent Hospital Appointment System

by Adnan Aktepe, A. Kursad Turker, Suleyman Ersoz
Abstract

In today2019s competitive service industry, the technology in service systems is used in a wide range of areas. The service companies are now providing service via internet or via other computer based systems in an increasing trend day by day. Expert systems are good examples of these applications. Today, expert systems are used in various fields such as design, planning, imaging, diagnosis, etc. For practical use, the expert systems are also used through internet. One of the most important service system institutions is the hospital. Increasing the service quality level in hospitals, internet based appointment systems are used in several hospitals in Turkey. There are also several internet based expert system applications today contributing the improvement of service levels in several industries. In this study, an internet based expert system is created that is used in outpatient department/polyclinic direction. The system architecture, algorithm and the role of such an expert system are discussed in this paper. With the help of internet based expert appointment system at hospitals, the queues in the hospitals decline, the number of consultations decreases, the patients and doctors save time and finally the customer satisfaction and quality level increases.

Volume: 21, Issue: 2

Comparison of Multi Layer Perceptron and Jordan Elman Neural Networks for Diagnosis of Hypertension

by Fuat Türk, Necaattin Barişçi, Aydın Çiftçi, Yakup Ekmekçi
Abstract

In this study, from 150 individuals over the age of 30 taken no drugs, sex, age, height, weight, HDL, LDL, Triglyceride, smoking and uric acid were measured. 65 of them are normal but 85 consist of the patients. This data was transferred to the computer by processing methods of quantitative analysis. Data obtained of each patient was applied Artificial Neural Network (ANN) models. The results obtained will be classified as either normal or the patient. Using Multi Layer Perceptron (MLP) neural network, 80.4% of patient individuals and 81.8% of normal individuals were classified correctly. Using Jordan Elman neural network, 85.3% of the patient individuals and 87.8% of normal individuals were classified correctly.

Volume: 21, Issue: 1

Aumann Shapley Method for Congestion Cost Allocation in Multilateral Transactions Framework of Restructured Power Market

by M. Murali, P. Sri Divya, M. Sailaja Kumari, M. Sydulu
Abstract

Deregulation of Electricity market has not only led to increase in competition among generators, but also reduced electricity prices. It has introduced several issues in the market; two of them are congestion management and market power. Due to open transmission access all the participants have equal right to access transmission network. However, they have to bear the costs incurred to accommodate their transaction. The cost allocation is still a problem to be tackled efficiently. The prevailing problem is how to allocate the congestion cost among the market participants. An efficient and fair allocation of congestion cost would result in smooth operation of transmission system. It also helps in tackling congestion and market power. This paper proposes a novel approach using Aumann Shapley (AS) method of game theory for congestion cost allocation in a deregulated electricity market. The results obtained using proposed method are compared with uplift and nodal pricing methods for IEEE 14 bus system, New England 39 bus system and 75 bus Indian power system (practical data). This comparison illustrates the pros and cons of each method.

Volume: 21, Issue: 1

Establishment of the Optimized Production Performance Detection Model with the Combination of GA and BPN

by Wen-Tsao Pan, Wen-Tsao Pan, Ching-pei Lin, Ming-Sheng Hu, Li-Li Lei
Abstract

As the activities to improve the process rationalization, optimized production assists the enterprises to improve the production and management processes, product quality, productivity and customer service efficiency. This study analyzed the data collected from the experiments made by a lean production simulation laboratory at a university in Taiwan, so as to investigate whether production optimization results of the enterprises can promote the overall performance of production and service. This study first compared the data envelopment analysis (DEA) with the experimental data, so as to evaluate whether the optimized production can improve the performance. It then analyzed main factors influencing the income with decision tree, and established the optimized production performance detection model respectively using three data mining technologies, namely the GABPN, BPN and decision tree. The analytic results showed that the output through optimized production does improve the overall performance of production and service. The main factors affecting the technical efficiency include the time consumed from serving all dishes on the table to leaving the table and the time consumed from leaving the table to paying the bill. Among these three data mining technologies, GABPN has the best detection ability.

Volume: 21, Issue: 1

Neural Identification of Thermochemical Processes for Solid Wastes Transformation

by R Carrasco, S Carlos-Hernandez, C Cadet
Abstract

This paper presents a neural network application to identify the behavior of the model for two thermochemical processes, which are used to transform organic solid wastes. The first model corresponds to the char reduction zone of a gasification process, including inputs signals. The second one corresponds to a fluidized bed sludge combustor focused on the dynamics of NO

Volume: 21, Issue: 1

A Pso-Based Maintenance Strategy in Wireless Sensor Networks

by Long Cheng, Yan Wang, Chengdong Wu, Quancheng Han
Abstract

This paper presents a new wireless sensor networks maintenance strategy using particle swarm optimization. This strategy considers three factors: Coverage rate, node energy consumption and node residual energy. The advantage of this method is to prolong the maintenance period and reduce the computational complexity. We first construct the networks health indicator to determine the locations of redeployed nodes. Therefore, the maintenance problem is formulated into cost optimization problem, and the linearly decreasing weight particle swarm optimization is employed to reduce the computational complexity. Simulation results show that the proposed particle swarm optimization based maintenance strategy (PSOMS) outperforms the random and uniform redeploy strategy with longer repair period.

Volume: 21, Issue: 1

Efficient and Verifiable Outsourcing Scheme of Sequence Comparisons

by Yansheng Feng, Hua Ma, Xiaofeng Chen
Abstract

With the rapid development of cloud computing, the techniques for securely outsourcing prohibitively expensive computations are getting widespread attentions in the scientific community. In the outsourcing computation paradigm, the clients with resource-constrained abilities can outsource the heavy computation workloads into the cloud server and enjoy unlimited computing resources in a pay-per-use manner. One of the most critical functionalities in outsourcing computation is the verifiability of the result. That is, the client should efficiently verify the validity of the result returned by the cloud servers. In this paper, we solve the problem of verifiable outsourcing computation of sequence comparisons by integrating the technique of Yao2019s garbled circuit with homomorphic encryption. Compared with the existing schemes, our proposed solution enables clients to efficiently detect the misbehavior of dishonest servers. Furthermore, our construction re-garbles the circuit only for malformed responses and thus is very efficient for real-world applications. Besides, we also present the formal analysis for our proposed construction.

Volume: 21, Issue: 1

Efficient Photo Image Retrieval System Based on Combination of Smart Sensing and Visual Descriptor

by Yong-Hwan Lee, Sang-Burm Rhee
Abstract

In this paper, we propose a novel efficient photo image retrieval method that automatically indexes for the searching of relevant images using a combination of geo-coded information and content-based visual features. A photo image is labeled with its GPS (Global Positioning System) coordinates at the moment of capture, and the label leads to generating a geo-spatial index with three elements of latitude, longitude and image view direction. Then, content-based visual features are extracted, and combined with the geo-spatial information for indexing and retrieving the photo images. For user0027s querying process, the proposed method adopts two steps as a progressive approach, filtering the relevant subset prior to using a content-based ranking function. To evaluate the performance of the proposed algorithm, we assess the simulation performance in terms of average precision and F-score using a natural photo collection. Comparing the proposed approach to search using visual feature alone, an improvement of 20.8% (61.6201340.8) was observed. The experimental results show that the proposed method exhibited a slight enhancement of around 7.2% (61.6201354.4) in retrieval effectiveness, compared to previous work. These results reveal that a combination of context and content analysis is markedly more efficient and meaningful than using only visual feature for image retrieval.

Volume: 21, Issue: 1

Fuzzy Relational Product for Collision Avoidance of Autonomous Ships

by Young-Il Lee, Seong-Gon Kim, Yong-Gi Kim
Abstract

This paper presents a heuristic search technique performing collision avoidance for autonomous ships. Unlike collision avoidance systems of other unmanned vehicles, the collision avoidance system for autonomous ships aims not only at deriving a reasonable and safe path to the goal but also complying with COLREGs (International Regulations for Preventing Collisions at Sea). The heuristic search based on Bandler and Kohout0027s fuzzy relational products and an evaluation function representing heuristic information of domain experts is adopted to achieve the general purpose of collision avoidance that derives a reasonable and safe path. The rule of 201Caction to avoid collision201D stipulated in the COLREGs is used for the other necessary and sufficient condition and complies with the COLREGs. The proposed heuristic search is verified using scenarios which consider encounter situations classified in the COLREGs.

Volume: 21, Issue: 1

Predictive Control Strategy Based on Extreme Learning Machine for Path-Tracking of Autonomous Mobile Robot

by Yimin Yang, Xiaofeng Lin, Zhiqiang Miao, Xiaofang Yuan, Yaonan Wang
Abstract

In this paper, we propose a novel nonlinear predictive control strategy based on an extreme learning machine to address the path-tracking control problem of wheeled mobile robots in the presence external disturbances. The hybrid chaotic optimization algorithm (HCOA), which can avoid being trapped in local minima and improve convergence in dealing with the large space and high-dimension optimization problems, is used to perform real-time nonlinear minimization of the cost function of a mobile robot to enhance the control accuracy. The proposed improved bidirectional extreme learning machine is employed to model the mobile robot plant and estimate future plant output. The experimental results of tracking the automation mobile robot indicate that the proposed controller can provide more accuracy and faster tracking performance than traditional neural network predictive controllers.

Volume: 21, Issue: 1

Analytic Study on Chaotic Characteristics of Viscoelastic Beams Based on the Evolution of Nonlinear Stiffness

by Jianxi Yang, Lizhang Qian, Jianting Zhou, Xiahui Li
Abstract

So far there exist deficiencies on monitoring methods of structures0027 stiffness among bridges0027 health monitoring. This paper introduces viscoelastic beams with nonlinear stiffness, explains physical nonlinear factors of beams through the sum of linear and cubic terms, and thus establishes a dynamic model of the viscoelastic beam with nonlinear stiffness under forced vibration. When the numerical simulation methods are applied, it studies systems of the bridge on nonlinear dynamic behaviors resulting from the variation of stiffness. The results prove that the bigger the coefficient of nonlinear stiffness is, the stronger the sensitivity of the beam0027s vibration against the initial conditions is. Since the beginning of periodic motion of the beam0027s vibration, there gradually appears quasi periodicity and period doubling bifurcation, then chaotic motion at last. The stronger the nonlinearity of the viscoelastic beam with nonlinear stiffness is, the stronger the sensitivity of the vibration against the initial value is, and thus the easier the chaotic vibration is produced. This paper serves as a theoretical reference for the safe performance of bridges and health monitoring.

Volume: 20, Issue: 4

Collaborative Mechanism of High-Speed Railway Dispatching System

by Peng Qiyuan, Wen Chao, Jiang Chaozhe, Wei Yanling
Abstract

The high-speed railway dispatching command system is the nerve center of a high-speed railway. The construction of such a system that has high reliability is of great significance in ensuring the safe operation and all-weather operation of trains. This study analyses the collaborative needs of a high-speed railway dispatching system, and investigates the organizational coordination of the system in terms of a collaborative plan, organizational coordination, information coordination, etc. The coordination mechanism of a high-speed railway dispatching command system should be based on three perspectives: (1) The vertical hierarchical level of the high-speed railway dispatching system0027s coordination, (2) The primary and auxiliary synergies of the horizontal professional subsystem, and (3) The cooperation between dispatching stations, as well as between the high-speed railway and the existing line command system. In this paper, we propose a theory of coordination between a high-speed railway dispatching command system and the existing lines, as well as a method for its implementation. Together, this theory and method will provide theoretical guidance and technical support in the development of a dispatching command system.

Volume: 20, Issue: 4

Experimental Study on Estimation of Global Mean with Preferential Spatial Samples

by Xuhong Ren, Ning Wei, Bingbo Gao, Yuchun Pan, Qing Guo, Yunbing Gao
Abstract

The conventional approach to estimate the global mean under preferential spatial sampling gets a larger deviation and further influences the precision of the subsequent model calculation and analysis. Zoning or declustering methods can effectively improve the estimation precision of preferential sampling. In this paper, we propose a novel method, which uses self-organizing dual-zoning method to estimate the global mean, in which the Self-Organizing Feature Map (SOFM) and the Voronoi diagram are utilized to realize classification and zoning. By comparing with arithmetic mean method, polygonal declustering method, and cell declustering method, we got that arithmetic mean method could not satisfy the special properties of the preferential sampling, and self-organizing dual-zoning method gets more accurate zoning results and more stable global means with different sample sizes and Feature Deviation Index (FDI).

Volume: 20, Issue: 4

Estimating Leaf Nitrogen Concentration In Barley By Coupling Hyperspectral Measurements With Optimal Combination Principle

by Xingang Xu, Chunjiang Zhao, Xiaoyu Song, Xiaodong Yang, Guijun Yang
Abstract

Leaf nitrogen concentration (LNC), as a key indicator of nitrogen (N) status, can be used to evaluate N nutrient levels and improve fertilizer regulation in fields. Due to the non-destructive and quick detection, hyperspectral remote sensing with hundreds of very narrow bands plays an unique role in monitoring LNC in crop, but most of the current methods using hyperspectral techniques are still based on spectral univariate analyses, which often bring about the unstability of the models for LNC estimates. By introducing the optimal combination principle to conduct multivariate analyses and form the combination model, this paper proposes a new method with hyperspectral measurments to estimate LNC in barley. First, this study analyzed the relationships between LNC in barley and three types of spectral parameters including spectral position, area features, vegetation indices, and established the quantitative models of determining LNC with the key spectral variables, then using the optimal combination method with linear programming algorithm conducted multivariate analyses for accuracy improvements by calculating the optimal weights to construct the combination model of evaluating LNC. The results showed that most of the three types of spectral variables had significant correlations with LNC under confidence level of 1%, and the univariate models with the key spectral variables (such as Dr and (03BBrplus03BBb)/03BBy)) could well describe the dynamic pattern of LNC changes in barley with the determination coefficients (R2) of 0.620 and 0.622, and root mean square errors (RMSE) of 0.619 and 0.620, respectively, but by comparison the combination model with Dr and 03BBb/03BBy exhibited the better fitting with R2 of 0.702 and RMSE of 0.574. This analysis indicates that hyperspectral measurements displays good potential to effectively estimate LNC in barley, and the optimal combination (OC) method has the better adaptability and accuracy due to the optimal selection of spectral parameters responding LNC, and can be applied for reliable estimation of LNC. The preliminary results of coupling hyperspectral measurements with optimal combination principle to estimate LNC can also provide new ideas for hyperspectral monitoring of other biochemical constituents.

Volume: 20, Issue: 4

Winter Wheat Cropland Grain Protein Content Evaluation through Remote Sensing

by Xiaoyu Song, Jihua Wang, Guijun Yang, Haikuan Feng
Abstract

Grain protein content (GPC) is generally not uniform across cropland due to changes in landscape position, nutrient availability, soil chemical, physical properties, cropping history and soil type. It is necessary to determine the winter wheat GPC quality for different croplands in a collecting area in order to optimize the grading process. GPC quality evaluation refers not only the GPC value, but also the GPC uniformity across a cropland. The objective of this study was to develop a method to evaluate the GPC quality for different croplands through remote sensing technique. Three Landsat5 TM images were acquired on March 27, April 28 and May 30, 2008, corresponding to erecting stage, booting stage and grain filling stage of wheat. The wheat GPC was determined after harvest. Then multi linear regression (MLR) analysis with the enter method was calculated using the TM spectral parameters and the measured GPC data. The GPC MLR model was established based on multi-temporal spectral parameters. The accuracy of the model was R2003E0.521, RMSE00A0003C00A00.66%. The GPC mean value and standard deviation value for each cropland was calculated based on the ancillary cropland boundary data and the grain protein monitoring map. Winter wheat filed GPC quality was evaluated by the GPC mean value and GPC uniformity parameter - coefficients of variation (CV). The evaluation result indicated that the super or good level winter wheat croplands mainly lie in Tongzhou, Daxing and Shunyi County, while the middle or low GPC level croplands are mainly distributed on the Fangshang county. This study indicates that the remote sensing technique provides valuable opportunities to monitor and evaluate grain protein quality.

Volume: 20, Issue: 4

Mineral Reserves Optimization Based on Improved Group AHP

by Chaozhe Jiang, Zhaoyang Bian, Jixue Yuan, Fang Xu, Yahui Cheng
Abstract

Mineral choice optimization is a basic key issue that related to whether the entire reserve system is able to adapt to the sustainable use of national mineral resources. In this paper, we may determine indicators0027 weight using improved group AHP (analytic hierarchy process) combined with objective weight of each decision-maker, and according to this, the most important index is 201Ctechnical difficulty of reserve201D, to which the government should pay more attention to. Then we added these weights into indicator variables of hierarchical clustering analysis in order to classify mineral reserve more reasonably taking the typical resources as an example, and the obtained result was consistent to the government0027s macroeconomic qualitative analysis. We divided typical mineral resources into four batches for reserving; among these batches, coal was taken granted as the intensive reserve object. Those scarce resources that China has advantage of should also be immediately included in the reserve object such as Tungsten, Tin, and Rare earth. To a certain extent, the results can lay foundation for the establishment of strategic reserve mechanism of mineral resources in China.

Volume: 20, Issue: 4

The Effect of Slab Track on Wheel/Rail Rolling Noise in High Speed Railway

by Xinwen Yang, Guangtian Shi
Abstract

The slab track of high speed railway has higher environmental noise emissions caused by a train running than the ballasted track. In order to predict and control wheel/rail rolling noise radiation due to roughness of wheel and rail running surface on slab track, a model is developed to calculate wheel/rail rolling noise of the slab track and analyze the influences of railway slab structure parameters on wheel/rail rolling noise. The results show that the rail noise occurs mainly in the middle and high frequency ranges of 50020132000Hz. The wheel noise is chiefly in the high frequency ranges of 160020134000Hz, and the track slab noise radiates in the frequency ranges of 1252013500Hz. Instantaneous noise pressure of the rail is first, the slab is second, the wheel is in between. The weights of the slab have more influence on emissions of the slab, but have little impact on noise emission of the wheel and rail. Below 500Hz, the greater weight of the slab is, the greater noise emission of the slab is, but above 500Hz, the result is the opposite. The CA mortar elastic modulus under the slab has little impact on noise emission of the wheel and rail, whereas has more influence on noise emissions of the slab. The greater elastic modulus of CA mortar under the slab is, the less noise emission of the slab is. The rubber pads installed under the slab have more influence on noise emissions of the slab than that of wheel and rail. All in all, the rubber pad under the slab is beneficial to wheel-rail noise reduction.

Volume: 20, Issue: 4

A Quadratic Lagrange Multipliers Based Approach to Feature Extraction in Face Recognition

by Dong Ren, Lin Yan, Simon Yang
Abstract

Fisher Linear Discriminant Analysis (LDA) has been widely used for feature extraction in face recognition. However, it cannot be used when each object has only one training sample because, within-class scatters cannot be statistically measured in this case. In addition, the respective axes of the projection matrix are not necessarily orthogonal in the strict sense. In this paper, a new method is proposed to solve those problems by quadratic Lagrange multipliers fisher linear discriminant analysis, which could not only eliminate the singular problem, but also obtain the optimal orthogonal projection matrix. The proposed approach is compared to the 2D-LDA method on the well-known ORL, CMU and YALE BplusExtend YALE B face databases. It shows that the proposed method achieves better recognition accuracy and faster computational speed than 2D-LDA method does, especially in solving the matrix singular problem with only one training sample.

Volume: 20, Issue: 4

An Effective Collaborative Filtering Via Enhanced Similarity and Probability Interval Prediction

by Tengfei Zou, Yan Wang, Xuyang Wei, Zhongliang Li, Guocai Yang
Abstract

In recent years, as one of the most successful recommendation methods, collaborative filtering has been widely used in the recommendation system. Collaborative filtering predicts the active user preference for goods or services by collecting a historical data set of users0027 ratings for items; the underlying assumption is that the active user will prefer those items that the similar users prefer. Usually the data is quit sparse, which makes the computation of similarity between users or items imprecise and consequently reduces the accuracy of recommendations. In this paper, we propose an enhanced similarity method that the common ratings and the all ratings are both taken into account. Additionally, we present a generative probabilistic prediction framework in which we first predict a missing data probability value interval instead of a certain value by using the defined range of similar neighbors0027 ratings, and the final missing data rating is produced in the interval. Empirical studies on two datasets (MovieLens and Netflix) show that the proposed algorithm consistently outperforms other state-of-the-art collaborative filtering algorithms.

Volume: 20, Issue: 4

An Empirical Study of Skew-Insensitive Splitting Criteria and its Application in Traditional Chinese Medicine

by Chong Su, Shenggen Ju, Yiguang Liu, Zhonghua Yu
Abstract

Learning from imbalanced datasets is a challenging topic and plays an important role in data mining community. Traditional splitting criteria such as information gain are sensitive to class distribution. In order to overcome the weakness, Hellinger Distance Decision Trees (HDDT) is proposed by Cieslak and Chawla. Despite HDDT outperforms the traditional decision trees, however, there may be other skew-insensitive splitting criteria. In this paper, we propose some new skew-insensitive splitting criteria which can be used in the construction of decision trees and applied a comprehensive empirical evaluation framework testing against commonly used sampling and ensemble methods, considering performance across 58 datasets. Based on the experimental results, we demonstrate the superiority of these skew-insensitive decision trees on the datasets with high imbalanced level and competitive performance on the datasets with low imbalanced level and K-L divergence-based decision tree (KLDDT) is the most robust among these skew-insensitive decision trees in the presence of class imbalance, especially when combined with SMOTE. Thus, we recommend the use of KLDDT with SMOTE when learning from high imbalanced datasets. Finally, we used these skew-insensitive decision trees to build the diagnosis model of chronic obstructive pulmonary disease in traditional Chinese Medicine. The results show that KLDDT is the most effective method.

Volume: 20, Issue: 4

Sensitivity Analysis of Transport Machinery Configuration in Underground Cavern Group Construction

by Tingyao Jiang, Zhonghu Jing, Jihua Wang
Abstract

The method of drilling and blasting is widely used in the construction of an underground cavern group. In the whole process, the slagging occupies 40201360% of overall cycle time. Therefore, it is important to optimize transport machinery configuration in order to reduce the time of slagging, and improve the overall efficiency of construction. In this paper, sensitivity analysis was applied to study the relations among the time of slagging, configuration of truck, and the number and capacity of loader when trackless transport was utilized in the construction of an underground cavern group. The analysis was based on the data of underground cavern group in JINPING II hydropower construction. The analysis results could be used to guide the configuration of mechanical equipment.

Volume: 20, Issue: 4

Application of Flexible Edge Matching Algorithm in the Field of Moving Object Detection

by L. X. Zhao, Q. Su, H. Liu, H. Peng
Abstract

Moving object detection is an important branch and foundation of computer vision; it has extensive application prospects in many fields, such as, traffic, military application, industries and bio-medical, et al, and becomes a hot research topic in computer vision field. Because of its inherent complexity, moving object detection still faces lots of challenges. In this paper, based on the existing research achievements, the methods of moving object detection in dynamic environment are studied deeply, a knowledge-based flexible edge matching algorithm is put forward. The effectiveness of the proposed matching algorithm in moving object detection is also demonstrated. The research results here can be provided as the reference for target detection and tracking and some other applications.

Volume: 20, Issue: 4

Comprehensive Safety Assessment Model of Road Long Tunnel Based on VISSIM

by Yingying Xing, Jian Lu, Linjun Lu, Chenming Jiang, Xiaonan Cai
Abstract

In recent years, the large number and long length of long road tunnels with wide structure dimensions in China attract the attention of the world. The traffic safety problems of long tunnels caused by complex construction technology and management difficulties are becoming more and more significant. Nevertheless, there are no systematic studies on the 201CTraffic201D safety of long tunnels. This paper makes use of speed variance index in place of the accident rate to analyze factors that have influence on tunnel safety by VISSIM, including lane number of tunnel, tunnel traffic volume, traffic composition, tunnel curve radius, tunnel slope gradient, and so on. Tunnel length is also one of the factors that have great effect on tunnel safety. Then, the studies try to model the relationship between the speed variation and some other selected traffic parameters related to the road tunnel traffic safety and build up a tunnel traffic safety assessment model. Finally, an application example is given to validate the feasibility and effectiveness of the model.

Volume: 20, Issue: 4

Analysis of Dynamic Behavior for Ballastless Track-Bridge with a Hybrid Method

by Wenjun Luo, Xiaoyan Lei
Abstract

A hybrid method combining finite element method (FEM) and statistical energy analysis (SEA) was recently presented for predicting the steady-state response of vibro-acoustic systems. Based on the structural characteristics of vehicle-CRTS II ballastless track-bridge system, the hybrid method of vehicle-track-bridge elements was presented. Using finite element method and Hamilton Theory, the coupled equation of vehicle-track-bridge can be established. Computational software is coded with Matlab. As an application, the vibration characteristics of the track-bridge system vertical profile assumed as random irregularity were calculated and the effect of random irregularity wavelength was analyzed. The computational results show that (1) The changes of the rail random irregularity wavelength have little influences on vertical displacement of track structure. (2) While it has very significant influences on vertical wheel/rail contact forces, and vertical acceleration amplitude of rail and slab, and the influences caused by rail short-wave random irregularity are greater, and it has influences on vertical acceleration amplitude of bridge. (3) The energy of rail vibration excitated by rail short-wave random irregularity is mainly distributed within the range of medium-high frequency and the maximum distribution in the range of the first natural frequency (123600A0Hz) of rail. While the energy of rail vibration excitated by rail medium-long wave random irregularity is mainly distributed within the range of medium-low frequency.

Volume: 20, Issue: 4

Omnidirectional Disturbance Rejection for a Biped Robot by Acceleration Optimization

by Zhangguo Yu, Fei Meng, Qiang Huang, Xuechao Chen, Gan Ma, Jing Li
Abstract

Biped robots are expected to keep stability after experiencing unknown disturbances which often exist in human daily environments. This paper presents a novel method to reject omnidirectional disturbances by optimizing the accelerations of the floating base of the robot. The optimized accelerations keep the desired external forces within their constraints and generate coordinated whole-body motion to reject disturbances from all directions. The effectiveness of the proposed method is confirmed by simulations with disturbance-rejection scenarios.

Volume: 20, Issue: 4

Robust Control Design Techniques Using Differential Evolution Algorithms Applied to the PVTOL

by David Lara, Marco Panduro, Gerardo Romero, EFRAIN ALCORTA
Abstract

In this paper, we present a strategy to stabilize the attitude of a planar vertical take off and landing (PVTOL) vehicle with variable pitch propeller (VPP) rotors. In the VPP configuration, the thrust is obtained with the propeller pitch angle, instead of changing the rotor speed, and this concept adds maneuverability to the vehicle. The PVTOL used in this paper is highly unstable in its natural hovering flight state, therefore the main goal is to achieve a stable attitude. First of all, a simplified dynamic model that includes the VPP dynamics is obtained. Then, a methodology to select the parameters of a nonlinear controller using Differential Evolution algorithms (DEA) will be presented. The controller0027s parameters are selected with two purposes: to guarantee the asymptotic stability of the closed-loop system while taking into account the uncertainty, and to improve its robustness margin. And finally, The results are validated with real-time experiments.

Volume: 20, Issue: 3

Emotional Learning Based Position Control of Pneumatic Actuators

by Naghmeh Garmsiri, Nariman Sepehri
Abstract

This paper presents a new scheme for position tracking of pneumatic actuators. The controller is built upon the Brain Emotional Learning Based Intelligent Control (BELBIC) concept proposed by Caro Lucas [Lucas, C., Shahmirzadi, D., 0026 Sheikholeslami, N. (2004). Introducing BELBIC: Brain emotional learning based intelligent controller. International Journal of Intelligent Automation and Soft Computing, 10, 11201321]. First the structure of BELBIC is analyzed to further understand its features. Next, different types of emotional signals, required by BELBIC, are experimentally evaluated to meet the challenges in position tracking of pneumatic actuators. The best performing BELBIC structure is then experimentally compared with a previously developed robust proportional integral controller. It is also successfully applied to a force reflecting tele-operated application of pneumatic actuator.

Volume: 20, Issue: 3

Expected Utility Based Decision Making Under Z-Information

by Lala Zeinalova
Abstract

Decision making theories are based on decision relevant information much of which is uncertain, imprecise or incomplete. An important qualitative attribute of information on which decisions are based is its reliability. The concept of Z-number relates to the issue of reliability of information, especially in the realms of economics and decision analysis. In this study we suggest Z-information based decision-making method which is more realistic in comparison with the existing methods. An example of investment problem is used to illustrate the proposed approach.

Volume: 20, Issue: 3

Optimization of Ensemble Neural Networks with Type-2 Fuzzy Integration of Responses for the Dow Jones Time Series Prediction

by Patricia Melin, Martha Pulido
Abstract

This paper describes an optimization method based on genetic algorithms for designing ensemble neural networks with fuzzy response aggregation to forecast complex time series. The time series that was considered in this paper, to compare the hybrid approach with traditional methods, is the Dow Jones data, and the results are presented for the optimization of the structure of the ensemble neural network with type-1 and type-2 fuzzy response integration. Simulation results show that the ensemble approach produces 99% prediction accuracy for the Dow Jones time series and that using type-2 fuzzy logic improves the performance of the predictor.

Volume: 20, Issue: 3

Endoscopy Video Summarization based on Multi-Modal Descriptors and Possibilistic Unsupervised Learning and Feature Subset Weighting

by Mohamed Maher Ben Ismail, Ouiem Bchir, Ahmed Emam
Abstract

The spread of capsule endoscopy systems has proved to be inherently constrained by the tedious diagnosis process when the physician has to review thousands of endoscopy video frames in order to detect pathology symptoms. In this paper, we propose a novel endoscopy video summarization approach based on possibilistic clustering and feature weighting algorithm. The algorithm generates possibilistic membership that represents the degree of typicality of the video frames, and that is used to identify and discard noise frames. The robustness to irrelevant features is achieved by learning optimal relevance weight for each feature subset within each cluster. We extend the proposed algorithm to find the optimal number of clusters in an unsupervised and efficient way by exploiting some properties of the possibilistic membership function. The system demonstrated promising performance in extensive testing on real-world datasets associated with the difficult problem of endoscopy video summarization. The endoscopy video collection was acquired on four patients at different geographic locations. It includes more than 90k video frames.

Volume: 20, Issue: 3

Designing A Healthcare Authorization Model Based On Cloud Authentication

by CHIN-LING CHEN, TSAI-TUNG YANG, FANG-YIE LEU, YI-LI HUANG
Abstract

With the rapid development of information technology and the continual progress of cloud computing technology, numerous studies have been focused on cloud services; many countries actively promote cloud systems which enhance the convenience of medical treatment and services, an important issue in remote areas. In this paper, we combine the cloud platform and mobile devices to compensate for the lack of medical resources in certain remote areas. We also use cryptographic technology to protect the patient0027s personal information; additionally, the technology can authorize the medical treatment. The proposed scheme can minimize the misuse of medical resources; patients can employ a simple password and use mobile devices to log in to the public cloud such that the authentication, integrity, privacy and non-repudiation issues can be satisfied. Our scheme can also satisfy other specific security requirements. For example: defend against offline password guessing attacks, replay attacks, impersonation attacks, man-in-middle attacks and insider attacks; this is done without the need to maintain a verification table.

Volume: 20, Issue: 3

Smart Real Time Adaptive Gaussian Filter Supervised Neural Network for Efficient Gray Scale and RGB Image De-noising

by Hassene Seddik, Sondes Tebbini, Ezzedine Ben Braiek
Abstract

Digital imaging may be corrupted by random variations in intensities due to external interferences or noise. Some common models of noise are similar to the real one such as: Salt and pepper, impulse, and Gaussian noise. These distortions may alter the perception or the interpretation of the processed data. They can also cause problems for post processing tasks such as patterns recognition, object detection and medical decisions. In this paper, a new and efficient method for grayscale and RGB image de-noising is presented. Neural networks are used to transform static Gaussian low-pass filter to dynamic smart filter that targets and eliminates different kinds and densities of noise in the image. Simulation results prove that the proposed method is able to filter efficiently corrupted data and reduce noise as well as preserve edges and forms. Applied on grayscale and color image, it overcomes the constraints of the static nature of the Gaussian core. The filtering strength varies with respect to the image characteristics. A comparison with the static filter is conducted to highlight the improvement allowed by this method.

Volume: 20, Issue: 3

An Efficient Refining Image Annotation Technique by Combining Probabilistic Latent Semantic Analysis and Random Walk Model

by Dongping Tian, Xiaofei Zhao, Zhongzhi Shi
Abstract

In this paper, we present a new method for refining image annotation based on a combination of probabilistic latent semantic analysis (PLSA) and random walk (RW). We first construct a PLSA model with asymmetric modalities to estimate the posterior probabilities of each annotation keywords for one image, and then a label similarity graph is constructed by a weighted linear combination of label similarity and visual similarity. Followed by a random walk process over a label graph is employed to further mine the correlation of the keywords so as to capture the refining annotation, which is very important for semantic-based image retrieval. The novelty of our method mainly lies in two aspects: exploiting PLSA to accomplish the initial semantic annotation task and implementing random walk process over the constructed label similarity graph to refine the candidate annotations generated by the PLSA. Compared with several state-of-the-art approaches on Corel5k and Mirflickr25k datasets, the experimental results show that our approach performs more efficiently and accurately.

Volume: 20, Issue: 3

User Adaptive and Fair Broadcasting in an Error-prone Environment

by Byoung-Hoon Lee, Deok-Hwan Kim, Sang-Min Lee
Abstract

User adaptive broadcasting service is a critical factor to implement ubiquitous computing for many clients and smart devices in the mobile environment. Service providers apply the user0027s preference to supply the user adaptive broadcasting service. However, in an error-prone environment, such as wireless networks, faults due to diverse conditions weaken the efficiency and fairness of the broadcasting service. It is crucial to broadcast to the user adaptively in the error-prone environment. This paper suggests a mechanism to enhance efficiency and maintain the fairness of the service by rescheduling the faulty item when the fault occurs. The proposed method uses the fault queue to get faulty items and can afford to provide fair broadcasting by supplementing the shortage item and provide adaptive broadcasting to the user groups by selecting the item of largest deviation value of preference among user groups in faulty items and new arrived items. Experimental results show that the proposed method reduces waiting time up to 31% compared to the retransmission scheme, while maintaining fairness.

Volume: 20, Issue: 3

Integrated Hybrid Intelligent Control Scheme For Methane Production In An Anaerobic Process

by K.J. Gurubel, E.N. Sanchez, S. Carlos-Hernandez, F. Ornelas-Tellez, M.A. Perez-Cisneros
Abstract

An integrated hybrid intelligent control strategy for an anaerobic digestion process is proposed in order to maximize methane production. The structure of this strategy is as follows: a) a control law calculates dilution rate and bicarbonate addition in order to track reference trajectories; this control law is based on speed-gradient inverse optimal neural control, b) a nonlinear discrete-time recurrent high order neural observer is used to estimate biomass concentration, substrate degradation and inorganic carbon, c) a Takagi-Sugeno supervisor controller detects the biomass activity, selects and applies the most adequate control action, allowing a smooth switching between open loop and closed loop, d) a Takagi-Sugeno supervisor calculates reference trajectories for the system states and methane production at different operating points of the process, and finally e) fuzzy gain scheduling for the dilution rate control law interpolates the respective gains in order to control methane production at different operating points. The applicability of the proposed scheme is illustrated via simulations considering a completely stirred tank reactor.

Volume: 20, Issue: 2

Neural Inverse Optimal Control via Passivity for Subcutaneous Blood Glucose Regulation in Type 1 Diabetes Mellitus Patients

by Blanca Leon, Alma Alanis, Edgar Sanchez, Fernando Ornelas-Tellez, Eduardo Ruiz-Velazquez
Abstract

This paper deals with subcutaneous blood glucose level control. Inverse optimal trajectory tracking for discrete time non-linear positive systems is applied. The scheme is developed for MIMO (multi-input, multi-output) affine systems. The control law calculates the subcutaneous insulin delivery rate in order to prevent hyperglycemia and hypoglycemia events. A neural model is obtained from an on-line neural identifier, which uses a recurrent neural network, trained with the extended Kalman filter (EKF); this neural model has an affine form, which permits the applicability of inverse optimal control scheme. The proposed algorithm is tuned to follow a desired trajectory; this trajectory reproduces the glucose absorption of a healthy person. Then this model is used to synthesize an inverse optimal controller in order to regulate the subcutaneous blood glucose level for a Type 1 Diabetes Mellitus patient the applicability of the proposed scheme is illustrated via simulation using a recurrent neural network in order to model the insulin-glucose dynamics.

Volume: 20, Issue: 2

Developing a Mini-heliostat Array for a Solar Central Tower Plant: A Practical Experience

by Victor Benitez, Jesus Pacheco-Ramirez, Nun Pitalua-Diaz
Abstract

This paper presents the modeling and control of an array of mini-heliostats developed for a solar central tower plant facility located outside of Hermosillo, Mexico. In order to deal with the real time implementation, an algorithm to significantly reduce the error that emerges in the solar tracking requirements is presented. Heliostats are oriented to reflect solar beam to a central receiver located in top of a tower. The heliostat tracks the apparent sun position with determined periodicity. A digital controller perform the tasks of calculate the control action to drive the actuators. The real time implementation of the control action introduces numerical issues that deviates the solar ray of the desired position. Results show that the proposed control strategy is able to track the solar sun position. The controller is implemented in real time via LabVIEW computational environment and is applied in a solar tower plant facility.

Volume: 20, Issue: 2

Fuzzy Visual Control for Memory-Based Navigation Using the Trifocal Tensor

by HECTOR BECERRA
Abstract

In this paper, a fuzzy control scheme for visual path-following of wheeled mobile robots is presented. It relies on the feedback of a geometric constraint: the trifocal tensor (TT). The TT is computed from the image currently seen by an onboard camera and the sequence of target images previously acquired, which defines the visual path. Only one element of the TT is needed for feedback, which provides information of the robot0027s deviation from the path. This is used in a set of Mamdani-type fuzzy rules that mimics the human action of driving a vehicle. The translational velocity is also adapted by a fuzzy system in function of the path0027s curvature, inferred from the TT computed a priori using the set of target images. The use of fuzzy control allows achieving an effective and simple controller that does not need a time-varying reference to be tracked while the resultant robot velocities are smooth or piece-wise constant. The validity and performance of the approach is shown through simulations using synthetic images.

Volume: 20, Issue: 2

Multiple Models Fuzzy Control: A Redistributed Fixed Models Based Approach

by Nikolaos Sofianos, Yiannis Boutalis
Abstract

A new fuzzy control architecture for unknown nonlinear systems in the framework of multiple models control is proposed in this paper. The architecture incorporates different kinds of identification models and controllers offering enhanced overall performance. More specifically, the fixed models which are widely used in multiple models control are becoming more flexible and they end up to be semi-fixed models. When semi-fixed models are combined with a free adaptive model and a reinitialized adaptive model, the result is very promising and offers many advantages in comparison with former control methods. All these models are represented by using the Takagi-Sugeno (T-S) method which is very useful for representing unknown or partially unknown nonlinear systems. The identification T-S models define the control signal at every time instant by updating their own state feedback fuzzy controllers and using the certainty equivalence approach. A performance index and an appropriate switching rule are used to determine the T-S model that approximates the plant best and consequently to pick the best available controller at every time instant. The semi-fixed models are updated according to a rule which leads the models towards a direction that minimizes the performance index. The asymptotic stability of the system and the adaptive laws for the adaptive models are given by using Lyapunov stability theory. The effectiveness and the advantages of the proposed method over other methods are illustrated by computer simulations.

Volume: 20, Issue: 2

Genetic Design of Optimal Type-1 and Type-2 Fuzzy Systems for Longitudinal Control of an Airplane

by Oscar Castillo, Leticia Cervantes
Abstract

This paper presents the design of fuzzy systems for the longitudinal control of an F-14 airplane using genetic algorithms. The longitudinal control is carried out by controlling only the elevators of the airplane. To carry out such control it is necessary to use the stick, the rate of elevation and the angle of attack. These 3 variables are the inputs to the fuzzy inference system, which is of Mamdani type we obtain as the output value of the elevators. After designing the fuzzy inference system we turn to the simulation stage. Simulation results of the longitudinal control are obtained using a plant in Simulink and those results are compared against the PID controller. For optimizing the fuzzy logic control design we use a genetic algorithm. The main contribution of the paper is the optimal design of type-1 and type-2 fuzzy system for longitudinal control of the airplane.

Volume: 20, Issue: 2

Intelligent Fault Diagnosis in Nonlinear Systems

by E. Alcorta-Garcia, S Saucedo-Flores, D.A. Diaz-Romero
Abstract

Fault diagnosis in nonlinear systems is a challenging and very active research area. One of the difficulties to detect and isolate faults in nonlinear systems via observer-based methods is the design of a residual generator. In this work an integrated procedure combining conventional decoupling methods and Fuzzy Takagi-Sugeno observers for fault diagnosis in nonlinear systems is proposed. The advantage of the proposed AI based approach is that the design condition for the observers is relaxed in contrast with conventional approaches. Furthermore the potential use of decoupling techniques is reinforced. The design methodology is shown using a three tank system.

Volume: 20, Issue: 2

Embedded Average of an Interval Type-2 Fuzzy Systems for Applications in FPGAs

by Yazmin Maldonado, Oscar Castillo, Patricia Melin
Abstract

This paper proposes the design of the embedded average of an interval type-2 fuzzy inference system (EAT2-FIS); four type-1 fuzzy inference systems are considered to model the footprint of uncertainty of inference process. The EAT2-FIS is designed in high level code, and this high level code is Very High Description Language (VHDL) for Field Programmable Gate Array (FPGA) applications. The goal is regulating the speed of a DC motor using Xilinx Spartan 3AN. The results obtained with the EAT2-FIS using different number bits are compared. The main contribution of the paper is the design and implementation of EAT2-FIS in VHDL, with the advantage of resolution change (bits number) of the EAT2-FIS for FPGA.

Volume: 20, Issue: 2

Fuzzy-Tuned PD Tracking Control of a 3-RRR Parallel Manipulator: Stability Analysis and Simulations

by FRANCISCO G. SALAS, Victor Santibañez, MIGUEL A. LLAMA
Abstract

In this work we propose a variable gains PD tracking controller, in which the gains are tuned by a Fuzzy controller, for a parallel robot. The stability analysis of the closed-loop system is performed by using the Lyapunov-based perturbed systems theory. The closed-loop system is formed by the PD with variable gains control law and a so-called reduced model of the robot, which exhibits some properties similar to those found on the model of a serial robot. As a result, we demonstrate that, under a proper selection of the bounds of the variable gains, the tracking errors are uniformly ultimately bounded. Simulation results of the Fuzzy-tuned PD controller, applied to a 3-RRR planar parallel manipulator, are presented. The superior performance of the proposed approach is validated when compared with a classic PD controller.

Volume: 20, Issue: 2

Stable Neural Pid Anti-Swing Control For An Overhead Crane

by Wen Yu, XIAOOU LI, FRANCISCO PANUNCIO
Abstract

PD with compensation or PID is the most popular algorithms for the overhead crane control. To minimize steady-state error with respect to uncertainties, PID control needs a big integral gain and the PD with compensator requires a large derivative gain. Both of them deteriorate transient performances of the crane control. In this paper, we propose a novel anti-swing control strategy which combines PID control with neural compensation. The main theoretical contributions of this paper are semi global asymptotic stability of the neural PID for the anti-swing control and local asymptotic stability of the neural PID control with a velocity observer are proven with standard weights training algorithms. These conditions give explicit selection methods for the gains of the linear PID control. An experimental study on an overhead crane with this neural PID control is addressed.

Volume: 20, Issue: 2

A Novel Topology in Modular Ann Approach for Multi-Modal Concept Identification and Image Retrieval

by SYED SAJJAD HUSSAIN, MANZOOR HASHMANI, MUHAMMAD MOINUDDIN, KAMRAN RAZA
Abstract

Concept identification and automated image annotation helps to overcome the non-trivial issue of image retrieval. It is accomplished by decomposing the image at object level and extracting concepts from objects. Several applications like biometrics, geographical information systems, and automatic target recognition possess highly diffused feature vectors. Hence, a conventional Artificial Neural Network (ANN) fails to deliver truthful results. In this work, we have developed a modified back propagation algorithm and have proposed a novel topology in Modular Artificial Neural Network (MANN) to rectify the problem faced in concept identification. In this topology we have trained each module of ANN for one object in one verse all fashion and highly ranked output is taken as classified output. Our argument is supported by combined application of afore-mentioned algorithm and novel topology using MATLAB simulations on two different datasets.

Volume: 20, Issue: 1

Spatial Prediction of Topsoil Texture in a Mountain-plain Transition Zone Using Unvariate and Multivariate Methods Based on Symmetry Logratio Transformation

by Shiwen Zhang, Weifang Kong, Yuanfang Huang, Chongyang Shen, Huichun Ye
Abstract

High-resolution soil texture maps are essential for land-use planning and other activities related to forestry, agriculture and environment protection. The objective of the article was to find suitable methods for predicting soil texture through comprehensive comparison of different prediction methods (e.g., univariate and multivariate methods) by completely taking account of its characteristics as composition data with the same auxiliary information. This article, taking elevation as auxiliary variable, predicted the soil texture using univariate [ordinary kriging (OK)] and multivariate [i.e. regression kriging (RK), simple kriging with locally varying means (SKlm), and cokriging (COK)] methods. Soil texture was transformed by symmetry log ratio (SLR) to meet the requirements of the spatial interpolation for the compositional data. The root mean squared errors (RMSE), the relative improvement (RI) values of RMSE and Aitchison0027s distance (D

Volume: 20, Issue: 1

A Weighted Dynamic Information Systems Reduction Method

by Yunfei Yin, Yafei Zeng, Haichao Guan, Xiaonan Wang
Abstract

Information systems reduction is an important research topic in Information Science; however, in distributed network, how to effectively conduct a dynamic distributed information systems reduction is a problem worthy of study. In this paper, we propose a weighted dynamic information systems reduction method used to conduct the information reduction in a distributed network. In this method, we (1) weigh and combine all the local information reduction results; (2) regard the users0027 preferences as core attributes; and (3) design recursive algorithms according to the properties. Experimental results show that this method is effective.

Volume: 20, Issue: 1

Design of Synthetic Optimizing Neuro Fuzzy Temperature Controller for Twin Screw Profile Plastic Extruder Using Labview

by S. RAVI, P.A. BALAKRISHNAN, C.N. MARIMUTHU, C. SUJITHA
Abstract

Controlling the temperature of the twin screw extruder is non linear and suffers from problems related to undesirable overshoot, longer settling time, coupling effects and disturbances. The system is designed with four different control techniques to control temperature at different set point changes and as well as to control sudden input disturbances. The technique involved is conventional PID controller, neural controller, mamdani type fuzzy Logic Controller and the proposed method is neuro fuzzy controller. The proposed method is smoothing of undesired control signal and maintains the temperature exactly at its set points. The software incorporates LabVIEW graphical programming for twin screw plastic extruder toolbox.

Volume: 20, Issue: 1

Joint Source-Channel Coding With Unequal Protection For Real-Time Scalable Video Transmission

by Xingjun Zhang, Yuxing Wu, Scott Fowler, Song Cen, Xiaoshe Dong
Abstract

In real-time video transmission, the distortion of video content may be caused by video compression in source coding and packets loss in transmission. Meanwhile the source coding distortion and transmission distortion are often mutually affected. There is a fundamental tradeoff between them for achieving the best video transmission quality. In order to make wise use of network resources, this paper proposes a video transmission scheme in which the source coding distortion and transmission distortion to the video quality are formulated and fountain code is used as the channel coding for unequal protection in scalable video transmission. The network packet loss and the characteristic of scalable video are quantitated in our proposed rate distortion model, which formulates the real-time video transmission system in a bandwidth limited network. We introduce a computerized algorithm. The computerized algorithm will determine the optimal source rate of the video coding and the optimal redundancy amount assigned to each scalable video layer. Our proposed algorithm can guarantee that the optimal quality of real-time video transmission will be achieved and the total real-time video transmission distortion will be reduced dramatically.

Volume: 20, Issue: 1

Performance Analysis of the Multiple Antenna Asynchronous Cognitive MAC Protocol in Cognitive Radio Network for IT Convergence

by CAIDAN ZHAO, LIANFEN HUANG, ZILONG GAO, SHALI ZHOU, DAN GUO, HAN-CHIEH CHAO
Abstract

Multiple antenna technology for wireless communications is becoming mature and wide ranging in the wireless broadband network. Many spectrum usage measurement reports have shown that inaccurate spectrum sensing will waste spectrum resources. A Multiple Antenna Asynchronous Cognitive MAC (MAAC-MAC) protocol is proposed through introducing a multiple antenna architecture and power control mechanism into the hardware-constrained cognitive MAC (MHC-MAC) protocol with Asynchronous-Assembly Line Mode in cognitive radio network. In the spectrum sensing stage, the protocol makes use of multiple antennas to perform spectrum sensing and positioning, aimed at improving spectrum sensing and detection performance. During the negotiation phase, the CU (Cognitive User) access power control mechanism fully utilizes the licensed band without interfering PU (Primary User) and also increases the transmission rate and system throughput. It may be used for improved positioning and location-based services both indoors and outdoors for IT convergence.

Volume: 20, Issue: 1

A Smart Service Robot Middleware on Ubiquitous Network Environments

by Jongsun Choi, Jaeyoung Choi, Hoon Ko, Kitae Bae, Kyung Jin An, Choong Kim, Jongmyung Choi
Abstract

As the importance of robot software has increased, a number of research on robot middleware within client-server architecture has been produced for the past several years. Those middleware usually provide convenient environments, where developers are able to develop robot services in conjunction with the existing libraries. Therefore, rich libraries are essential in the robot middleware. This paper introduces experience learned while developing robot software using the middleware. The middleware allows a robot0027s functions to be described in XML, to be registered to a Web Service server, and to be reused as a Web Service. The approach has some advantages: separation a robot0027s abstract behaviors from hardware dependent implementation, reuse of the existing robot software implementation, and finally, the possibility of cooperation among various robots.

Volume: 20, Issue: 1

Ontology-based Dynamic and Semantic Similarity Calculation Method for Case-based Reasoning

by MYE SOHN, Jun HYEOK YIM, SEONGIL LEE, HYUN JUNG LEE
Abstract

In this paper, we propose an Ontology-based DYnamic and Semantic similaritY computation method for CBR (ODYSEY). ODYSEY is developed for the computation of dynamic similarity as well as semantic using dynamically changed ontology structure according to occurred context using CBR. Context is defined as any information that can be used to characterize the occurrence situation of a new problem and cases. To compute dynamic and semantic similarity, ontology is restructured by the context. The domain ontology is developed by consideration of contexts, and partial ontologies are extracted from the ontology including a set of shared contexts with same features and values between a new problem and cases. We implemented a mobile e-mentoring system, named MintStory00A9 to show the applicability and feasibility of ODYSEY. As experimental results show, the satisfaction value of similarity of MintStory00A9 is higher than the coordinator-recommended result. It shows that the ODYSEY is significantly efficient in a similarity calculation problem in CBR.

Volume: 20, Issue: 1

A Heuristic Field Navigation Approach for Autonomous Underwater Vehicles

by Hui Miao, Xiaodi Huang
Abstract

As an effective path planning approach, Potential Field Method has been widely used for Autonomous Underwater Vehicles (AUVs) in underwater probing projects. However, the complexity of the realistic environments (e.g. the three dimensional environments rather than two dimensional environments, and limitations of the sensors in AUVs) have limited most of the current potential field approaches, in which the approaches can only be adapted to theoretic environments such as 2D or static environments. A novel heuristic potential field approach (HPF) incorporating a heuristic obstacle avoidance method is proposed in this paper for AUVs path planning in three dimensional environments which have dynamic targets. The contributions of this paper are: (1) The approach is able to provide solutions for more realistic and difficult conditions (such as three dimensional unknown environments and dynamic targets) rather than hypothetic environments (flat 2D known static environments); (2) The approach results in less computation time while giving better trade-offs among simplicity, far-field accuracy, and computational cost. The performance of the HPF is compared with previous published Simulated Annealing (SA) and Genetic Algorithm (GA) based methods. They are analyzed in several environments. The performance of the heuristic potential field approach is demonstrated through case studies not only to be effective in obtaining the optimal solution but also to be more efficient in processing time for dynamic path planning.

Volume: 20, Issue: 1

IT Convergence with Traditional Industries and Short-Term Research and Development Strategy in Korea

by SANG CHEOL HAN, YOUN-HEE HAN
Abstract

In recent years, the convergence phenomenon of the IT (information technology) industry with other traditional industries has deepened. Particularly, the IT industry has been strongly characterized as a base industry leading the convergence era and also pointed out as a key sector in the convergence era. In this paper, we describe what IT convergence is and introduce the Korea0027s five core traditional industries which are supported by combining with IT. And, we present how it is contributing to enhance the competitiveness of the traditional industries in Korea. We also explain the Korea0027s technology development roadmap and look at the current development status set by the Korea0027s traditional industries. We finally present the short and long-term strategy for national research and development plan for IT convergence technology development in the five core traditional industries.

Volume: 20, Issue: 1

Cost-Efficient Environmentally-Friendly Control of Micro-Grids Using Intelligent Decision-Making for Storage Energy Management

by Y. S. MANJILI, Rolando Vega, Prof Mo Jamshidi
Abstract

A smart decision-making framework based on genetic algorithms (GA) and fuzzy logic is proposed for control and energy management of micro-grids. Objectives are to meet the demand profile, minimize electricity consumption cost, and to modify air pollution under a dynamic electricity pricing policy. The energy demand in the micro-grid network is provided by distributed renewable energy generation (coupling solar and wind), battery storage and balancing power from the electric utility. The fuzzy intelligent approach allows the calculation of the energy exchange rate of the micro-grid storage unit as a function of time. Such exchange rate (or decision-making capability) is based on (1) the electrical energy price per kilowatt-hour (kWh), (2) local demand (load), (3) electricity generation rate of renewable resources (supply), and (4) air pollution measure, all of which are sampled at predefined rates. Then, a cost function is defined as the net dollar amount corresponding to electricity flow between micro-grid and the utility grid. To define the cost function one must consider the cost incurred by the owner of the micro-grid associated to its distribution losses, in addition to its demand and supply costs, in such a way that a positive cost translates to owner losses and a negative cost is a gain. Six likely scenarios were defined to consider different micro-grid configurations accounting for the conditions seen in micro-grids today and also the conditions to be seen in the future. GA is implemented as a heuristic (DNA-based) search algorithm to determine the sub-optimal settings of the fuzzy controller. The aforementioned net cost (which includes pricing, demand and supply measures) and air pollution measures are then compared in every scenario with the objective to identify best-practices for energy control and management of micro-grids. Performance of the proposed GA-fuzzy intelligent approach is illustrated by numerical examples, and the capabilities and flexibility of the proposed framework as a tool for solving intermittent multi-objective function problems are presented in detail. Micro-grid owners looking into adopting a smart decision-making tool for energy storage management may see an ROI between 5 and 10.

Volume: 19, Issue: 4

Stock Market Prediction Using a Combination of Stepwise Regression Analysis, Differential Evolution-based Fuzzy Clustering, and a Fuzzy Inference Neural Network

by David Enke, Nijat Mehdiyev
Abstract

This paper discusses a hybrid prediction model that combines differential evolution-based fuzzy clustering with a fuzzy inference neural network for performing an index level forecast. In the first phase of the proposed model, stepwise regression analysis is implemented to determine the combination of inputs that have the strongest forecasting ability. Next, the selected variables are grouped by means of a differential evolution-based fuzzy clustering method, allowing the extraction rules to be determined. For the final stage, a fuzzy inference neural network is implemented to predict the market prices by using the extraction rules from the previous stage.

Volume: 19, Issue: 4

A Temperature Field Detection System for Blast Furnace Based on Multi-Source Information Fusion

by JIANQI AN, Min Wu, Yong He
Abstract

With regard to the difficulty in detecting the burden surface temperature of blast furnace (BF), an online detection system based on multi-source information fusion is put forward in this paper. Firstly, a method for estimating the temperature of burden surface based on heterogeneous single information is proposed. Then an approach of multi-source information fusion based on reliability theory and Kalman Filter is presented. Lastly, the system implementation and industrial application is introduced. The result from a 2200 m3 BF indicates that this system can directly and accurately reflect the real-time state of burden surface temperature distribution. This paper provides an effective solution to control and monitor the process of complicated metallurgy.

Volume: 19, Issue: 4

FDA Performance Analysis of Job Shop Schedule in Uniform Parallel Machines

by Jin Chen, Yufeng Deng, Xueming He
Abstract

The challenging problem of a non-homogeneous machine load planning is to distribute jobs to the different machines with the same ability at the operation for the minimum cost. Each operation can be performed by a set of machines with the same or different characters, and each machine can handle a job many times with different operations. A new heuristics method (FDA, Four Dimension Algorithm) for uniform parallel machine scheduling is proposed in this paper to tackle this load distribution problem. The FDA method is to select a sequence, a machine set and an operation such that the minimum evaluation of the defined indexes is achieved. These indexes consist of four independent parameters. Essentially, this method consists of three main iteratives in implementation: iterative for job numbers, iterative for the operation turns and iterative for the non-homogeneous machines. Each operation is associated with a set of evaluation indexes. The proposed algorithm has shown a significant improvement over Genetic Algorithm and Branch and Bound Algorithm. The key of this new method is partial indexes calculated in every cycle only. As a result, the complexity of this method is greatly improved from exponential to only polynomial (O(mnplus3m2n2) at most). The most noticeable innovation in this proposed method is the practical efficient algorithm capable of analyzing non-homogeneous machine and job relations while reducing complexity of computation. Of equal importance, a various examples and experiments are shown in detail.

Volume: 19, Issue: 4

A Gradient Descent Sarsa(03BB) Algorithm Based on the Adaptive Reward-shaping Mechanism

by Quan Liu, Fei Xiao, Qiming Fu, Yuchen Fu
Abstract

Based on the adaptive reward-shaping mechanism, we propose a novel gradient descent (GD) Sarsa(03BB) algorithm to solve the problems of ill initial performance and low convergence speed in the reinforcement learning tasks with continuous state space. Adaptive normalized radial basis function (ANRBF) network is used to shape reward. The reward-shaping mechanism propagates model knowledge to the learner in the form of the additional reward signal so that the initial performance and convergence speed can be improved effectively. A function approximation algorithm named ANRBF-GD-Sarsa(03BB) is proposed based on the ANRBF network. The convergence of ANRBF-GD-Sarsa(03BB) is analyzed theoretically. Experiments are conducted to show the good initial performance and high convergence speed of the proposed algorithm.

Volume: 19, Issue: 4

The Study of Fatigue Crack Growth Rate When Consider the Impact of the Crack Tip Plastic Zone of the Concrete

by Chen Yue, Zhou Jian-Ting, Liu Lu, Sun Ma
Abstract

The traditional Paris formula of concrete structures is not suitable to describe the rate of fatigue crack growth problem, we take into account the impact of the plastic zone and use the restoring force, model was proposed to consider the size effect and residual stress correction into Paris formula. Through the three-point bending concrete beams structural response analysis showed that the modified reaction Paris formula to better physical condition of concrete structures.

Volume: 19, Issue: 4

Study of Tower Surface Crack Size Effect Based on Weibull Theory

by Song Jun, Zhou Jianting, Liu Lu, Sun Ma, Zhang Hong, Yang Juan, Liu Fangping
Abstract

This paper presents the evaluation of safety state of for tower surface with Weibull and Fractal theory which simulate stress situation of the different scales on the cable-stayed tower surface with PFC. The combination of the Weibull theory and the Fractal theory evaluated the crack condition on the surface of the cable-stayed tower. Calculation results of three specimens showed that the number of the cracks increased when the load step and surface size increased. At the same time, the largest size of specimen had the biggest fractal dimension. The discrete element numerical simulation shows that Weibull modulus was successfully used to evaluate the crack extension of the cable bent tower.

Volume: 19, Issue: 4

A Robust Object Tracking Algorithm Based on Surf and Kalman Filter

by Yin Hongpeng, Peng Chao, Chai Yi, Fan Qu
Abstract

In this paper, an efficient robust object tracking approach based on SURF and Kalman Filter is proposed. SURF as an outstanding local invariant feature is employed. Based on the SURF feature, a SURF match method is proposed. A combination method using an ingenious method and KF is used to predict the possible region, in which the tracking object may appear. Only in this region, SURF features are extracted and matched. It can significantly reduce the computational complexity. A histogram-based re-match process is employed to dislodge failure tracking after SURF matching. To verify the performance of the proposed algorithm, several comparative experiments are conducted. The results reveal that the proposed method achieves better performance and accuracy than conventional methods.

Volume: 19, Issue: 4

A Novel 3-D Bio-Inspired Neural Network Model for the Path Planning of An Auv in Underwater Environments

by Mingzhong Yan, Daqi Zhu, Feng Ding, SIMON YANG
Abstract

A three-dimensional (3-D) neural network model based on bio-inspired neurodynamics is proposed for the path planning of an autonomous underwater vehicle (AUV) in underwater environments. The model is topologically organized according to the 3-D underwater workspace of the AUV. Each neuron in the neural network uniquely represents a discretized subspace in the workspace. The excitatory and inhibitory inputs to the neural network come from the mission of the AUV and obstacles in the workspace respectively. The AUV is globally attracted through excitatory neural activity and the propagation among the network for its mission. Meanwhile, it is locally pushed away by the inhibitory neural activities to avoid collisions. Simulation results show that the proposed 3-D bio-inspired neural network model is suitable for an AUV to plan various paths in a 3-D underwater workspace.

Volume: 19, Issue: 4

An Identification of the Growing Area of Longjing Tea Based on the Fisher0027s Discriminant Analysis with the Combination of Principal Components Analysis

by Wenshen Jia, Zhihong Ma, Jihua Wang, Yubin Lan, Wenfu Wu, Dong Wang
Abstract

In recent years, the fake Xihu Longjing Tea has damaged its brand image and reputation. This paper based on the Fisher0027s discriminant analysis using the fixed-size moving window evolving factor analysis to find characteristic spectra through analyzing the near infrared spectroscopy of Xihu Longjing Tea and Zhejiang Longjing Tea. The Fisher0027s discriminant analysis was used to reduce the data dimension combined with the principal component analysis. A discrimination model was set up for the identification of the Xihu Longjing Tea and Zhejiang Longjing Tea. The model0027s accuracy is 97.3%. The results proved that this model is feasible to identify the differences between the Xihu Longjing Tea and Zhejiang Longjing Tea. Unlike other methods, the tea does not need to be made into a powder. It also lays out a theoretical foundation for developing an identification instrument for Xihu Longjing Tea.

Volume: 19, Issue: 4

Level Set Priors Based Approach to the Segmentation of Prostate Ultrasound Image Using Genetic Algorithm

by Yongtao Shi, Yiguang Liu, Pengfei Wu
Abstract

We propose a level set priors based approach to segment the prostate ultrasound image using the genetic algorithm (GA) optimization. Firstly, the ground truths are manually outlined in the training sets to generating the corresponding training level sets, and we derive the implicit boundary curve representation by using Principal Component Analysis (PCA). Secondly, a novel narrow band boundary feature is presented to determine the prostate edge. Thirdly, we use the genetic algorithm to optimize the parameters of the implicit curve representation. The experimental results demonstrate that the level set priors based method using genetic algorithm is robust and efficient.

Volume: 19, Issue: 4

Directional Weight Based Contourlet Transform Denoising Algorithm for Oct Image

by Fangmin Dong, Qing Guo, Shuifa Sun, Xuhong Ren, Liwen Wang, Shiyu Feng, Bruce Gao
Abstract

Optical Coherence Tomography (OCT) imaging system has been widely used in biomedical field. However, the speckle noise in the OCT image prevents the application of this technology. The validity of existing contourlet-based denoising methods has been demonstrated. In the contourlet transform, the directional information contained by spatial domain is reflected in the corresponding sub-bands, while the noise is evenly distributed to each sub-band, resulting in a big difference among the coefficients0027 distribution of sub-bands. The traditional algorithms do not take these features into account, and only use uniform threshold shrinkage function to each sub-band, which limits the denoising effect. In this paper, a novel direction statistics approach is proposed to build a directional weight model in the spatial domain based on image gradient information to represent the effective edge information of different sub-bands, and this weight is introduced into threshold function for denoising. The experiments prove the effectiveness of this method. The proposed denoising framework is applied in contourlet soft threshold and bivariate threshold denoising algorithms for a large number of OCT images, and the results of these experiments show that the proposed algorithm effectively reduces noise while preferably preserves edge information.

Volume: 19, Issue: 4

Crop Discrimination in Shandong Province Based on Phenology Analysis of Multi-year Time Series

by Qingyun Xu, Guijun Yang, Huiling long, Chongchang Wang
Abstract

Crop type identification plays an important role in extracting crop acreage, assessing crop growth and arable land productivity. In this study, the main crops (winter wheat, summer maize and cotton) of Shandong Province as research objects, and the SPOTlowbarVGT normalized difference vegetation index (NDVI) remote sensing datasets from 1999 to 2011 covering Shandong Province were acquired. The NDVI characteristic curves of typical features were extracted by combining the SPOTlowbarVGT NDVI time series datasets, the HJ-1B image and the phenological information. Moreover, the reasonable dynamic thresholds were settled, the non-cultivated land areas were removed and the crop patterns and the crop types were identified based on the annual NDVI variation and the phenological information of the typical features. The accuracy assessment was performed through the spatial contrast and quantitative description. The overall accuracy is 77.10% in the spatial accuracy assessment compared with standard land cover classification map, and the overall relative errors of winter wheat, summer maize and cotton are 25.52%, 25.97% and 7.11% in the quantitative accuracy assessment compared with the statistical datasets. The results of research show that it is feasible to identify the crop planting patterns and crop types using the proposed classification method by combining the SPOTlowbarVGT NDVI time series datasets with the phenological information.

Volume: 19, Issue: 4

Selection of Spectral Channels for Satellite Sensors in Monitoring Yellow Rust Disease of Winter Wheat

by Lin Yuan, Jingcheng Zhang, Chenwei Nie, Liguang Wei, Guijun Yang, Jihua Wang
Abstract

Remote sensing has great potential to serve as a useful means in crop disease detection at regional scale. With the emerging of remote sensing data on various spectral settings, it is important to choose appropriate data for disease mapping and detection based on the characteristics of the disease. The present study takes yellow rust in winter wheat as an example. Based on canopy hyperspectral measurements, the simulative multi-spectral data was calculated by spectral response function of ten satellite sensors that were selected on purpose. An independent t-test analysis was conducted to access the disease sensitivity for different bands and sensors. The results showed that the sensitivity to yellow rust varied among different sensors, with green, red and near infrared bands been identified as disease sensitive bands. Moreover, to further assess the potential for onboard data in disease detection, we compared the performance of most suitable multi-spectral vegetation index (MVI)-GNDVI and NDVI based on Quickbird band settings with a classic hyperspectral vegetation index (HVI) and PRI (photochemical reflectance index). The validation results of the linear regression models suggested that although the MVI based model produced lower accuracy (R200A0003D00A00.68 of GNDVI, and R200A0003D00A00.66 of NDVI) than the HVI based model (R200A0003D00A00.79 of PRI), it could still achieve acceptable accuracy in disease detecting. Therefore, the probability to use multi-spectral satellite data for yellow rust monitoring is illustrated in this study.

Volume: 19, Issue: 4

Cooperative Attack Strategy of Unmanned Aerial Vehicles in Adversarial Environment

by Min Yao, Min Zhao
Abstract

This paper addresses the problem of risk in adversarial environments and presents a new attack model in which the cooperative action is considered. The actions of each UAV not only get benefit from attacking targets but also reduce the risk in the environment for other UAVs. The implications of the survival probabilities and the time discount factor in this model are analyzed and the results will help to build the optimal attack strategy. An improved genetic algorithm is applied to quickly build the optimal strategy. Simulation results show that this algorithm provides better solutions than genetic algorithm.

Volume: 19, Issue: 3

Modified Radial Basis Function Neural Network Integrated with Multiple Regression Analysis and its Application in the Chemical Industry Processes

by Yang Wang, Chao Chen, Xuefeng Yan
Abstract

The construct of a radial basis function neural network (RBFNN) plays an important role in predicting performance. However, determining the optimal construct is difficult. A modified RBFNN integrated with correlation pruning algorithm-least squares regression (CPA-LSR) was proposed to optimize the number of hidden neurons as well as the weights and bias. First, an initial RBFNN was built by superposing each center to a training set point. This RBFNN was then trained. Next, CPA-LSR was applied to eliminate the redundant information of the initial network and to improve the predicting performance by optimizing the structure as well as the weights and bias. Finally, the developed naphtha dry point soft sensor and the industrial oxidation of p-xylene to terephthalic acid were employed to illustrate the performances of the modified RBFNNs. The result reveals an improvement in the predicting performances of the RBFNNs integrated with CPA-LSR. Conclusively, RBFNNs integrated with CPA-LSR are recommended because the redundant neurons are effectively eliminated, and the optimal structure of the RBFNN is obtained by CPA-LSR. Moreover, such RBFNNs are intuitive and eliminate the need for parameterization.

Volume: 19, Issue: 3

Recycling Plants Layout Design by Means of an Interactive Genetic Algorithm

by Laura Garcia-Hernandez, Antonio Arauzo-Azofra, Lorenzo Salas-Morera, Henri Pierreval, Emilio Corchado
Abstract

Facility Layout Design is known to be very important for attaining production efficiency because it directly influences manufacturing costs, lead times, work in process and productivity. Facility Layout problems have been addressed using several approaches. Unfortunately, these approaches only take into account quantitative criteria. However, there are qualitative preferences referred to the knowledge and experience of the designer, which should also be considered in facility layout design. These preferences can be subjective, not known in advance and changed during the design process, so that, it is difficult to include them using a classic optimization approach. For that reason, we propose the use of an Interactive Genetic Algorithm (IGA) for designing the layout of two real recycling plants taking into consideration subjective features from the designer. The designers knowledge guides the evolution of the algorithm evaluating facility layouts in each generation adjusting the search to his/her preferences. To avoid the fatigue of the designer, he/she evaluates only the most representative individuals of the population selected through a soft computing clustering method. The algorithm is applied on two real world waste recycling plant layout problems: a carton packs recycling plant and chopped plastic one. The results are compared with another method, proving that the new approach is able to capture the designer preferences in a reasonable number of iterations.

Volume: 19, Issue: 3

A collaborative scheme for boundary detection and tracking of continuous objects in WSNs

by Chauhdary Hussain, Myong-Soon Park, Ali Bashir, Sayed Shah, Jeongjoon Lee
Abstract

With rapid advancements in MEMS technologies, sensor networks have made possible a broad range of applications in real time. Object tracking and detection is one of the most prominent applications for wireless sensor networks. Individual object tracking and detection has been intensively discussed, such as tracking enemy vehicles and detecting illegal border crossings. The tracking and detection of continuous objects such as fire smoke, nuclear explosions and hazardous bio-chemical material diffusions pose new challenges because of the characteristics of such objects, i.e., expanding and shrinking in size, changing in shape, splitting into multiple objects or merging multiple objects into one over time. A continuous object covers a large area over the network and requires extensive communication to detect and track. Tracking continuous objects accurately and efficiently is challenging, whereas extensive communication consumes massive energy in the network, and handling energy in wireless sensor network (WSNs) is a key issue in prolonging the lifetime of a network.In this paper, we propose collaborative boundary detection and tracking of continuous objects in WSNs. The proposed scheme adopts local communication between sensor nodes for the purpose of finding boundary nodes; boundary nodes enhance energy efficiency. The scheme uses an interpolation algorithm to find the boundary points and to acquire a precise boundary shape for the continuous object. The proposed scheme not only improves the tracking accuracy but also decreases energy consumption by reducing the number of nodes that participate in tracking and minimizing the communications cost. Simulation results show a significant improvement over the existing solutions.

Volume: 19, Issue: 3

Improved Particle Swarm Optimization by Updating Constraints of PID Control for Real Time Linear Motor Positioning

by Yi-Cheng Huang, Ying-Hao Li
Abstract

This paper proposes an Improved Particle Swarm Optimization (IPSO) technique for adjusting the gains of a Proportional-Integral-Derivative PID controller. The new approach introduces particle space constraints to improve velocity updating performance and position updating capability. This study presents numerical simulations and experimental results based on PID, PSO-PID, and IPSO-PID control systems. Real time experimental results show that the proposed IPSO algorithm has great computational convergence and ensures the stability of the controlled system without strict constraints on updating velocity. Tests on a linear synchronous motor (LSM) using a digital signal Microchip (dsPIC) processor demonstrate the effectiveness and robustness in positioning with disturbance excitation.

Volume: 19, Issue: 3

Energy-Aware Discrete Probabilistic Localization of Wireless Sensor Networks

by Amena Amro, Imad Elhajj, Mariette Awad
Abstract

Localizing sensor nodes is critical in the context of wireless sensor network applications. It has been shown that, for some applications, low-overhead discrete localization achieves results comparable to costly fine localization. This research presents a hybrid energy-aware discrete localization method that requires no transmission overhead from the sensor nodes. The proposed method, E-KalmaNN, is a combination of a Kalman-inspired localization and Artificial Neural Networks estimation that updates the position of a node with respect to a mobile reference. E-KalmaNN runs on the sensor nodes and supports different listening/wakeup frequencies for different nodes to balance power requirements with localization accuracy for each node. Simulation results show that the method converges to the correct position of the node in a relatively short time with high average location accuracy. Compared to the localization methods found in the literature, E-KalmaNN localizes with comparable accuracy, lower transmission costs and/or fewer motion restrictions.

Volume: 19, Issue: 3

The Chemical Stain Inspection of Polysilicon Solar Cell Wafer by the Fuzzy Theory Method

by Chern-Sheng Lin, Chih-Wei Lin, Shih-Wei Yang, Shir-Kuan Lin, Chuang-Chien Chiu
Abstract

This study proposed an automatic optical inspection (AOI) technique to improve the inspection of chemical stains on solar wafers. Poly-silicon solar cell wafers were inspected for chemical stains, and the inspection was rapid and stable. The system used a laser-reflection-point-based AOI method for solar wafer chemical stain inspection. Based on the fuzzy theory, the image binarization algorithm could efficiently filter irrelevant image information, and the back-propagation method was also utilized to determine if the image was stained. The inspection algorithm integrated fuzzy theory and the back-propagation method in order to shorten the comparison time and quickly find the target. The experiment proved that the validity of the proposed method could achieve a recognition rate of 98% from among 1000 images.

Volume: 19, Issue: 3

Integrated Intelligent Control and Fault System for Wind Generators

by Pedro Ponce, Arturo Molina, Brian MacCleery
Abstract

The goal of this paper is to show the possibility of combining fault detection analysis, detection, modeling, and control of the doubly-fed induction generator (DFIG) wind turbine using intelligent control and diagnostic techniques. To enable online detection of problems inside the power electronics converter we apply the wave direct analysis method which enables a complete model for fault detection that includes the power electronic stage itself. A neural network system based on Hebbian networks is applied for fault classification with good detection results in simulation. For controlling the wind turbine a number different artificial intelligence techniques are presented including fuzzy logic and an adaptive fuzzy inference systems (ANFIS) which combines the characteristics of fuzzy logic and neural networks. A Grey predictor is also integrated in the control scheme for predicting the wind profile. The combined fault detection and control scheme are validated using simulation results. The software development and control platform is LabVIEW which is one of the most powerful tools for simulating and implementing industrial control systems.

Volume: 19, Issue: 3

Using Iceberg Concept Lattices and Implications Rules to Extract Knowledge from Ann

by Sérgio Dias, Luis Zárate, Newton Vieira
Abstract

Nowadays, Artificial Neural Networks are being widely used in the representation of physical processes. Once trained, the networks are capable of solving unprecedented situations, keeping tolerable errors in their outputs. However, humans cannot assimilate the knowledge kept by these networks, since such knowledge is implicitly represented by their structure and connection weights. Recently, the FCANN method, based on Formal Concept Analysis, has been proposed as a new approach to extract, represent and understand the behavior of the processes based on rules. However, the extraction of those rules set is not an easy task. In this work, two main proposals to improve the FCANN method are presented and discussed: 1) the building of concept lattice using frequent item sets, which provides a threshold on the formal concepts number, and 2) the extraction of implications rules from the concept lattice, which provides clearer and more direct rules, thus facilitating the learning of the process by the user. As a case study, the new approach will be applied in a solar energy system – thermosiphon.

Volume: 19, Issue: 3

Cluster analysis of citrus genotypes using near-infrared spectroscopy

by Qiuhong Liao, Yanbo Huang, Shaolan He, Rangjin Xie, Qiang Lv, Shilai Yi, Yongqiang Zheng, Xi Tian, Lie Deng, Chun Qian
Abstract

There are many genotypes and varieties in the citrus family. Currently, citrus classification systems have significant divergences in varieties of species, and subgenus classification as well. In this study, near-infrared spectroscopy technique was used to acquire spectral information on the surface of citrus fruits. Cluster analysis was consequently conducted to identify citrus genotypes. Results indicated that the combination of 9-point moving average smoothing and multiplicative scattering correction was optimal for preprocessing spectral data. In the spectral range of 1,180–1,220nm, the cumulative reliability of the first two principal components were greater than 99.4%, and sweet oranges were clustered into an independent class. In 1,280–1,320nm, systematic clustering performed better than principal component clustering, and all other sour oranges, except Goutoucheng, were clustered into a single clade. With dimensions reduction, the cumulative reliability of first five principle components in full band of 1,000–2,350nm reached up to 99.1%. Using principal component cluster analysis, pomelo and loose-skin mandarin were clustered together; sweet and sour oranges were clearly separated. Pomelo being clustered with loose-skin mandarin, implies that they may have a hybrid origin; Jiaogan Mandarin, Daoxian yeju Mandarin, Goutoucheng sour oranges, and Zhuhongju sour tangerine were clustered with sweet orange, which implies old varieties may contain similar characteristic matters as sweet orange; Given that Jinlong lemon and Ranpour lime were clustered with sour orange, they were proved to originated from sour orange. The study indicates the great potential of spectral analysis for citrus genotype identification and classification.

Volume: 19, Issue: 3

A New Decision Support Model Based on Impact Factor Analysis for Optimized Scheduling of Tomato Harvesting

by Dan Wang, Hong Zhang, Guocai Yang
Abstract

In fructescence, abnormal weather has great effects on tomatoes economic benefits. For example, heavy rain will bring great losses. Generally speaking, picking fruits in advance can reduce the losses. However, the market value of fruit is closely related to the maturity. This indicates that a dedicated system supporting decisions that affect tomato quality is required. In this work, a novel model designed for the scheduling of the tomato harvest is proposed. What is more, a feasibility study is carried out to evaluate the functionality and viability of a potential integrated decision support system. Besides, we establish factors hierarchical relationships to analyse the inner relationship between the factors and the effect to the economic benefit. Meanwhile, we study tomatoes ripeness algorithm and the corresponding performance evaluation methods to provide harvest scheduling for farmers, especially those making harvest plans in advance to avoid abnormal weather. Preliminary results show that the use of a dedicated decision support model for the tomato harvest has the potential of significantly reducing the costs and improving the quality of the harvest. In addition, simulation test platform proves the effectiveness of the algorithm and historical data is used to verify the proposed models accuracy and feasibility. To a certain extent, our model can improve tomatoes economic benefit.

Volume: 19, Issue: 3

Calculation and control of flow path length in superelevation sections

by Zhuo Zhang, Xiaofeng Wang
Abstract

For the sake of researching the drainage on superelevation section, according to the character of the superelevation gradient process, the calculation models of flow path length for superelevation gradient sections with the same change direction and the opposite are built by FDM and multiple linear regression method. Then, Considered traffic safety and unfavorable drainage combination state with the critical water film thickness, the formula of the flow path maximum control length is deduced based on flow continuity and momentum theory. The maximum length of flow path should not exceed 41m for highway in 80km/h. Finally, the calculation model is verified to be workable and reasonable by the engineering example, and flow path length can be reduced by setting appropriate longitudinal slope or superelevation to improve the road surface drainage condition.

Volume: 19, Issue: 3

A comparative analysis of spectral vegetation indices to estimate crop leaf area index

by Yuanyuan Fu, Guijun Yang, Jihua Wang, Haikuan Feng
Abstract

Leaf area index (LAI) is a key variable to reflect crop growth status and forecast crop yield. Many spectral vegetation indices (SVIs) suffer the saturation effect which limits the usefulness of optical remote sensing for crop LAI retrieval. Besides, leaf chlorophyll concentration and soil background reflectance are also two main factors to influence crop LAI retrieval using SVIs. In order to make better use of SVIs for crop LAI retrieval, it is significant to evaluate the performances of SVIs under varying conditions. In this context, PROSPECT and SAILH models were used to simulate a wide range of crop canopy reflectance in an attempt to conduct a comparative analysis. The sensitivity function was introduced to investigate the sensitivity of SVIs over the range of LAI. This sensitivity function is capable of quantifying the detailed relationship between SVIs and LAI. It is different with the regression based statistical parameters, such as coefficient of determination and root mean square, can only evaluate the overall performances of SVIs. The experimental results indicated that (1) LAI3 was an appropriate demarcation point for comparative analyses of SVIs; (2) when LAI was no more than three, the variations of soil background had significant negative effects on SVIs. LAI Determining Index (LAIDI), Optimized Soil-adjusted Vegetation Index (OSVI) and Renormalized Difference Vegetation Index (RDVI) were relatively optimal choices for LAI retrieval; (3) when LAI was larger than three, leaf chlorophyll concentration played an important role in influencing the performances of SVIs. Enhanced Vegetation Index 2(EVI2), LAIDI, RDVI, Soil Adjusted Vegetation Index (SAVI), Modified Triangular Vegetation Index 2(MTVI2) and Modified Chlorophyll Absorption Ratio Index 2 (MCARI2) were less affected by leaf chlorophyll concentration and had better performances due to their higher sensitivity to LAI even when LAI reached seven. The analytical results could be used to guide the selection of optimal SVIs for crop LAI retrieval in different phenology periods.

Volume: 19, Issue: 3

Prediction of Bridge Monitoring Information Chaotic Using Time Series Theory by Multi-step BP and RBF Neural Networks

by Jianxi Yang, Yingxin Zhou, Jianting Zhou, Yue Chen
Abstract

This paper uses time series and chaos theory of phase space reconstruction. First, it monitors information phase space reconstruction parameters from the deflection of the mid-span in Masangxi Bridge. As a result, the delay value is 4, the embedded dimension for 15, the maximum number of predictable of 10. Then, it constructs the multiple-step recursive BP neural network and RBF neural network model and realizes the analysis and prediction of monitoring information based on space reconstruction parameters. As the results show, the BP neural network and RBF neural network are all effective in monitoring information prediction and RBF shares more advantages than the BP in keeping the structural dynamic performance.

Volume: 19, Issue: 3

Estimation of leaf area index by using multi-angular hyperspectral imaging data based on The two-layer canopy reflectance model

by Qinhong Liao, Chunjiang Zhao, Guijun Yang, Craig Coburn, Jihua Wang, Dongyan Zhang, Zhijie Wang
Abstract

This study aims to investigate the effects of observation angle on the estimation of leaf area index (LAI) by using multi-angular hyperspectral imaging data. First, the bidirectional reflectance was simulated with a two-layer canopy reflectance model (ACRM), the obvious bell-shaped and bowl-shaped pattern can be found in the blue, red and NIR wavebands. Subsequently, the three most commonly used vegetation indexes, the normalized difference vegetation index (NDVI), the simple ratio index (SRI) and enhanced vegetation index (EVI) were used to exploit the effect of different observation angles. Through the analysis of simulated data, SRI and EVI displayed a greater potential for estimating LAI due to the fact that they are more sensitive to the variation of observation angle, thus the partial least square regression (PLS) based on the cross validation was applied both to the single observation angle and to various combinations of multiple observation angles. The result shows that SRI has obtained the highest estimation accuracy (R20.47, RMSE0.30) by the combination of six observation angles, which agreed well with the simulated result, indicating that multi-angular observation can improve the estimation accuracy of LAI.

Volume: 19, Issue: 3

Remote sensing of regional crop transpiration of winter wheat based on MODIS data and FAO-56 crop coefficient method

by Heli Li, Yi Luo, Chunjiang Zhao, Guijun Yang
Abstract

Crop evapotranspiration is one of the most important parameters of farmland water cycle, which consists of crop transpiration (

Volume: 19, Issue: 3

A Fast and Reliable VANET Routing Protocol for Cooperative Anti-collision Warning on Highway

by Shouzhi Xu, Bo Xu, Pengfei Guo, Qing Wang
Abstract

How to prevent rear to rear traffic accidents on highways is an increasingly important topic, but there is no efficient solution yet. To reduce the delay effect of drivers, this paper aims at designing an effective network communication protocol in highly dynamic and fast moving scenarios. A VANET cooperative anti-collision routing protocol is presented based on the strategy of shortest delay first (SDF, in brief), whose main idea is to select a node of the least transmission delay as the forward node from its behind nodes to ensure the least set of forwarding nodes. Comparing with the typical Directional Flooding (DF) protocol, simulation results show that the proposed protocol reduces the number of emergency warning message (EWM) and the transmission latency obviously, costs lower transmission delay and offers higher network reliability.

Volume: 19, Issue: 3

Mobile smart device-based vegetable disease and insect pest recognition method

by Kaiyi Wang, Shuifa Zhang, Zhibin Wang, Zhongqiang Liu, Feng Yang
Abstract

Computer vision and image processing technology have been rapidly developed and widely applied in many fields. There are many potential applications in modern agriculture. In this paper, a novel vegetable disease and insect pest recognition method is proposed based on the current computer vision and image processing methods. To investigate the vegetable disease and insect pest state, it is convenient to use images captured using smart phones for judgment. To implement this application, the disease area and the insect number on the leaves should be detected and figured out. So a new extraction and classification algorithm is firstly introduced to recognize leaves from images. Then a region-labeling algorithm is applied to calculate the insect number and disease areas in the segmented images. To deal with the areas of adhesion, a mathematical morphology algorithm is used for separating the objects. The proposed method is implemented on mobile smart devices and tested with field experiments. The experimental results show that the proposed method has good recognition performance with high efficiency.

Volume: 19, Issue: 3

Dynamic change Forecast of the Saline-Alkali Farmland Based on the Non-Equal Time Interval Grey Model

by Huaji ZHU, Huarui WU
Abstract

Salinization has become one of the most important questions about farmland in China. How to monitor and forecast the salinization trend of the farmland and to carry out valid prevention and cure measures is important for farmland protection in China. The grey model based on the grey system theory is different from the traditional regression forecast analysis. It doesnt need a large number of original data and doesnt require complex computation. It can avoid the fatal weakness of insufficient data and avoid the subjective factors on the forecast precision. By the mechanism of the grey model, the internal rules of the change of the saline-alkali farmland can be scientifically forecasted. It is usually executed with the mode of GM (1, 1). However, the GM (1,1) requires the equal time interval original data sequence. The saline-alkali degree always cant be monitored with the equal time interval. So, a new model by integrating the GM (1,1) with the least square method is proposed. With the new model, the original data sequence, the intervening results and the final forecast results are modified with the least square method. The results showed that the forecast precision was improved with the new model.

Volume: 19, Issue: 3

Segmentation on Ripe

by Lvwen Huang, Dongjian He, Simon Yang
Abstract

In this work we developed a novel approach for the automatic recognition of red Fuji apples in natural scenes using Lab color model and fuzzy two dimensional (2D) entropy based on 2D histogram in order to achieve intelligent harvesting. The Lab model is applied to detect the fruit under different lighting conditions because the red Fuji apple has the highest red color among the objects in the image. The fuzzy 2D entropy, which could discriminate the object and the background in grayscale images, is obtained from the 2D histogram. The genetic algorithm (GA), compared to the heuristic searching method, is optimized to increase the precision of segmentation of Fuji apples under complex backgrounds with partially occluded branches and reflective lights. A series of morphological operations are applied to eliminate segmental fragments. Finally, the proposed approach is validated on apple images taken in natural orchards. The contributions reported in this work, is the whole effective approach which recognizes and segments apples under different natural scenes regardless of the recognition accuracy.

Volume: 19, Issue: 3

Prediction of The Total Starch and Amylose Content in Barley using Near-Infrared Reflectance Spectroscopy

by Hua Ping, Jihua Wang, Guixing Ren
Abstract

Near-infrared reflectance (NIR) spectroscopy combined with reference data was used to predict the total starch content (TSC) and amylose content (AC) in different barley varieties. One hundred and twelve barley samples were analyzed for TSC and AC by AOAC methods. Out of the total samples, ninety were used to develop the calibration models, and twenty two were used to validate the models. The total starch model showed very good correlation with coefficient of determination (

Volume: 19, Issue: 3

Guest Editorial: Intelligent Information Technology in Agriculture

by Dong Ren, Chunjiang Zhao, Simon Yang
Abstract

Volume: 19, Issue: 3

Characteristics and Reliability Analysis of the Complex Network In Guangzhou Rail Transit

by Chaozhe Jiang, Lu Wu, Fang Xu, Jixue Yuan
Abstract

Researching for the characteristics and reliability of urban rail transit network by using complex network theory has certain significance on rail transits planning and efficiency improvement. In this paper, the topological structure of the Guangzhou urban rail transit network was made by space L and space P methods. The network characteristics, such as average path length, clustering coefficient and degree distribution were selected in each method. Then the networks reliability was discussed, and the results show that the Guangzhou rail transit network at present is classified as a random network. When suffered from selected attack, the average travel length and the global efficiency are increasing; the local efficiency keeps the same. As long as most of the stations work well, the entire rail network structure is relative stable.

Volume: 19, Issue: 2

Remote Sensing of the Seasonal Naked Croplands Using Series of Ndvi Images and Phenological Feature

by Zhengying Shan, Qingyun Xu
Abstract

Naked cropland elimination is an important part of Beijing Olympic ecological project. In this paper, Multi-temporal satellite data were used to monitor and position the naked croplands. Three Landsat TM images and two “Beijing-1” Small-Satellite images were selected to calculate NDVI series according to crop phenological calendars and investigated information of agricultural cropping structures in Beijing suburb. Based on the phenological spectral characteristics of main agricultural land use types, a classification scheme was proposed to extract the naked croplands. Considering the structural characteristic hierarchical classification and various demands of feature selection in different periods, decision tree algorithm and a stepwise masking technology were employed to extract typical crops in each season, and hence the naked croplands were left. Accuracy assessment of the naked croplands in winter and spring were performed with comparison of the monitoring areas with statistical data. The results show that the area of the naked croplands in winter and spring was 170368.1ha in Beijing. The areas of the top five districts (Yanqing, Shunyi, Daxing, Miyun and Tongxian) were 17933.3ha, taking a percent of 69.2% of that of Beijing. The areas of the naked cropland were 25719.6ha, 4485.4ha and 3325ha in summer, autumn and all the year round respectively. Experimental results demonstrated that our method would fast and simply monitor agricultural land use.

Volume: 19, Issue: 2

Detection of Smell Change of Flue-Cured Tobacco Based on an Electronic Nose

by Wei Ding, Zhongbin Zhu, Changhua Zhang, Xiaoming Chen, Wei Jiang, Yongjiang Liang, Cheng Sun, Simon Yang, Fengchun Tian
Abstract

In order to make the flue-cured tobacco represent better aroma quality, an electronic nose with an array of metal-oxide gas (MOS) sensors are used to monitor the smell change during tobacco curing process. The design of dedicated electronic nose and experiment steps are described in detail. After signal preprocessing, the smell synthesized curve, of which four obvious peaks were detected, was obtained by the use of principal component analysis (PCA). The results indicate that, the peak time would be of important significance to the smell synthesized curve, which can clearly reflect the overall trend of smell during tobacco curing process. Therefore, back-propagation neural network (BPNN) is proposed to forecast peak time. This paper presents a study on smell change based on electronic nose during flue-cured tobacco process and detection of obvious peaks on the basis of sensor outputs. The law of smell changes obtained from analysis is in agreement with flue-cured tobacco theory.

Volume: 19, Issue: 2

Analysis on Minimal Path Sum Algorithm of Air-to-ground Combat Scenarios

by Shufang Xu, Dazhuan Xu, F. Xu
Abstract

Path selecting and task allocation are often studied in many military application. Some focus on the preparation stage and others on the attack stage. In this paper, we will model a path selecting problem before the mission starts and investigate the problem of finding better attack paths in the air-to-ground combat scenarios. We propose a minimal path sum algorithm (MPSA) for path selecting to realize the higher killing probability in one attack. Many necessary factors and their relationships are also studied in this algorithm. Finally, we conduct simulations of our proposed algorithm with multi-UAVs to multi-targets. The simulation results show that the missiles range is the most sensitive factor in many attack scenarios.

Volume: 19, Issue: 2

Web Service System Structure based on Trusted Computing Platform

by Feng Xu, Hongxu Ma
Abstract

By introducing trusted computing techniques into web service security mechanisms, a web service security framework based on trusted computing platform is proposed. It changes the original passive defense mechanism into active one. Combining the integrity of terminal platform and the trustiness of platforms identity, the measuring mechanism can effectively resist the security threats from “malicious terminal” and “risky terminal”. “Trusted Connection Layer” in the framework bridges upper web application and the user. Trusted web access is achieved by adding Access Requester (AR) and Trusted Service Decision Point (TSDP) into the system.

Volume: 19, Issue: 2

Reliability Evaluation of Cloud Computing Systems Using Hybrid Methods

by Zhang Xuejie, Wang Zhijian, Xu Feng
Abstract

Cloud computing is a recently developed new technology for complex systems with massive-scale service sharing. Cloud reliability analysis and modeling are not easy tasks because of the complexity and large scale of the system. Due to the complexity of cloud computing systems, the reliability models for pure software/hardware or conventional networks cannot be simply applied to study the cloud reliability. This paper focuses on reliability evaluation using hybrid methods, which combine MTTF/MTTR and CTMC models. We take into account not only service faults, but also the effect of physical-resource breakdowns. Based on the proposed model, the formal calculation of the systems reliability is given. The effectiveness of the approach is illustrated through an example.

Volume: 19, Issue: 2

A Novel Cloud Load Balancing Mechanism in Premise of Ensuring QoS

by Ye Feng, Wang Zhijian, Xu Feng, Zhou Yuanchao, Zhou Fachao, Yang Shaosong
Abstract

In premise of ensuring users QoS, the paper proposes a novel load balancing mechanism in cloud computing environment. The idea takes into account not only the energy efficiency of cloud providers, also the needs of users QoS. The main work includes: construct QoS model, resource model of cloud infrastructure and the mapping between low-level resource metrics and QoS attributes; model load status of the virtual machine instance, and estimate the resource utilization ratio of the virtual machine cluster quantitatively; design the task scheduling algorithm and elastic scaling algorithm to achieve the tasks distribution and the elastic scaling of the virtual machines cluster respectively. By simulation using CloudSim platform, the result shows that this novel method has better load balance degree and complement time compared with other common algorithms such as round robin and green load, achieving the purpose of optimal load balancing in premise of ensuring QoS.

Volume: 19, Issue: 2

Identity-Based Xtr Blind Signature Scheme

by Qiaoying Tang, Fengxian Shen
Abstract

XTR is a new type of public-key cryptosystem, under which the algorithms have outstanding advantages both in efficiency and space. Considering privacy-preserving, an identity-based XTR blind signature scheme is proposed. It satisfies the properties of blind signature and has a favorable computational efficiency, making it suitable for many application scenarios, such as e-voting and e-bank.

Volume: 19, Issue: 2

A Three-Dimensional Simulation System for Truck Crane Hoisting Based on Cloud Computing

by Jianqi An, Shilong Shu, Min Wu, Yonghua Xiong
Abstract

Aiming at the problem of real-time calculation in the complex hoisting algorithm employed in the three-dimensional hoisting simulation system, this paper proposes a system based on cloud computing. First, the system structure based on cloud computing is designed. Then, to meet the requirements of the practical hoisting, the complex hoisting algorithms based on cloud computing are put forward, which involve collision detection, hoisting path planning, and so on. Finally, web publishing and complex computing in the background are realized by employing Browse/Server (B/S) structure and software Hadoop. The results show that the method proposed in this paper can improve the real-time calculation in the complex algorithms and achieve a smooth process of simulation.

Volume: 19, Issue: 2

Authentication Scheme for Cluster-Structured Ad Hoc Network

by Feng Xu, Xuan Liu
Abstract

In this paper, an authentication scheme is proposed for cluster-structured Ad Hoc network. All the nodes in the network are classified into two levels: cluster head nodes and cluster member nodes, so there are three kinds of authentication between them. To satisfy the requirements of each kind, two authentication protocols are proposed: constant-sized traceable Ring Signature based authentication protocol and self-updating one-time password mutual authentication protocol. The analysis shows that the whole process of authentication does not need the participation of the third trusted party, which is fully self-organizing, and the anonymity is also achieved. Due to the cluster structure, the network has better scalability.

Volume: 19, Issue: 2

Cryptographic Cloud Storage with Public Verifiability: Ensuring Data Security of the YML Framework

by Xin Lv, Feng Xu, Serge Petiton
Abstract

YML framework is a well-adapted advanced tool to support designing and executing portable parallel applications over large scale peer to peer and grid middlewares. This work studies the problem of ensuring data security of the YML framework. We define and construct a mechanism that enables us to move the data repository to a public cloud infrastructure where the service provider is not completely trustworthy. To achieve confidentiality, we encrypt the data using the encryption algorithm in our prior work before uploading to the cloud, and then attach pre-classified keywords to them for ciphertext-searching, which are generated by a statistically consistent public-key encryption with keyword search (PEKS) scheme, so the service provider can use the corresponding trapdoor to identify all data containing some specific keywords without learning anything else. To ensure integrity, an elegant verification scheme is proposed, enabling a third party auditor (TPA), on behalf of data owner, to verify the integrity of the (encrypted) data stored in the cloud. The introduction of TPA eliminates the involvement of client through the auditing of whether his data stored in the cloud is indeed intact, which can be important in achieving economies of scale for cloud computing.

Volume: 19, Issue: 2

Information Security and Processing

by Feng Xu, Simon Yang
Abstract

Volume: 19, Issue: 2

In-Field Recognition and Navigation Path Extraction For Pineapple Harvesting Robots

by Bin Li, Maohua Wang
Abstract

Fruit recognition and navigation path extraction are important issues for developing fruit harvesting robots. This manuscript presents a recent study on developing an algorithm for recognizing “on-the-go” pineapple fruits and the cultivation rows for a harvesting robotic system. In-field pineapple recognition can be difficult due to many overlapping leaves from neighbouring plants. As pineapple fruits (Ananas comosus) are normally located at top of the plant with a crowned by a compact tuft of young leaves, image processing algorithms were developed to recognize the crown to locate the corresponding pineapple fruit in this study. RGB (Red, Green, and Blue) images were firstly collected from top-view of pineapple trees in the field and transformed into HSI (Hue, Saturation and Intensity) colour model. Then, Features of pineapple crowns were extracted and used for developing a classification algorithm. After the pineapple crowns were recognized, locations of the crowns grown in one row were determined and linearly fitted into a line, which could be used for navigating the harvesting robots to conduct the harvest. To validate the above algorithms, 100 images were taken in a pineapple field under different environments in Guangdong province as a validation set. The results showed that pineapple recognition rate can reach 94% on clear sky day, which was much better than that on overcast sky day and the navigation path was well fitted.

Volume: 19, Issue: 1

Chaotic Differential Evolution Algorithm Based On Competitive Coevolution And Its Application To Dynamic Optimization Of Chemical Processes

by Xin Li, Chunping Hu, Xuefeng Yan
Abstract

A chaotic differential evolution algorithm based on competition coevolution is proposed to improve the performance of the differential evolution (DE) algorithm. In the proposed algorithm (named CO-CDE), at first the population is divided into several sub-populations, each sub-population evolves individually, using different differential schemes. At the end of the evolution each sub-population will have one individual with best fitness. After that all the individuals with best fitness compete with each other, at this time the fitness of one individual is defined as the number of times one individual is superior to others. Therefore one individual with best fitness is picked out and its information is shared by the whole population. To avoid premature convergence and raise the probability of escaping from local optima, a chaotic evolutionary operation based on chaotic variables is introduced into the algorithm and implemented to the whole population. The simulation experiment shows that the CO-CDE algorithm generally outperforms the original differential evolution algorithm for a suite of benchmark functions. Furthermore, the CO-CDE algorithm is applied to a dynamic optimization of chemical process. Experimental results have proved the proposed approach effective, statistically consistent, and promising.

Volume: 19, Issue: 1

Honeybee Mating Optimization Algorithm For Task Assignment In Heterogeneous Computing Systems

by Qinma Kang, Hong He
Abstract

Effective task assignment is essential for achieving high performance in heterogeneous distributed computing systems. This paper proposes a new technique based on the honeybee mating optimization (HBMO) algorithm for solving the problem with the objective of minimizing the total execution and communication costs. We discuss the adaptation and implementation of the HBMO search strategy to the task assignment problem. Through simulations over a wide range of parameters, we demonstrate the performance of our method by comparing it with three existing task assignment techniques from the literature.

Volume: 19, Issue: 1

Fuzzy-Genetic Identification and Control Stuctures for Nonlinear Helicopter Model

by Jasmin Velagic, Nedim Osmic
Abstract

The paper exploits advantages of the genetic algorithm and fuzzy logic in identification and control of 2DOF nonlinear helicopter model. The genetic algorithm is proposed for identification of the helicopter system, which contains a helicopter body, main and tail motors and drivers. The quality of helicopter model achieved was validated through simulation and experimental modes. Then, this model is used to design of elevation and azimuth Mamdani type fuzzy controllers. The main objective of the paper is to obtain robust and stable controls for wide range of azimuth and elevation angles changing during the long time flight. The robustness and effectiveness of both fuzzy controllers were verified through both simulations and experiments. Also, a comparative analysis of proposed fuzzy and traditional PID controllers is performed.

Volume: 19, Issue: 1

Voluntary Disconnected Operations for Energy Efficient Mobile Devices in Pervasive Computing Environments

by Sung-Hwa Lim, Byoung-Hoon Lee, Jai-Hoon Kim
Abstract

It is important to support disconnected operations in mobile computing, because wireless communication requires substantial power consumption by mobile devices. Suitable usage of voluntary disconnected operations with pre-fetching of data in mobile environments can effectively save energy and money for mobile devices in wireless pervasive computing. In this paper, we analyze energy expenditure, including voluntary disconnected operations, to evaluate the energy efficiency of mobile computing. We estimate the energy expenditure using various parameters. The results of our analysis will assist efficient employment of disconnected operations and performance prediction.

Volume: 19, Issue: 1

Real-Time Decentralized Neural Control for a Five Dof Redundant Robot

by Ramon Garcia-Hernandez, Jose Ruz-Hernandez, Edgar Sanchez, Maarouf Saad
Abstract

This paper presents a discrete-time decentralized control scheme for trajectory tracking of a five degrees of freedom (DOF) redundant robot. A modified recurrent high order neural network (RHONN) structure is used to identify the plant model and based on this model, a discrete-time control law is derived, which combines block control and sliding mode technique. The neural network learning is performed on-line by Kalman filtering. The local controllers for each joint use only local angular position and velocity measurements. The proposed control scheme is implemented in real-time. The experimental results for trajectory tracking show the effectiveness of the proposed approach.

Volume: 19, Issue: 1

Updating the Finite Element Model of Large-Scaled Structures using Component mode Synthesis Technique

by Yang Liu, Hang Sun, Dejun Wang
Abstract

It is difficult to update the finite element (FE) model of the large-scale or complex structures such as bridges by using the regular methods since the dynamic analysis of complex structures always result in the heavy computational burden. A new updating method based on component mode synthesis (CMS) was proposed to improve the efficiency of FE model updating of large-scale structures. The effectiveness of the proposed method was validated by updating the FE model of a scaled bridge model with measured modal parameters.

Volume: 19, Issue: 1

Heart Arrhythmia Detection using support vector machines

by Ali Khazaee, Ataollah Ebrahimzadeh
Abstract

This paper deals with the discrimination of premature ventricular contraction (PVC) arrhythmia using support vector machine (SVM) and genetic algorithm (GA). Feature extraction module extracts ten electrocardiogram (ECG) morphological features and two timing interval features. Then a number of SVM classifiers with different values of C and the GRBF kernel parameter, sigma, are designed and compared their ability for classification of three different classes of ECG signals. However, the parameters were not optimum choices. So, GAs are used to find the optimum values of C and sigma. An overall classification accuracy of detection of 99.8112% were achieved using proposed method over nine files from the MIT/BIH arrhythmia database.

Volume: 19, Issue: 1

An Improved Pca Fusion Method Based on Generalized Intensity–Hue–Saturation Fusion Technique

by Dong Ren, Yanmei Liu, Xiaodong Yang, Jihua Wang, Haiyang Yu
Abstract

Among various image fusion methods, principal component analysis (PCA) technique is capable of quickly merging the massive volumes of data. For IKONOS imagery, PCA can yield satisfactory “spatial” enhancement but may introduce spectral distortion, appearing as a change in colors between compositions of resembled and fused multi-spectral bands. To solve this problem, a fast improved PCA fusion method based on Intensity–Hue–Saturation Fusion Technique with Spectral Adjustment is presented. The experimental results demonstrate that the proposed approach can provide better performance than the original PCA method both in processing speed and image quality.

Volume: 18, Issue: 8

A Study of High Fill Embankment Chain Effect of the Disease Feature

by Ming Zhang, Shanlin Tang, Lei Wu
Abstract

This paper reveals the chain rule of road diseases, and through the analysis of three types of chain effects in common diseases of high embankment--their form and characteristics, discusses the law of diseases on the basis of chain mechanism analysis and prevention and control technology, so as to proposes the application of broken chains principle.

Volume: 18, Issue: 8

Image Segmentation Method for Crop Nutrient Deficiency Based on Fuzzy C-Means Clustering Algorithm

by Jing Hu, Daoliang Li, Guifen Chen, Qingling Duan, Yeiqi Han
Abstract

As the fact that the emergence and development of crop nutrient deficiency has become more common nowadays, this research aims to find a method to segment and determine nutrient deficiency regions of crop images based on image processing technology. The experiment starts by obtaining 256 images of various crops such as oat, wheat, beet, maize, rye, potato, kidney been and sunflower with nutrient deficiency. Secondly all the experimental images are pre-processed by color transformation and enhancement to improve quality. Finally the nutrient deficiency diseased regions of crop images were segmented by fuzzy c-means clustering (FCM) algorithm based on fuzzy clustering algorithm. In the experimental course, color space of image was transformed from RGB to HSV and images were enhanced by use of median filter method, which not only remove the noise of the image, but also keep clear edge and efficiently highlight the disease regions. To test the accuracy of segmentation, other common algorithms such as threshold, edge detection and domain division were compared with FCM. Results showed that the FCM algorithm was the appropriate algorithm for segmentation of complexity and uncertainty images of crop disease. Applying fuzzy set theory in dividing the nutrient deficiency regions is the new point of the research, and this research has great practical significance in variable rate fertilization based on image processing technology.

Volume: 18, Issue: 8

Application of GPS GPRS Technology to Field Data Acquisition in Coal Mine Dump Area

by Yingyi Chen, Xing Wu, Daoliang Li, Liang Yong, Dandan Li
Abstract

Because of the complexity and diversity of the terrain and cover types in coal mine dump, it is difficult to set up the research points in the wild dump. This paper introduces an embedded GPS and GIS technology and develops portable environmental information acquisition system applied to coal mine dump investigation. The system mainly achieves the functions of soil, vegetation, microclimate sampling layout and information collection. According to the different sampling requirements, the system can draw the layout grid on the base map, which achieving sampling points positioning and information prompting. The environmental information we collected can be transmitted to the remote server real-time through GPRS. Experimental results show that the system provides a viable collection and layout scheme, includes real-time acquisition, transmission, information and data processing. Thus it reduces workload of the sampling and information collection, and it has strong practicality.

Volume: 18, Issue: 8

A Hybrid Registration Approach of Remote Sensing Images for Land Consolidation

by Li Li, Yingyi Chen, Hongju Gao, Daoliang Li
Abstract

Significant developments in the field of remote sensing have widened the application fields of remote sensing. One of the application fields is a land consolidation project. Multi-temporal andor multi-sensor remote sensing provides a unique tool to track land utilization dynamics but requires precise registration of thousands of satellite images. However, automatic registration between remote sensing images is a challenging problem due to the different geometric distortion within the images, the illumination variation and varying resolution. To address this problem, we propose a hybrid automatic image registration scheme (technique), which combines Phase Correlation (PC) method and SIFT descriptor registration together. Based on the specific characteristic of the remote sensing imagery, we apply a Phase Correlation (PC) method first to coarsely pro-register the input image to the reference image. Then, a fine-scale registration process based on the scale invariant feature transform (SIFT) method is constructed. Experiments with Quickbird, CBERS-lremote-sensing images of the land Consolidation area in Beijing demonstrate that the proposed hybrid method is fully automatic and fast. Moreover, the registration accuracy is higher than traditional methods.

Volume: 18, Issue: 8

Research Vertical Distribution of Chlorophyll Content of Wheat Leaves Using Imaging Hyperspectra

by Dongyan Zhang, Xiu Wang, Wei Ma, Chunjiang Zhao
Abstract

Chlorophyll content is an important indicator for judging crop photosynthesis ability and monitoring growth status. Hyperspectral imaging is one of the hot spots in quantitative remote sensing research. As an image-spectrum merging technology, it could be used to explore and develop new methods for diagnosing of crop nutrition, diseases and insect pests. In this study, an auto-development pushbroom imaging spectrometer (PIS) was applied to measure the chlorophyll content of wheat leaves. The tested sites of spectrum and the chlorophyll content measured positions were on the same area of single leaf. Partial least square (PLS) regression was used to establish prediction models of chlorophyll content. The model accuracy of single leaf with values from different positions was evaluated; and the model accuracy of leaves from different layers was also studied. The results showed that the model of the leaf with 2, 4, 6 sites was better than that of 1, 3, 5 sites; those models of leaves from vertical levels were medium layer

Volume: 18, Issue: 8

Application of Electro-Mechanical Impedance Sensing Technique for Online Aging Monitoring of Rubber

by Yuxiang Zhang, Fuhou Xu, Tongda Zhang, Cuiqin Wu
Abstract

Electro-mechanical impedance (EMI) sensing technique based on smart piezoelectric materials has emerged as a potential tool for the implementation of an online monitoring system for structural health monitoring. Many nondestructive methods have limited capabilities in monitoring of the rubber aging in a continuous manner. In this study, the feasibility of the EMI sensing technique for the online aging monitoring of rubber is investigated. The finite element method (FEM) is employed to analyze the impedance of piezoelectric patch which is bonded on the aging rubber. The results show that the EMI signatures not only can detect the aging of rubber but also can evaluate the aging extent, and the EMI signatures are more sensitive to the early aging rubber with increasing excites frequency.

Volume: 18, Issue: 8

Directory Services and Data Sharing for Distributed Agricultural Information Resources

by Qiong An, Gao Wanlin, Jianjia Wu, Lina Yu, Jianing Zhao
Abstract

The agricultural information resources is an important data assets, how to rationally plan, share and use of distributed storage of agricultural information resources, efficiently develop agricultural information resources, is the important issue of agricultural information resources needed to resolve. Based on the fully research on the current status of agricultural information systems, management processes and practices based on the user, using a distributed technical architecture, integration of J2EE framework, metadata model and directory services technology and other advanced means to build a distributed directory of agricultural information resources and data-sharing platform, the working principle of the platform and key technologies are analyzed, and from the application model, the overall structure, functions, data architecture, deployment options such as distributed agricultural information resources directory and information sharing platform was designed, without changing the data structure of information resources, achieve the efficient sharing of agricultural information resources, provide effective information support for rural economic development.

Volume: 18, Issue: 8

Research on Grid Framework of Agricultural Remote Sensing Monitoring Data Sharing

by Wanlin Gao, Qiong An, Lina Yu, Jianing Zhao, Xiaomiao Zuo
Abstract

In order to effectively implement sharing and integrated application of agricultural remote monitoring data, which is of distributed areas, various types, different systems and a large amount of data, across different areas, departments and systems, new networked framework is needed to be introduced to promote sharing of resources. In this paper, based on grid, considering the current situation of agricultural remote sensing monitoring data, Grid Framework of Agricultural Remote Sensing Monitoring Data Sharing is proposed, which is designed from both the logical and physical aspect. With the explanation of its components and the relationship among them, it offers well high level instruction supporting framework for agricultural remote monitoring data. Meanwhile, based on the framework, agricultural remote monitoring data sharing grid prototype system is built in the paper, after implementing the system, the difficulties of data sharing is overcame, and the efficiency is improved, data resource distributed in different departments is regularly integrated, the results is good and the system is feasible and practicable.

Volume: 18, Issue: 8

A New Multi-Feature Approach to Object-Oriented Change Detection Based on Fuzzy Classification

by Xuejiao Du, Chao Zhang, Jianyu Yang, Wei Su
Abstract

Remote sensing technologies have been widely used in the detection of Land UseLand Cover change (LUCC). In the past few decades, lots of methods have been proposed attempting to detect changes using multi-temporal satellite images, most of which are on the pixel level. In this paper, a new synthetic method based on object-oriented is proposed. Several customized difference features such as difference of band value, Normalized Difference Vegetation Index (NDVi), texture and so on are applied to the change detection, and also the fuzzy classification. The classified elements are image objects with the object-oriented approach which improve the salt-and-pepper problem effectively. Experiment results show that this method has a stronger advantage than the traditional method to high resolution remote sensing image change detection.

Volume: 18, Issue: 8

Analysis on the Relationship and its Dynamics Between Rural Settlements and Linear Features Using Gis and Rs

by Qiaoqin Liu, Yuchun Pan, Baisong Chen, Shuhua Li, Aiqing Guo
Abstract

The relationship between spatial patterns of rural settlements and environment and its dynamics were analyzed in Daxing District, Beijing in the recent 20 years using landsat TM images. Results showed that too much of land in total was used for the rural settlements in the study area with a rapid growth, and it had a great potential for land intensive utilization; the rural settlements were rather small, with weak aggregation and high random expanse in spatial distribution; highways, rivers and channels were important factors that affected the spatial distribution of rural settlements, while railways and expressways exhibited little influence. Accordingly, to guarantee the unified and balanced development of rural and urban construction, planning for villages and towns are required, and considering the convenience of road transportation and water use is an important principle for the selection of central towns and the systematic planning of rural settlements; on the other hand, areas along the highways, rivers and channels become the hotspot of land use change, and it ought to take these areas as the key areas for monitoring rural land use change.

Volume: 18, Issue: 8

The Application of Unmanned Aerial Vehicle Remote Sensing in Quickly Monitoring Crop Pests

by Jianwei Yue, Tianjie Lei, Changchun Li, Jiangqun Zhu
Abstract

It is particularly important for the development of agriculture to strengthen the agricultural pests monitoring, prevention and cure. The traditional methods of remote sensing (RS) for agricultural pests monitoring cannot meet the needs of agricultural development, because of their long time-consuming, high cost, and low accuracy. However, Unmanned Aerial Vehicle (UAV) remote sensing, as a new means of remote sensing, is introduced in this paper for agricultural monitoring. UAV has advantages of strong real-time, quickness, convenience, low cost, high accuracy and abundant data. With the great flexibility of UAV remote sensing, it is not only easy to focus on regional and long-term agricultural pests monitoring, but also feasible to provide scientific basis for crop pests control. Thus, it is better to meet the time needs of pests’ control. In the article, Baiyangdian agricultural area is chosen as the study area. It is discussed how to process UAV images rapidly and extract disease crop information, especially which improved scale invariant feature transform (SIFT) algorithm and object-oriented information extraction are used for image-processing. Fortunately, it has been received good results for the local crop pest control in terms of fact of treatment. With the advantages of UAV remote sensing, there is a broad application prospect in precision agriculture.

Volume: 18, Issue: 8

Monitoring Winter Wheat Freeze Injury Based on Multi-Temporal Data

by Huifang Wang, Gu Xiaohe, Jihua Wang, Yingying Dong
Abstract

Winter wheat freeze injury is one of the most serious damages in northern China. Timely and accurately monitoring freeze injury can provide quantitative damage assessment and decision support for after-injury field management. Freeze injury stress is directly related to the deviation of winter wheat population. This paper aimed to monitor the spatial-temporal distribution as well as the severity of winter wheat freeze injury with multi-temporal data. The field investigations were conducted in the wintering and setting stages, corresponding with the acquisition moderate resolution date of TM data and HJ-lA data. A multiple linear regression (ML.R) model proposed in this paper by analyzing the relationship between plants number per mu and varieties of vegetation indices having strong correlation with wheat growth vigor. The survival rate serving as an efficient indicator of freeze injury severity was applied to generate freeze injury distribution map. The results show that assessment of survival rate grade can be utilized to not only monitor the freeze injury area and distribution of winter wheat, but also provide a scientific basis for measuring after-injury field management.

Volume: 18, Issue: 8

Extraction of Planting Areas of Major Crops and Crop Growth Monitoring in Northeast China

by Qing Huang, Qingbo Zhou, Wenbin Wu, Limin Wang, Li Zhang
Abstract

This paper presents a method used in China Agriculture Remote Sensing Monitoring System (CHARMS) for automatically identifying crop planting areas and monitoring crop growth conditions at a large scale based on time-series of MODIS NDVI Datasets. In doing that, the characteristics of NDVI time series of spring wheat, spring corn, soybean and rice in Northeastern China were firstly analyzed to determine the threshold values used for extracting different crops. Then using these thresholds, extraction models for above-mentioned four major crops were established and applied to obtain the spatial distribution of these four crops in Northeastern China in 2009. In comparison with the average statistic data of several years, the total extraction accuracy is over 87, which suggests its feasibility to extract planting areas of different crops at a large scale using MODIS data. Based on the extracted crop planting areas, the same MODIS NDVI time series data were used to monitor crop growth conditions in 2009 and compared with the average crop growth of last five years. The crop growth conditions were categorized into three classes, better than usual, usual and worse than usual. The results showed that crop growth conditions in Northeastern China varied over both spatial and temporal scales.

Volume: 18, Issue: 8

Semi-Automatic Road Tracking using Parallel Angular Texture Signature

by Xiangguo Lin, Jing Shen, Yong Liang
Abstract

Road tracking is a promising technique to increase the efficiency of road mapping. In this paper, asemi-automatic road tracker, Parallel Angular Texture Signature (PATS), is presented. The tracker is object-oriented in some sense, because it makes best use of the texture signature of road primitives on high-resolution remotely sensed imagery. Our tracker uses parabolas to model the road trajectory and predicts the position of next road centerline point. It employs PATS to get the moving direction of current road centerline point, and it will move on one predefined step along the moving direction to reach a new position, and then it uses curvature change to verify the newly added road point. Moreover, we also build compactness of Angular Texture Signature polygon to check whether the PATS is suitable for subsequent tracking. Repeat the above steps until the whole task is finished. Extensive experiments demonstrate that the proposed tracker is capable of efficiently extracting most of main roads from medium and low resolution imagery, and reliably and robustly extracting most of ribbon roads from high resolution SAR and optical imagery.

Volume: 18, Issue: 8

Analysis of Rice Growth Using Multi-Temporal Radarsat-2 Quad-Pol Sar Images

by Fan Wu, Bo Zhang, Hong Zhang, Chao Wang, Yixian Tang
Abstract

Three time series of quad-polarization RADARSAT-2 images have been acquired from transplanting to harvesting of rice crop. Ground truth data such as rice height and biomass etc. were measured during acquisition of RADARSAT-2 data in Hainan province, southern China. Among different observations, dry biomass and fresh biomass of rice crop have shown a temporal signature with a clear correlation with backscattering coefficient in HVVH polarization, whose correlation parameters are mainly larger than 0.8, while HH and VV polarizations show unfavorable correlation with crop dryfresh biomass, whose correlation parameters are lower than 0.6. Variations of scatter mechanism of rice crop from transplantation to maturity have been investigated based on Pauli decomposition and HalphaA-wishart classification. For Pauli decomposition, Pauli A component is the main backscatter of rice crop, which valued between 0.2 and 0.6 in the whole rice growth stage. Pauli B component ranks second, valued from 0.1 to 0.3. Pauli C component is low from 0.02 to 0.1. However, Pauli C component of rice crop shows the best correlation with days after transplantation. The experiment and analysis results show the quad-polarization RADARSAT-2 SAR data have great potential for monitoring rice growth. Furthermore, when rice crops are in reproductive or ripening stage, the SAR data can obtain good results for rice mapping. With the information of biomass and mapping area of rice, rice yield estimation can be made.

Volume: 18, Issue: 8

A Special Issue of Intelligent Automation and Soft Computing

by Daoliang Li, Simon Yang
Abstract

Volume: 18, Issue: 8

Application Of FAHP for Stone Arch Bridge Strengthening Scheme Optimization

by Jianting Zhou, Lu Liu, Ping Lu
Abstract

In order to establish the appraisal target system, the principles and methods of the Fuzzy Analytic Hierarchy Process (FAHP) were described in aspects of structuring the comparative judgment matrix described by interval numbers, solving both the interval weight vector and the comprehensive weights of the general target detemuned by various layers, and selecting the strengthening proposal out of the various schemes. By an actual project of strengthening stone arch bridge, application of the principles and methods of the FAHP to detemune the optimal proposals was introduced. From the paper, it is visual to know how to select the optimal proposal from the various schemes by mathematical principle. It will promote the development of the systemization, standardization and theorization of bridge strengthening.

Volume: 18, Issue: 7

Classification of Electronic Nose Data in Wound Infection Detection Based on PSO-SVM Combined with Wavelet Transform

by Qinghua He, Jia Yan, Yue Shen, Yutian Bi, Guanghan Ye, Fengchun Tian, Zhengguo Wang
Abstract

In this paper, a new method for classifying electronic nose data in rats wound infection detection based on support vector machine (SVM) and wavelet analysis was developed. Signals of the sensors were decomposed using wavelet analysis for feature extraction and a PSO-SVM classifier was developed for pattern recognition. The sensor array was optimized and model parameters were selected to achieve the maximum classification accuracy of SVM. Particle swarm optimization (PSO) was used to achieve optimization of the sensor array and the SVM model parameters. A classification rate of 97.5 was achieved by the proposed method for data discrimination. Compared with the methods of radial basis function (RBF) neural network classifier with maximum or wavelet coefficients feature and SVM without sensor array optimization, this method gave better performance on classification rate and time consumption in rats wound infection data recognition.

Volume: 18, Issue: 7

Evaluation of Covariance Matrix in Distributed MIMO Radar

by Jurong Hu, Ning Cao, Hao Lu, Fei Wang
Abstract

It has been shown that the detection performance can be improved by exploiting the covariance matrix of the channel vector in the distributed MIMO radar. In this paper, we firstly introduce a system model in which the target is modeled as the sum of a finite number of independent scatterers without limitation on transmitter-receiver configurations. Using this system model, we make a detailed analysis of the covariance matrix under various target RCS models. The formula to calculate the elements of covariance matrix for the MIMO radar with arbitrary transmitter-target-receiver collocations is derived. We also propose an approach to evaluate correlations between channels based on the local maximum potential detection and the received signals. This approach makes it possible to predict the detection performance for the actual distributed MIMO radar. The theoretical probability of detection for this approach is developed and validated by simulation.

Volume: 18, Issue: 7

MIMO Radar with Arbitrarily Distributed Array-Target Configuration in the Presence of Possible Non-Gaussian Clutter-Plus-Noise

by Ning Cao, Weiwei Liu, Jurong Hu, Hao Lu
Abstract

For amultiple-input multiple-output (MIMO) radar system adopting the Neyman-Pearson (NP) criterion, a detection algorithm for MIMO radar with arbitrarily correlated observation channels in the presence of possible Non-Gaussian clutter-plusnoise is analyzed. Simulation results show that both the correlation of the observation channels and the correlation of the clutter-plus-noise will reduce the detection performance. The algorithm can be utilized to compute and analyze the detection performance of a MIMO radar system for any given transmitterreceiver geometry and varying levels of correlation in the target.

Volume: 18, Issue: 7

Password Cracking Based On Rainbow Tables With A Dynamically Coarse Grain Reconfigurable Architecture

by Chao Li, Xiwei Zhang
Abstract

Rainbow attack is a very efficient attack which uses rainbow tables to offer an almost optimal time-memory tradeoff in the process of recovering the plaintext password from ciphertext hash. In this paper, we proposed a new method which can crack DES password quickly with less power consumption on a coarse grain reconfigurable architecture (CGRA) named reconfigurable encrypt-decrypt system (REEDS). To the best of our knowledge, this is the first try for password cracking based on “Rainbow Tables” under a dynamically CGRA platform presented in the literature. High parallel computing capability and good flexibility make the platform an excellent candidate to process multimedia application, encryption and decryption algorithm etc. In this paper, the whole work of DES password cracking based on rainbow tables is split into several sub tasks, which are mapped onto REEDS respectively and executed in parallel using pipeline approach. Experimental results show that the proposed system with 200 MHz clock rate can fulfill the DES password cracking task with great performance, which is up to 2000 times faster than the corresponding software approach. Moreover, it only consumes 194mW power which is less than the FPGA-based system or the GPU-based system.

Volume: 18, Issue: 7

Multi-Scale Model Of Dam Safety Condition Monitoring Based On Dynamic Bayesian Networks

by Fang Weihua, Xu Lanyu
Abstract

In order to monitor dam safety condition better, a Dynamic Bayesian Networks (DBN) model is developed to overcome the shortcomings of the ordinary monitoring methods in this paper. Ordinary methods include comprehensive assessment methods and numerical simulation methods. Comprehensive assessment methods have shortcomings such as weight detemunation, scale difference, variables correlation, etc. In addition, comprehensive assessment methods cannot describe the multi-scale characteristics of monitoring data and dynamic property of large dam. Numerical simulation methods need complex mathematical theory, mechanics methodologies and high performance computer. DBN is a novel model with the consideration of correlations, delay and multi-scale characteristics of the variables such as deformation, seepage, stress and water load and temperature loads, as well as duration at every state. And the new model is also a simpler method with less experience, computational complexity and fewer experiments comparing with numerical simulation. Case study shows that better effect has been achieved in dam safety condition monitoring because the model can take the specific properties of dam into account. The main novel technical contributions of this paper are as follows: applying DBN to establish amulti-scale dynamic model on large engineering conditions monitoring at the first time; providing a new concept of latent durational-state time and analyzing its meaning of dam in the physical sense; using mechanics simulation analysis to verify the new model.

Volume: 18, Issue: 7

A Tuning Method Of Passive Filter Based On Variable Reactors

by H. P. Lin, J. Chen, W. Wu, Y. X. Yuan, H. B. Zhu
Abstract

The principles of the traditional passive filter for suppressing harmonics are qualitatively analyzed in this study. The auto-tuning principles based on TCR are also analyzed. According to the deficiencies of these filters, a passive filter based on variable reactors is presented and the tuning method is investigated principally. This filter utilizes the anti-parallel thyristor controlled reactor as a variable reactor. The secondary equivalent inductance of the variable reactor is adjusted by the triggering angle of the thyristor. Then the equivalent inductance of the variable reactor in the filter branch is indirectly changed, to make the filter resonant after the capacitance is varied, so as to achieve the auto-tuning of this passive filter. Based on the auto-tuning principles of the filter, the tuning parameters of the filter are deduced emphatically. Finally, the tuning steps are summarized.

Volume: 18, Issue: 7

An Indoor Mobile Visual Localization Algorithm Based On Harris-Sift

by Huiqing Zhang, Chen Xu, Xuejin Gao, Luguang Cao
Abstract

Through the installation of a monocular vision sensor on a target and shooting sequence images of the floor, combined with the characteristics of the sequence images, an indoor mobile visual localization algorithm based on Hams-SIFT is proposed. The algorithm is first to establish first-order DOG scale space of sequence images of the floor, to extract feature points in each layer images of DOG scale space by using Hams operator, then to select scale-invariant feature points to calculate each feature point descriptor and match, and thus to depict the trajectory of the movement of the target to achieve positioning. Using several different indoor sequence images under the different environment respectively, the algorithm is verified, positioning experimental results show the effectiveness of the algorithm.

Volume: 18, Issue: 7

Hyperspectral Imaging Target Detection Based on Improved Kernel Principal Component Analysis

by Fengchen Huang, Feng Xu, Jiming Hu
Abstract

The kernel principal component analysis (KPCA) algorithm has been extensively used in target detection and classification for hyperspectral imaging. Kernel parameters are a key component of KPCA, but no optimal method for selecting appropriate parameters has been proposed. We study the largest eigenvalue and sum of the eigenvalues of characteristic equation of kernel matrix, and then put forward a proposal for parameter selection. Experiments show that the proposal enables best parameter selection.

Volume: 18, Issue: 7

An Auto-Sorting Algorithm For Image Sequences With Different Sizes In Image Stitching

by Xuewen Wu, Mingxing Cai
Abstract

An auto-sorting algorithm for image sequences with different sizes is presented in this paper. The algorithm is suitable for sorting image sequences, such as images in matrix form. Zero padding is used in the stern of image matrices for images with different sizes in the image sequence. As a result, all images adopt the same size. Based on phase correlation, the position relationship between two images is detemuned. The approach used is extended, and anauto-sorting algorithm is applied on image sequences with different sizes. Experimental results show that the algorithm can be implemented effectively without manual intervention. It can overcome the size limitation of images and can be applied in image stitching.

Volume: 18, Issue: 7

Study on 3D Simulation Optimization for Digital Basin of Large-Scale Landform

by Ou Jian, Zhang Xingnan, Yang Biao, Wu Wei
Abstract

For the need of digital basin 3D simulation, the paper proposes a visualization optimization method for large-scale landform and flow pattern. Based on call of landform data partitioning organization and dynamic processing of visual regional data, for the present situation of commonly using ADO(ActiveX Data Objects) developing interface to realize the operation for database, OCI (Oracle Call Interface) programming is adopted in order to improve exchange efficiency with database, which can realize call of database partitioning data, improve 3D space database loading efficiency and realize quick plotting of landform model in flight view. The water body flow effect is represented by means of texture movement mode on basis of flow field visualization. It is indicated by results that optimization strategy and method improves graph plotting efficiency, intensifies visualization effect, effectively solves the contradiction between complicated landform model and limited plotting capability of graphic display hardware and realizes smooth running of digital basin flight view system of large-scale landform on ordinary PC.

Volume: 18, Issue: 7

Target Detection In Sar Images Based On Sub-Aperture Coherence And Phase Congruency

by Zhen Zhang, Xin Wang, Lizhong Xu
Abstract

For target detection in SAR images, the sub-aperture coherence analysis is employed widely by calculating coefficient of coherence to express the differences of the target signals in sub-aperture images. However the calculation of coherence coefficients is non-adaptive so that when the amplitude difference of coherence coefficient between a target and background is small target detection probability is low. In this paper, with the region growing algorithm, we improve the adaptability of coherence coefficient. We introduce phase congruency algorithm based on sub-aperture coherent method to realize target detection, which also uses the differences of texture feature in sub-aperture images. Experimental results demonstrate that detection probability is as high as 75.8 under the false alarm probability of 0. The largest area under an ROC curve is 0.9175.

Volume: 18, Issue: 7

Short-Term Travel Time Predication for Urban Router

by Wenting Liu, Ruikai Pan, Xiaoqing Guo, Xu Feng
Abstract

With the rapid development of various technologies and equipment of location based services, predicting the motion laws of moving objects based on traffic networks has become a hot research field. Advanced travel information systems and route guidance systems are an important part of the traffic operation and management. As a key parameter, travel time is the most important index to identify traffic state and the most direct evidence for travellers to make travel decisions. For the issue of the low accuracy of the travel time prediction, the paper introduces a method for travel time predication with multi-source data fusion. The method combines the technique of travel time prediction with pattern matching to create traffic pattern rules with multi-source data fusion. Simulation experiments for vehicle navigation systems with multi-source data fusion prove that the travel time predication method based on multi-source data fixsion can effectively lift the accuracy of travel time prediction.

Volume: 18, Issue: 7

Improved Shuffled Frog Leaping Algorithm And Its Application In Node Localization Of Wireless Sensor Network

by Fan TangHuai, Lü Li, Zhao Jia
Abstract

To improve the unreasonable distribution of sensors’ random deployment and increase network coverage rate, an optimization method of wireless sensor networks coverage based on improved shuffled frog leaping algorithm (ISFLA) was proposed in this paper. During the process of updating the frog, a novel learning strategy is introduced, in which the poor frog learns not only from the best frog of its own ethnic group, but also from the best frog of the population. In addition, a diversity factor is considered in updating the frog. Experimental results show that the algorithm yields better optimization coverage results.

Volume: 18, Issue: 7

Delay-Bounded Data Forwarding in Low-Duty-Cycle Sensor Networks

by Yingchi Mao, Ambassa Yves, Feng Xu
Abstract

In many sensor network applications, sink node needs to actively communicate with other sensor nodes in order to perform data forwarding operations. For those applications, there is usually adelay-bounded associated with them and require the messages sent to be received within a designated time bound. In energy harvesting sensor networks, limited energy from environment necessitates sensor nodes to operate at alow-duty-cycle. Sensor nodes work active briefly and stay asleep most of time. Such low-duty-cycle operation leads to communication delays in comparison with the always-active networks. In this paper, we address the data forwarding problems in an energy harvesting sensor network where energy efficiency and data freshness need to be balanced. To solve this problem, we propose autility-based delay bounded scheme for data forwarding MaxOpUtility-based scheme. MaxOpUtility scheme offers the ability to increase reliability through relay nodes selection as well as to ensure the timeliness. In addition, we show how nodes in the network can cooperatively bound end-to-end delay with maximum utility. Extensive simulations are conducted to verify the effectiveness of our approach compared with the optimal one. Meanwhile, we demonstrate that our solution is able to effectively provide delay bounded guarantee in energy harvesting networks.

Volume: 18, Issue: 7

Real-Time Multicast With Network Coding In Mobile Ad-Hoc Networks

by Guoping Tan, Xinyang Ni, Xiuquan Liu, Chuanyu Qu, Luyao Tang
Abstract

In this paper, a Network Coding based Real-time Multicast (NCRM) protocol is proposed for real-time multicast services in Mobile Ad-hoc Networks (MANET). Through reducing the forwarding times for data packets in MANET, NCRM can not only decrease the energy consumption but also improve the throughput performance. To satisfy the maximum end-to-end delay deadline requirements for real-time services, NCRM adopts a mechanism with strict delay constraints. Simulation results show that, in those real-time multicast scenarios with many receivers andor high node motilities, NCRM outperforms those traditional protocols such as PUMA and MAODV in terms of transmission reliability and energy consumption significantly.

Volume: 18, Issue: 7

Guest Editorial

by Xiaofang Li, Simon Yang
Abstract

Volume: 18, Issue: 7

Polygonal Approximation Based on Multi-Objective Optimization

by Xiaojing Xuan, Fangmin Dong, Bangjun Lei, Dong Ren, Qing Guo
Abstract

In order to solve multiple constraints in the existing polygonal approximation algorithms of digital curves, a new algorithm is proposed in this article. Each control constraint is taken as the optimization objective respectively and the idea of multi-objective optimization is also applied. Vertex positions of the intermediate approximation polygon are represented by a binary sequence, and Hamming distance that often used in communication encoding and vertex position average of each polygonal are introduced to get the position average of the intermediate approximation polygon. Those make the selection of the intermediate global and local optimum more reasonable when updating particles. Experimental show that the proposed algorithm can get better effective results.

Volume: 18, Issue: 6

Abscission Point Extraction for Ripe Tomato Harvesting Robots

by Lvwen Huang, Simon Yang, Dongjian He
Abstract

Due to the randomicity of the natural growth of tomatoes in greenhouses and different storage days for market needs, it is difficult to fmd appropriate methodologies for certain ripe-tomato-harvesting robot systems. This paper proposes a novel approach for recognizing ripe tomatoes from the natural background in greenhouses and extracting abscission points after color segmentation for autonomous robot systems. The ripe tomatoes are recognized and segmented using Lab color space method from complex tomato plants containing clutter and occlusion in tomato greenhouses. The bi-level partition fiizzy logic entropy, which could discriminate the object and the background in grayscale images, is improved to segment the ripe tomatoes. The improved exhausted search method based on the maximum value of the histogram is proposed to increase the precision of segmentation threshold and the efficiency of searching. The mathematical morphology operations are used to eliminate binary image noises after segmentation. Finally the abscission point of cluster of tomatoes is obtained for the robot to pickup tomatoes precisely. The proposed approach is validated on tomato images taken in natural greenhouses. Experimental results show that the proposed method is capable of obtaining the abscission point of ripe tomatoes effectively and precisely.

Volume: 18, Issue: 6

Schnorr Ring Signature Scheme with Designated Revocability

by Xin Lv, Zhijian Wang, Feng Qian, Feng Xu
Abstract

Ring signatures enable a user to sign a message so that a ring of possible signers is identified, without revealing exactly which member of that ring actually generated the signature. In some situations, however, an actual signer may possibly want to expose himself, for instance, if doing so, he will acquire an enormous benefit. In this paper, we present a Schnorr ring signature scheme with designated revocability, providing a confirmation procedure, in which the real signer is able to convince a designated party that he is the one who generates the signature. The confirming proof is non-transferable, which only can be triggered by the signer. Meanwhile, the scheme satisfies unconditional anonymity, and has been proven to be existentially unforgeable under adaptive-chosen message attack in the random oracle.

Volume: 18, Issue: 6

The Whole Tendency of the Degradation of Concrete Bridges

by Sun Ma, Jianting Zhou, Jun Song
Abstract

Analyze the major tendency of degradation of concrete bridges through the study of the main causes of their degradation firstly; then analyze the process fold line of their degradation via the aspect of reliability; thirdly, study the law and the characteristics of the degradation of bridges from structural elements; eventually got the entire trend of the degradation of concrete bridges.

Volume: 18, Issue: 6

The Study on Designing of Hinged Plates Model Test

by He Jiang, Jianting Zhou
Abstract

The paper will research on the cracking mechanism of hinged plates, it designed on the condition of different cracking length the extension of cracks and cracking mechanism in the longitudinal side panel and middle panel and put forward the corresponding reinforcement scheme and test plan.

Volume: 18, Issue: 6

The Predictable Time Study Based on the Structure of the Kolmogorov Entropy Structure Condition

by Jianxi Yang, Jianting Zhou, Ruiqiang Yue, Lei Wu
Abstract

Due to long-term unpredictability of nonlinear system, based on the analysis of the bridge structure nonlinear characteristics and chaotic time series kohnogorov entropy extraction algorithm, calculate kohnogorov entropy of responding information time series to obtain the most predictable points of complex structure system state, so providing the theoretical support for predictable confidence interval of structural state, at the same time laid a foundation for further using nonlinear time series to analysis the bridge state.

Volume: 18, Issue: 6

The Discussion on Deflected Pre-Stressed Tendons and Diagonal Cracks of Webs in PSC Box Girders

by Zhou Jianting, Liu Fangping, Jun Song, Jianxi Yang
Abstract

In order to find the relationship between deflected pre-stressed tendons and diagonal cracks of webs and provide basis to design and construction of bridges, a characteristics section model of a long span pre-stressed concrete continuous box girder, which is now under construction in Chongqing is analyzed by using nonlinear finite element program Midas-FEA. A numerical simulation analysis to the tensile stress in the webs was down based on the actual construction situation. As the research shows: 1, Stress concentration exists at the point where pre-stressed tendons are anchored, and the stress value was higher in anchorage area, which accord with the Saint-Venant Principle.2, The main tensile stress of webs in the anchorage area is lower than 1.04MPa, which did not surpass the standard 2.74MPa of the concrete tensile stress of C55, as a result, webs would not craze from finite element analysis. 3, Cracks brought by other normal or abnormal reasons existed in the process of casting the cantilever concrete girder could get released for some extent by timely tensioning transverse or vertical presstressed steel.

Volume: 18, Issue: 6

Security Identification Technologies for Stone Arch Bridges at Service

by Haijun Wu, Yue Chen, Lu Liu
Abstract

The typical diseases of stone arch bridge in the Three Gorges Reservoir area are snmmarized through the investigation, and four kinds of typical diseases of stone arch bridges are investigated and evaluated by spatial fmite element software. Based on these investigations, the limited value index of the security and the identification technology of the four typical diseases of the bridge are obtained, which is of certain referential significance for the rank-and-file technical personnel to evaluate the security of stone arch bridge effectively and expediently.

Volume: 18, Issue: 6

Safety Evaluation of Long-Range Monitoring of Bridge Health based on Internal Force Envelope Theory

by Jianxi Yang, Shaolin Fang, Juan Yang, Bing Luo
Abstract

The safety evaluation model, the resistant internal force diagram and real-time internal force diagram based on the real-time monitoring of a bridge as well as the application are expounded. The internal force envelope method in bridge design is app lied to the remote monitoring of bridge health, and a kind of safety evaluation method based on internal force envelope theory is proposed. Comparing with the current method, this method solves the problems that the measuring points are limited and cant reflect the state of unmeasured location and the overall bridge. Therefore, this method is advantageous to be more intuitive and more objective, at the same time, it does better in overcoming environmental and operational conditions and has a promising application potential.

Volume: 18, Issue: 6

New Support Vector Machine for Imbalance Data Classification

by Fengqing Han, Ming Lei, Wenjuan Zhao, Jianxi Yang
Abstract

Support vector machine has a better classification and prediction performance on balance data classification, but has a poor performance for imbalance data. In this paper, the reason of the poor results which were produced by imbalance data is explained. And a new approach is proposed to solve the imbalance data classification. By this method the imbalance data problem is converted to several independent classifications with balance data and can be trained in parallel. The experiments show that new method has a best effect in some classifying strategy.

Volume: 18, Issue: 6

A New Strategy for Structural Health Monitoring based on Structural Destroyed Mode and Data Correlation

by Liu SiMeng, Zhang Liang-Liang, Zhou JianTing
Abstract

This paper provides anew strategy for study of Structural Health Monitoring, and discusses establishment of structural safety monitoring system based on the structural destroyed modes and data correlation. Two concepts, structural destroyed mode and data correlation, are given and discussed. The structural destroyed mode refers to the pattern of certain structural state or situation that some kind of failure occurred. The study on data correlation focuses on the relations existed between different sensors and locations. A structural safety monitoring system for a simply supported beam including several damage indices is built, and the numerical experiment shows it works effectively.

Volume: 18, Issue: 6

The Selection of Reference Anchor Nodes and Benchmark Anchor Node in the Localization Algorithm of Wireless Sensor Network

by Xiaohui Chen, Jinpeng Chen, Jing He, Chen Chen
Abstract

This paper analyzes the source of the localization error in the least square localization (LSL) algorithm, and introduces the thought of distance clustering to select the reference anchor nodes, and gives a new principle to select the benchmark anchor node. Based on the above analysis, this paper proposes an improved least square localization algorithm based on the distance clustering and the selection of benchmark anchor node (LSL DCRB). Although the improved localization algorithm decreases the number of anchor nodes, the simulation results indicate that the improved localization algorithm can improve the localization precision effectively.

Volume: 18, Issue: 6

An Improved Mean-Shift Tracking Algorithm using PSO-Based Adaptive Feature Selection

by Yin Hongpeng, Yang Jin, Chai Yi, Simon Yang
Abstract

Traditional mean-shift tracking algorithm use pre-defined tracking feature. Its trends to lead tracking failure in the complex background scenes and fast-changing background scenes. In this paper, an improved mean-shift tracking algorithm using Particle swarm optimization (PSO) based adaptive feature selection is presented to improve the tracking performance. We assume that the features with best discrimination between object and background are also the best for tracking the object. A two-class variance ratio is employed to measure the discrimination. PSO algorithm is used to optimize the different feature combination to adaptively generate the best tracking feature. Experimental results show that the proposed method can improve the performance of mean-shift tracker significantly in the complex and fast-changing background scenes.

Volume: 18, Issue: 6

Design of Wireless Sensor Networks for Monitoring at Construction Sites

by Xia Wei, Yan Xijun, Wei Xiaodong
Abstract

An integrated hardware and software system for a three dimensional wireless sensor networks is designed and developed for construction monitoring system Compared to traditional monitoring system, the construction monitoring system based on wireless sensor networks has many advantages. In this paper, we discuss the design and evaluation of the three dimensional wireless sensor networks for construction monitoring system. We proposed the ensemble structure of the system We proposed the topological structure of the three dimensional wireless sensor networks. The sensor node is designed, developed, and calibrated to meet the requirements for construction monitoring wireless sensor networks. Software components have been implemented within the TinyOS operating system to provide a flexible software platform and scalable performance for construction monitoring applications. We also proposed the deployment of three dimensional wireless sensor networks for construction monitoring system.

Volume: 18, Issue: 6

Overview of Intelligent Railway Transportation Systems in China

by Chaozhe Jiang, Jin Yang, Jixue Yuan, Fang Xu
Abstract

As the intelligent railway is the trend of current railway development, the paper aims to show an overview of the Railway Intelligent Transportation System (RTIS) in China. Firstly, the current transportation situation in China is given and then some basic concepts of RTIS are talked about as well. Secondly, the necessity of RTIS is analyzed to show its strategic position. Then, the overall architecture of RTIS is introduced here to make a clear picture of RTIS and how it works. Finally, apart from key technologies in RTIS, its sustainable strategies are mentioned to show the future development of RTIS.

Volume: 18, Issue: 6

A Secure Threshold Proxy Signature Scheme

by Feng Xu, Wenhuan Zhou, Xuan Liu
Abstract

Aiming at the security and performance problems existed in most threshold proxy signature (TPS) schemes, this paper presents a new TPS scheme based on bilinear pairings. In this work, the security level of the proposed TPS scheme has been significantly enhanced by using improved short signatures and interactive secret-sharing. Our analysis indicates that the proposed scheme achieves the six accepted security properties that are required for a strong-secure TPS scheme. It can also resist a severe attack against TPS, i.e., original signer changing attack.

Volume: 18, Issue: 6

Guest Editorial

by Jianting Zhou, Jianxi Yang, Simon Yang
Abstract

Volume: 18, Issue: 6

Bilinear Pairings-Based Threshold Identity Authentication Scheme For Ad Hoc Network

by Feng Xu, Yuqi Zhu, Hongxu Ma, Bin Cai
Abstract

Aiming at specific security threats in ad hoc network, this paper presents a bilinear pairings-based threshold identity authentication scheme without the trusted center. In this work, the security of certificate has been significantly enhanced by using improved GDH signature and interactive secret-sharing, that is, any attacker cannot forge a valid certificate for the unfrosted nodes. The proposed scheme can effectively reduce the storage and computation amounts of each node, and some security problems (e.g., passive attack, man-in-the-middle attack and so on) can also be solved. Compared to the congener schemes, ours is much more efficient in the process of certificate generating.

Volume: 18, Issue: 5

Research On The Performance Of The Ultrasonic Measurement System Of The Tree Canopy Volume

by Kaiqun Hu, Zetian Fu
Abstract

Variable-rate spraying technology can change the application rate by the target information. And the tree canopy volume was a very important target information. An ultrasonic measurement method of canopy volume was presented, and an ultrasonic measurement system of canopy volume was developed.The average of Wheaton and Albrigo manual measurement values was defined as the manual measurement values in this paper.150 peach trees having different canopy volumes were selected to as the experimental objects. Experiment results indicated that the ultrasonic measurement system had high stability (R20.968, Std. Error of the Estimate was 0.061719), the manual measurement method had high stability(R20.973, Std. Error of the Estimate was 0.060214), and the manual measurement values and the ultrasonic measurement values were in a good agreement(R20.968, Std. Error of the Estimate was 0.065314), that is the ultrasonic measurement system had high precision.

Volume: 18, Issue: 5

Machine Vision Based Classification Of Tobacco Leaves For Automatic Harvesting

by D. S. Guru, P. B. Mallikarjuna, S. Manjunath, M. M. Shenoi
Abstract

A machine vision based approach for classification of tobacco leaves for automatic harvesting in a complex agricultural environment is proposed in this paper. The CIELAB color space model is used to segment the leaf from the background. The segmented leaves are classified into three classes viz., ripe, unripe, and over-ripe. Models based on various textural features such as GLTP (Gray Level Local Texture Patterns), LBP (Local Binary Pattern) and LBPV (Local Binary Pattern Variance) are studied in this work. The K-Nearest Neighbor (K-NN) classifier based on the Euclidean distance measure has been used for classification. Experiment is conducted on our own dataset consisting of 244 images of tobacco leaves captured in both sunny and cloudy lighting conditions in a real tobacco field. The experimental results show that GLTP model achieves significant improvement in classification accuracy over traditional LBP and LBPV.

Volume: 18, Issue: 5

Prototype of an Aquacultural Information System Based on Internet of Things E-Nose

by Daokun Ma, Qisheng Ding, Zhenbo Li, Daoliang Li, Yaoguang Wei
Abstract

Aquaculture is the fastest growing food-producing sector in the World, especially, in P. R. China. In the ongoing process of Internet growth, a new development is on its way, namely the evolution from a network of interconnected computers to a network of interconnected objects that is called the Internet of Things (IoT). Constructing an affordable, easy-to-use, aquaculture information system based on IoT is the future trend for modern aquaculture. The prototype IoT of aquaculture information system is developed with following functions: (1) Sense in real time the water quality in aquaculture ponds and send water quality data in time to remote IoT Platform with wireless sensor network and mobile internet; (2) Forecast the change trend of water quality based real-time data and operating suggestion will be created for special aquaculture application automatically; (3) Real-time and efficient exchange with special users and common users with WEB, WAP and SMS; (4) Some control devices may be operated automatically with authorization. Five control nodes, twelve water quality sensor nodes and a wireless weather sensor node are deployed in seven aquaculture ponds belongs to five farmers, in Yixing Peng-yao eco-agricultural demonstration farm located in Wuxi, Jiangsu province, P. R. China. Each farmer may access web pages of aquaculture service platform to monitor the variation of water quality of their ponds, and control their own aerators via internet or cell phone. At the same time, real time water quality and weather information will be sent to related farmers as the public service. The real time monitoring water quality and weather data are reliable, aquaculture information service has been accepted by farmers and local government, which revealed that the prototype IoT of aquaculture information system is meaningful.

Volume: 18, Issue: 5

An Improved Gray Model For Aquaculture Water Quality Prediction

by Zhenbo Li, Yu Jiang, Jun Yue, Lifeng Zhang, Daoliang Li
Abstract

Water quality management is one of the key problems for intensive aquaculture. In order to predict the aquaculture water quality accurately, the paper proposes an Improved Gray Model for aquaculture water quality prediction. The Gray Model is improved and combined with the BP Neural Networks to implement the prediction. The Improved Gray Model serves as the main body for prediction and BP Neural Networks are used to judge the prediction results. Experiments using the temperature and DO data collected from the aquatic factories of Yixing, Dongying in China proved that the prediction accuracy improved 15 than the traditional prediction method.

Volume: 18, Issue: 5

A Monitoring System Based On Environment Indicator Technology For Land Rehabilitation In Coal Mine Dump Areas

by Yingyi Chen, Yuan Wang, Daoliang Li, Xing Wu
Abstract

Minesite rehabilitation needs to be monitored with easily measured indicators so that trends can be plotted and assessed over time to address closure criteria. According to the monitoring content, this paper adopts wireless sensor networks, network, multimedia and geographic information system technology emphatically to carry out the key research on the integration and development hardware and software of the system. The system establishes the real-time monitoring device of mine land soil, climate and ecology. Meanwhile, wireless network sensor data, images, video and other multimedia data processing and analysis technology are applied for mine vegetation restoration monitoring. Finally, a system for formulating indicators to describe biophysical, socio-cultural and economic processes for minesite rehabilitation is developed, which provides the basic for the coal enterprise environmental data acquisition and monitoring, and provides information mediums for land reclamation engineering supervision of the coal enterprise.

Volume: 18, Issue: 5

Monitoring Winter Wheat Maturity By Hyperspectral Vegetation Indices

by Qian Wang, Cunjun Li, Jihua Wang, Yuanfang Huang, Xiaoyu Song, Wenjiang Huang
Abstract

It is very important to harvest wheat in optimum time which greatly affects grain quality, mainly referred to as protein content in the research. Because either too early harvest shortens grain-filling process or too late harvest leads to yield losses and poor quality caused by high grain respiration rate in dry and hot wind weather and sprouting in rainy weather. Research was conducted during 2007–2008 to determine if vegetation indices could be used as indicators of winter wheat maturation. The cultivar Jingdong 12 was planted under four nitrogen treatments, and reflectance and agronomy parameters were measured on five different harvest dates. In maturation process, increasing grain protein content ranged from 12.2 to 16.5, declining ear water content ranged from 36 to 60, chlorophyll, carotenoids content of both leaf and ear decreased, and ratio of carotenoids to chlorophyll increased on the whole. Seven maturation monitoring models were established by corresponding vegetation indices, which were chosen by comparing correlation coefficients between vegetation indices and agronomy parameters. Compared with the other models, the eaz water content model was chosen as the best one due to the least average absolute relative error and high prediction accuracy in validation, with 0.03, 0.04 in cross test and 0.98, 0.98 in training samplings test. The results suggest that hyperspectral vegetation indices could potentially aid in predicting winter wheat maturation.

Volume: 18, Issue: 5

Design Of High Energy-Efficiency Air Conditioner With Asymmetric Fin Condenser Tube

by Haiyan Zhu, Wenqin Cao, Jinliang Nie
Abstract

This paper presents the detailed design and analysis of an asymmetric fm tube condenser. And the heat transfer performance of the asymmetric fin tube condenser is simulated with the fluent software. Based on the simulation results, an optimal model of the asymmetric-finned tube condenser is designed. The actual model is experimentally studied on air conditioners and contrasted with the symmetric-finned tube condenser under the same conditions. The experimental results show that the overall power is decreased 1.7, the refrigerant capacity is increased as 1 and the energy efficiency ratio is enhanced 2.6. The good actual effects provided a new way to obtain high energy-efficient of air conditioner.

Volume: 18, Issue: 5

Nondestructive Evaluation of Welding Crack Defects in Structural Component Of Track Crane Using Acoustic Emission Technique

by Wei Wang, Hongxing Wei, Yu Zhen, Yantao Dou, Huiming Heng
Abstract

On-line defects detection of structural component of track crane such as crane boom, outriggers, turntable and vehicle frame, is a difficult problem of nondestructive testing (NDT). In the present study, the usefulness of acoustic emission (AE) measurements for the detection of welding crack defects in the specimen made of HG70 steel, which is widely used for manufacturing structural component of track crane, has been investigated. Firstly, three-point bending test on the standard specimen made of HG70 steel is conducted. Then the analysis of AE source location and AE source characteristics are introduced respectively. As the result, the rudimentary location analysis can be attained using the linear location method. For AE source characteristics, procedural map of amplitude, RMS or energy rate, energy accumulation map, in connection with the corresponding conditional filter means, are available analysis methods to mirror welding crack defect. We fmd that the maximum of the amplitude, energy rate and RMS appear approximately at the same time, and the time is the point at which energy accumulation has a remarkable jump as shown in the energy accumulation map. In the end, we deduce that the reason why there is such a sudden energy leap is because of the activities of welding crack, and the multistage energy release also because of the step by step propagation of welding crack.

Volume: 18, Issue: 5

A Rapid Measurement System For Transmission Efficiency Of Automotive Powertrain

by Ai-Guo Ouyang, Cha-Gen Luo, Xiao-Ling Dong
Abstract

The current annual inspection of automotives is non-disassembly detection. It can merely measure the power output of automotive chassis, and get inaccurate results. There are few cumbersome and low precision methods for measuring the power output of the engine, and it is difficult to obtain transmission efficiency of automotive powertrain. Anew measuring system is developed by adding reverse dragging system on the basis of the present chassis dynamometer. The system can be used for swift testing of power output of the engine at a particular speed and efficient calculation of the transmission efficiency of automotive powertrain without disassembly. Calibration test is experimented on the force cell. The results show that the proposed system is theoretically correct and practically usable.

Volume: 18, Issue: 5

A Novel Non-Synchronous Sampling Method for HarmonicInterharmonic Measurement sn Power Systems

by Jingwen Yu, Hui Xue, Boying Wen
Abstract

A novel non-synchronous sampling method for harmonicinterharmonic measurement in power system is proposed. The method consists of two main steps. In the first step, the leakage effect of DFT is reduced through transfom’s on the consecutive points of DFT. In the second step, the amplitude, frequency and phase angle of the harmonics are calculated through the corresponding interpolation methods. Compared to the commonly used windowed DFT method, the proposed method does not need the construction and storage of window functions. Therefore, it simplifies the implementation and improves the precision of harmonic measurement. The performance of the novel method is compared with DFT interpolation methods and windowed DFT interpolation method through simulation experiments.

Volume: 18, Issue: 5

The Updating System Of Sub-Compartment Data For Effective Forest Resource Management

by Baoguo Wu, Yan Qi, Song Zhu, Fei Gao, Enying Guo
Abstract

In order to meet the demand of modern forest resources management and data update, this paper analyses the reasons of the forest resource data changes in the forest resource information management, introduces the concept of changing event to describe the reasons of the changes, codes the events changing the sub-compartment data coding, and designs the spatio-temporal database structure of forest resource sub-compartments’ data changes. The study includes the growth model of the Chinese fir and the Pinus massoniana and the growth rate of broad-leaved forest. This study designs the sub-compartment date update process and standards, the system function of update, and the forest sub-compartment data update system of the county forest resources management information system based on the geographical information system (GIS). The forest resource database can be updated by applying the annual changes data, so as to realize the dynamic management of the county forest resources.

Volume: 18, Issue: 5

Intelligent Pearl Disease Diagnosis Based on Rough Set - Neural Network

by Longqin Xu, Shuangyin Liu
Abstract

In view of large amount of monitoring data for Pearl disease, complexity of network structure of the traditional diagnostic neural network method, validity of disease data issues and slow training, this paper introduces the rough set theory to intelligent Pearl disease diagnosis. A method for disease diagnostics is proposed based on rough set -neural network. The rough set is used to remove the redundant attributes of decision table in order to reduce the number of input neurons and optimize neural network topology. The experimental simulation shows that the proposed algorithm can effectively improve the diagnostic rate and diagnostic accuracy. The proposed algorithm is a new way of methods for the diagnosis aquaculture technology

Volume: 18, Issue: 5

A Variable-Rate Fertilizer Control System for Disc Fertilizer Spreaders

by Rui Zhang, Chunjiang Zhao, Xiu Wang, Zhijun Meng, Liping Chen, Wei Ma
Abstract

Fertilizer application control in reason is the key technology to improve operation quality, reduce fertilizer pollution, and cut down costs of production. A control system of variable rate fertilizer is developed based on microcontroller chip in the paper. The system can automatically control flow dosage by adjusting feed gate opening size and changing hydraulic motor speed of fertilizer conveyor chain at the same time for precise variable rate fertilization. The fact experiments show that the feed gate opening size and fertilizer amount are well linear, which is R20.988. Experiments of fertilizer by pre-setting different dosage show that the errors of fertilizer amount are well in fact, and the errors are less than 6.5. The system resolves well to automatically adjust feed gate size, fertilizer dropping continuously, and reduce error of fertilizer machine system.

Volume: 18, Issue: 5

Color Image Filtering Methods for Variable Spray Systems

by Huihui Yu, Ronghua Ji, Jingyuan Li, Tiantian Wang
Abstract

The color images of the actual field were collected and processed in the variable spray system based on machine vision. The variable spray decision was made according to the identified results. The variable spraying was realized by the sprayer for the variable spray decision. However, there were many noises in the field images because of the complexity of the field image acquisition condition. The identification result could be affected by the noises. By analyzing the noise distribution of the color images that were acquisitioned in the actual field, it could be found that there were more noises in the images which were collected in sunny day and with shadows. Meanwhile, there were fewer noises in the images which were collected in cloudy and with the simple soil background. There was mainly random impulse noise in the actual field images. According to the noises characteristics in the color images, four color image filtering algorithms were tested. The test results showed that the filter based on RGB scalar had poor effect because some new colors appeared, and it was the slowest; the rest filters nearly had no difference, the filter based on RGB vector was the fastest.

Volume: 18, Issue: 5

Differentiation of Mechanical Damages of Rice Plants Using E-Nose

by Zhou Bo, Jun Wang
Abstract

Plants change the emission of induced volatiles in response to damage and herbivore attack, and monitoring the change of such volatiles could provide a nondestructive means of plant health measurement. Current monitoring techniques for plant volatiles are time-consuming and costly. The main objective of this research is to figure out whether electronic nose (e-nose) technique can be used to differentiate rice plants with different degrees of mechanical damage. A portable e-nose (PEN2) is used to characterize and classify rice plants subjected to three degrees of mechanical damage compared with undamaged control plants. Principle component analysis (PCA), Linear discriminant analysis (LDA), Stepwise discriminant analysis (SDA), and Back-propagation neural network (BPNN) are applied to evaluate the data. Different degrees of damaged rice plants are better distinguished using LDA than using PCA. The average correction ratio of testing set of BPNN is 75. The results obtained indicate that it is possible to classify different degrees of damaged rice plants using a-nose signals. This study demonstrates the feasibility of using an e-nose to rice plant damage assessment.

Volume: 18, Issue: 5

Guest Editorial

by Daoliang Li, Simon Yang
Abstract

Volume: 18, Issue: 5

Efficient Architecture For Island Genetic Algorithm in Reconfigurable Hardware

by Chien-Min Ou, Tsung-Yi yu, Wen-Jyi Hwang, Tsung-Che Chiang
Abstract

A novel VLSI architecture for an island genetic algorithm (GA) is presented in this paper. The island GA is based on steady-state GA for reducing the hardware resources consumption. Alook-up table based fast string migration architecture is proposed for lowering the computational overhead while enhancing the performance for the island GA. As compared with its single-island GA hardware counterpart, the proposed architecture attains superior performance with less computation time subject to the same total population size. In addition, the proposed architecture has significantly lower computational time as compared with its software counterparts running on cluster computer with multithreading for GA-based optimization.

Volume: 18, Issue: 4

Tuning of a PID Controller using Soft Computing Methodologies Applied to Moisture Control in Paper Machine

by B. Nagaraj, P. Vijayakumar
Abstract

Proportional -Integral -Derivative control schemes continue to provide the simplest and effective solutions to most of the control engineering applications today. However PID controller is poorly tuned in practice with most of the tuning done manually which is difficult and time consuming. This research comes up with a soft computing approach involving Genetic Algorithm, Evolutionary Programming, Particle Swarm Optimization, Bacterial Foraging Optimization and heuristic algorithm bacterial foraging combine with particle swarm optimization. The proposed soft computing is used to tune the PID parameters and its performance has been compared with the conventional method Ziegler Nichols. The results obtained reflect that use of soft computing based controller improves the performance of process in terms of time domain specifications, set point tracking, and regulatory changes and also provides an optimum stability. This research addresses comparison of tuning of the PID controller using soft computing techniques on moisture control system in paper machine (Machine Direction).

Volume: 18, Issue: 4

Adaptive Positioning Of Mems Production System With Nano – Resolution

by Gregor Škorc, Riko Šafari
Abstract

This paper describes an experimental approach to the adaptive positioning of a MEMS production system with nano - scale resolution. It is known that accurate positioning within nano - scale requires higher move resolutions which cause drastic speed reductions. Production processes which are running within such production systems are therefore very time consuming or very limited due to their working spaces. We are proposing an adaptive approach to the positioning within nano -scale, which will help us extend the size of the systems working space in such a way that it will not be time consuming. The basic idea of our approach is that the move resolution and the move speed are adapted according to the size of positioning error. Large positioning error sets the system to low resolution and high speed. As a separate axis approaches the target point, positioning error decreases, move resolution increases, and move speed decreases. The so called adaptive approach was tested on the positioning system with five axes based on Piezo LEGS® linear motors, with the use of bang-bang, fuzzy and polynomial control techniques. The results of the experiments with these techniques, and the practical implementation of them, are given in this paper. The practical use of such a system with three different micro-grippers is also presented.

Volume: 18, Issue: 4

Design Rule Extraction from a Trained Ann Model using Ga for Product form Design of Mobile Phones

by K.Y. Fung, C.Y. Tang, Eric Lee, G.T.S. Ho, Michael Siu, W.L. Mou
Abstract

An artificial neural network (ANN) model and rule extraction from a trained ANN using genetic algorithm (GA) are applied to predict and advise on the rules for optimal product form design for a particular customer feeling. To map design elements and the affected impressions, principal component analysis (PCA) is employed to determine the essential dimensions for data analysis. By using ANN to examine the relationships between perceptual value and form elements, black-box ANN knowledge can be extracted by applying GA to generate design rules. A case study on the product form of a mobile phone design was conducted to implement the proposed approach. The resultant rules can be used to help product designers to better understand key design elements and to verify optimal solutions suggested by using ANN models.

Volume: 18, Issue: 4

Fuzzy Sliding Mode Controller for a PH Process in Stirred Tanks

by Luis Zárate, Peterson Resende
Abstract

The pH control is a difficult problem. This is due to the strong non-linearity and extreme sensitivity to disturbances in the neighborhood of the neutrality point. A Fuzzy Sliding Mode Controller (FSMC) which combines a Variable Structure Control with sliding modes (VSC) and the fuzzy logic theory to adjust the controller gains, for pH control (neutralization and regulation), is presented in this work. The FSMC was designed by considering the non-linearities and the uncertainties of the pH process. The controller uses the Smith predictor structure to deal with the effect of the time delay characteristic of this kind of process. It is shown how the FSMC can improve the response of the conventional VSC applied to pH process It will be possible to observe that the required times to reach the steady-state is significantly improved. Simulation results for the pH process are presented with comparisons between the VSC and FSMC.

Volume: 18, Issue: 4

Low Level Control Layer Definition for Autonomous Vehicles Based on Fuzzy Logic

by J.E. Naranjo, F. Jiménez, O. Gómez, J.G. Zato
Abstract

The intelligent control of autonomous vehicles is one of the most important challenges that intelligent transport systems face today. The application of artificial intelligence techniques to the automatic management of vehicle actuators enables the different Advanced Driver Assistance Systems (ADAS) or even autonomous driving systems, to perform a low level management in a very similar way to that of human drivers by improving safety and comfort. In this paper, we present a control schema to manage these low level vehicle actuators (steering throttle and brake) based on fuzzy logic, an artificial intelligence technique that is able to mimic human procedural behavior, in this case, when performing the driving task. This automatic low level control system has been defined, implemented and tested in a Citroen C3 testbed vehicle, whose actuators have been automated and can receive control signals from an onboard computer where the soft computing-based control system is running.

Volume: 18, Issue: 4

Development of a Hybrid Artificial Neural Network Model and its Application to Data Regression

by Eric Lee, L.T. Wong, K.W. Mui
Abstract

Simulating the behaviour of a nonlinear system from its historical noise corrupted data is one of the major applications of the Artificial Neural Network (ANN). The objective of this paper is to develop an autonomous incremental growth neural network model for carrying out the tasks of regression and classification in noisy environment. A hybrid ANN model called GRNNFA is proposed. It is a fusion of the Fuzzy ART (FA) and the General Regression Neural Network (GRNN) and facilitates the removal of the noise embedded in the training samples. The performance of the GRNNFA model was examined by two benchmarking problems which are the Approximation-Of-Noisy-Mapping and the Fisher’s Iris Data. The results demonstrate the superior performance of the GRNNFA model working in the noisy environments.

Volume: 18, Issue: 4

Channel Estimation Based on Neural Network With Feedback for Mimo Ofdm Mobile Communication Systems

by M. Nuri Seyman, Necmi Tapinar
Abstract

multiple input multiple output (MIMO) orthogonal frequency division multiplexing (OFDM) has received a great deal of attention of recently in achieving high data rate in wireless communication systems such as WIMAX. Channel estimation is, however, a critical issue for coherent demodulation. In this paper, a new channel estimator based on neural network with feedback for MIMO-OFDM mobile system is designed and its performance is compared to the least square error (LS), least mean square error (LMS), minimum mean square error (MMSE) algorithms and neural network without feedback by using computer simulations. Simulation results demonstrate that our proposed system is an effective solution to channel estimation in time varying fast fading channels without any knowledge of channel statistics and noise information.

Volume: 18, Issue: 3

Fuzzy-Based Routing Scheme in Mobile AD Hoc Networks

by Jin-Long Wang
Abstract

In mobile ad hoc networks, mobile nodes have to communicate to each other without any infrastructure or base station. Thus, a lot of critical routing decision factors should be taken into considerations, such as reputation, reliability, availability, security, and mobility. Specially, the misconduct nodes due to unreliability, malfunction, migration, or malice may result in the loss and delay of packets. This paper proposes a fuzzy-based routing scheme based on reputation, bandwidth, and distance to overcome the misconduct mobile nodes and to enhance the performance of throughput. The scheme utilizes the ad hoc on-demand distance vector, the exponential smoothing model, and the fuzzy multiple-criteria group decision-making method to perform the routing path decision. Finally, the simulation is used to illustrate the performance of proposed scheme and existing methods. From the simulation results, the proposed scheme has better throughput than other schemes.

Volume: 18, Issue: 3

Multi-Connect Architecture (MCA) Associative Memory: A Modified Hopfield Neural Network

by Emad I Kareem, Wafaa A.H Alsalihy, Aman Jantan
Abstract

Although Hopfield neural network is one of the most commonly used neural network models for auto-association and optimization tasks, it has several limitations. For example, it is well known that Hopfield neural networks has limited stored patterns, local minimum problems, limited noise ratio, retrieve reverse value of pattern, and shifting and scaling problems. This research will propose multi-connect architecture (MCA) associative memory to improve the Hopfield neural network by modifying the net architecture, learning and convergence processes. This modification is to increase the performance of associative memory neural network by avoiding most of the Hopfield neural network limitations. In general, MCA is a single layer neural network uses auto-association tasks and working in two phases, that is learning and convergence phases. MCA was developed based on two principles. First, the smallest net size will be used rather than depending on the pattern size. Second, the learning process will be performed to the limited parts of the pattern only to avoid learning similar parts several times. The experiments performed show promising results when MCA shows high efficiency associative memory by avoiding most of the Hopfield net limitations. The results proved that the MCA net can learn and recognize unlimited patterns in varying size with acceptable percentage noise rate in comparison to the traditional Hopfield neural network.

Volume: 18, Issue: 3

Identification and Compensation of A Capacitive Differential Pressure Sensor Based on Support Vector Regression Using Particle Swarm Optimization

by M. Hashemi, J. Ghaisari, A. Salighehdar
Abstract

Capacitive Differential Pressure Sensors (CDPSs) are highly utilized in industry. However, the accuracy of CDPSs is limited because of the adverse effects of ambient temperature on their output characteristics. In this paper, the effect of temperature on a CDPS output is identified and compensated for using a Support Vector Machine for Regression (SVR) method. To achieve a better performance, a Particle Swarm Optimization (PSO) method is employed to optimize the parameters of SVR. Also, a test bench is designed and implemented to obtain data under real environmental conditions. The experimental results obtained verify the performance of modeling and compensation for the non-linear behavior of the CDPS based on SVR using PSO. Simulation results show that the proposed identifier and compensator estimates and compensates the output accurately. Finally, the performances of the proposed methods are also compared with those of Artificial Neural Network (ANN) techniques.

Volume: 18, Issue: 3

Real-Time Step-Count Detection and Activity Monitoring Using A Triaxial Accelerometer

by Yun Kim, Sung-Mok Kim, Hyung Lho, We-Duke Cho
Abstract

We have developed a wearable device that can convert sensor data into real-time step counts and activity levels. Sensor data on gait were acquired using a triaxial accelerometer. A test was performed according to a test protocol for different walking speeds, e.g., slow walking, walking, fast walking, slow running, running, and fast running. Each test was carried out for 36 min on a treadmill with the participant wearing a portable gas analyzer(K4B2), an Actical device, and the device developed in this study. The signal vector magnitude(SVIvi) was used to process the X, Y, and Z values output by the triaxial accelerometer into one representative value. In addition, for accurate step-count detection, we used three algorithms: a heuristic algorithm(HA), the adaptive threshold algorithm(ATA), and the adaptive locking period algorithm(ALPA). A regression equation to estimate the energy expenditure(EE) was derived by using acceleration data and information of the participants. The recognition rate of our algorithm was 97.34, and the performance of the activity conversion algorithm was better than that of the Actical device by 1.61 .

Volume: 18, Issue: 3

A Chromosome Representation Encoding Intersection Points for Evolutionary Design of Fuzzy Classifiers

by Joon-Yong Lee, Joon-Hong Seok, Ju-Jang Lee
Abstract

Unlike the conventional chromosome representation to search the shape of fuzzy membership functions, a novel encoding scheme to search the optimal intersection points between adjacent fuzzy membership functions is originally presented for evolutionary design of fuzzy classifiers. Since the proposed representation contains the intersection points directly related to the boundary of classification, it is intuitively expected that redundancy of the search space is reduced and the performance is better in comparison with the conventional encoding scheme. The experimental results show that the proposed encoding scheme gives superior or competitive performance in two real-world datasets and gives more interpretable fuzzy classifiers. This short paper has provided additional explanation to the previous works introduced in the latest conference.

Volume: 18, Issue: 3

Gpax: Genetic Parabolic Adaptive Crossover Operator

by José Luis Álvarez, Manuel Gegúndez, José Arjona
Abstract

In this paper we propose a new crossover operator for real coded evolutionary algorithms that is based on a parabolic probability density function. This density function depends on two real parameters α and β which have the capacity to achieve exploration and exploitation dynamically during the evolutionary process in relation to the best individuals. In other words, the proposed crossover operator is able to handle the generational diversity of the population in such a way that it can either generate additional population diversity, therefore allowing exploration to take effect, or use the diversity previously generated to exploit the better solutions.In order to test the performance of this crossover, we have used a set of test functions and have made a comparative study of the proposed crossover against other classic crossover operators. The analysis of the results allows us to affirm that the proposed operator displays a very suitable behavior, although, it should be noted that it offers a better behavior applied to complex search spaces than simple ones.

Volume: 18, Issue: 3

Reliability Analysis of Structure for Fuzzy Safety State

by Zhonglai Wang, Yanfeng Li, Hong-Zhong Huang, Yu Liu
Abstract

Due to the existing of various kinds of uncertainties, predicting structural performance is always a great challenge in practical applications. The reliability of structure under the probabilistic uncertainty has been well studied in the past decade. However, the randomness is not the only attribute of reality. This paper attempts to investigate approach for the structural reliability analysis via the fuzzy safety state which is defined by state variable and fuzzy random allowable interval. The fuzziness of safety criterion, the fuzziness and randomness of generalized stress and generalized strength are considered in the limit state models, and the membership function of the fuzzy structural reliability are derived by the defined fuzzy safety state.

Volume: 18, Issue: 3

Negative Dielectrophoretic Particle Positioning in A Fluidic Flow

by Tomoyuki Yasukawa, Junko Yamada, Hitoshi Shiku, Fumio Mizutani, Tomokazu Matsue
Abstract

In this work, we report the control of a microparticle position within fluid flow based on its size by using a repulsive force generated with negative dielectrophoresis (n-DEP). The n-DEP based fluidic channel, which was consisted of navigator and separator electrodes, was used to manipulate the particle flow in the center of channel and to control the particle position in the fluidic flow. The mixture of 10 μm-and 20 μm-diameter particles was introduced into the channel with 30 μm height at 700 μms. On applying an AC voltage (23 V peak-peak and 7 MHz) to the navigator electrodes on the upper and lower substrates in a n-DEP frequency region, the suspended microparticles were guided to the center of the fluidic channel and then channelled through the passage gate positioned at the center of the channel. The AC electric field was also applied to separator electrodes, resulting in a formation of flow paths with low electric fields. The separator was consisted of the five band electrodes with the different gap spaces with the adjacent band, which allow to fomvng the flow paths with different electric fields. The microparticles separately flow in line along the paths formed between the band electrodes, the 10 μm-diameter particles mainly flow through the narrow path and 20 μm-diameter particles through the wide path arranged at the outside from the center. These results indicated that positions of two types of microparticles in the fluidic channel were easily separated and controlled using the n-DEP.The present procedure therefore yields a procedure for the DEP based simple and miniaturized separators.

Volume: 18, Issue: 2

Scanning Dielectrophoresis for the Directed-Assembly and Non-Contact Manipulation of Colloidal Particles

by Hiroshi Frusawa, Masaichi Inoue
Abstract

The scanning dielectrophoresis system consists of a pair of microneedles connected to a waveform generator of arbitrary ac electric fields. Consequently, our scanning method is able to place and move the strong electric field spot between the electrode tips anywhere, just as optical tweezers have done the focus of light beam. In this paper, we demonstrate non-contact manipulations unique to our scanning method. We also propose that the use of the frequency modulated electric field provides a novel way for one-step assay of crossover frequency in dielectrophoresis of a single colloid, which has been validated by the scanning method. Furthermore, the dielectrophoretic assembly forms amicron-film with stripe pattern of single-walled carbon nanotube bundles surrounded by polymer beads, similarly to depletion induced clusters in binary colloids.

Volume: 18, Issue: 2

Control of Cellular Organization Around Collagen Beads using Dielectrophoresis

by Shogo Miyata, Yu Sugimoto
Abstract

Tissues formed by cells seeded in hydrogels are used in biotechnology, cell-based assays, and tissue engineering. We previously presented a cell micro-patterning technique that localizes live cells within hydrogels using dielectrophoretic (DEP) forces. We also demonstrated the ability to modulate tissue function through the control of microscale cell architecture. In this study, we developed a novel cell patterning technology for accumulating cells around collagen microbeads using DEP forces. As a case study, we produced collagen-alginate microbeads as acell-adhesive scaffold and accumulated bovine chondrocytes to cover the microbeads by combining the DEP forces with a flow of buffer solution. This approach allows studies to better examine the influence ofthree-dimensional cellular architecture on microscale levels.

Volume: 18, Issue: 2

Optical Counting of Trapped Bacteria in Dielectrophoretic Microdevice with Pillar Array

by Satoshi Uchida, Ryota Nakao, Chihiro Asai, Takayuki Jin, Yasuharu Shiine, Hiroyuki Nishikawa
Abstract

The development of rapid, accurate and simple screening system is essential in actual processes for large-volume water treatment from a standpoint of recent strict regulations on environmental water pollution. in the present work, we newly produced a dielectrophoretic microdevice with three dimensional microstructures and sophisticated microchannel to trap and detect bacteria efficiently. We measured the fluorescence area and intensity of stained bacteria in the gap between electrodes, and estimated the number of trapped bacteria quantitatively. The dependence of the trapping efficiency of bacteria on flow velocity was also discussed.

Volume: 18, Issue: 2

Fundamental Studies of Dielectric Characteristics of Heat-Injured

by Takaharu Enjoji, Satoshi Uchida, Fumiyoshi Tochikubo
Abstract

Rapid and high sensitivity methods for microorganism detection and constant monitoring system in fermentation processes have been required for advanced quality preservation in food and beverage industries. In the present work, the metabolic states of heat-injured Saccharomyces cerevisiae (S. cerevisiae) in a micro-cell were investigated using dielectrophoretic impedance measurement (DEPIM) method. Temporal change in the conductance between micro-gap (δG) was measured for various heat treatment temperatures (HTT). As a result, there is an obvious correlation between HTT and δG as well as our previous works for Escherichia coli (E. coli). These results suggest that DEPIM method should be available for an effective monitoring method for complex change in various biological states of microorganisms.

Volume: 18, Issue: 2

Separation of Leukemia Cells from Blood by Employing Dielectrophoresis

by Hiroko Imasato, Takeshi Yamakawa, Masanori Eguchi
Abstract

Our goal is to develop a new automatic blood cell counter that can detect a small amount of leukemia cells in the blood of patients. in this paper, we examine whether living blood cells can be separated by employing the dielectrophoresis (DEP). Leukocytes were separated from erythrocytes by dielectrophoresis at 60MHz, 1OVpp. Then BALL-1 (Human B-cell Acute Lymphotropic Leukemia Cell Line) which is one kind of leukemia cells were separated from the normal leukocytes (Mononuclear) at 37KHz, 14Vpp. HL-60 (Human promyelocytic Leukemia cell line) cells were separated from the normal leukocytes (Granurocyte) at 45KHz, 14Vpp.

Volume: 18, Issue: 2

Particle Separation by Employing Non-Uniform Electric Fields, Traveling-Wave Electric Fields and Inclined Gravity

by Masanori Eguchi, Hiroko Imasato, Takeshi Yamakawa
Abstract

In this study, we present the methods of the particle separation by employing electrokinetic phenomena (dielectrophoresis (DEP) and traveling-wave electroosmosis (TWEO)) and the inclined gravity. The methods can separate particles of same sign DEP. However, it is very difficult to separate particles on a plane electrode. To cope with this problem, we create the "Bottle neck Fork-trace electrode (BF electrode)" for separation of all particles in the chamber. Polystyrene beads, glass beads, yeast cells and barium titanate beads in NaCl solutions were separated by this electrode.

Volume: 18, Issue: 2

Guest Editorial

by Hiroko Imasato, Takeshi Yamakawa
Abstract

Volume: 18, Issue: 2

Soft Computation Using Artificial Neural Estimation And Linear Matrix Inequality Transmutation For Controlling Singularly-Perturbed Closed Timeindependent Quantum Computation Systems, Part B: Hierarchical Regulation Implementation

by Anas Al-Rabadi
Abstract

A new method of intelligent control for time-independent closed quantum computation systems is introduced in this second part of the paper. The goal is to apply a new implementation of intelligent hierarchical control method within quantum computing systems where the obtained results are satisfying for the robust control of time-independent quantum computations. The new method utilizes supervised recurrent artificial neural networks (ANN) to estimate parameters of the [ A ]transformed system matrix After system matrix estimation is performed, linear matrix inequality (LMi) is used to detemvne the permutation matrix [P] so that a complete system transmutation {[ B ], [ C ], [ D ]} is accomplished. The transformed system model is then reduced using singular perturbation and state feedback control is implemented for system performance enhancement. In quantum computing and mechanics, a closed system is an isolated system that can’t exchange energy or matter with its surroundings and doesn’t interact with other quantum systems. In contrast to open quantum systems, closed quantum systems obey the unitary evolution and thus are information lossless. The experimental simulations were implemented upon the time-independent closed quantum computing system using the important quantum case of a particle in afinite-walled box for an m-valued quantum computing in which the resulting distinct energy states are used as the orthonormal basis states. Although several other diverse conventional control methodologies and schemes can exist for the purpose of controlling computational circuits and systems, the introduced intelligent hierarchical control method simplifies the order of the ANN-estimated LMI-transformed eigenvalue-preserved quantum model, and thereafter synthesizes - as demonstrated -simpler controllers for the utilized closed time-independent quantum computation devices, circuits and systems, that achieve the desired enhanced quantum system performance.

Volume: 18, Issue: 1

Soft Computation Using Artificial Neural Estimation And Linear Matrix Inequality Transmutation For Controlling Singularly-Perturbed Closed Time-Independent Quantum Computation Systems, Part A: Basics And Approach

by Anas Al-Rabadi
Abstract

This article presents basic background and approach methods that are needed for implementing a new method of intelligent control for time-independent quantum computing systems. This includes basic background on quantum computing, supervised neural networks, linear matrix inequalities, and model order reduction. The presented basics will be utilized in the second part of the paper for applying a new implementation of intelligent hierarchical control method within quantum computing where the obtained results are satisfying for the robust control of time-independent closed quantum computing systems.

Volume: 18, Issue: 1

Comparing Traditional Statistics, Decision Tree Classification And Support Vector Machine Techniques For Financial Bankruptcy Prediction

by Mu-Yen Chen
Abstract

Recently, several spectacular bankruptcies, including Fannie Mae, Freddie Mac, Washington Mutual, Merrill Lynch, and Lehman Brothers, have caught the world by surprise. To improve the accuracy of financial distress predictions, this research compares traditional statistical methods (i.e., linear discriminant analysis, logistic regression), decision tree classification methods (i.e., C5.0, CART, CIIAID, QUEST) and artificial neural network techniques (i.e., multi-layer perceptron, support vector machine) to distinguish their respective capabilities for predicting financial distress. The experimental results showed that the support vector machine (SVM) technique could be amore suitable method for predicting financial distress than traditional statistical or DT techniques.

Volume: 18, Issue: 1

Automatic Reconstruction Of 3D Environment Using Real Terrain Data And Satellite Images

by Chen Wang, Tao Wan, Ian Palmer
Abstract

This paper presents a novel 3D reconstruction method for large-scale 3D environments. There are three core components of our work: dynamic terrain modelling, river and water region identification and modelling using an active contour model and primitive shape matching method. Real-time environment reconstruction is constructed using real measurement data of GIS, in terms of digital elevation data and satellite image data. A Nona Tree Space Partitions (NTSP) algorithm is proposed for dealing with very large data processing and visualisation. A new geometric active contours model is used to automatically segment interesting image areas such as water or flooded regions, forest region and residential region. A primitive shape matching method is proposed to detect the residential objects, such as buildings and houses. The experimental results demonstrate that our approach is a promising one, which is able to deal with large environment reconstruction effectively.

Volume: 18, Issue: 1

Evolutionary Search For Entertainment In Computer Games

by Zahid Halim, A. Rauf Baig, Mujtaba Hasan
Abstract

Games have always been of interest to all age groups. With the advancement in technology and increase in number of users of personal computers, increased number of games is introduced in market. This is resulting in efforts, both for the developers in writing scripts for games and for the end users to select a game which is more entertaining. In this work we present a solution to both the issues. Initially a quantitative measure is devised, which calculates the entertainment value of games. Based upon the proposed measure we use evolutionary algorithm to generate games for different genres on the fly. The evolutionary algorithm needs to be given an initial set of games which it optimizes for entertainment using the proposed entertainment measure as the fitness criteria. In order to compare the entertainment value of the new games generated with the human’s entertainment value we conduct a human user survey.

Volume: 18, Issue: 1

Monotonic Fuzzy Systems As Universal Approximators For Monotonic Functions

by Jinwook Kim, Jin-Myung Won, Kyungmo Koo, Jin Lee
Abstract

In this paper, we propose a constructive method to develop a fuzzy system having a monotonic input-output relationship and prove that the developed fuzzy system can approximate any continuously differentiable monotonic function with any desired degree of accuracy. The fuzzy system is constructed with complete and consistent input membership functions and imposes special parametric constraints on the consequent part of the fuzzy rules. The monotonicity property and approximation capability of the developed fuzzy system are demonstrated using numerical examples.

Volume: 18, Issue: 1

Anti-Swing Control For An Overhead Crane With Fuzzy Compensation

by Xiaoou Li, Wen Yu
Abstract

This paper proposes a novel anti-swing control strategy for an overhead crane. The controller includes both position regulation and anti-swing control. Since the crane model is not exactly known, fuzzy rules are used to compensate friction, gravity as well as the coupling between position and anti-swing control. Ahigh-gain observer is introduced to estimate the joint velocities to realize PD control. Real-time experiments are presented comparing this new stable anti-swing PID control strategy with regular crane controllers.

Volume: 18, Issue: 1

Soft Sensor Based on a Pso-Bp Neural Network for a Titanium Billet Furnace-Temperature

by Yan Lv, Min Wu, Qi Lei, Zhuo-Yun Nie
Abstract

This paper builds a soft sensor model based on a PSO-BP neural network for titanium billet heating furnace-temperature. An improved particle swarm optimization algorithm is proposed. This algorithm is used to optimize the initial weights of the neural network, which can overcome the disadvantages of the random initial weights of the conventional BP neural networks. The proposed algorithm is based on an adaptive particle swarm optimization method with ajump-factor and ajump-strategy added on the position states, and can improve the ability of the global searching. The results of the simulation based on industrial data show that the precision of the sensor by using the proposed model meets the practical requirements.

Volume: 17, Issue: 8

Automatic Memory Management for Embedded Real-Time Java Processor Jpor-32

by Guang Hu, Zhilei Chai, Shiliang Tu
Abstract

Currently, Java has been gradually applied in embedded real-time areas like robotics, control system, etc. owning to its advantages like robustness, security, etc. In order to improve the performance of Java's execution engine for embedded real-time applications, JPOR-32, an embedded real-time Java processor, is designed. Based on it, this paper presents the automatic memory management (AMM) mechanism for embedded real-time Java processor. JPOR-32 provides architectural support as well as instruction level support for AMM. Its preprocessing mechanism reduces the complexity of the implementation of AMM, enhances the run-time efficiency, and promotes predictability of the worst-case execution time. The system design of JPOR-32 makes AMM of class azea avoided, and the optimized design of instruction set provides effective support for space checking and garbage collection scheduling. This paper also proposes an object reference format which provides supports for objects tracing, heap scanning, synchronization, etc., and lays the foundation for the implementation of suitable garbage collection algorithm. Moreover, this paper gives the analysis of the feature of runtime environrnent of embedded Java processor, and implements a suitable improved generational garbage collector.

Volume: 17, Issue: 8

Improved Affine Partition Algorithm for Compile-Time and Runtime Performance

by Yuan Xinyu, Li Ying, Deng Shuiguang, Cheng Jie
Abstract

The Affine partitioning framework, which unifies many useful program transforms such as unimodular transformations, loop fusion, fission, scaling, reindexing, and statement reordering, has been proved to be successful in automatic discovery of the loop-level parallelization in programs. The affine partition algorithm was improved from the aspects of compile-time and runtime efficiency in this paper. Firstly, it improves compile-time speed of affine partition algorithm by using of generalized GCD test which is a basic dependence testing algorithm. This paper proved that generalized GCD test has a strong relationship with affine partition algorithm which can improve the compiling speed of the affine partition algorithm. Secondly, a method is put forward to select an optimal solution among the infinite legal solutions of the affine partition algorithm which ensures the minimum communication volume and the simplified processor space expression. Proved by experiments, the two innovations mentioned above can promote the compile-time and runtime efficiency of the affine partition algorithm.

Volume: 17, Issue: 8

Image Super-Resolution Fusion Based on Hyperacutiy Mechanism and Half Quadratic Markov Random Field

by Aiye Shi, Chenrong Huang, Mengxi Xu, Fengchen Huang
Abstract

In the image super-resolution reconstruction (SRR) process, the uncertainty factors such as the accuracy level of registration and the constraint method to solution will affect the reconstructed result. In this paper, we propose an SRR method using the combined hyperacuity mechanism with half quadratic Markov random field (MRF) in the frame of maximum a posteriori (MAP). Asteepest-descent optimization algorithm is used to fmd the high resolution image. In the process of optimization, the initial estimate of high resolution image is fustly obtained by fusing the whole low resolution images inspired by the visual hyperacuity mechanism of flying insects. Then, the registration pazameters and high resolution image are implemented jointly in order to reduce the uncertainty of image registration. Moreover, the adaptive regularization method is used to reduce the effect of randomness by man-made adjustment. The experimental results demonstrate our proposed method effective.

Volume: 17, Issue: 8

The Research of A New Streaming Media Network Architecture Based on the Fusion of P2P And CDN

by Wuyao Shen, Xingming Zhang, Shubin Mai, Lei Huang, Xueyun Liu, Chong Zhang, Huaixi Chen
Abstract

A new three-layered streaming media network architecture based on the fusion of P2P and CDN which can be used for mobile device users is proposed in this paper. It uses P2P network as the backbone, selects nodes of high-performance, high-bandwidth, and stable online time as CDN edge servers, and provides mobile device users with streaming media services under the schedule of the load-balancing servers. Since P2P has the inherent drawback of flow disorder, an optimized idea of P4P has been introduced into P2P network in this architecture to make the flow ordered. Theoretical analysis and experimental results show that this architecture has optimized P2P network with higher transmission efficiency, lower bandwidth utilization of the backbone network and lower cost and fewer resources to provide streaming media services as higher network scalability and quality as possible. A streaming media system for videoon-demand which uses the method proposed in the paper has good performance when a large number of mobile users access concurrently.

Volume: 17, Issue: 8

An Effective Network Coding Strategy With Scalable Video Coding for Peer-to-Peer Streaming

by Pengyu Zeng, Yong Jiang
Abstract

Undoubtedly, Peer-to-Peer (P2P) technology is an effectively method for delivering streaming media content over the Internet. However, current P2P stream systems also suffer many problems, such as huge bandwidth consumption, less effective in heterogeneous environment, etc. Scalable video coding (SVC), as an extension of H.264AVC standard, has attracted many researchers for its flexible scalability and adaptability to the heterogeneous network. Network coding, an innovation of the field of information theory, is proved to be valuable in P2P application. In this paper, we propose two different network coding schemes (multi-layer network coding and infra-layer network coding), combining with SVC technology to solve the above problems. Simulation result shows system gain significant improvement by employing network coding, especially by infra-layer network coding.

Volume: 17, Issue: 8

Reliable Multi-Path Routing With Bandwidth and Delay Constraints

by Fang Wang, Zhaocheng Wang, Yong Li, Lieguang Zeng
Abstract

The increased demand of real-time video applications, including IPTV in home networks and remote monitoring in Internet of Things, requires new routing mechanism to support rapid and reliable real-time data transmission, especially in the wireless networks with bandwidth limitation and at the same time with the high packet loss rate. In this paper, we study the problem of reliable multi-path routing with bandwidth and delay constraints. Considering the link loss rate, which affects the actual bandwidth, we search a set of source-destination paths such that the valid aggregated bandwidth satisfies the requirements and meanwhile the maximum delay is minimized. Since this multi-path routing problem is NP-hard, we first propose a heuristic algorithm as the benchmark, and then present a polynomial time approximation algorithm to obtain a ( 1+ )-approximation solution. Simulations on well-known Internet topologies and random architectures verify the performance of our algorithms.

Volume: 17, Issue: 8

Research on the Radio Waves Propagation in Complex Coal Mine Workface

by Ding Enjie, Zhao Duan, Li Xiao
Abstract

In order to study the transmission characteristics of the radio waves in a complex coal mine workface, we proposed a novel ray-tracing based radio waves propagation (RTRWP) law suitable for under-tunnel complex coal mine workface. Based on this RTRWP law, both the simulation model of the impulse responses of the radio waves and its corresponding energy consumption evaluation in complex coal mine workface can be derived. Theoretical analysis about the effect of the multiple reflections of the radio waves on the final transmission delay is also provided. Computer simulations and field tests in tunnel show that this proposed RTRWP law can work effectively to describe the actual radio wave propagation environments in complex coal mine workface.

Volume: 17, Issue: 8

A Self-Organizational Back-Off Algorithm Based on Local Topology Analysis

by Huibin Wang, Lili Zhang, Jie Shen, Jie Yang
Abstract

The back-off algorithm is the key point that directly affects the message transmission delay, during the design of the MAC layer of the vehicular ad hoc network communication protocol. However, thanks to the back-off time is inversely proportional to the seizing channel ability of the nodes, the unreasonable choice of the contention window usually leads to the nodes “starvation”. This paper investigates aback-off algorithm to deal with the unfairness problem by changing the size of the contention window by analyzing the connectivity of local topology and the polymerization degrees of the nodes. The algorithm can effectively reduce the node’s unfairness problem. The simulation results show that: comparing to the Binary Exponential back-off algorithm and the Multiplicative Increase, Linear Decrease back-off algorithm, the proposed algorithm can significantly enhance the nodes’ fairness, which also reduces the transmission delay.

Volume: 17, Issue: 8

A Novel Square Antenna Array to Improve the Capacity Stability of 4-Element Compact Mimo Systems

by Yueheng Li, Cheng Cheng, Guoping Tan
Abstract

In a compact multiple-input-multiple-output (MIMO) communication systems, the channel capacity of a traditional 4-element uniform linear array (1JLA) will decrease rapidly with the increment of the mean angle of arrival (AoA) of the incident waves. We will show first in this paper that this channel capacity decrement is caused by the antenna radiation pattern distortion, which owes to the so-called mutual coupling (MC) effect among the dense array elements. In order to overcome this channel capacity decrement flaw of ULA, a 4-element square antenna array (SAA) with space symmetric structure is proposed by re-arranging the antenna elements layout according to the idea of reducing the final radiation pattern distortion. Three-dimensional capacity matrix eigenvalue distribution and generalized condition number (GCN) are also developed in this paper to analyze the final capacity stability of 4-element compact MIMO systems. Theoretical analysis and numerical simulations show that comparing with the traditional 4-element ULA, the new 4-element SAA with space symmetry is much less sensitive to the AoA of incident waves, and can maintain a more stable channel capacity.

Volume: 17, Issue: 8

A Novel Rssi-Based Position Algorithm for Wireless Sensor Networks and Design of an Experimental System

by Shi Shuo, Sun Hao, Gu Xuemai, Jiang Yanjun
Abstract

At present, although the position theory and algorithm of WSN (Wireless Sensor Network) have been widely researched, however, there still exist many problems on position algorithm needed to be solved, such as low position accuracy and high realization complexity, eta This paper based on the common trilateral position algorithm uses weighted method to improve the algorithm. The simulation results show that the performance and position accuracy of the improved algorithm is much better than that of the common one. Meanwhile, this paper designs an indoor experimental position system based on the improved algorithm, which selects trilateral ranging method as position method and utilizes CC2510 chip as wireless node. This experimental system realizes indoor position in the 7.4x7.4m2 meeting room on the 2A Building’s 10th floor in Science Garden of Harbin Institute of Technology. From the test results, it can be concluded that the practical performance of the improved algorithm is well proved.

Volume: 17, Issue: 8

A Novel Trust Routing Scheme Based on Node Behaviour Evaluation for Mobile AD Hoc Networks

by Hongsong Chen, Fu Zhongchuan
Abstract

With the rapid application and development of mobile ad hoc network, security and trust aze important to the network. As the vazious attack models, we need trust routing scheme to guarantee the security and trust for the network. So trust routing scheme is the main issue in security mobile ad hoc network. As the cooperation chazacter of the network, trust routing is related to node behaviours. A novel trust routing scheme based on node behaviour evaluation is proposed to enhance security of the network. The defmition of trust routing is proposed in the paper. Ad hoc On-Demand Distance Vector (AODV) routing protocol is used to validate trust routing scheme, Route Reply (RREP) Message is extended to record node behaviour.Ns-2 simulator is used to simulate the trust scheme under different node behaviours and experiment scenarios. Simulation results show that trust routing scheme can improve the security and performance of network effectively.

Volume: 17, Issue: 8

A Mac Protocol of Single Covered -Multiple Sensors Monitoring System

by Xijun Yan, Yadong Li, Shufang Xu
Abstract

In cost efficient monitoring projects, the single-covered technology is usually used for the design of multiple-sensor monitoring system. This paper studied MAC protocols with star topology. After studying the MAC protocol based on scheduling and competition, and considering that the energy of the sink node is unlimited, we improved the traditional MAC protocol, get a new protocol-STAR MAC protocol. The new protocol uses frames to realize synchronization, in non-handshake. It can reduce the energy consumption of sensor nodes, extend network life cycle. Theoretical analysis and simulation results show that STAR-MAC protocol has higher energy efficiency than the traditional MAC protocol. Experiments also show that the data packet loss rate under STAR-MAC protocol is less than 1, which can meet the requirements of applications.

Volume: 17, Issue: 8

Formal Threat Analysis for Ad-Hoc Routing Protocol: Modelling and Checking the Sybil Attack

by Gui Jing-Jing, Wang Jin-Shuang, Zhang Yu-Sen, Zhang Tao
Abstract

The threat analysis for routing protocol is a significant issue in the ad-hoc network security. In this paper we propose a framework in which the vulnerabilities from the Sybil attack for ad-hoc routing protocol can be modeled in a mathematic approach, and the formal analysis is carried out with a specific proof system under the extended strand space model in order to further verify the validity of the threats from the Sybil attack. As an example, a security route protocol, called endairA, is analyzed in our framework, and the result shows that endairA has the flaw of creating false routes in present of the Sybil attack by utilizing some specific topology leak.

Volume: 17, Issue: 8

An Effective Data Gathering Scheme in Heterogeneous Energy Wireless Sensor Networks

by Yingchi Mao, Xiaofang Li, Simon Yang
Abstract

Data gathering is a major function of many applications in wireless sensor networks (WSNs). In real-world applications, it is unrealistic to guarantee that all sensors have the same lifetime because they have different energy consumption. Moreover, sensor redeployment also results in the heterogeneous energy capacities. In this paper, an Effective Data GAthering (EDGA) scheme for heterogeneous energy WSNs is proposed. Based on weighted election probabilities of each node’s energy, EDGA selects nodes to become cluster head, which can handle better the heterogeneous energy capacities. Moreover, EDGA adopts a simple but efficient method to solve the area coverage problem in a cluster range, namely intra-cluster coverage. Finally, the collected data are forwarded to the sink node via the routing tree. EDGA is capable of achieving a good performance in terms of lifetime by minimizing energy consumption for in-network communications and balancing the energy load. The simulation results demonstrate that the proposed EDGA significantly outperforms LEACH and HEED in terms of network lifetime and the amount of data gathered in the heterogeneous energy network.

Volume: 17, Issue: 8

Guest Editorial

by Lizhong Xu, Xiaofang Li, Simon Yang
Abstract

Volume: 17, Issue: 8

Prediction Of Bridge Life Based On Svm Pattern Recognition

by Jianting Zhou, Jianxi Yang
Abstract

Based on the conception of bridge life and the traditional methods for evaluation of bridge life, in combination with the monitoring data obtained by bridge health monitoring system, this paper proposes a novel research method for predicting life of the bridge structure based on one SVM pattern recognition. Key indicators of bridge life are fustly extracted from monitoring information. The indicators constitute the “property” of structural state, then the assessment and prediction of bridge life based on monitoring information turns into a matter of pattern recognition. These “properties” are continuously extracted with time evolution, and the structure life is described as the period till these properties begin to appear in the “negative” area. Finally the SVM model is modified by using geometric chaotic analysis.

Volume: 17, Issue: 7

Tobacco Dry Weight Estimation Based On Artificial Neural Network

by Pan Wenjie, Jiang Chaoying, Tang Yuanju, Simon Yang
Abstract

Artificial neural network is a powerful tool for modelling and prediction in various fields. Tobacco management specialists need simple and accurate techniques to evaluate tobacco growth and cultivation in the growth period. The objective in this study is to develop aback-propagation neural network (NN) model to accurately predict tobacco growth under various climatic and soil conditions in China. The proposed model used the field test data at 11 locations in main tobacco azeas in China. As the output of the NN, the dry weight of tobacco plants was significantly influenced by dry weigh preceding stage, time for sampling, average temperature, relative humidity, soil organic matter and available P value, so these 6 environrnent factors were taken as the NN input. In the study, an optimal NN model was developed with 6 inputs, 1 output and 19 neurons in the hidden layer. With the same test data, the proposed NN model could perform better than the conventional regression model in terms of the relative error ranges and relative coefficient.

Volume: 17, Issue: 7

Extended Probabilistic Latent Semantic Analysis Model For Topics In Time-Stamped Images

by Xiaofeng Liao, Yongji Wang, Liping Ding, Jian Gu
Abstract

This paper considers the problem of modelling the topics in a sequence of images with known time stamp. Detecting and tracking of temporal data is an important task in multiple applications, such as finding hot research point from scientific literature, news article series analysis, email surveillance, search query log mining, etc. In contrast to existing works mainly focusing on text document collections, this paper considers mining temporal topic trends from image data set. An extension of the Probabilistic Latent Semantic Analysis(PLSA) model, which includes an additional variable associated with the time stamp to better model the temporal topics, is presented to extract topics among images and tract how topics change over time. Experiments show the effectiveness of this method.

Volume: 17, Issue: 7

An Effective Graph-Based Hierarchy Image Segmentation

by Weihong Cui, Yi Zhang
Abstract

This paper explores a Minimum Span Tree (MST) based multi-level image segmentation method. We define an edge weight based optimal criterion (merging predicate) which based on statistical learning theory (SLT), a scale control parameter is used to control the segmentation scale. On the other hand, a data structure is designed to keep adjacency of the objects during the MST based segmenting process. This make it is a simple and easy way to realize multi-level hierarchy image segmentation. Experiments based on the natural image and high resolution remote sensing images show the proposed merging predicate can keep the integrity of the objects and do well on preventing over-segmentation; the multi-level segmentation can avoid less segmentation. It also proves its efficiency in segmenting the high resolution remote sensing images. It will provide multilevel spatial neighbours’ information for image interpretation.

Volume: 17, Issue: 7

Optimal Feature Matching for 3D Reconstruction by Combination of Global and Local Information

by Shengyong Chen, Zhongjie Wang, Hanyang Tong, Sheng Liu, Beiwei Zhang
Abstract

For feature matching in 3D computer vision, there are two main kinds of methods, i.e. global-based and local-based algorithms. Although both have achieved some useful results, they still have own disadvantages. This paper proposes a novel method which combines the global and local information as much as possible so that it can take both advantages. A series of sub-pixel window correlation method is employed with the guidance of fronto-parallel result to produce some local results. These local results are then repeatedly merged by quadratic pseudo-boolean optimization under the guidance of global information. After several sub-pixel local optimizations, the error rates at high resolution are tremendously reduced. When combining the global and local traits together, the third step optimization can both reduce the low resolution error as well as keep high-accuracy resolution error low. Compared with other existing algorithms, the proposed approach performs well when the scene is comprised with planar or curved surfaces. Practical experiments are carried out in this research to illustrate the method and typical results.

Volume: 17, Issue: 7

Fusion of Ikonos Imagery Based on Maximum Likelihood Estimation

by Aiye Shi, Min Tang
Abstract

In order to improve the fusion quality of IKONOS multispectral (MS) and panchromatic (Pan) images, this paper proposes a fusion method using maximum likelihood (ML,) estimation. The proposed method firstly uses the sensor characteristics to model the observation process of both MS and Pan images. Then, the cost function with respect to the estimated high-resolution MS images is constructed based on the ML estimation. Finally, the fused images are obtained using a steepest descent optimization algorithm. The experimental results demonstrate that the proposed method can have better spectral result compared with the WT fusion method and perform as well as the maximum a posteriori (MAP) fusion method with a lower computational cost.

Volume: 17, Issue: 7

Content-Aware Retargeting For Soccer Video Adaptation

by Shenghong Hu, Yufu Jia, Shenglong Tan
Abstract

A content-aware retargeting method is proposed for adapting soccer video to heterogeneous terminals. According to domain-specific knowledge, ball, player and player’s face are defined as user interested objects (UIOs) in different view-types. The UIOs are extracted by semantic analysis on soccer video, and then a region of interest (ROI) of each shot is determined jointly by three factors: terminal size, scaling factor and aspect ratio. The proposed method optimizes the retargeted region to contain more semantic content while adapting the constraint of terminal screen. The simulation results prove that the proposed CAR system wins better viewing experiences than the traditional methods such as resizing in a “Letter box” mechanism or cropping directly.

Volume: 17, Issue: 7

An Svm Method of Lda and its Kernel Algorithm With Application to Face Recognition

by Zixue Qiu, Xiaojun Wu, Wenming Zhang
Abstract

Face recognition has been a research topic of pattern recognition and feature extraction is an important step toward face recognition. In this paper, we first propose a method to transform from LDA to PCA with the discriminative information embedded in a whitening transformation, and then we propose a support vector machine (SVM) method of LDA. The kernel algorithm of the SVM solution to LDA has been developed by mapping the data to kernel space using the kernel trick implicitly. The two proposed methods have been applied to face recognition. The results of experiments obtained on ORL and XM2VTS databases show the effectiveness of proposed methods including both linear method and nonlinear method respectively.

Volume: 17, Issue: 7

An Approach for Target Detection and Extraction Based on Biological Vision

by Huibin Wang, Shengnan Zheng, Xin Wang
Abstract

Inspired by the mechanism of multi-scale image fusion of insect compound eye, this paper proposed a target detection and extraction method based on insect compound eye and human visual attention mechanism. The main feature of this method is that multi-scale visual attention mechanism is designed for improving the detection accuracy of interested target, meanwhile image is pre-segment based on it, then the target is extracted based on the LEGION (Locally Excitatory Globally Inhibitory Oscillator Networks) selection network model. For multi-scale visual attention mechanism, on intensity channel, this approach adopts multi-scale analysis to get the intensity saliency map on both global-level and local-level; on orientation channel, according to the mechanism of the large vision field of compound eye, directional information is added to improve the orientation sensitivity; on color channel, according to the characteristic of color sensitivity of human vision system, HSI color space is used instead of RGB to enhance color features. Compared with the experiment results of Itti visual attention model, the proposed approach can locate the interested targets in clutter scene more accurately, and complete fast extraction of the target without increasing of computation and time-consuming.

Volume: 17, Issue: 7

Multi-Agent System Computing And Simulation Of Inter-Basin Water Transfer

by Wei Huang, Xingnan Zhang, Jianying Wang
Abstract

The South-to-North Water Diversion Eastern Route Project (SNWDERP) of China is a large inter-basin water transfer project. It is an extremely complex process management of water resources allocation and scheduling for such amulti-basin, multi-source, multi-objective complex engineering systems. The SNWDERP is difficult to accurately construct the mathematical model because this kind of complex system has natural and artificial quality in the same time. This paper proposes a model for the SNWDERP based on multi-Agent calculation and complex adaptive system (CAS). Experiments aze conducted on the SWARM simulation platform. Simulation results show that the interaction within all kinds of objects and the behaviour of system evolvement in the course of water resources allocation and scheduling is effective.

Volume: 17, Issue: 7

A Neural Network-Based Intelligent Image Target Identification Method And Its Performance Analysis

by Xiaofang Li, Yanhong Sun, Ming Tang, Xijun Yan, Yanping Kang
Abstract

The image sensor-based target identification is one of the important for vehicle identification and intelligent transportation applications. Based on neural network classification algorithms, one vehicle identification method is proposed. The main work includes: (1) designing the parameter extraction of vehicle shape based on Sobel operator and mathematical morphology; (2) introducing a vehicle feature extraction method based on “i”-shape model; (3) designing a momentum back-propagation algorithm with the smallest error and a variable learning rate of an exponential fimction, verify the algorithm by simulation experiments. The proposed algorithm combines the advantages of the algorithm with additional momentum and variable learning rate while the convergence speed is accelerated and jumps out of local minimum points. The results of simulation experiments show that the proposed algorithm can quickly and effectively identify the various types of vehicle models.

Volume: 17, Issue: 7

Solution Method Of Optimal Scheme Set For Water Resources Scheduling Group Decision-Mahing Based On Multi-Agent Computation

by Chenming Li, Fengzhou Wang, Xiaodong Wei, Zhenli Ma
Abstract

Water resources allocation and scheduling management is a typical group decision-making problem. Detemvning how to realize the effective coordination between the decision makers, and how to generate the optimal solution set by computation are the keys to the research and development of water resources scheduling group decision support system. However, in the existing group decision support systems and literatures about water resources scheduling, the effective solution methods have not been reported. Firstly, this paper analyzes and designs amulti-Agent computation platform which can support the optimal scheme set, and build a functional structure model of multi-Agent computation platform. Then, it provides an algorithm of group decision making optimal scheme set based on Agent computation. The proposed algorithm generates the optimal scheme set by using the multi-Agent computation platform based on the genetic algorithm. Experimental results show that the proposed algorithm can make the coordination satisfaction degree of each group decision-making converge, and generate the optimal solutions set through the effective computation.

Volume: 17, Issue: 7

A Multi-Agent Simulation Approach To Rumor Spread In Virtual Commnunity Based On Social Network

by Renbin Xiao, Tongyang Yu
Abstract

In general, rumor spread has certain impact on public safety and it has become a universal phenomenon in virtual community. Based on the data collected from Kaixin website, which is a famous social network service and website launched in 2008 in China, it is briefly analyzed the network properties, such as maximum degree, average degree and so on. Then amulti-agent rumor spread model in virtual community is established based on the analysis of rumor spread pattern. According to the proposed model, some simulations are conducted to show the impact of network structure, tolerance frequency, and believing rate to rumor spread. It is also concluded that in this kind of virtual community, rumor cannot be eliminated and its spread is also insufficient; each node’s tolerance frequency can greatly reduce the prevalence of rumor duration; believing rate of each node can change nodes’ trusting states to rumor obviously in the spread process. Finally, it is also suggested that some strategies on Internet culture should be adopted to resist on rumor spread in virtual community.

Volume: 17, Issue: 7

Optimization Of Three-Phase Pwm Rectifier Robust Control System Based On Improved PSO Algorithm

by Guojun Tan, Hui Zhang, Miaowang Qian, Ruiwen Yu
Abstract

This paper proposes a robust H control method for three-phase PWM rectifier. Based on the mathematical model of the PWM rectifier, the state equations which include outside interference are established for the standard setup H control problem with suitable weighting functions. The robust H controller can be derived by solving the Riccati inequality. An improved PSO algorithm is proposed in this paper by bringing the ideas of SA algorithm and chaotic search into the PSO algorithm. And it is used to optimize the parameters of proposed controller. Simulation and experiment results verify that the designed robust H controller has a better external disturbance rejection capability compared with the conventional PI controller. And the improved PSO algorithm has ability to detemune the parameters effectively for the proposedcontroller.

Volume: 17, Issue: 7

Traching Control Of A Redundant Manipulator With The Assistance Of Tactile Sensing

by Jingguo Wang, Yangmin Li
Abstract

This paper presents a method to control the end-effector of a redundant manipulator tracking the surface closely with the assistance of tactile sensing, which is based on the idea of hybrid impedance control method. Not only should the position and force control be implemented, but also the actual contacting between the end-effector and the object’s surface should be monitored and sensed. The feedback contains both the force-torque information and the tactile sensing such as contact state, contact area and so on. Then several strategies of tactile sensing feedback are proposed to combine into the control algorithm. A 5-DOF redundant manipulator equipped with forcetorque and tactile sensors is applied, and simulations and two groups of real experiments for the planar tracking task are made respectively. The results confirmed the effectiveness of the proposed strategies at last.

Volume: 17, Issue: 7

A Special Issue of Intelligent Automation and Soft Computing

by Lizhong Xu, Xiaofang Li, Simon Yang
Abstract

Volume: 17, Issue: 7

A Security Mechanism For RFID With Dependable Proxy

by Jun Zhou, Yongjun Xu, Xiaowei Li
Abstract

RFID (Radio Frequency Identification) is a non-contact auto identification technology widely applied in many fields nowadays, while its security issues also get much concern in practical applications. So far, experts from industry and academia have proposed a series of solutions, mainly including physical methods, security protocols based on cryptography, hardware encryption technique and so on. However, various defects still exist in all the three categories, which may lead to the failure to achieve the security requirements of RFID systems. Aiming to make a further improvement to these solutions, we propose a security mechanism designed with dependable proxy in this paper, which demonstrates a good fusion of physical methods and security protocols. In allusion to the new scheme, we use BAN logic to do the formal analysis to derive the security objectives of our mechanism. Subsequently, given the corresponding theoretic analysis and comparison, it is indicated that the new mechanism can efficiently defend RFID systems against monitoring, deception, tracking, replay attacks, and greatly decrease the possibility of suffering denial of service. The asynchronous problem that may arise on systemic information is also discussed to lessen the authentication failure of the legal tags.

Volume: 17, Issue: 6

AC-RMT: A Fault-Tolerance Smt Architecture Based On Asynchronous Checkpoint

by Jie Yin, Jianhui Jiang, Yu Liu
Abstract

In existing redundant multithreading (RMT) architectures, master threads have to wait for slave threads for comparison at some positions, which may delay the release of rename register file. The quantitative analysis presented in the paper shows that the rename register file is one of the important resources affecting the performance. In order to avoid the waiting of master threads, an asynchronous checkpoint-based redundant multithreading (AC-RMT) architecture is proposed. In such a RMT architecture, two contexts saving rooms are set aside for each thread, one for detecting faults and the other for saving the last checkpoint used for fault restoration, so resources can be released timely. Experiments show that AC-RMT can boost performance more efficiently with lower overhead than that of register value queue free recovery (RVIZF) scheme.

Volume: 17, Issue: 6

Accuracy Analysis Of Earthwork Calculation Based On Triangulated Irregular Network (Tin)

by Xingyao Hao, Yuchun Pan
Abstract

In most of conventional methods for calculating earthwork, the assessment of accuracy contains many uncertainties. This paper discusses the relationship between the accuracy of terrain data and that of the earthwork calculation. The main research method is propagation of error, and a formula is created which expresses the quantity relationship that how the errors of terrain data effects the accuracy of earthwork calculation. Setting the different values of parameters, the relative errors can be calculated. Through adjusting values of parameters, it is easy to make a plan of data acquisition that is cost-optimal and meets the accuracy requirement of the land consolidation. In the end, the calculating methods with different sources of terrain data are discussed.

Volume: 17, Issue: 6

A Simulation Approach To The Control Mechanism Of Individual And Web Site In Malware Spread

by Renbin Xiao, Xiaoguang Gong, Tongyang Yu
Abstract

Malware has become the major threat to security and health of computers and Internet. The multi-agent simulation model of malware spreading is designed based on the Scale-Free network theory, which focuses on the control of its spread. Through simulation experiments, it is found that the speed of malware spreading is inhibited with the increasing of long-lasting immunity node, and is accelerated with that of the infection rate. It is also found that the (learning) ability plays a decisive role on the control of malware spreading and the average connection has important impact on the speed of spreading. The results show that in order to control the spread of malware, intemet users should enhance awareness of malware and solving ability, while the government should strengthen the supervision on the Internet and promote the development of security software through effective measures.

Volume: 17, Issue: 6

Oware: Operand width Aware Redundant Execution for Whole-Processor Error Detection

by Yu Hu, Chen Zhongliang, Xiaowei Li
Abstract

As the feature size of semiconductor technology continues to shrink, high-performance microprocessors are increasingly susceptible to soft errors. Exploiting the fact that narrow-width values universally exist in applications, prior in-register duplication approaches for improving reliability of register file and other data-holding components mitigate performance cost but leave the rest of datapath highly vulnerable. This paper presents a novel whole-processor soft error detection technique to reduce performance degradation by alleviating resource racing, while providing whole-processor error detection via redundant operations. Experimental results show that the IPC of our scheme outperforms conventional symmetric redundant execution by approximately 72.

Volume: 17, Issue: 6

An Effective Bi-Criteria and Contention Awareness Scheduling in Heterogeneous Distributed Systems

by Weipeng Jing, Zhibo Wu, Hongwei Liu, Jian Dong
Abstract

Fault-tolerant scheduling is an important issue for optimal heterogeneous distributed systems because of a wide range of resource failures. In this paper we propose a fault-tolerant scheduling heuristics for precedence task that is based on primary-backup replication scheme on a realistic platform model where communication contention is taken into account. We focus on a bi-criteria approach, where we aim at minimizing makespan (or the schedule length), and the other way take account into the failure probability of the application. We are able to let the user choose atrade-off between reliability maximization and makespan minimization. Major achievements include a low complexity and reduction of the number of additional communications by the replication and clustering mechanism. Simulation results show that in comparison to existing scheduling algorithms, the proposed scheduling algorithm improves the reliability and performance.

Volume: 17, Issue: 6

Reliability Analysis Of SA based Software Deployment with Consideration of System Deployment

by Xihong Su, Zhibo Wu, Hongwei Liu, Xiaozong Yang, Xiao-Zong Yang
Abstract

Software architecture (SA) has been widely advocated as an effective abstraction for modeling, implementing, and evolving complex software systems such as those in distributed, decentralized, heterogeneous and mobile environments. There are two important facets related to this domain: software deployment and reliability. SA based software deployment models help to analyze reliability of system deployments. Though there exist many approaches for architecture-based reliability estimation, little work has been done in incorporating the influence of system deployment and hardware resources. In this paper, a new approach of estimating system reliability at architectural level is proposed. The approach incorporates the influence of system deployment and hardware resources. Additionally, there are many factors influencing system deployment, such as possible restrictions on component location. In order to make fully use of these factors, the multi-dimension factors on system deployment are translated into degree matrices of component dependence and host node dependence. An approximate algorithm, Greedy Deploy algorithm based on greedy algorithm is presented. On the basis of matrices of component dependence and host node dependence, the Greedy Deploy algorithm is used to deploy software components on host nodes. In the evaluation, Greedy Deploy algorithm shows better performance than adaptive greedy algorithm.

Volume: 17, Issue: 6

A Fault-Tolerant Algorithm of Wireless Sensor Network Based on Recoverable Nodes

by Chuang Ma, Hong-Wei Liu, Hai-Ying Zhou, Zhi-Bo Wu, Xiao-Zong Yang, Xi-Bo Xiao
Abstract

In wireless sensor networks, the node faults often occurred in scenes because of severe disturbance, harsh environment and inherent weakness of the networks. In this paper, a fault tolerance directed diffusion protocol, termed fault-tolerant algorithm with recoverable nodes (FTA-RN), is proposed to extend the lifetime of the wireless sensor network based on recoverable host nodes, in which a heartbeat detection algorithm is applied to detect node failure. In FTA-RN, each recoverable intermediate node has a redundant node for fault tolerant function to increase the rate of successful data reception. The nodes are designed based on a kind of recoverable model to prevent the disadvantage of nodes instantaneous fault. Moreover, the energy of host nodes can be used efficiently in recovering model. The simulation result shows that more messages can be received on the sink node using FTA-RN compared with the traditional DD protocol in tolerable amount of delay time and energy consumption, and the lifetime of wireless sensor network in FTA-RN are remarkable longer than traditional DD protocol because of the effect of backup nodes and recoverable host nodes.

Volume: 17, Issue: 6

Modeling Activity Diagrams with Extended Petri Nets

by Nianhua Yang, Huiqun Yu, Hua Sun, Zhilin Qian
Abstract

To enhance trustworthiness, UML (unified modeling language) activity diagrams are transformed into Petri nets for verification and analysis. Data concerned Petri net (DCPN) is proposed for activity diagrams’ modeling. Mapping rules for transfomvng elements in an activity diagram into DCPNs are proposed in both graphical and formal forms. Weaving method is used to compose DCPNs. This paper provides foundation for developing a tool which can automatically transform an activity diagram into an analyzable Petri net. A case study shows the feasibility and applicability of the proposed method.

Volume: 17, Issue: 6

Task Synchronization Process based on Petri Net

by Shuang’e Zhou, Guoping Xiong
Abstract

Task synchronization means that each redundant module has the same executing schedule in each task scheduling cycle of the operating system in the Triple Modular Redundancy (abbreviated TMR) fault-tolerant systems; it faces how to realize the coordination among the three modules. Therefore, it is necessary to investigate the task synchronization process of the TMR fault-tolerant system. In the paper, a task synchronization process of the TMR fault-tolerant system is given, the model is built up by Petri Nets, and the model is analyzed by reachable graphs. The correctness of the Petri Net model is analyzed by reachable graph from the reachability, activity, boundness, and integrity of the model. It shows that the merits of describing the task synchronization process model of Triple Modular Redundancy fault-tolerant system with Petri Net are intuitive, clear, and easy to understand the implementation mechanism of the task synchronization process, and the model is correct.

Volume: 17, Issue: 6

Integrating Geospatial Web Services Into Enterprise Business System based on Service Intelligent Agents and Bayesian Network

by Yang Xiaodong, Cui Weihong, Yang Hao, Li Cunjun, Huang Wenjiang, Jihua Wang
Abstract

Integrating Geospatial Web Services into the enterprise business system seamlessly has become one of the major tasks in the enterprise information construction projects. The authors design a services integration system based on Bayesian Network and Service Intelligent Agents which provides an effective way for the enterprise to integrate Geospatial Web Services into the existing business system seamlessly. In this system, the authors build a Quality of Service reasoning model based on Bayesian network and propose a method of services executing result monitoring and feedback based on Service Intelligent Agents, which can effectively improve the efficiency and the success rate of services discovery and composition. The feedback information of services chains executing will be send to a QoS database, which can help to correct and update the QoS information.

Volume: 17, Issue: 6

A Robust Processing Chain for Face Recognition under Varying Illumination

by Dong Ren, Yuanyuan Fu, Fangmin Dong, Guangzhu Xu
Abstract

In order to make face recognition more reliable under varying illumination, a robust processing chain is presented in this paper. Most of the illumination normalization methods treat all face images in the same way without considering the specific illumination condition of each probe image. For the nearly well-lit face images, they may be misclassified after illumination normalization. But they can be correctly classified without illumination normalization. To address this problem, the illumination quality index (IQI) of face image is proposed. According to the IQI of a probe face image, it can be detemvned whether the illumination normalization should be applied to it. In the proposed processing chain, the probe face image needing no illumination normalization will directly be used for recognition using normalized correlation. Otherwise a new illumination normalization approach, based on the Retinex theory and the total variation under L2 norm (TVL2) constraint model, is conducted on it. The proposed illumination normalization approach utilizes the edge-preserving capability of the TVL2 model, and can effectively weaken the halo effect. Gradient direction and magnitude are extracted from the illumination normalized face image, and then fused at decision level for recognition. The experimental results on Yale B  Extended Yale B’ face database demonstrate the robustness of the proposed processing chain.

Volume: 17, Issue: 6

An Improved Localization Algorithm for Wireless Sensor Network

by Xiaohui Chen, Jing He, Jinpeng Chen
Abstract

In wireless sensor network (WSN), the basic requirement is the localization of nodes, it can make the application of wireless sensor network effectively, so it is the key to improve the accuracy of localization. This paper aims at the accuracy problem by range-based localization model, and two improved algorithms that based on negative gradient method are proposed for nodes localization errors in wireless sensor networks: segmentation Learning rate algorithms based on negative gradient; FR conjugate negative gradient algorithms. Under some priori assumptions, this paper gives the basic principles and two implementation algorithms. It is proved that these two improved algorithms are more accurate than some existing algorithms through the simulation results using Matlab.

Volume: 17, Issue: 6

A Novel Security Mechanism for Hybrid Encryption in Mineral Management Information System

by Tingyao Jiang, Heng Yu, Lele Cui
Abstract

To increase the tax collection of mineral resources and enhance the management of resource exploitation, computerized mineral management information systems (HMIs) are developed and applied to the government management system. The MMIS are run at checking portals that inspect trucks transporting minerals. Only the trucks with the special pemut (also called ticket) can pass through the checking portal after the ticket is correctly authenticated by the MMIS. Due to the huge economic benefits of the special tickets, the tickets are frequently fabricated. Because tickets are exoteric and current, the security of them becomes an urgent problem. In this paper, a novel security mechanism is proposed for the mineral management information system, in which the hash fixnction, AES (Advanced Encryption Standard) encryption algorithm and ECC (Elliptic Curve Cryptography) encryption algorithm are hybrid to encode and encrypt the data involved with the ticket according to different sensitivity levels. Data are dynamically and separately stored into the disk storage or RFID (Radio Frequency Identification) tags storage. Identity authentication of the ticket is achieved by data in the two-dimensional bar code printed on the ticket, the RFID tag that is installed on the truck with the ticket, and the disk storage of MMIS database. MMIS equipped with the presented ticket encryption mechanism is secure and results in great economic and society interests.

Volume: 17, Issue: 6

A Special Issue of Intelligent Automation and Soft Computing

by Dong Ren, Tingyao Jiang, Simon Yang
Abstract

Volume: 17, Issue: 6

An Improved Integrated Tender Evaluation Method Based On Analytic Hierarchy Process

by Tingyao Jiang, Xiao Chen, Dewei Shu
Abstract

The integrated evaluation method is the most important tender evaluation method in material procurements. However, subjective consciousness of the evaluation experts and the weights of the technical conditions, business conditions and price conditions have excessive impact on the evaluation results, which make the process and result of tender evaluation undependable. In this paper an improved integrated evaluation method is presented based on the analytic hierarchy process (AHP). The technical conditions, business conditions and price conditions of integrated evaluation method are used as the criterion level of AHP; and the choice of suppliers is used as the scheme level of AHP by the proposed method. Through single-level sorting and then total- sorting among levels according to the judgment matrix, the winning bidder list is achieved. Analysis and actual case computing results show that the proposed method improves the dependability on the process and result of bid evaluation, which ensures the fairness of procurement activities and quality of the product purchased.

Volume: 17, Issue: 5

Analysis Of Bridge Safety Assessment With Correlation Between Measuring Points For Bridge Health Monitoring

by Jianting Zhou, Jianxi Yang
Abstract

By using linear statistical coefficients such as Pearson, Spearman and Kendall’s, and nonlinear methods such as time-delayed transfer entropy and mutual information, the correlation between the observation stations of bridge health monitoring system is obtained. A model for predicting structural safety based on the correlation coefficients is established, which provides a new research method for bridge health assessment and prediction based on bridge health monitoring system

Volume: 17, Issue: 5

Weighted Fusion Of Gradient, Vertical Gradient And Horizontal Gradient In Logarithm Domain For Face Recognition Under Varying Lighting

by Yuanyuan Fu, Dong Ren, Guangzhu Xu, Simon Yang
Abstract

In this paper, the illumination problem of face recognition is investigated. A conclusion is drawn that the logarithm vertical gradient (LVG) of face images is more robust to varying lighting than the logarithm horizontal gradient (LHG). When the variation of illumination is large, LVG will be a better representation of the face image than logarithm gradient (LG). Although both LUG and LHG can weaken shadow boundaries, they lose the vertical component, horizontal component of facial objects' edges, respectively. LG can capture more information of the facial objects' edges, although it has no ability to weaken the shadow edges. By taking advantages of LVG, LHG and LG, a new algorithm integrating the LUG, LHG and LG at the decision level is proposed for robust face recognition under varying lighting conditions. The experimental results on the Yale B, CMLJ-PIE, and ‘Yale B Extended Yale B’ face databases demonstrate the effectiveness of the proposed method.

Volume: 17, Issue: 5

Speed Up Reliability Model Optimization With Hypervolume Contribution Calculating Algorithm

by Xiuling Zhou, Ping Guo, C. L. Chen
Abstract

Software dependability modelling involves simultaneous consideration of several incompatible and often conflicting objectives, while hypervolume-based multi-objective evolutionary algorithm (MOEA) has been shown to produce better results for multi-objective problem in practice. A frame of reliability model optimization with hypervolume based MOEA is presented. Focusing on the key issue of hypervolume based MOEA, a new algorithm, set hypervolume contribution by slicing objective (SHSO), is proposed for calculating the exclusive hypervolume contribution of a subset to the whole nondominated set directly for small dimension. For the special case of SHSO, CHSO (the contribution of a point to hypervolume by slicing objective) is improved with heuristics. The feasibility and efficiency of developed algorithms are shown by experiments.

Volume: 17, Issue: 5

Implementation And Optimization For Tate Pairing

by Guangming Dai, Maocai Wang, Lei Peng, Ruijie Qin
Abstract

Tate pairings has found several new applications in cryptography. However, how to compute Tate pairing is a research focus in all kinds of applications of pairing-based cryptosysterns (PBC). In the paper, the structure of Miller's algorithm is firstly analyzed, which is used to implement Tate pairing. Based on the characteristics that Miller's algorithm will be improved tremendous if the order of the subgroup of elliptic curve group is low hamming prime, a method of generating primes with low hamming is presented. Then, a new method for generating parameters for PBC is put forward, which enable it feasible that there is certain some subgroup of low hamming prime order in the elliptic curve group generated. Moreover, an optimization implementation of Miller's algorithm for computing Tate pairing is given. Finally, the computation efficiency of Tate pairing using the new parameters for PBC is analyzed, which saves 25.4 of the time to compute the Tate pairing.

Volume: 17, Issue: 5

Anomaly Detection Using Neighborhood Negative Selection

by Dawei Wang, Yibo Xue, Dong Yingfei
Abstract

Negative Selection Algorithms (NSAs) have been widely used in anomaly detection. As the security issue becomes more complex, more and more anomaly detection schemes involve high-dimension data. NSAs however perform poorly on effectiveness and efficiency when dealing with high-dimension data. To address these issues, we propose a Neighborhood Negative Selection (NNS) algorithm in this paper. Instead of a single data point, NNS uses a neighborhood to represent a self sample (or a detector). As a result, the training efficiency is greatly improved. We further introduce a special matching mechanism to limit the negative effect of the dimensionality of a shape space and improve the detecting performance in high dimensions. The experimental results show that NNS can provide a more accurate and stable detection performance. Meanwhile, both theoretical analysis and experimental results show that NNS further improves the training efficiency.

Volume: 17, Issue: 5

Fast Navigation-Placement Tree Algorithm For Reconfigurable Computing System

by Huan-Huan Song, Shu-Zong Wang, Li-Bing Shao, Li Chen, Xiao-Fang Zhang
Abstract

Due to its potential to greatly accelerate a wide variety of applications, reconfigurable computing has become a subject of a great deal of research. In this paper, we explore the representation models of 1D routing structure and 2D routing structure, we also focus on the task scheduling that targets these representation models. A fast navigation-placement tree algorithm is proposed to formulate the scheduling problem for guarantee-based scheduling of hardware tasks. We present two heuristics, the horizon and the vertical technique, which can be summarized on bin-packing and applied to the problem of placing tasks on guarantees. Simulation experiments evaluate the performance and runtime efficiency of the proposed algorithm.

Volume: 17, Issue: 5

Roubust Visual Object Tracking Using Covariance Features In Quasi-Monte Carlo Filter

by Xiaofeng Ding, Lizhong Xu, Xin Wang, Guofang Lv, Xuewen Wu
Abstract

Image covariance features, enabled with efficient fusion of several different types of image features without any weighting or normalization, have low dimensions. The covariance-based trackers are robust and versatile with a modest computational cost. This paper investigates an object tracking algorithm using a sequential quasi-Monte Carlo (SQMC) filter combined with covariance features. The covariance features are used not only to model target appearance, but also to model background. The dissimilarity of target and background is integrated in the SQMC filter as an additional measurement for the particle weight. A target model update strategy using the element of Riemannian geometry is proposed for the variation of the target appearance. Comparison experiments are conducted on several image sequences, and the results show that the proposed algorithm can successfully track the object in the presence of appearance changes, cluttered background and even severe occlusions.

Volume: 17, Issue: 5

A Hybrid Queueing Model With Imperfect Debugging For Component Software Reliability Analysis

by Chun-Yan Hou, Gang Cui, Hong-Wei Liu, Lian-Ke Zhou
Abstract

With the growing size and complexity of software applications, research in the area of component software reliability analysis has gained prominence. To ensure analytical tractability, traditional approaches usually ignore fault correction process of component software based on simplifying assumptions of instantaneous and perfect debugging. As a result, the estimates obtained from these models tend to be optimistic. To obtain realistic estimates, it is desirable that the assumptions of instantaneous and perfect debugging be amended. In this paper we discuss fault repair policy according to which debugging to various components may be conducted in integration testing of component software. We then propose a hybrid infinite server queueing model to describe fault correction process with consideration of the possibility of imperfect debugging. Based on fault detection process and repair policy, the model can be resolved to represent component software reliability growth during integration testing. Finally, the evaluation experiment shows the effectiveness of the model.

Volume: 17, Issue: 5

A New Verifiable Threshold Decryption Scheme Without Trusted Center

by Xu Feng, Lv Xin, Jia Likun
Abstract

In the most of scenarios, the trusted center dose not existed. A new verifiable threshold decryption scheme without trusted center was proposed in this paper. In order to publicly verify the identity of decryption member, the scheme forces the member to submit a commitment at the beginning of the process. Moreover, the scheme has several advantages such as cheat-proof and dynamic member revoking.

Volume: 17, Issue: 5

A Novel Aco-Based Multicast Path Algorithm In Hypercube Networks

by Hongwei Wang, Zhibo Wu, Xiaozong Yang, Hongwei Liu
Abstract

This paper proposes an ACO (ant colony optimization)-based multicast path algorithm (AMPA) to reduce the multicast communication traffic in hypercube networks. According to the high regularity of hypercube networks, an optimized distributed algorithm (Opt-AMPA) for AMPA is proposed by clustering strategy, whose time cost can fill the requirements of the actual networks. The simulation results indicate that effectiveness of the algorithm can be improved drastically using Opt-AMPA.

Volume: 17, Issue: 5

Continuous Wavelet Analysis Based Spectral Feature Selection For Winter Wheat Yellow Rust Detection

by Zhang Jingcheng, Luo Juhua, Huang Wenjiang, Wang Jihua
Abstract

This study aims at identifying some mechanisms based on spectral features through continuous wavelet (CWT) analysis, and examining their estimating and discriminating power. In 2003, an inoculation of yellow rust fungal was conducted. Field measurements of canopy reflectance and biochemical properties were made with 5–7 day intervals during key growing period. Through atwo-tailed paired student t-test, it was found that the variation of chlorophyll content is closely associated with the yellow rust infection. Based on this relationship, four spectral features were thus identified by CWT analysis. According to the results of stepwise linear regression and partial least squares (PLS) regression, the estimating power of those spectral features was not satisfied. However, the discriminating power of those features was revealed by linear discrimination analysis (LDA) and quadratic discriminate analysis (QDA), which yielded a success rate of 76.4. Therefore, the CWT analysis and discriminate analysis are of great potential in identifying the yellow rust infected wheat plants.

Volume: 17, Issue: 5

Multiple Classifier Combination For Recognition Of Wheat Leaf Diseases

by Yuan Tian, Chunjiang Zhao, Shenglian Lu, Xinyu Guo
Abstract

Wheat industry is an important constituent of Northern China's overall agricultural economy. Proper disease detection using computer vision and pattern recognition has being investigated to minimize the loss, and fmally achieve intelligent healthy farming. This paper proposes a new strategy of Multi-Classifier System based on SVM (support vector machine) for pattern recognition of wheat leaf diseases for higher recognition accuracy. Diseased leaf samples with Powdery Mildew, Rust Puccinia Triticina, Leaf Blight, Puccinia Striifomus were collected in the field and images were captured before a uniform black background. Three feature sets including color feature set, shape feature set and texture feature set were created for classification analysis. The proposed combination strategy was based on stacked generalization and included two-level structure: base-level was a module of three kinds of SVM-based classifiers trained by three feature sets and meta-level was one module of SVM-based decision classifier trained by meta-feature set which are generated through a new data fusion mechanism. Compared with other single classifiers and other strategy of classifier ensembles for wheat leaf diseases, this approach is more flexible and has higher success rate of recognition.

Volume: 17, Issue: 5

Automatic Segmentation Of Infrared Image Based On x

by Xinsai Wang, Yu Liu, He Jing
Abstract

The accuracy and detecting distance of infrared imaging system is greatly affected by segmentation and recognition algorithms. By analyzing gradient distribution characteristics of infiared images, the gradient histogram of both targets and background can be fitted by xz distribution density fimction with different degrees of freedom.According to the above statistical characteristics and the least classified probability, an adaptive segmentation algorithm with automatic estimation of gradient histogram threshold is proposed. The algorithm is validated with infrared images and is proved to be effective and practical.

Volume: 17, Issue: 5

Edge-Based Blotch Identification Algorithm In Archive Film Restoration

by Junqing Liu, Dangui Xie, Dong Ren, Yong Liu, Shuifa Sun
Abstract

Previous blotch identification algorithms perform identification process for each pixel in current frame, which are time-consuming. To deal with this issue, a blotch identification algorithm based on edge detection is proposed in this work. Above all, edges of current frame are extracted by edge detectors, blotch identification area is limited to edge pixels and pixels nearby. To further reduce time complexity, anon-blotch edge removal approach is proposed to remove partial non-blotch edge points.T hen, a scanning method combined with local motion compensation (MC)-based SROD blotch identification algorithm is used to identify all blotches from remaining edge points. Abundant experiments have been done to verify our method and compared our method to other two typical previous algorithms. Experimental results show that the proposed approach gives successful identification performances, provides the computational simplicity and outperforms classical algorithms.

Volume: 17, Issue: 5

A Special Issue of Intelligent Automation and Soft Computing

by Tingyao Jiang, Dong Ren, Simon Yang
Abstract

Volume: 17, Issue: 5

A Sliding Mode Based Neural Network For Data Fusion And Estimation Using Multiple Sensors

by Seta Bogosyan
Abstract

In this study, a Neural Network (NN) based data fusion and estimation algorithm is developed, which could be applied in monitoring and detection applications that use measurements coming from multiple sensors. For NN based applications, a fast training approach which can also guarantee the global minimum is highly desirable and is subject for ongoing research. For this purpose, in this study, a novel robust training approach is developed for NNs, using achattering-free sliding mode (SM) technique derived based on the Lyapunov theory. The proposed training approach exploits the robustness of SM theory, ensures fast training and global convergence while also providing a smoother estimation output due to eliminated chattering. In this study, Kalman filters are also designed to filter and fuse the data output of high bandwidth sensors with an aim to reduce the number of inputs, hence computational complexity in NNs. The developed NN algorithm, which has a feedforward structure and three layers, is tested using actual data collected from multiple sensors in a nuclear power plant, with the specific aim of estimating the neutron detector output. The performance of the novel SM based training approach is compazed against the Levenberg-Marquardt (LM), which is currently the most commonly method for fast training in NNs. The performance of the proposed scheme demonstrates a considerable improvement over LM in terms of estimation accuracy and convergence rate. The results motivate the utilization of the SM based NN configuration in a vaziety of monitoring, detection and diagnostic applications which involve measurements from multiple sensors.

Volume: 17, Issue: 4

Fuzzy Information Retrieval Based On A New Similarity Measure Of Generalized Fuzzy Numbers

by Shi-Jay Chen
Abstract

This study presents a new similarity measure based on the geometric-mean averaging operator to handle the similarity measure problems of generalized fuzzy numbers. Some properties of the proposed similarity measure are demonstrated, and 26 sets of generalized fuzzy numbers are used to compare the proposed method with existing similarity measures. Comparison results indicate that the proposed similarity measure is better than existing methods. Finally, the proposed similarity measure is applied to propose an algorithm for handling fuzzy-number information retrieval problems.

Volume: 17, Issue: 4

Automatic Summarization Based On Automaticallyinduced Ontology

by Hei-Chia Wang, Tian-Hsiang Huang, Chia-Tzung Liu
Abstract

In this paper, we proposed an ontology-based method for summazizing documents. Automatic summarization based on ontology is considered better than other methods. However, existing methods require the ontology to be manually constructed and maintained, which is subjective and time-consuming, so the ontology-based method is not used often. In addition, existing summarization methods consider only similazities between sentences and ontology terms, ignoring “semantics,“ “reading comprehension,” and “topic-related” features. To improve the summarization, we propose a novel method of fully automatic ontology construction and text summarization. The proposed method fast ”learns” the ontology from selected documents. After the domain ontology is generated, other technologies are used to evaluate semantics, reading comprehension, and topic relatedness. We evaluate the proposed method by using it to summarize journal papers, and find that it outperforms existing methods.

Volume: 17, Issue: 4

Fast KNN Classification Based On Softcore Cpu And Reconfigurable Hardware

by Hui-Ya Li, Yao-Jung Yeh, Wen-Jyi Hwang
Abstract

This paper presents a novel architecture for k-nearest neighbor (kNN) classification using field programmable gate array (FPGA). In the architecture, the first k closest vectors in the design set of a kNN classifier for each input vector are first identified by perfomung the partial distance search (PDS) in the wavelet domain. To implement the PDS in hardware, subspace search, bitplane reduction, multiple-coefficient accumulation and multiple-module computation techniques are employed for the effective reduction of the area complexity and computation latency. The proposed implementation has been embedded in a softcore CPU for physical performance measurement. Experimental results show that the implementation provides acosteffective solution to the FPGA realization of kNN classification systems where both high throughput and low area cost are desired.

Volume: 17, Issue: 4

Robust Fuzzy Corner Detector

by Erik Cuevas, Daniel Zaldivar, Marco Pérez-Cisneros, Edgar Sánchez, Marte Ramírez-Ortegón
Abstract

Reliable corner detection is an important task in detemvning the shape of different regions within an image. Real-life image data are always imprecise due to inherent uncertainties that may arise from the imaging process such as defocusing, illumination changes, noise, eta Therefore, the localization and detection of corners has become a difficult task to accomplish under such imperfect situations. On the other hand, Fuzzy systems are well known for their efficient handling of impreciseness and incompleteness, which make them inherently suitable for modelling corner properties by means of a rule-based fuzzy system. The paper presents a corner detection algorithm which employs such fuzzy reasoning. The robustness of the proposed algorithm is compared to well-known conventional corner detectors and its performance is also tested over a number of benchmark images to illustrate the efficiency of the algorithm under uncertainty.

Volume: 17, Issue: 4

Feature Selection For The Prediction Of Tropospheric Ozone Concentration Using A Wrapper Method

by C. Sakar, Goksel Demir, Olcay Kursun, Huseyin Ozdemir, Gokmen Altay, Senay Yalcin
Abstract

High concentrations of ozone (03) in the lower troposphere increase global warming, and thus affect climatic conditions and human health. Especially in metropolitan cities like Istanbul, ozone level approximates to security levels that may threaten human health. Therefore, there are many research efforts on building accurate ozone prediction models to develop public warning strategies. The goal of this study is to construct a tropospheric (ground) ozone prediction model and analyze the effectiveness of air pollutant and meteorological vaziables in ozone prediction using artificial neural networks (ANNs). The air pollutant and meteorological variables used in ANN modeling are taken from monitoring stations located in Istanbul. The effectiveness of each input feature is determined by using backward elimination method which utilizes the constructed ANN model as an evaluation function. The obtained results point out that outdoor temperature (OT) and solar irradiation (SI) are the most important input features of meteorological variables, and total hydrocarbons (THC), nitrogen dioxide (NOz) and nitric oxide (NO) are those of air pollutant variables. The subset of pazameters found by backward elimination feature selection method that provides the maximum prediction accuracy is obtained with six input features which are OT, SI, NO2, THC, NO, and sulfur dioxide (SOZ) for both validation and test sets.

Volume: 17, Issue: 4

A Hybrid Approach for Multi-Agent Learning Systems

by Jong Kuo, Fu Huang
Abstract

This study proposes a hybrid approach to construct amulti-agent learning system that is applied to RoboCup (Robot world cup tournament) soccer game. RoboCup competitive games involve the complicated system behavior of multiple agents, which makes it a popular research domain in recent years. The goal of RoboCup is to promote AI and robotics, and some researchers even hope that RoboCup robots can eventually defeat human soccer players. A hybrid approach called the Case Based Reasoning-Genetic Algorithm (CBR-GA) was used to provide a better strategy for robots in their planning under all kinds of conditions and to store experiences learned during the game for further use. In addition, the Rule Based Reasoning (RBR) method was integrated to improve the defect of CBR-GA. Using the hybrid approach without pre-defined knowledge and a complicated mathematical basis, robots can learn and accumulate experience to become smarter. The robots not only learn how to score but also figure out how to avoid making same mistakes. Finally, the effectiveness of this proposed method was verified by implementing it in the RCBR-GA multi-agent learning system and by making comparisons with other learning approaches to prove its superiority.

Volume: 17, Issue: 3

Marine Vessels Acoustic Radiated Noise Classification in Passive Sonar Using Probabilistic Neural Network and Spectral Features

by Mehdi Farrokhrooz, Mahmood Karimi
Abstract

Development of intelligent systems for classifying marine vessels based on their acoustic radiated noise is of major importance in the sonar systems. This paper focuses on three topics. The fast topic is applying some modifications to the conventional Probabilistic Neural Network (PNN), as a common classifier in supervised pattern recognition, and suggesting a new configuration of PNN which we call it, Multi-Spread Probabilistic Neural Network (MSPNN). The second topic is proposing a method for estimating the required spread values of MSPNN from training data. The third topic is introducing discriminating features which can be used for ship noise classification. These features are: the poles of autoregressive (AR) model with proper order, the coefficients of AR model with proper order and six features which are directly extracted from Power Spectral Density (PSD) of acoustic radiated noise of marine vessels. The performance of the conventional PNN and the suggested multi-spread PNN in classifying real ship noise data will be examined in this paper. A bank of 71 files of real radiated ship noise data is used for this performance evaluation. The results of this performance examination show that the proposed features are suitable for ship noise classification and the performance of the multi-spread PNN is generally better than the conventional PNN.

Volume: 17, Issue: 3

Optimal-Feedrate Interpolation for Machining Parametric Curves

by Yih-Fang Chang, Truong-Giang Nguyen
Abstract

In high-speed precision machining systems, trajectory requires very small feedrate fluctuation and contour error, which can be achieved with pazametric interpolation. This paper proposes aspeed-controlled interpolation method based on optimal-feedrate algorithm. The real-time interpolation method was taken full its advantages, the chord error and the difference between the orientation of tangent vector of the curve at current point and previous point were repeatedly checked through the whole interpolation process. If either chord error exceeded the prescribed tolerance or sharp corner was detected, the feedrate in the proposed interpolation method was automatically adjusted in order to confine the chord error within the prescribed tolerance. A parametric curve, determined by the non-uniform rational B-spline (NiJRBS), was employed to test the feasibility and precision of the proposed interpolation method.

Volume: 17, Issue: 3

Simultaneous Vibration Reduction and Attitude Control of Flexible Spacecraft with On-Off Actuators

by Qinglei Hu
Abstract

This paper deals with the problem of simultaneous vibration suppression and robust attitude control of orbiting spacecraft with flexible appendages. Based on integral vaziable structure control theory, a discontinuous attitude control law is fustly derived to achieve the desired position of the spacecraft, taking explicitly into account external disturbance and nonlinearity. To reconstruct estimates of the system states for use in a full information variable structure control law, an asymptotic variable structure observer is also employed and the fulfillment of sliding condition, including the case when estimated states aze used, is also verified. To achieve the residual vibration free, the shaped input attenuator is also employed after the end of input in an open-loop manner. In addition, the system also employs the pulse-width modulation technique to achieve the desired control torque signals. The performance and efficacy of the proposed control scheme are illustrated with a compazison of different maneuvering strategies by extensive simulation.

Volume: 17, Issue: 3

TWO-WHEELED PIEZOELECTRIC SYSTEM

by Li-Hong Juang
Abstract

Two-wheeled piezoelectric system is proposed for applications in micro-stepping displacement devices. The system includes a beam and two displacement members which are respectively pivoted on the beam. Two displacement members are not rotatable. In addition, each displacement member includes a wheel sheet and a piezoelectricity element embedded on its surface. When the piezoelectricity element generates and transmits power to the wheel sheet, the wheel induces vibration and deformation. Therefore, due to the wheel sheets and the touched ground involving their relative motion, the displacement device can move and orient its motion direction in a micro manner. The wheel piezoelectric system is direct movement, no rotor requirement. In this research, a 3-D mechanical element with an extra electrical degree of freedom is employed to simulate the dynamic vibration modes of the linear piezoelectric, mechanical and piezoelectric-mechanic behaviours of the wheel piezoelectric system.

Volume: 17, Issue: 3

Forming Neural Networks by Means of Assembler Encoding–Preliminary Report

by Tomasz Praczyk
Abstract

This paper presents anew neural network encoding method called Assembler Encoding. The method was tentatively tested in the predator-prey problem. In the experiments, neural networks created by means Assembler Encoding, were used to control predators whose task was to capture a fast moving prey behaving according to a simple strategy. To compare Assembler Encoding with another neural network encoding method, in the experiments, a co-evolutionary version of simple connectivity matrix was also applied. The results of the experiments are included in the final part of the paper.

Volume: 17, Issue: 3

An Intelligent and Automatic Face Shape Prediction System From Fingerprints

by Seref Sagiroglu, Necla Ozkaya
Abstract

Volume: 17, Issue: 3

A New Mixed Least Mean Square and Least Mean Fourth Algorithm for Multilayer Perceptron Fast Training

by Sabeur Abid, Farhat Fnaiech, B. W. Jervis
Abstract

In this work a new fast training algorithm for the multilayer perceptron (MLP) is proposed. This algorithm is based on optimising a criterion formed from the Mean Squared and Mean Fourth power errors, resulting in a modified form of the Standard Back-Propagation (SBP) algorithm. In this criterion, the mean fourth power error signal is appropriately weighted. The choice of the weighting parameter is evaluated via rank convergence series analysis and asymptotic constant error values. The same minimisation procedure is used as for the SBP algorithm. The performance of the proposed algorithm is compared with other existing algorithms such as the SBP algorithm, the Conjugate Gradient algorithm (CG), the Variable Learning Coefficient algorithm (VLC) and others. Many experiments have been performed to highlight the superiority of the new proposed algorithm in terms of reducing the number of learning iterations, computation time, sensitivity to weight initialisation and its effective generalisation setting.

Volume: 17, Issue: 3

Image Forgery Using An Enhanced Bayesian Matting Algorithm

by I-Cheng Chang, Chieh-Jung Hsieh
Abstract

The development of forgery techniques for multimedia has recently become an important research topic This paper proposes a new method to construct a tampered image based on the matting approach. Most previous matting techniques focus on the alpha estimation of the source image, since an accurate alpha matte can help with compositing the selected object into a new scene. However, the lighting conditions are inherently assumed to be coherent between the source image and the new scene. We propose an enhanced Bayesian matting method that adopts a new nearest-neighbor sampling method to extract color information. It can produce a more accurate alpha matte than previous methods, especially on the fuzzy boundaries. Furthermore, the paper deals with the lighting consistency problem. The proposed approach analyzes the color variations and shading effect and then adjusts the extracted foreground object to combine with the new background. Experimental results demonstrate the effectiveness of our method by showing the forged results of the fuzzy object and the new background under different lighting conditions.

Volume: 17, Issue: 2

An Efficient Block-Based Fragile Watermarking System for Tamper Localization and Recovery

by Venkata Edupuganti, Frank Shih, I-Cheng Chang
Abstract

In this paper we present an efficient block-based fragile watermarking system for tamper localization and recovery of images. We apply the Cyclic Redundancy Checksum (CRC) to authenticate the feature of a block stored in a pair of mapping blocks. The proposed scheme localizes the tampered area irrespective of the gray value, and the detection strategy is designed in such a way to reduce false alarms. It can be applied on grayscale and color images. We conduct malicious attacks using different cropping patterns, sizes, and shapes. Experimental results show that the proposed system outperforms the existing schemes in terms of the recovery rate of modified blocks, the support against the vector quantization attack, the localization of the tampered area, and the reduction of false alarms.

Volume: 17, Issue: 2

Hierarchical Fragile Watermarking Scheme For Image Authentication

by Shinfeng Lin, Zong-Lin Yang
Abstract

In this paper, a simple hierarchical fragile watermarking scheme for image authentication is proposed. The important features and parity bits of an image are embedded by modifying the pixel value of the host image. Once an image is tampered by other users or corrupted by transmission, the parity bits and important features can be used to detect and recover the image. The method is effective because the detection and recovery is hierarchical structured such that the accuracy of damaged location and the quality of recovered image can be ensured and enhanced. Experimental results demonstrate the effectiveness of the proposed method.

Volume: 17, Issue: 2

Reversible Visible Watermarhing with Lossless Data Embedding Based on Difference Value Shift

by Xinpeng Zhang, Shuozhong Wang, Guorui Feng
Abstract

This paper fast proposes a novel lossless data-hiding method, in which the magnitudes of gray level differences in pixel pairs are slightly increased so that the region of differences with small magnitudes can be used to carry the hidden message. Overflow is avoided by prohibiting modifications to certain pixel pairs, and a small number of labels marking these exceptions are also embedded for perfect recovery of the host. Since this method avoids embedding any compressed data of location map or original data, the payload-distortion performance is very high. The lossless data-hiding method is then applied to implement a reversible visible watermarking scheme. After semi-transparently embedding a binary watermark image, some additional data about the mark and the host image are also inserted in the lossless manner. When the visible watermark is extracted from the marked version, the original host image can be completely restored without any error.

Volume: 17, Issue: 2

A New Robust Watermarhing Scheme Based on Shuffled Frog Leaping Algorithm

by Xia Li, Lingjun Liu, Na Wang, Jeng-Shyang Pan
Abstract

A new robust watermarking method, named SFLA-QIM, is proposed based on the shuffled frog leaping algorithm (SFLA) and quantization index modulation (QIM). The shuffled frog leaping algorithm is utilized to find out the optimal embedding position and adaptive quantization step for embedding watermark into a carrier image in the framework of QIM. A carefully chosen fitness function is designed in terms of the Peak Signal to Noise Ratio (PSNR) and the Normalized Correlation (NR) value to achieve high transparency and robustness. The proposed scheme is blind. Compared with other quantization index related watermarking methods, SFLA-QIM exhibits satisfactory robustness against a wide variety of attacks such as amplitude scaling, filtering, noise addition, cropping and JPEG compression.

Volume: 17, Issue: 2

On the Secure Watermark Embedding Scheme Based on Selective Encryption

by Xi Chen, Shiguo Lian
Abstract

Some joint fingerprinting and decryption (JFD) schemes used for copyright protection were reported recently. However, most of them need to be investigated before practical applications. In this paper, the typical one proposed by Lemma et al. is investigated and evaluated. Since this scheme aims to distribute multimedia content by encryption and watermarking, some important performances determine its practicability, including the perceptual security of the encryption operation, the imperceptibility of the embedded watermark and the robustness of the embedded watermark. Some flaws are found in the scheme, such as the low encryption strength, the data overflow caused by encryption decryption and the low correlation value caused by collusion, which degrade the performances greatly. To improve the scheme, some means are proposed, including adaptive media preprocessing, chaos-based selective media module addition and collusion-resistant fingerprint encoding. Comparative experiments show that better performances are obtained by the improved means. The analysis method proposed in this paper can be used to investigate some other JFD schemes.

Volume: 17, Issue: 2

Game-Theoretic Analyses and Simulations of Adoptions of Security Policies for Drm in Contents Sharing Scenario

by Zhiyong Zhang, Qingqi Pei, Jianfeng Ma, Lin Yang
Abstract

A legitimate contents sharing is an essential functionality of DRM (Digital Rights Management)-enabling contents industry and its value chain extension. In order to effectively choose and deploy some typical security policies in a contents sharing scenario, we introduced game theory to analysis the mutual influence of adoptions of trusted computing enabling enhanced security policies on benefits of two stakeholders, which are DRM Providers and contents Sharer who is a category of consumers. A dynamic and mixed game and its algorithm were proposed, where Sharer’s strategies were whether to employ the trusted computing enabling devices and related components or not, as well as Providers’ strategies included entirely general security, entirely enhanced security and dynamic security policies. We concluded from both game-theoretic analyses and Swarm simulation experiments that the number of acquired sharable digital rights and security cost have a direct effect on Sharers choices of the enhanced security policy, and also their different basic sharing modes including partial, modest and extensive sharing, further influence the choice of Providers. Besides, with respect to the mixed sharing mode far more similar to a real contents sharing scenario, Dynamic security strategy is superior to the entirely enhanced security in the context of limited sharable rights and higher security costs, but with the acquisition of much more rights and the decrease of enhanced security overhead, the latter strategy would be optimal and stable as a Nash Equilibrium for stakeholders, in combination with the exploitation of effective business models of contents industry.

Volume: 17, Issue: 2

A Special Section of Intelligent Automation and Soft computing

by Shiguo Lian, Frank Shih
Abstract

Volume: 17, Issue: 2

A Hybrid Method for Intrusion Detection With Ga-Based Feature Selection

by Zhi-Xian Chen, Hao Huang
Abstract

Traditional intrusion detection techniques examine all features to detect intrusion or misuse patterns. But all features are not relevant and some of them may be redundant and contribute little to the detection process. Irrelevant and redundant features may lead to complex intrusion detection model as well as poor detection accuracy. In this paper, we propose and investigate a novel hybrid feature selection method to intrusion detection based on fusion of Extension Matrix (EM) and Genetic Algorithm (GA), which employs a combination of EM and GA through genetic operation, and it is capable of building an optimal detection model with only selected important features and their specific values. Experiment results show the achievement of high correct detection rates and tolerable low false positive rates based on benchmark KDD Cup 99 data sets.

Volume: 17, Issue: 2

Fuzzy Pid Control Via Modified Takagi-Sugeno Rules

by B.M. Mohan
Abstract

We present a novel simplest fuzzy PID controller using a modified Takagi – Sugeno (TS) rule base. Mathematical model of the controller is derived and analyzed. The unique features of the controller are: (i) the consequent part of the rule base is closely related to the rule base of Mamdani type controller, and (ii) the proportional, integral and derivative gains vary as the output of the system under control varies. Two illustrative examples are included, and demonstrated how the proposed controller outperforms its linear counterpart.

Volume: 17, Issue: 2

Ship Course-Keeping Algorithm Based On Knowledge Base

by Piotr Borkowski, Zenon Zwierzewicz
Abstract

The article presents an original ship course-keeping algorithm based on a knowledge base. Its integral part is a computer-borne ship movement dynamical model based on a set of signals obtained from the object's input and output. This way problems occurring while designing classic control algorithms for a complex, non-linear ship model have been avoided. The presented methodology is general in the sense that it can be also applied in other ship control tasks or other dynamic objects. The proposed intelligent course-keeping system has been verified via simulation. The designed algorithm was compared to LQR controller as well as feedback linearizing one. The results prove high-quality performance of the proposed method. It concerns minimizing the steering quality criterion, control time and over-regulation at a step change of the preset course.

Volume: 17, Issue: 2

An Optimized Recursive Learning Algorithm for Three-Layer Feedforward Neural Networks For Mimo Nonlinear System Identifications

by Daohang Sha, Vladimir Bajic
Abstract

Back-propagation with gradient method is the most popular learning algorithm for feed-forward neural networks. However, it is critical to determine a proper fixed learning rate for the algorithm. In this paper, an optimized recursive algorithm is presented for online learning based on matrix operation and optimization methods analytically, which can avoid the trouble to select a proper learning rate for the gradient method. The proof of weak convergence of the proposed algorithm also is given. Although this approach is proposed for three-layer, feed-forward neural networks, it could be extended to multiple layer feed-forward neural networks. The effectiveness of the proposed algorithms applied to the identification of behavior of a two-input and two-output non-linear dynamic system is demonstrated by simulation experiments.

Volume: 17, Issue: 2

Design Of A Prototype Underwater Research Platform For Swarm Robotics

by Matthew Joordens, Mo Jamshidi
Abstract

To perform under water robotic research requires specialized equipment. A few pieces of electronics atop a set of wheels is not going to cut it. An underwater research platform must be waterproof, reliable, robust, recoverable and easy to maintain. It must also be able to move in 3 dimensions. Also it must be able to navigate and avoid obstacles. Further if this platform is to be part of a swarm of like platforms then it must be cost effective and relatively small. To purchase such a platform can be very expensive. However, for shallow water, a suitable platform can be built from mostly off the shelf items at little cost. This article describes the design of one such underwater robot including various sensors and communications systems that allow for swarm robotics. Whilst the robotic platform performs well, to explore what many of them would do, that is more than are available, simulation is required. This article continues to study how best to simulate these robots for a swarm, or system of systems, approach.

Volume: 17, Issue: 2

Rollover Prediction and Control in Heavy Vehicles Via Recurrent High Order Neural Networks

by Edgar Sanchez, Luis Ricalde, Reza Langari, Danial Shahmirzadi
Abstract

In this paper, a predictor is developed in order to estimate roll angle and lateral acceleration for tractor-semitrailers. Based on this prediction, an active control system is designed to prevent rollover for these vehicles. In order to develop this control structure, a high order recurrent neural network is used to model the unknown tractor semitrailer system; a learning law is obtained using the Lyapunov methodology. Then a control law, which stabilizes the reference tracking error dynamics, is developed using Control Lyapunov Functions and the inverse optimal control approaches. Via simulations, the control scheme is applied to the speed-only and speed-yaw rate trajectory tracking in atractor-semitrailer during a cornering situation.

Volume: 17, Issue: 1

Bayesian Normalized Gaussian Network and Hierarchical Model Selection Method

by Junichiro Yoshimoto, Masa-Aki Sato, Shin Ishii
Abstract

This paper presents a vaziational Bayes (VB) method for normalized Gaussian network, which is a mixture model of local experts. Based on the Bayesian framework, we introduce ameta-learning mechanism to optimize the prior distribution and the model structure. In order to search for the optimal model structure efficiently, we also develop a hierazchical model selection method. The performance of our method is evaluated by using function approximation problems and an system identification problem of a nonlinear dynamical system. Experimental results show that our Bayesian framework results in the reduction of generalization error and achieves better function approximation than existing methods within the fmite mixtures of experts family when the number of training data is fairly small.

Volume: 17, Issue: 1

Joint Routing and Radio Resource Management in Multihop Cellular Networks Using Particle Swarm Optimization

by Mohamed Saad, Sanaa Muhaureq
Abstract

Given a multihop cellular network with fixed relays, this paper considers the problem of jointly selecting (possibly multiple) routing paths for each communication session and allocating transmit powers and bandwidth across all network links, such that the total network utility (i.e., social welfare) is maximized. This paper focuses on quality of service (QoS-) sensitive applications that are captured by sigmoidal-like utility functions. The non-concavity of the user utility functions, the complex relation between routing decisions and network resource allocation, and the dependence of link capacities on all interfering powers render the problem non-convex, and extremely difficult to solve. However, we present ameta-heuristic based on particle Swann optimization that solves the problem successfully, efficiently and globally.

Volume: 17, Issue: 1

Meta-Tacs: A Trust Model Demonstration Of Robustness Through A Genetic Algorithm

by Félix Mármol, Gregorio Pírez, Javier Marín-Blázquez
Abstract

Ensuring trust and confidence in virtual communities' transactions is a critical issue nowadays. But even more important can become the use of robust and accurate trust models allowing an entity to decide which other entity to interact with. This paper aims to study the robustness of TAGS (Trust Ant Colony System), a previously proposed bio-inspired P2P trust model, when applying a genetic algorithm in order to fmd the range of values of its working parameters that provides the best TAGS performance. The optimization of those parameters has been carried out using the CHC genetic algorithm. Experiments seerns to demonstrate that TAGS can achieve high performance ratios due to the enhancement provided by META-TAGS, and to achieve them for a wide range of working parameters, hence showing a remarkable robustness.

Volume: 17, Issue: 1

Discovering Accurate and Common Characteristic Rules from Large Tables

by Yu-Chin Liu, Ping-Yu Hsu
Abstract

With the wide installation of eBusiness and database softwaze in enterprises, mountains of data are accumulating in the form of relational tables. Discovering valuable information from the sea of data is of interest to researchers and managers worldwide. In this paper, an algorithm is proposed to find characteristics from a large database table. It can be applied to fmd chazacteristics of customers in a particular segments or the characteristics of patients, .., etc. In contrast to traditional data generalization or induction methods, the proposed new method, named Char, does not need a concept tree in advance and can generate a manual set of characteristic rules that are precise enough to describe the main characteristics of the data. The simulation results show that the characteristic rules found by Char are efficient as well as consistent regardless of the number of records and of attributes in the dataset.

Volume: 17, Issue: 1

Simulation and Verification of a Dynamic Model of the Electric Forklift Truck

by Kristjan Baša, Andrej emva
Abstract

While the automobile industry of electrically powered cars is still in its infancy stage, the industry vehicles, such as forklift trucks and other material handling equipment have been in production for several decades. Such systems are designed to operate at low speeds which are not critical in terms of occupational safety. On the other hand, material transportation involves extreme driving maneuvers, including high steering and fast acceleration and braking. These maneuvers impose some critical driving situations which can be fatal for operators or any other nearby working person. This paper describes a simple nonlinear vehicle model appropriate for simulation of the three-wheel forklift trucks. The equations model the vehicle behavior at low speeds and at extreme driving maneuvers. To allow model verification, an acquisition system built around a digital signal processor and low-cost sensors was developed. The proposed system provides a platform for further vehicle controller development to be used in signalization or prevention on hazardous situations in the vehicle operating states.

Volume: 17, Issue: 1

A Robust Fuzzy Clustering Approach and its Application to Principal Component Analysis

by Ying-Kuei Yang, Chien-Nan Lee, Horng-Lin Shieh
Abstract

A robust fuzzy clustering approach is proposed to simplify the task of principal component analysis (PCA) by reducing the data complexity of an image. This approach performs well on function curves and character images that not only have loops, shazp corners and intersections but also include data with noise and outliers. The proposed approach is composed of two phases: fustly, input data are clustered using the proposed distance analysis to get good and reasonable number of clusters; secondly, the input data are further re-clustered by the proposed robust fuzzy c-means (RFCM) to mitigate the influence of noise and outlier data so that a good result of principal components can be found. Experimental results have shown the approach works well on PCA for both curves and images despite their input data sets include loops, corners, intersections, noise and outliers.

Volume: 17, Issue: 3

Employing Object-Based Storage Devices to Embed File Access Control in Storage

by Youhui Zhang, Hongyi Wang, Dongsheng Wang, Weimin Zheng
Abstract

This paper presents a proposal to embed the file access control into object-based storage devices (OSD) to achieve powerful storage security with rich semantics; and two application prototypes, the OSD-based intrusion detection (ID) and the finer-grained (than the file-level) access control, are implemented to show its feasibility. To embed file access control into storage, one of vital challenges is how to connect a file with its corresponding storage units and its access control rule. In this design, OSD itself can complete the connection for ID, the one (file) to one (object) relationship is used to link files and their storage objectsaccess rules together by the storage. As the relationship is extended to one to more, one file can be divided into several objects in accordance with its access control semantics; then assigning users with different access permissions based on the file’s internal structure (which is the meaning of the finer-grained access control) is feasible. In addition, the OSD standard is discussed to extend to define new object attributes for file access control. Both prototypes are built based on the OSD reference implementation provided by Intel. Testing results show that the extra overheads introduced by this design are acceptable.

Volume: 17, Issue: 1

Plant-Based Remediation Of Heavy-Metal-Contaminated Roadside Soils Of Guangdong, China

by Xuya Peng, Fei Xiao, Qingwei Yang
Abstract

Two vazieties of Bechmeria nivea (L.) Gaud. (Ramie), namely, triploid Tri-2 and diploid Xiangzhu-3, were potted with soils from Guangdong for fifteen weeks and treated with 10 mmol-kg-1 EDTA or EGTA before harvest at seventeenth week. Lead, Zn and Cd in plant and soil materials were analyzed and their potential ecological risk in soils was simultaneously evaluated. These three metals in soils was found to be above 14.4, 3.0 and 29.9 times higher than the national (China) background value, 10.9, 6.19 and 96.7 times higher than the local (Guangdong) background value and 1.25, 1.20 and 9.67 times higher than the maximum permissible concentration for soils, respectively. An ecological risk analysis of metals using Hakänson’s method indicated an extremely high contamination and a significantly high potential ecological risk by these three metals in soils. The both ramie varieties contained respective concentration exceeding the concentration of

Volume: 16, Issue: 5

Study On Regularity Of Fumes Emitting From Asphalt

by Xuya Peng, Zhiyang Li
Abstract

A large number of poisonous asphalt fames are emitted into environrnent during the mix and compaction process on the pavement placement, which pollute environrnent and do harm to people’s health In this paper, the regularity of emitting fumes from asphalt and the effect of suppressing smoke with additives on asphalt were investigated. The results showed that the amount of fumes emitted was related to temperature, thickness and surface area of the asphalt; the amount of fumes was reduced by using melamine, polyethylene, and activated carbon additives respectively, and good effects were achieved It has great significance on improving working condition on the pavement placement and promoting the construction of environrnent-friendly society.

Volume: 16, Issue: 5

Modified Asphalt From Domestic Waste Old Plastic

by Xiwu Yang, Xinghua Jiang, Ze He, Xinyu Zhang
Abstract

By in house experiment a study is made on the asphalt modifying agent made from plastic basin, stool and bucket so on domestic waste plastic and the effect of modifying asphalt. The result shows: (1) the modifying agent made of waste plastic can considerably improve softening point of asphalt and reduce needle penetration; (2) for the modifying asphalt made from waste plastic by different making method its storage stability differs and PMA modifying asphalt made from waste plastic grain by plastic extruder has severe segregation and poor storage stability. But the PMB waste plastic modifier specially made under given study can not only improve the high temperature stability of asphalt but also no segregation of modifying asphalt and has sound storage stability; (3) Waste plastic can apparently improve the degree of stability of asphalt mixture and the effect of stability degree improvement of PMB is better than that of PMA and the improvement of flow value is not obvious. The achievement of study has referential application value for reuse of waste plastic and improvement of asphalt road pavement performance.

Volume: 16, Issue: 5

DSC Analysis Of Domestic Waste Old Plastic Modifying Agents And Its Modified Asphalt Property

by Xiwu Yang, Mei Feng
Abstract

In study of waste old plastic modified asphalt majority study staff generally cut and then directly the waste old plastic recycled into asphalt for shear cutting, mixing modifying. The modified asphalt made by this kind of method easily segregates, and makes a poor stability and uniformed property of modified asphalt. This obviously has become a difficult problem of waste old plastic modified asphalt. This article through DSC (Differential Scanning Calorimeter) test studies the property of domestic waste old plastic modifying agent and granular waste old plastic modifying agent made by special method. A study by comparison on the fundamental property and storage stability of this two types modifying agent modified asphalt is made. The result from study shows that the crystallization degree of specially made waste old plastic modifying agent decreases compared with decrease of molecule amount and molecule amount distribution becomes wide and inner cohesive energy becomes small and branching degree becomes great and flexibility is reduced. The waste old plastic modifying agent (PMB) can not only improve softening point of asphalt but also has sound storage stability without segregation and solve the segregation problem of modified asphalt. The mechanism of segregation of waste old plastic modified asphalt is analyzed. The study achievement has certain reference and applicable values in respects of recycling for use of waste old plastic and cost saving of modified asphalt and improving road pavement property.

Volume: 16, Issue: 5

Fume Suppression Agents For Environmental-Friendly Asphalt Pavement

by Fei Xiao, Hongzhang Zhang, Xinyu Zhang, Shilin Qian, Xiwu Yang
Abstract

According to the impact of fumes generated by asphalt pavement construction under poor air ventilation conditions on environrnent and operators at pavement construction and mechanism of asphalt fumes generation this article provides the method of addition of addictives in asphalt to suppress fumes due to asphalt pavement construction. Through in house test the variation law of asphalt fames with heating temperature and heating time. The result shows that with an increase of heating time, the yield of asphalt fumes appears increasing tendency. During the initial stage, the smoke yielding rate is fast; in later stage the vaziation slows down; and finally the variation becomes stable. With higher heating temperature, the yield of asphalt fumes is greater. Paztial addictives aze selected for smoke suppression effect test from flame retardant agent, plastic smoke suppression agent, physical adsorbent, polymer modified agent and aging resistant agent. SBS and manometer calcium carbonate compound type modifying agent of better smoke suppression effects are selected for test and asphalt fumes is reduced around 29.0 in total.

Volume: 16, Issue: 5

A Study On Addictives For Fume Reduction In Asphalt Pavement Construction

by Fei Xiao, Xuya Peng, Shilin Qian
Abstract

In the construction process, asphalt pavement will generate a lot of asphalt fumes, which contain a large number of toxic and hazardous substances that will produce injury to the construction workers and residence around. In order to radically reduce asphalt fumes in the construction process, through self-designed test equipment, we added SBS (Styrene Butadiene Styrene), melamine and activated carbon which may have fumes suppression effect to the asphalt and experimented, obtained: (1) SBS which had fumes suppression effect, could be used as an additive to reduce asphalt fumes; (2) Melamine and activated carbon, which would produced a number of side effects, could not be used as an additive to reduce asphalt fume. At present the research of modified asphalt which produces lower fumes is basically still in a blank state, but the paper will fill the gaps in this aspect and provide a test device and method to study on modified asphalt with low fumes.

Volume: 16, Issue: 5

Online Public Key Cryptographic Scheme For Data Security In Bridge Health Monitoring

by Xu Feng, Lv Xin, Jiang Rongyan
Abstract

the paper proposes an online-public-key cryptographic scheme for data security in bridge health monitoring. The scheme solves the problem such as how to preserve and manipulate the users’ private key securely. Meanwhile, it realizes the mutual authentication between user and server, in which the private key splitting method is proven to be secure in the RO model. The analysis shows that the scheme is able to resist the normal attacks such as the denial of service or the off-line password guess effectively. It has strong robustness in applications.

Volume: 16, Issue: 5

A Study Of Wavelet Kernel In Support Vector Regression

by Lefei Zhang, Fengqing Han
Abstract

A new kernel function is proposed for support vector regression (SVR). A one-to-one mapping is adopted for dimensionality reduction and then continuous wavelettransform is utilized to construct the nonlinear mapping φ(x) from the input space Sto the feature space. So we call it continuous wavelet kernel function (CWKF). This wavelet kernel is not translation invariant kernel, instead inner product kernel and need not parameter selecting. The quadratic program of support vector regression has feasible solution if we use CWKF. Numerical experiments demonstrate the effectiveness of this method.

Volume: 16, Issue: 5

A New Multi-Resolution Wavelet Neural Network For Bridge Health Monitoring

by Fengqing Han, Jianxi Yang
Abstract

Anew multi-resolution wavelet network for bridge health monitoring is proposed in this paper, which has one hidden layer and multiple outputs. In this network, training data and forecasting data collected from bridge health monitoring are important factors in determining structure. By learning algorithm the finite wavelets and scaling functions are selected for the active function. All outputs vary from the coarsest resolution to the finest one. The theoretical analysis shows that the finest output convergence becomes uniformly.

Volume: 16, Issue: 5

A Crack Monitoring Method For Concrete Structures

by Benniu Zhang, Zhixiang Zhou, Xingxing Li
Abstract

Structural health monitoring is thought to be a promising way to monitor and evaluate the health state of bridges. Crack identification is one of the most important aspects in structural health monitoring, because the collapses of concrete bridges are mostly due to occurrence and propagation of initial cracks. In this paper, metal wire has been adopted to form a regular crack detecting network on concrete structures. The method along with the related system has the ability to real time monitor the occurrence, propagation, length and location of initial crack on the surface of concrete structures.

Volume: 16, Issue: 5

Application Of The Arma Model To Bridge Structural Health Monitoring

by Rui Fang, Yang Jianxi
Abstract

Based on the stability analysis of the monitoring data of bridge structural health, the autoregressive moving average (ARMA) model is adopted to make an experimental analysis that the data of the typical deflection monitoring site of Masangxi Bridge in Chongqing (a part of Yangtze River) could be predicted and estimated. Combined with Akaike information criterion (AIC), the fifth-order ARMA model has been constructed in accordance with the required efficiency and precision, accomplishing the estimation in case of data distortion or default. Additionally, this model provides reliable monitoring samples for predicting the state of the bridge structure.

Volume: 16, Issue: 5

Bi-Parameters Method For Structural Vulnerability Analysis

by Limin Sun, Gang Yu, Zhi Sun
Abstract

Vulnerability originally means the susceptibility of a system to damage. In civil engineering, structural vulnerability can be defined as the susceptibility of structural performance to local damage. This paper studied the quantitative analysis method on structural vulnerability. The proposed method bases on two parameters, the influence of damage scenazio to structural performance, and the proportion of the damage scenario in the overall structural system. By using these two parameters, the vulnerability of a structure can be quantitatively evaluated for different damage degree under a certain damage scenario, or for different damage degree under different damage scenarios.

Volume: 16, Issue: 5

Lyapunov Exponent Analysis On Real-Time Monitoring Information Of Extractive Structure Health Based On Chaos Time Sequence

by Jianting Zhou, Yue Chen, Lu Yin
Abstract

The article has made a maximum Lyapunov exponent analysis of ASCE Benchmark and the health monitoring data of Chongqing Masangxi Bridge using non-linear theory and chaos time sequence. The maximum Lyapunov exponents in the two kinds of structural monitored data are both over zero, indicating that in the structural system chaos phenomenon has appeared. Meanwhile, experiments have shown that the maximum Lyapunov exponent is sensitive of the amount of samples and the time delay. So, to compute the chaos index, the amount of samples and the time duration are of importance.

Volume: 16, Issue: 5

PCA Based Bridge Health Model Identification

by Zhangli Lan, Lina Xiang, Jianqiu Cao
Abstract

Structure safety assessment is one of the fundamental objectives for bridge health monitoring. In order to identify whether a bridge is safe, a bridge health model identification method based on Principle Component Analysis (PCA) is presented in this paper. A matrix made up of vazious measurement data of a bridge is constituted, the method of determine the similazity of bridges using the eigenvalue distance between data matrixes of bridges is addressed, and some issues related to the collection of the measurement data and the establishments of data model are also discussed.

Volume: 16, Issue: 5

Fatigue Cumulative Damage Evaluation Based On Smart Stay Cables

by C.M. Lan, H. Li, Y. Ju
Abstract

Stay cables are one of the most critical structural components of cable-stayed bridge. However, stay cables readily suffer from fatigue damage, corrosion damage and their coupled effects. Thus, health monitoring of stay cables is important for ensuring the integrity and safety of a bridge. The application of the smart stay cables on the Tianjin Yonghe Bridge is demonstrated and the vehicle fatigue load effects smart stay cables are evaluated based on field monitoring data. Secondly, based on fatigue test results of corroded wires obtained from dissection of actual parallel wire cables which were used on Tianjin Yonghe cable-stayed bridge, the fatigue properties of corroded cable are investigated by the method of Monte Cazlo simulation. The results of fatigue lives and corrosion degree of corroded wire are presented. Comparisons between the original design information and fatigue test results, it can be seen that corrosions make the fatigue lives of wires decreasing sharply and the cable fatigue lives are controlled by a small fraction of the cable wires with the shortest fatigue lives. Finally, based on the simulated S-N curves of cable and Miner's law, the fatigue cumulative damages of cables in reference periods are evaluated.

Volume: 16, Issue: 5

A Non-Linear Study On Chaos In Bridge Structures sBased On Chaos Time Sequence

by Jianxi Yang, Zhi Deng, Jiaomei Wu
Abstract

This article analyzes characteristics of bridge structure and possibility of occurrence of chaos of structure under intense loads during operation period .then, Using the method of Melnikov analyzed the chaos’ critical state appearing in vibration formula of bridge structural system. The paper puts forward to the chaos index, such as correlation dimension, the Maximum Lyapunov exponent, etc. The relevant dimensions are all greater than 2 and are not integer and the max Lyapunov index is also greater than 0. These have fully proved that the bridge under vibration appears chaos phenomena.

Volume: 16, Issue: 5

Condition Assessment Of A Cable-Stayed Bridge Using The Dynamic Analysis Based On A Statistic Categorization Strategy

by Yang Liu, Feng Zhou, Hui Li, Zhiming Guo, Hongzhi Ding
Abstract

The statistical analysis of the structural dynamic properties is carried out in order to evaluate the safety condition of structures. Firstly, the changing process of the dynamic properties of acable-stayed bridge is obtained by identifying the acceleration response measured from the structural health monitoring (SHM) system, which was implemented on this bridge in 2006. Secondly, the effects of the traffic load and wind load on the dynamic properties of this bridge aze eliminated by categorizing the loads and wind load into different grades, and the probability density function of modal parameters at each grade are obtained statistically. Finally, the frequencies and damping ratio measured during 3 months in 2006 are applied to found the confidence interval of safety condition of this bridge, and then the measured modal parameters in 2009 are applied to evaluate the safety condition of this bridge by using above safety confidence interval.

Volume: 16, Issue: 5

Acquirement And Analysis Of Bridge Crack Images

by Feiquan Li, Zhangli Lan, Jianqiu Cao
Abstract

It is very important to evaluate the health status of a bridge by accessing cracks information of its key parts. Images of cracks aze obtained by analyzing the video information of the bridge’s key positions, the identifying algorithm and rebuilding algorithm are proposed based on the surface images of the bridge. Crack images pre-processing, crack images extracting, vector image storage and crack images rebuilding are addressed in this paper.

Volume: 16, Issue: 5

Fatigue Damage Evolution Of Steel Bridges Based On Multi-Scale Finite Element Modeling

by Shunlong Li, Hui Li, Yang Liu, Zhiming Guo, Hongzhi Ding
Abstract

This paper presents the fatigue damage evolution approach of steel bridges by employing multi-scale fmite element modeling. In order to achieve the accurate fatigue damage evolution results, the accurate stress analysis should be performed ahead. The global fmite element model should be updated with the help of identified modal parameters and the cable forces. The results show that the updated fmite element model can represent the global dynamic and static characteristics in good agreement with the measured ones. However, for the local fatigue damage regions, the stress analysis of global fmite element model may not suitable for the stress concentration analysis. A detailed fmite element model of local bridge deck is incorporated in the global FE model and the standard truck loads were applied to the multi-scale FE model. With the help of monitored traffic loading, the fatigue damage evolution of bridge deck would be performed and provides basis for the safety assessment of the steel bridge.

Volume: 16, Issue: 5

Structural Condition Assessment Based On Canonical Correlation Analysis And Outlier Analysis

by Zhihua Min, Limin Sun
Abstract

A method of structural condition assessment based on the canonical correlation analysis and outlier analysis was proposed. Assuming that the structural features field and the environrnental factors field were known, the predictors of two fields can be calculated by the canonical correlation analysis and a lineaz regression model or other method can be used to describe the relationship of the predictors. So the residual was calculated by the difference between the structural features field measured and the structural features field estimated. The statistical method, such as extreme value distribution theory, is proceeding to estimate the threshold of the residual. The novelty of the structural condition can be detected by outlier analysis. Finally a numerical example of cable-stayed bridge validated the method proposed in this paper. The results show that this method can effectively distinguish different structural conditions.

Volume: 16, Issue: 5

A Study On Mechanical Property Of High Strength Steel Wires In Cables Based On Corrosive State Analysis

by Yi Hao, Yue Chen, Jianhua Du, Xuan He, Xue Han
Abstract

The change laws of stress distribution of high strength steel wire because of the existence of the corrosion were analyzed. Study of the effects of the geometric parameters (including the depth, shape, cleaz spacing of the pit, steel wire diameter after general corrosion) of the corrosion pit on the yield load of the rusty wire were performed. It is discovered that exponential relation exists between the ratio for the depth of pit and diameter of wire and the ratio of yield load for real and theoretical, the obvious bending effect in the wire is also appeazed when the depth is large. The spacing of the pits has the very tremendous influence on yield load similazly, while the influence of shape is relative small. The stress concentration nearby the pit has created the mechanical properties of the wire obvious degeneration.

Volume: 16, Issue: 5

A Dynamic Life Evolution Method Based On Timedependent Reliability By Health Monitoring Data

by Jianxi Yang, Jianting Zhou
Abstract

This paper aims at the disadvantage of traditional calculation mode of reliability and proposes a new idea to calculate resistance of structure. This idea uses identification of structure modal parameters, structure physical parameters and EWMA control map. Reliability by load effects taken and life prediction can be achieved. In addition, by utilization of high order statistical predicting function for life prediction, meanwhile, modification of dynamic evolution in reliability calculation and life prediction are completed.

Volume: 16, Issue: 5

A New Safety Evaluation Method For Long-Span Bridges With Tele-Monitoring Systems

by Jianting Zhou, Yue Chen, Xiaogang Li, Hongwei Sun, Lei Zhang
Abstract

In this paper, a novel evaluation method is proposed for long-span bridges with remote-monitoring system, which is based on reliability theory by detailing such procedures as acquiring functional parameters, acquiring the load effect parameters, conducting the calculation of dynamic reliability, and the like. A reliability index is introduced. The proposed system has proved to be effective in actual bridge monitoring tasks.

Volume: 16, Issue: 5

A Special Issue of Intelligent Automation and Soft Computing

by Jianting Zhou, S. Abenda
Abstract

Volume: 16, Issue: 5

Context-Aware Workflow Management For Intelligent Navigation Applications In Pervasive Environments

by Feilong Tang, Ilsun You, Minyi Guo, Song Guo
Abstract

Pervasive computing is auser-centric distributed computing paradigm, allowing users to access their preferred services even while moving around. To make this vision a reality, context-aware workflow management is one of key issues because the context of pervasive applications is changing dynamically. In this paper, we propose a context model for intelligent navigation applications and then present acontext-aware workflow management algorithm (CAWM) which can adaptively adjust workflow execution behaviours based on current context information. The correctness of the CAWM algorithm has been also verified theoretically by formulating it as a Petri-net model. Furthermore, the proposed context model and workflow management algorithm can apply to other applications by simply revising the corresponding context structures only.

Volume: 16, Issue: 4

Two Distributive Key Management Schemes In Mobile Ad Hoc Networks

by Mohammad Al-Shurman, Seong-Moo Yoo, Bonam Kim, Seungjin Park
Abstract

Today’s ever smaller computing systems are increasingly spreading in our ubiquitous environrnent. Being available ubiquitously in the devices and appliances that we use everyday and everywhere, these embedded computing systems are accessible to mobile users via hand-held devices connected over wireless networks. A mobile ad hoc network (MANET) is one of the important wireless networks. In a MANET a reliable key management system is required to generate and distribute symmetric encryption decryption keys. The key management schemes proposed in MANETs so far have used trusted third parties (TTP) which have limitations because of the mobility of nodes. A Distributed Key Pre-distribution Scheme was proposed based on a probabilistic method without relying on any TTP but with results identical to TTP-based schemes. The scheme utilized cover-free family (CFF) properties. However, the precondition of the probabilistic method was claimed to be falsely deduced.In this paper, we propose two distributive key management schemes using maximum distance sepazable codes (MDS). First, we will construct a practical (n, t + 1)-threshold key management system. Second, we propose a key pre-distribution scheme achieving CFF properties. We use a global MDS code instead of the probabilistic method to generate node keys. The scheme is secure enough against malicious nodes’ fraud and tapping. The effects of block size and network parameters are also studied.

Volume: 16, Issue: 4

Fuzzy-Based Filtering Solution Selection Method For Dynamic Sensor Networks

by Hee Seo, Hae Lee, Seung Lee, Deok Lee
Abstract

In wireless sensor networks, adversaries can compromise sensor nodes and use them to inject forged reports, which can lead to false alarms and energy depletion. Recently, several reseazch solutions have been proposed to detect and drop such forged reports during the forwarding process. Since each of them has its own energy consumption characteristics, employing only a single filtering solution for a network is not a recommended strategy, in terms of energy savings. While a technique for the adaptive selection of filtering solutions has been proposed, it considers only static networks. This paper presents afuzzy-based filtering solution selection method for dynamic sensor networks. In order to save energy, a fuzzy rule-based system chooses between two filtering solutions and controls detection power by considering network status. The effectiveness of the proposed method is shown by the simulation results described in the final pazt of the paper.

Volume: 16, Issue: 4

Routing Protocol With Scalability, Energy Efficiency And Reliability In WSN

by Inbo Sim, Jaiyong Lee
Abstract

The world around us will soon be interconnected as a pervasive network of intelligent devices. Through this, people will be able to get online and have almost continuous access to their preferred services. WSN (Wireless Sensor Networks) is the emerging technology expected to prevail in the pervasive computing environrnent of the future. Geographic routing protocol is an attractive localized routing scheme for wireless sensor networks because of its directional routing properties and scalability. Sensor nodes are highly energy-constrained. Also, sensor nodes can be deployed in a hostile condition. This is the reason why we have to consider not only the energy but also the wireless link condition. In this paper, we propose a geographic routing scheme considering the wireless link condition. If wireless link condition is not considered, the node which is at the end distance of the transitional region where packets can be received with errors can be selected as the next hop. This draws out retransmissions because of received packet errors. In addition, because of these retransmissions, additional energy is consumed. This proposed scheme guarantees that reliable data transmission is made and consumed energy is minimized. For this, we fast determined the distance doP. That is, in this scheme, sensor nodes send packets to the neighbor closest at the place which is doP distance from the source to the sink. Also, method handling communication voids and loop avoidance is proposed. Through simulation, we validate that this proposed scheme is more efficient in terms of performance than general geographic routing, which selects the neighbor closest to the sink in the connected region or in the transmission range as the next hop.

Volume: 16, Issue: 4

A Proxy-Based System For Dynamic Content Negotiation And Collaborative Optimization In Heterogenic Environments

by Xavier Sanchez-Loro, Victoria Beltran, Jordi Casademont, Marisa Catalan
Abstract

Ubiquitous and Pervasive Computing relies on ubiquitous network access and applications’ context-awareness. This pervasive access implies exchanging traffic with a wide spectrum of devices across heterogenic networks. Services and applications deployed on these networks should adapt its operation and presentation to the characteristics of the underlying network technologies and the actual client device capabilities. Cellulaz wide azea networks like UMTS are used as Internet access networks for particular users but, in some cases, they can also be employed to provide Internet access to other smaller networks. The main inconvenient is that cellular networks have not the same bandwidth as wired networks and therefore, the cellular channel becomes a network bottle-neck. To help to mitigate this situation and in order to improve the user’s experience different optimization techniques exist, especially in web traffic. This paper studies the existing synergies at HTTP layer between device capabilities expression, content negotiation, channel optimization and content adaptation. And secondly, it presents a system where HTTP requests transmission is optimized by means of HTTP header reduction over the cellulaz channel, showing a significant improvement in response time. In order to allow content negotiation, headers should be restored when reaching the Internet. This dynamic header reconstruction allows giving enriched and more expressive information about user’s device and browser capabilities. Thus navigation speed and user’s QoE can be enhanced by means of dynamic content negotiation in order to obtain adapted and lighter content and responses from web servers and adaptation proxies alike.

Volume: 16, Issue: 4

Conserving Bandwidth In A Wireless Sensor Network For Telemedicine Application

by Shuo-Jen Hsu, Chin-Hsing Chen, Show-Hong Chen, Wen-Tzeng Huang, Yuan-Jen Chang, You-Yin Chen
Abstract

Telemedicine aided by wireless sensor networks (WSN) has recently become a healthcare trend. Many previous studies have adopted the ZigBee-based WSN to implement a platform andor a telemedicine system. However, the low data rate and bandwidth have limited the maximum number of nodes in a WSN during continuous and simultaneous transmission. The issue of low data throughput has not been addressed in previous reseazch. In this study, we propose an arrhythmia-aware system, and a new DSPbased WSN platform is developed for the high compression performance of physiological data in a ZigBee-based WSN. Proven by simulations and several real tests, the combination of the proposed platform and the sensor nodes can lead to more bandwidth conservation and extend the WSN scale for the next generation of telemedicine system.

Volume: 16, Issue: 4

Adaptive Authentication And Registration Key Management Scheme Based On AAA Architecture

by Jong-Hyouk Lee, Moonseong Kim, Byoung-Soo Koh, Tai-Myoung Chung
Abstract

The demand for mobile communications has been increasing significantly while inducing more challenges to security issues, especially in authenticating mobile hosts. In order to provide secure communications in mobile networks, the Authentication, Authorization, and Accounting (AAA) architecture is currently in use within the Internet access service. The AAA architecture is used to establish authentication between the communication hosts. However, the current azchitecture has an inefficient authentication procedure when a mobile host hands off from a home domain to foreign domains because the architecture assumes that the only reliable source of authenticating the mobile host is the AAA server located in the home domain. This problem becomes more significant when the mobile host traveling far way from its home domain establishes a mobility security association with mobility entities. To solve these problems, we propose in this paper an adaptive authentication and registration key management scheme. Within the proposed scheme, the mobile host is authenticated by the AAA server located in the previous domain and obtains the required key material to establish the mobility security association when the mobile host performs the inter-domain handoff. In the infra-domain handoff case, the mobile host is simply authenticated by the AAA server located in the current domain and obtains the required key material. The results of a performance evaluation show that the proposed scheme reduces the authentication failure rate up to 58.46 compared to the current AAA architecture.

Volume: 16, Issue: 4

The Case Study Of System Architecture In Wireless Sensor Networks: The Kindergarten Safety System (Kss)

by Junmo Yang, Sang-Hun Jung
Abstract

In Wireless Sensor Networks (WSNs) and Wireless Personal Area Networks (WPANs), IEEE802.15.4 [1] has emerged as one of the most promising radio specifications for physical layer and medium access control, guaranteeing low rate and low power communication independent of infrastructure. To utilize the collected information under WSNs, the data typically pass through gateways or sinks. Simultaneously, the control messages for sensor networks reach each sensor node via gateways. However, practical constraints such as multiple destinations for the sensing data and multiple types of devices at each sensor node make the design of both gateways and application servers very challenging. In this paper, a practical system azchitecture for WSNs is proposed for gateways and service brokers. Areal-world deployment of the proposed WSN system azchitecture is also suggested.

Volume: 16, Issue: 4

A New Adaptive Grey Decision-Energy Aware Management System Based On The Optimal Read Only-Write Buffer Architecture For Flash Memory In Embedded And Mobile Devices

by W.T. Huang, C.H. Chen, H.-D.J. Jeong, C.T. Chen
Abstract

It is generally accepted that the trends in ubiquitous and pervasive computing are rapidly increasing the diversity and heterogeneity of wireless technologies and their constituent devices. However, there are significant problerns to overcome when integrating embedded and mobile devices into a ubiquitous and pervasive computing environrnent. One can consider that flash memory is an essential storage medium for embedded and mobile devices, because it is small, lightweight, nonvolatile, and vibration-resistant; and it has a high storage capacity. Lower energy consumption is an important consideration in the design, and one solution for reducing energy consumption is to include a buffer layer in a flash memory storage system. A buffer layer can let data be updated in situ, reduce energy consumption, and increase access performance. We developed an optimal-read only-write buffer architecture tailored to the properties of flash memory, since a traditional readwrite buffer architecture is unsuitable for flash memory. Further, we propose an adaptive grey decision policy for considering temporal and spatial localities to obtain better results. Our proposed method shows that the number of flash memory write operations to reduce energy consumption is decreased from 15 to 35 compared to the least recently used and flash-aware buffer policies based on the optimal-read only-write buffer architecture.

Volume: 16, Issue: 4

A Special Issue of Intelligent Automation and Soft Computing

by Ilsun You, Bonam Kim, Hui-Hu Hsu, Seong-Moo Yoo
Abstract

Volume: 16, Issue: 4

A New Fingerprint Biometric Remote User Authentication Scheme Using Chaotic Hash Function On Mobile Devices

by Eun-Jun Yoon, Kee-Young Yoo
Abstract

ABSTRACT A remote user authentication is a mechanism to authenticate remote users over insecure communication networks. It is evident that, with the passage of time, the volume of mobile user authentication is increasing because of the ease in accessing resources from any remote location. In 2008, Khan et al. proposed an efficient and practical chaotic hash-based fmgerprint biometric remote user authentication scheme for mobile devices. The current paper, however, demonstrates that Khan et al.’s scheme is vulnerable to a privileged insider’s attack and impersonation attacks by using lost or stolen mobile devices. In order to isolate such problems, the current paper also presents an improved remote user authentication scheme based on Khan et al.’s scheme.

Volume: 16, Issue: 3

A New Primitive For Stream Ciphers Applicable To Pervasive Environments

by Jun Choi, Dukjae Moon, Sangjin Lee
Abstract

omputing devices in pervasive environments have limitations on the following attributes: calculation capacity, power consumption, and chip size. The huge amount of operation required for applications of cryptographic primitives restricts the implementation of these primitives in pervasive environments. In order to overcome such limitations, we propose a new primitive for stream ciphers called PC-AddRotR (Pervasive Computing -Adder Right Rotation). PC-AddRotR is easily implemented by light-weight hardware and fast word-based software. PC-AddRotR efficiently generates sequences of long period and multi-bit sequences. In addition, using aword-based adder with a nonlinear property, it has more resistance against algebraic attacks, which are known to be the strong analysis methods for stream ciphers.

Volume: 16, Issue: 3

Efficient OnlineOffline Signcryption Scheme

by Baodian Wei, Fangguo Zhang, Xiaofeng Chen
Abstract

In this paper, we propose a new signcryption scheme and its onlineoffline version from pairings. Based on the assumption of k’1 square roots, the scheme is proven, without random oracles, to be secure against the existential forgery under an adaptive chosen-message attack. It is also proven that its IND-CPA security also implies its IND-CCA2 security. A comparison is made with existing schemes from the viewpoint of computational cost and the size of ciphertexts.

Volume: 16, Issue: 3

A Pervasive Secret Sharing Scheme For Embedded Visual Communication System

by Chul-Ung Lee, Hyoung Kim, Jong Park, Sang-Soo Yeo, Jaesoo Yang
Abstract

A simple secret sharing scheme for secure visual communications is presented in this paper. Secret sharing schemes allow a group of participants at different locations to share a secret (i.e., an image) among them by splitting it into n pieces (“shazes” or “shadows”. In case of the (k, n) secret shazing scheme only a group of k qualified participants among n (where kn) can reconstruct the secret. This paper presents an (n, n) secret sharing scheme. This scheme randomizes one shaze after another by executing XOR operations with random seeds derived from an initial seed. This scheme can also be used as an image encryption scheme. This scheme is resistant to collusion attacks.

Volume: 16, Issue: 3

Coverage And Connectivity Problems Under Border Effects In Wireless Sensor Networks

by Yan Jin, Ling Wang, Yoohwan Kim, Xiao-Zong Yang
Abstract

Wireless sensor networks can be used to monitor the interested region by multi-hop communication. Coverage is a primary metric to evaluate the capacity of monitoring. In this paper, we focus on the coverage issue under border effects, where the sensor nodes are distributed in acircle-shaped region randomly. Under this scenario, the expected coverage of the sensor node and the total network coverage provided by n sensor nodes are derived accurately by probability. These fmdings are useful to determine the related parameters (i.e., sensing range, number of sensor nodes and radius of monitored region) for a specific network coverage ratio. Besides, to guarantee the collected data to be arrived at the sink node, the lower bound of network connectivity probability is also calculated when border effects are considered. Simulation results demonstrate that our analysis is correct and effective.

Volume: 16, Issue: 3

Device Authentication In Wireless And Pervasive Environments

by Georgios Kambourakis, Stefanos Gritzalis, Jong Park
Abstract

Security can only be guaranteed as long as the hardwaze and other key parameters, including software components, secret keys etc, of a device remain genuine and unrnodified. Under this context, device authentication must be considered as a key security issue, complementary and of equal importance to user authentication, in today’s wireless and forthcoming ubiquitous realms. This paper classifies and analyses possible major solutions proposed until now towazds solving the device authentication issue. We constructively argue on each solution presented examining its advantages and disadvantages. A qualitative comparative analysis for the device authentication schemes in question is also offered, probing its applicability for both infrastructure and ad-hoc deployments. Inter-domain device authentication, where applicable, and users’ privacy as aside-effect are investigated as well.

Volume: 16, Issue: 3

A Special Section of Intelligent Automation and Soft Computing

by Ching-Hsien Hsu
Abstract

Volume: 16, Issue: 3

Look-Ahead Linear Jerk Filter For A Computerized Numerical Control

by Yih-Fang Chang, Truong-Giang Nguyen
Abstract

In high-speed precision machining systems, reference trajectory generation with smooth kinetic profiles plays a key role in the computerized numerical control (CNC). In this paper, look-ahead linear jerk filter algorithm is proposed to ensure smooth and accurate motion with a linear jerk change. The look-ahead algorithm detected the step-changing points of the speed curve. At each step-changing point, the speed curve was modified by proposed method in order to approach given maximum acceleration deceleration and jerk for the purpose of saving machining time. The number of filter registers depended on time was also generated base on fluctuation of speed curve. The commands filtered by the look-ahead linear jerk filter stabilize the beginning, the end and the step-changing speed points of the motion of the machine table. Amultiple-stepchanging speed curve of a CNC machine and a speed curve of a measurement system were constructed in order to verify the feasibility and precision of the proposed algorithm.

Volume: 16, Issue: 3

Multi-Level Optimization Of Negative Selection Algorithm Detectors With Application In Motor Fault Detection

by X. Z. Gao, S.J. Ovaska, X. Wang, M.-Y. Chow
Abstract

This paper proposes amulti-level optimization strategy for the Negative Selection Algorithm (NSA) detectors, based on both the Genetic Algorithms (GA) and clonal selection principle. The NSA is a natural immune response-inspired pattern discrimination method In our hierazchical optimization scheme, the NSA detectors are fast optimized by the GA to occupy the maximal coverage of the nonself space. Next, these detectors are further fine-tuned and optimized using the Clonal Selection Algorithm (CSA) so as to achieve the best fault detection performance. This novel NSA detectors optimization approach is also examined with artificial data and a practical motor fault detection example.

Volume: 16, Issue: 3

An Intelligent System Based On Adaptive CTBN For Uncertainty Reasoning In Sensor Networks

by Dongyu Shi, Xinhuai Tang, Jinyuan You
Abstract

;onsisting of various sensing and computing devices deployed in a changing environrnent, a sensor network’s raw sensed data have many uncertainties. A natural way to deal with them is generating belief messages. Sensing objects continuously change with time, so are their beliefs. Therefore, dynamic models are required to monitor distributed states in the system. This paper presents a CTBN based intelligent system for modeling dynamics and processing uncertainties in sensor networks. Algorithms for message passing and parameter updating for adapting the model to the changing environrnent are provided. The effectiveness of the system is shown in experiments.

Volume: 16, Issue: 3

A Practical Implementation Of A Distributed Control Approach For Microgrids

by P.F. Lyons, P. Trichakis, P.C. Taylor, G. Coates
Abstract

Public low voltage feeders containing a mixture of several micro-sources, distributed energy storage units (ESUs) and controllable loads, which appear to the upstream distribution network as controllable entities, are known as MicroGrids. Through intelligent co-ordination of micro-generators and ESUs, coupled with demand side management techniques, MicroGrids have the potential to offer significant improvements in the commercial value and environrnental impact of installed micro-generators. Furthermore, using appropriate active control techniques, MicroGrids could potentially overcome the low voltage distribution network constraints associated with high levels of micro-generation. The reseazch described in this paper builds upon previous research carried out at Durham University, which proposed a preliminary distributed control approach for MicroGrids. The fast steps in this approach have now been implemented using agent technology on the laboratory based Experimental MicroGrid at Durham University. Results from this practical implementation of fast-stage agent-based control are presented and discussed. Finally, the agent-based controllers are evaluated based on their suitability to satisfy the specific control requirements of MicroGrids.

Volume: 16, Issue: 2

Intelligent Load Control For Frequency Regulation In Microgrids

by Bieshoy Awad, Janaka Ekanayake, Nick Jenkins
Abstract

During autonomous operation, MicroGrids may suffer large fluctuations in frequency due to th