Computational Intelligence and Neuroscience The latest articles from Hindawi Publishing Corporation © 2015 , Hindawi Publishing Corporation . All rights reserved. The Intelligence of Dual Simplex Method to Solve Linear Fractional Fuzzy Transportation Problem Tue, 24 Feb 2015 13:43:46 +0000 An approach is presented to solve a fuzzy transportation problem with linear fractional fuzzy objective function. In this proposed approach the fractional fuzzy transportation problem is decomposed into two linear fuzzy transportation problems. The optimal solution of the two linear fuzzy transportations is solved by dual simplex method and the optimal solution of the fractional fuzzy transportation problem is obtained. The proposed method is explained in detail with an example. S. Narayanamoorthy and S. Kalyani Copyright © 2015 S. Narayanamoorthy and S. Kalyani. All rights reserved. The Construction of Support Vector Machine Classifier Using the Firefly Algorithm Mon, 23 Feb 2015 12:53:13 +0000 The setting of parameters in the support vector machines (SVMs) is very important with regard to its accuracy and efficiency. In this paper, we employ the firefly algorithm to train all parameters of the SVM simultaneously, including the penalty parameter, smoothness parameter, and Lagrangian multiplier. The proposed method is called the firefly-based SVM (firefly-SVM). This tool is not considered the feature selection, because the SVM, together with feature selection, is not suitable for the application in a multiclass classification, especially for the one-against-all multiclass SVM. In experiments, binary and multiclass classifications are explored. In the experiments on binary classification, ten of the benchmark data sets of the University of California, Irvine (UCI), machine learning repository are used; additionally the firefly-SVM is applied to the multiclass diagnosis of ultrasonic supraspinatus images. The classification performance of firefly-SVM is also compared to the original LIBSVM method associated with the grid search method and the particle swarm optimization based SVM (PSO-SVM). The experimental results advocate the use of firefly-SVM to classify pattern classifications for maximum accuracy. Chih-Feng Chao and Ming-Huwi Horng Copyright © 2015 Chih-Feng Chao and Ming-Huwi Horng. All rights reserved. Numerical Computation of Homogeneous Slope Stability Sun, 15 Feb 2015 09:29:52 +0000 To simplify the computational process of homogeneous slope stability, improve computational accuracy, and find multiple potential slip surfaces of a complex geometric slope, this study utilized the limit equilibrium method to derive expression equations of overall and partial factors of safety. This study transformed the solution of the minimum factor of safety (FOS) to solving of a constrained nonlinear programming problem and applied an exhaustive method (EM) and particle swarm optimization algorithm (PSO) to this problem. In simple slope examples, the computational results using an EM and PSO were close to those obtained using other methods. Compared to the EM, the PSO had a small computation error and a significantly shorter computation time. As a result, the PSO could precisely calculate the slope FOS with high efficiency. The example of the multistage slope analysis indicated that this slope had two potential slip surfaces. The factors of safety were 1.1182 and 1.1560, respectively. The differences between these and the minimum FOS (1.0759) were small, but the positions of the slip surfaces were completely different than the critical slip surface (CSS). Shuangshuang Xiao, Kemin Li, Xiaohua Ding, and Tong Liu Copyright © 2015 Shuangshuang Xiao et al. All rights reserved. An Adaptive Image Enhancement Technique by Combining Cuckoo Search and Particle Swarm Optimization Algorithm Sun, 15 Feb 2015 08:10:18 +0000 Image enhancement is an important procedure of image processing and analysis. This paper presents a new technique using a modified measure and blending of cuckoo search and particle swarm optimization (CS-PSO) for low contrast images to enhance image adaptively. In this way, contrast enhancement is obtained by global transformation of the input intensities; it employs incomplete Beta function as the transformation function and a novel criterion for measuring image quality considering three factors which are threshold, entropy value, and gray-level probability density of the image. The enhancement process is a nonlinear optimization problem with several constraints. CS-PSO is utilized to maximize the objective fitness criterion in order to enhance the contrast and detail in an image by adapting the parameters of a novel extension to a local enhancement technique. The performance of the proposed method has been compared with other existing techniques such as linear contrast stretching, histogram equalization, and evolutionary computing based image enhancement methods like backtracking search algorithm, differential search algorithm, genetic algorithm, and particle swarm optimization in terms of processing time and image quality. Experimental results demonstrate that the proposed method is robust and adaptive and exhibits the better performance than other methods involved in the paper. Zhiwei Ye, Mingwei Wang, Zhengbing Hu, and Wei Liu Copyright © 2015 Zhiwei Ye et al. All rights reserved. Study on the Calculation Models of Bus Delay at Bays Using Queueing Theory and Markov Chain Wed, 11 Feb 2015 06:21:20 +0000 Traffic congestion at bus bays has decreased the service efficiency of public transit seriously in China, so it is crucial to systematically study its theory and methods. However, the existing studies lack theoretical model on computing efficiency. Therefore, the calculation models of bus delay at bays are studied. Firstly, the process that buses are delayed at bays is analyzed, and it was found that the delay can be divided into entering delay and exiting delay. Secondly, the queueing models of bus bays are formed, and the equilibrium distribution functions are proposed by applying the embedded Markov chain to the traditional model of queuing theory in the steady state; then the calculation models of entering delay are derived at bays. Thirdly, the exiting delay is studied by using the queueing theory and the gap acceptance theory. Finally, the proposed models are validated using field-measured data, and then the influencing factors are discussed. With these models the delay is easily assessed knowing the characteristics of the dwell time distribution and traffic volume at the curb lane in different locations and different periods. It can provide basis for the efficiency evaluation of bus bays. Feng Sun, Li Sun, Shao-wei Sun, and Dian-hai Wang Copyright © 2015 Feng Sun et al. All rights reserved. A Lane-Level LBS System for Vehicle Network with High-Precision BDS/GPS Positioning Sun, 08 Feb 2015 08:39:37 +0000 In recent years, research on vehicle network location service has begun to focus on its intelligence and precision. The accuracy of space-time information has become a core factor for vehicle network systems in a mobile environment. However, difficulties persist in vehicle satellite positioning since deficiencies in the provision of high-quality space-time references greatly limit the development and application of vehicle networks. In this paper, we propose a high-precision-based vehicle network location service to solve this problem. The major components of this study include the following: (1) application of wide-area precise positioning technology to the vehicle network system. An adaptive correction message broadcast protocol is designed to satisfy the requirements for large-scale target precise positioning in the mobile Internet environment; (2) development of a concurrence service system with a flexible virtual expansion architecture to guarantee reliable data interaction between vehicles and the background; (3) verification of the positioning precision and service quality in the urban environment. Based on this high-precision positioning service platform, a lane-level location service is designed to solve a typical traffic safety problem. Chi Guo, Wenfei Guo, Guangyi Cao, and Hongbo Dong Copyright © 2015 Chi Guo et al. All rights reserved. A Framework for Final Drive Simultaneous Failure Diagnosis Based on Fuzzy Entropy and Sparse Bayesian Extreme Learning Machine Thu, 05 Feb 2015 06:50:02 +0000 This research proposes a novel framework of final drive simultaneous failure diagnosis containing feature extraction, training paired diagnostic models, generating decision threshold, and recognizing simultaneous failure modes. In feature extraction module, adopt wavelet package transform and fuzzy entropy to reduce noise interference and extract representative features of failure mode. Use single failure sample to construct probability classifiers based on paired sparse Bayesian extreme learning machine which is trained only by single failure modes and have high generalization and sparsity of sparse Bayesian learning approach. To generate optimal decision threshold which can convert probability output obtained from classifiers into final simultaneous failure modes, this research proposes using samples containing both single and simultaneous failure modes and Grid search method which is superior to traditional techniques in global optimization. Compared with other frequently used diagnostic approaches based on support vector machine and probability neural networks, experiment results based on -measure value verify that the diagnostic accuracy and efficiency of the proposed framework which are crucial for simultaneous failure diagnosis are superior to the existing approach. Qing Ye, Hao Pan, and Changhua Liu Copyright © 2015 Qing Ye et al. All rights reserved. A Novel Multiple Instance Learning Method Based on Extreme Learning Machine Tue, 03 Feb 2015 06:40:36 +0000 Since real-world data sets usually contain large instances, it is meaningful to develop efficient and effective multiple instance learning (MIL) algorithm. As a learning paradigm, MIL is different from traditional supervised learning that handles the classification of bags comprising unlabeled instances. In this paper, a novel efficient method based on extreme learning machine (ELM) is proposed to address MIL problem. First, the most qualified instance is selected in each bag through a single hidden layer feedforward network (SLFN) whose input and output weights are both initialed randomly, and the single selected instance is used to represent every bag. Second, the modified ELM model is trained by using the selected instances to update the output weights. Experiments on several benchmark data sets and multiple instance regression data sets show that the ELM-MIL achieves good performance; moreover, it runs several times or even hundreds of times faster than other similar MIL algorithms. Jie Wang, Liangjian Cai, Jinzhu Peng, and Yuheng Jia Copyright © 2015 Jie Wang et al. All rights reserved. Training Spiking Neural Models Using Artificial Bee Colony Sun, 01 Feb 2015 13:20:50 +0000 Spiking neurons are models designed to simulate, in a realistic manner, the behavior of biological neurons. Recently, it has been proven that this type of neurons can be applied to solve pattern recognition problems with great efficiency. However, the lack of learning strategies for training these models do not allow to use them in several pattern recognition problems. On the other hand, several bioinspired algorithms have been proposed in the last years for solving a broad range of optimization problems, including those related to the field of artificial neural networks (ANNs). Artificial bee colony (ABC) is a novel algorithm based on the behavior of bees in the task of exploring their environment to find a food source. In this paper, we describe how the ABC algorithm can be used as a learning strategy to train a spiking neuron aiming to solve pattern recognition problems. Finally, the proposed approach is tested on several pattern recognition problems. It is important to remark that to realize the powerfulness of this type of model only one neuron will be used. In addition, we analyze how the performance of these models is improved using this kind of learning strategy. Roberto A. Vazquez and Beatriz A. Garro Copyright © 2015 Roberto A. Vazquez and Beatriz A. Garro. All rights reserved. Golden Ratio Genetic Algorithm Based Approach for Modelling and Analysis of the Capacity Expansion of Urban Road Traffic Network Sat, 31 Jan 2015 11:41:49 +0000 This paper presents the modelling and analysis of the capacity expansion of urban road traffic network (ICURTN). Thebilevel programming model is first employed to model the ICURTN, in which the utility of the entire network is maximized with the optimal utility of travelers’ route choice. Then, an improved hybrid genetic algorithm integrated with golden ratio (HGAGR) is developed to enhance the local search of simple genetic algorithms, and the proposed capacity expansion model is solved by the combination of the HGAGR and the Frank-Wolfe algorithm. Taking the traditional one-way network and bidirectional network as the study case, three numerical calculations are conducted to validate the presented model and algorithm, and the primary influencing factors on extended capacity model are analyzed. The calculation results indicate that capacity expansion of road network is an effective measure to enlarge the capacity of urban road network, especially on the condition of limited construction budget; the average computation time of the HGAGR is 122 seconds, which meets the real-time demand in the evaluation of the road network capacity. Lun Zhang, Meng Zhang, Wenchen Yang, and Decun Dong Copyright © 2015 Lun Zhang et al. All rights reserved. Log-Linear Model Based Behavior Selection Method for Artificial Fish Swarm Algorithm Mon, 26 Jan 2015 13:05:15 +0000 Artificial fish swarm algorithm (AFSA) is a population based optimization technique inspired by social behavior of fishes. In past several years, AFSA has been successfully applied in many research and application areas. The behavior of fishes has a crucial impact on the performance of AFSA, such as global exploration ability and convergence speed. How to construct and select behaviors of fishes are an important task. To solve these problems, an improved artificial fish swarm algorithm based on log-linear model is proposed and implemented in this paper. There are three main works. Firstly, we proposed a new behavior selection algorithm based on log-linear model which can enhance decision making ability of behavior selection. Secondly, adaptive movement behavior based on adaptive weight is presented, which can dynamically adjust according to the diversity of fishes. Finally, some new behaviors are defined and introduced into artificial fish swarm algorithm at the first time to improve global optimization capability. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and reasonable convergence speed compared with the standard artificial fish swarm algorithm. Zhehuang Huang and Yidong Chen Copyright © 2015 Zhehuang Huang and Yidong Chen. All rights reserved. Self-Adaptive Prediction of Cloud Resource Demands Using Ensemble Model and Subtractive-Fuzzy Clustering Based Fuzzy Neural Network Mon, 26 Jan 2015 09:37:31 +0000 In IaaS (infrastructure as a service) cloud environment, users are provisioned with virtual machines (VMs). To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential. For this purpose, this paper proposes a self-adaptive prediction method using ensemble model and subtractive-fuzzy clustering based fuzzy neural network (ESFCFNN). We analyze the characters of user preferences and demands. Then the architecture of the prediction model is constructed. We adopt some base predictors to compose the ensemble model. Then the structure and learning algorithm of fuzzy neural network is researched. To obtain the number of fuzzy rules and the initial value of the premise and consequent parameters, this paper proposes the fuzzy c-means combined with subtractive clustering algorithm, that is, the subtractive-fuzzy clustering. Finally, we adopt different criteria to evaluate the proposed method. The experiment results show that the method is accurate and effective in predicting the resource demands. Zhijia Chen, Yuanchang Zhu, Yanqiang Di, and Shaochong Feng Copyright © 2015 Zhijia Chen et al. All rights reserved. An Extended Affinity Propagation Clustering Method Based on Different Data Density Types Wed, 21 Jan 2015 14:20:59 +0000 Affinity propagation (AP) algorithm, as a novel clustering method, does not require the users to specify the initial cluster centers in advance, which regards all data points as potential exemplars (cluster centers) equally and groups the clusters totally by the similar degree among the data points. But in many cases there exist some different intensive areas within the same data set, which means that the data set does not distribute homogeneously. In such situation the AP algorithm cannot group the data points into ideal clusters. In this paper, we proposed an extended AP clustering algorithm to deal with such a problem. There are two steps in our method: firstly the data set is partitioned into several data density types according to the nearest distances of each data point; and then the AP clustering method is, respectively, used to group the data points into clusters in each data density type. Two experiments are carried out to evaluate the performance of our algorithm: one utilizes an artificial data set and the other uses a real seismic data set. The experiment results show that groups are obtained more accurately by our algorithm than OPTICS and AP clustering algorithm itself. XiuLi Zhao and WeiXiang Xu Copyright © 2015 XiuLi Zhao and WeiXiang Xu. All rights reserved. Traffic Signal Synchronization in the Saturated High-Density Grid Road Network Tue, 13 Jan 2015 06:33:15 +0000 Most existing traffic signal synchronization strategies do not perform well in the saturated high-density grid road network (HGRN). Traffic congestion often occurs in the saturated HGRN, and the mobility of the network is difficult to restore. In order to alleviate traffic congestion and to improve traffic efficiency in the network, the study proposes a regional traffic signal synchronization strategy, named the long green and long red (LGLR) traffic signal synchronization strategy. The essence of the strategy is to control the formation and dissipation of queues and to maximize the efficiency of traffic flows at signalized intersections in the saturated HGRN. With this strategy, the same signal control timing plan is used at all signalized intersections in the HGRN, and the straight phase of the control timing plan has a long green time and a long red time. Therefore, continuous traffic flows can be maintained when vehicles travel, and traffic congestion can be alleviated when vehicles stop. Using the strategy, the LGLR traffic signal synchronization model is developed, with the objective of minimizing the number of stops. Finally, the simulation is executed to analyze the performance of the model by comparing it to other models, and the superiority of the LGLR model is evident in terms of delay, number of stops, queue length, and overall performance in the saturated HGRN. Xiaojian Hu, Jian Lu, Wei Wang, and Ye Zhirui Copyright © 2015 Xiaojian Hu et al. All rights reserved. Genetic Algorithm for Multiple Bus Line Coordination on Urban Arterial Tue, 13 Jan 2015 05:52:29 +0000 Bus travel time on road section is defined and analyzed with the effect of multiple bus lines. An analytical model is formulated to calculate the total red time a bus encounters when travelling along the arterial. Genetic algorithm is used to optimize the offset scheme of traffic signals to minimize the total red time that all bus lines encounter in two directions of the arterial. The model and algorithm are applied to the major part of Zhongshan North Street in the city of Nanjing. The results show that the methods in this paper can reduce total red time of all the bus lines by 31.9% on the object arterial and thus improve the traffic efficiency of the whole arterial and promote public transport priority. Zhen Yang, Wei Wang, Shuyan Chen, Haoyang Ding, and Xiaowei Li Copyright © 2015 Zhen Yang et al. All rights reserved. Study Partners Recommendation for xMOOCs Learners Mon, 12 Jan 2015 13:08:56 +0000 Massive open online courses (MOOCs) provide an opportunity for people to access free courses offered by top universities in the world and therefore attracted great attention and engagement from college teachers and students. However, with contrast to large scale enrollment, the completion rate of these courses is really low. One of the reasons for students to quit learning process is problems which they face that could not be solved by discussing them with classmates. In order to keep them staying in the course, thereby further improving the completion rate, we address the task of study partner recommendation for students based on both content information and social network information. By analyzing the content of messages posted by learners in course discussion forum, we investigated the learners’ behavior features to classify the learners into three groups. Then we proposed a topic model to measure learners’ course knowledge awareness. Finally, a social network was constructed based on their activities in the course forum, and the relationship in the network was then employed to recommend study partners for target learner combined with their behavior features and course knowledge awareness. The experiment results show that our method achieves better performance than recommending method only based on content information. Bin Xu and Dan Yang Copyright © 2015 Bin Xu and Dan Yang. All rights reserved. Analysis of the Seismic Performance of Isolated Buildings according to Life-Cycle Cost Mon, 05 Jan 2015 13:31:24 +0000 This paper proposes an indicator of seismic performance based on life-cycle cost of a building. It is expressed as a ratio of lifetime damage loss to life-cycle cost and determines the seismic performance of isolated buildings. Major factors are considered, including uncertainty in hazard demand and structural capacity, initial costs, and expected loss during earthquakes. Thus, a high indicator value indicates poor building seismic performance. Moreover, random vibration analysis is conducted to measure structural reliability and evaluate the expected loss and life-cycle cost of isolated buildings. The expected loss of an actual, seven-story isolated hospital building is only 37% of that of a fixed-base building. Furthermore, the indicator of the structural seismic performance of the isolated building is much lower in value than that of the structural seismic performance of the fixed-base building. Therefore, isolated buildings are safer and less risky than fixed-base buildings. The indicator based on life-cycle cost assists owners and engineers in making investment decisions in consideration of structural design, construction, and expected loss. It also helps optimize the balance between building reliability and building investment. Yu Dang, Jian-ping Han, and Yong-tao Li Copyright © 2015 Yu Dang et al. All rights reserved. AITSO: A Tool for Spatial Optimization Based on Artificial Immune Systems Mon, 05 Jan 2015 08:42:39 +0000 A great challenge facing geocomputation and spatial analysis is spatial optimization, given that it involves various high-dimensional, nonlinear, and complicated relationships. Many efforts have been made with regard to this specific issue, and the strong ability of artificial immune system algorithms has been proven in previous studies. However, user-friendly professional software is still unavailable, which is a great impediment to the popularity of artificial immune systems. This paper describes a free, universal tool, named AITSO, which is capable of solving various optimization problems. It provides a series of standard application programming interfaces (APIs) which can (1) assist researchers in the development of their own problem-specific application plugins to solve practical problems and (2) allow the implementation of some advanced immune operators into the platform to improve the performance of an algorithm. As an integrated, flexible, and convenient tool, AITSO contributes to knowledge sharing and practical problem solving. It is therefore believed that it will advance the development and popularity of spatial optimization in geocomputation and spatial analysis. Xiang Zhao, Yaolin Liu, Dianfeng Liu, and Xiaoya Ma Copyright © 2015 Xiang Zhao et al. All rights reserved. New Algorithms for Computing the Time-to-Collision in Freeway Traffic Simulation Models Wed, 31 Dec 2014 13:22:59 +0000 Ways to estimate the time-to-collision are explored. In the context of traffic simulation models, classical lane-based notions of vehicle location are relaxed and new, fast, and efficient algorithms are examined. With trajectory conflicts being the main focus, computational procedures are explored which use a two-dimensional coordinate system to track the vehicle trajectories and assess conflicts. Vector-based kinematic variables are used to support the calculations. Algorithms based on boxes, circles, and ellipses are considered. Their performance is evaluated in the context of computational complexity and solution time. Results from these analyses suggest promise for effective and efficient analyses. A combined computation process is found to be very effective. Jia Hou, George F. List, and Xiucheng Guo Copyright © 2014 Jia Hou et al. All rights reserved. Combined Prediction Model of Death Toll for Road Traffic Accidents Based on Independent and Dependent Variables Wed, 31 Dec 2014 08:10:05 +0000 In order to build a combined model which can meet the variation rule of death toll data for road traffic accidents and can reflect the influence of multiple factors on traffic accidents and improve prediction accuracy for accidents, the Verhulst model was built based on the number of death tolls for road traffic accidents in China from 2002 to 2011; and car ownership, population, GDP, highway freight volume, highway passenger transportation volume, and highway mileage were chosen as the factors to build the death toll multivariate linear regression model. Then the two models were combined to be a combined prediction model which has weight coefficient. Shapley value method was applied to calculate the weight coefficient by assessing contributions. Finally, the combined model was used to recalculate the number of death tolls from 2002 to 2011, and the combined model was compared with the Verhulst and multivariate linear regression models. The results showed that the new model could not only characterize the death toll data characteristics but also quantify the degree of influence to the death toll by each influencing factor and had high accuracy as well as strong practicability. Feng Zhong-xiang, Lu Shi-sheng, Zhang Wei-hua, and Zhang Nan-nan Copyright © 2014 Feng Zhong-xiang et al. All rights reserved. Context Transfer in Reinforcement Learning Using Action-Value Functions Wed, 31 Dec 2014 00:10:41 +0000 This paper discusses the notion of context transfer in reinforcement learning tasks. Context transfer, as defined in this paper, implies knowledge transfer between source and target tasks that share the same environment dynamics and reward function but have different states or action spaces. In other words, the agents learn the same task while using different sensors and actuators. This requires the existence of an underlying common Markov decision process (MDP) to which all the agents’ MDPs can be mapped. This is formulated in terms of the notion of MDP homomorphism. The learning framework is -learning. To transfer the knowledge between these tasks, the feature space is used as a translator and is expressed as a partial mapping between the state-action spaces of different tasks. The -values learned during the learning process of the source tasks are mapped to the sets of -values for the target task. These transferred -values are merged together and used to initialize the learning process of the target task. An interval-based approach is used to represent and merge the knowledge of the source tasks. Empirical results show that the transferred initialization can be beneficial to the learning process of the target task. Amin Mousavi, Babak Nadjar Araabi, and Majid Nili Ahmadabadi Copyright © 2014 Amin Mousavi et al. All rights reserved. A Study of Driver’s Route Choice Behavior Based on Evolutionary Game Theory Mon, 29 Dec 2014 11:30:50 +0000 This paper proposes a route choice analytic method that embeds cumulative prospect theory in evolutionary game theory to analyze how the drivers adjust their route choice behaviors under the influence of the traffic information. A simulated network with two alternative routes and one variable message sign is built to illustrate the analytic method. We assume that the drivers in the transportation system are bounded rational, and the traffic information they receive is incomplete. An evolutionary game model is constructed to describe the evolutionary process of the drivers’ route choice decision-making behaviors. Here we conclude that the traffic information plays an important role in the route choice behavior. The driver’s route decision-making process develops towards different evolutionary stable states in accordance with different transportation situations. The analysis results also demonstrate that employing cumulative prospect theory and evolutionary game theory to study the driver’s route choice behavior is effective. This analytic method provides an academic support and suggestion for the traffic guidance system, and may optimize the travel efficiency to a certain extent. Xiaowei Jiang, Yanjie Ji, Muqing Du, and Wei Deng Copyright © 2014 Xiaowei Jiang et al. All rights reserved. Test Generation Algorithm for Fault Detection of Analog Circuits Based on Extreme Learning Machine Mon, 29 Dec 2014 10:32:43 +0000 This paper proposes a novel test generation algorithm based on extreme learning machine (ELM), and such algorithm is cost-effective and low-risk for analog device under test (DUT). This method uses test patterns derived from the test generation algorithm to stimulate DUT, and then samples output responses of the DUT for fault classification and detection. The novel ELM-based test generation algorithm proposed in this paper contains mainly three aspects of innovation. Firstly, this algorithm saves time efficiently by classifying response space with ELM. Secondly, this algorithm can avoid reduced test precision efficiently in case of reduction of the number of impulse-response samples. Thirdly, a new process of test signal generator and a test structure in test generation algorithm are presented, and both of them are very simple. Finally, the abovementioned improvement and functioning are confirmed in experiments. Jingyu Zhou, Shulin Tian, Chenglin Yang, and Xuelong Ren Copyright © 2014 Jingyu Zhou et al. All rights reserved. Image Superresolution Reconstruction via Granular Computing Clustering Sun, 28 Dec 2014 13:29:22 +0000 The problem of generating a superresolution (SR) image from a single low-resolution (LR) input image is addressed via granular computing clustering in the paper. Firstly, and the training images are regarded as SR image and partitioned into some SR patches, which are resized into LS patches, the training set is composed of the SR patches and the corresponding LR patches. Secondly, the granular computing (GrC) clustering is proposed by the hypersphere representation of granule and the fuzzy inclusion measure compounded by the operation between two granules. Thirdly, the granule set (GS) including hypersphere granules with different granularities is induced by GrC and used to form the relation between the LR image and the SR image by lasso. Experimental results showed that GrC achieved the least root mean square errors between the reconstructed SR image and the original image compared with bicubic interpolation, sparse representation, and NNLasso. Hongbing Liu, Fan Zhang, Chang-an Wu, and Jun Huang Copyright © 2014 Hongbing Liu et al. All rights reserved. A Study on Urban Road Traffic Safety Based on Matter Element Analysis Mon, 22 Dec 2014 07:11:18 +0000 This paper examines a new evaluation of urban road traffic safety based on a matter element analysis, avoiding the difficulties found in other traffic safety evaluations. The issue of urban road traffic safety has been investigated through the matter element analysis theory. The chief aim of the present work is to investigate the features of urban road traffic safety. Emphasis was placed on the construction of a criterion function by which traffic safety achieved a hierarchical system of objectives to be evaluated. The matter element analysis theory was used to create the comprehensive appraisal model of urban road traffic safety. The technique was used to employ a newly developed and versatile matter element analysis algorithm. The matter element matrix solves the uncertainty and incompatibility of the evaluated factors used to assess urban road traffic safety. The application results showed the superiority of the evaluation model and a didactic example was included to illustrate the computational procedure. Qizhou Hu, Zhuping Zhou, and Xu Sun Copyright © 2014 Qizhou Hu et al. All rights reserved. Application of Wireless Intelligent Control System for HPS Lamps and LEDs Combined Illumination in Road Tunnel Sun, 21 Dec 2014 00:10:18 +0000 Because of the particularity of the environment in the tunnel, the rational tunnel illumination system should be developed, so as to optimize the tunnel environment. Considering the high cost of traditional tunnel illumination system with high-pressure sodium (HPS) lamps as well as the effect of a single light source on tunnel entrance, the energy-saving illumination system with HPS lamps and LEDs combined illumination in road tunnel, which could make full use of these two kinds of lamps, was proposed. The wireless intelligent control system based on HPS lamps and LEDs combined illumination and microcontrol unit (MCU) Si1000 wireless communication technology was designed. And the remote monitoring, wireless communication, and PWM dimming module of this system were designed emphatically. Intensity detector and vehicle flow detector can be configured in wireless intelligent control system, which gather the information to the master control unit, and then the information is sent to the monitoring center through the Ethernet. The control strategies are got by the monitoring center according to the calculated results, and the control unit wirelessly sends parameters to lamps, which adjust the luminance of each segment of the tunnel and realize the wireless intelligent control of combined illumination in road tunnel. Jinxing Lai, Junling Qiu, Jianxun Chen, Yaqiong Wang, and Haobo Fan Copyright © 2014 Jinxing Lai et al. All rights reserved. Improvement of Adaptive GAs and Back Propagation ANNs Performance in Condition Diagnosis of Multiple Bearing System Using Grey Relational Analysis Thu, 18 Dec 2014 09:23:55 +0000 Condition diagnosis of multiple bearings system is one of the requirements in industry field, because bearings are used in many equipment and their failure can result in total breakdown. Conditions of bearings commonly are reflected by vibration signals data. In multiple bearing condition diagnosis, it will involve many types of vibration signals data; thus, consequently, it will involve many features extraction to obtain precise condition diagnosis. However, large number of features extraction will increase the complexity of the diagnosis system. Therefore, in this paper, we presented a diagnosis method which is hybridization of adaptive genetic algorithms (AGAs), back propagation neural networks (BPNNs), and grey relational analysis (GRA) to diagnose the condition of multiple bearings system. AGAs are used in the diagnosis algorithm to determine the best initial weights of BPNNs in order to improve the diagnosis accuracy. In addition, GRA is applied to determine and select the dominant features from the vibration signal data which will provide good diagnosis of multiple bearings system in less features extraction. The experiments results show that AGAs-BPNNs with GRA approaches can increase the accuracy of diagnosis in shorter processing time, compared with the AGAs-BPNNs without the GRA. Lili A. Wulandhari, Antoni Wibowo, and Mohammad I. Desa Copyright © 2014 Lili A. Wulandhari et al. All rights reserved. Freeway Travel Speed Calculation Model Based on ETC Transaction Data Mon, 15 Dec 2014 00:10:25 +0000 Real-time traffic flow operation condition of freeway gradually becomes the critical information for the freeway users and managers. In fact, electronic toll collection (ETC) transaction data effectively records operational information of vehicles on freeway, which provides a new method to estimate the travel speed of freeway. First, the paper analyzed the structure of ETC transaction data and presented the data preprocess procedure. Then, a dual-level travel speed calculation model was established under different levels of sample sizes. In order to ensure a sufficient sample size, ETC data of different enter-leave toll plazas pairs which contain more than one road segment were used to calculate the travel speed of every road segment. The reduction coefficient α and reliable weight θ for sample vehicle speed were introduced in the model. Finally, the model was verified by the special designed field experiments which were conducted on several freeways in Beijing at different time periods. The experiments results demonstrated that the average relative error was about 6.5% which means that the freeway travel speed could be estimated by the proposed model accurately. The proposed model is helpful to promote the level of the freeway operation monitoring and the freeway management, as well as to provide useful information for the freeway travelers. Jiancheng Weng, Rongliang Yuan, Ru Wang, and Chang Wang Copyright © 2014 Jiancheng Weng et al. All rights reserved. Game Theory Model of Traffic Participants within Amber Time at Signalized Intersection Mon, 15 Dec 2014 00:10:22 +0000 The traffic light scheme is composed of red, green, and amber lights, and it has been defined clearly for the traffic access of red and green lights; however, the definition of that for the amber light is indistinct, which leads to the appearance of uncertainty factors and serious traffic conflicts during the amber light. At present, the traffic administrations are faced with the decision of whether to forbid passing or not during the amber light in the cities of China. On one hand, it will go against the purpose of setting amber lights if forbidding passing; on the other hand, it may lead to a mess of traffic flow running if not. And meanwhile the drivers are faced with the decision of passing the intersection or stopping during the amber light as well. So the decision-making behavior of traffic administrations and drivers can be converted into a double game model. And through quantification of their earnings in different choice conditions, the optimum decision-making plan under specific conditions could be solved via the Nash equilibrium solution concept. Thus the results will provide a basis for the formulation of the traffic management strategy. Weiwei Qi, Huiying Wen, Chuanyun Fu, and Mo Song Copyright © 2014 Weiwei Qi et al. All rights reserved. Risk Evaluation of Bogie System Based on Extension Theory and Entropy Weight Method Wed, 10 Dec 2014 00:10:40 +0000 A bogie system is the key equipment of railway vehicles. Rigorous practical evaluation of bogies is still a challenge. Presently, there is overreliance on part-specific experiments in practice. In the present work, a risk evaluation index system of a bogie system has been established based on the inspection data and experts’ evaluation. Then, considering quantitative and qualitative aspects, the risk state of a bogie system has been evaluated using an extension theory and an entropy weight method. Finally, the method has been used to assess the bogie system of four different samples. Results show that this method can assess the risk state of a bogie system exactly. Yanping Du, Yuan Zhang, Xiaogang Zhao, and Xiaohui Wang Copyright © 2014 Yanping Du et al. All rights reserved.