Mathematical Problems in Engineering The latest articles from Hindawi Publishing Corporation © 2015 , Hindawi Publishing Corporation . All rights reserved. Bayes-Nash Equilibrium of the Generalized First-Price Auction Thu, 08 Oct 2015 16:18:23 +0000 We analyze the generalized first-price auction under incomplete information setting. Without setting a reserve price, the efficient symmetrical Bayes-Nash equilibrium is characterized and found to be increasing as the number of bidders is sufficiently large. Then, the explicit expression for the expected revenue of the search engine is found and the effect of the click rates of all the positions on the expected revenue is obtained. Finally, with setting of the reserve price, we have found the optimal reserve price and examine how the difference of the search engine’s revenues with setting reserve price and without setting reserve price varies with the reserve price. Xiaohu Han and Shulin Liu Copyright © 2015 Xiaohu Han and Shulin Liu. All rights reserved. Modeling and Simulation of Ballistic Penetration of Ceramic-Polymer-Metal Layered Systems Thu, 08 Oct 2015 14:51:12 +0000 Numerical simulations and analysis of ballistic impact and penetration by tungsten alloy rods into composite targets consisting of layers of aluminum nitride ceramic tile(s), polymer laminae, and aluminum backing are conducted over a range of impact velocities on the order of 1.0 to 1.2 km/s. Computational results for ballistic efficiency are compared with experimental data from the literature. Simulations and experiments both demonstrate a trend of decreasing ballistic efficiency with increasing impact velocity. Predicted absolute residual penetration depths often exceed corresponding experimental values. The closest agreement between model and experiment is obtained when polymer interfaces are not explicitly represented in the numerical calculations, suggesting that the current model representation of such interfaces may be overly compliant. The present results emphasize the importance of proper resolution of geometry and constitutive properties of thin layers and interfaces between structural constituents for accurate numerical evaluation of performance of modern composite protection systems. J. D. Clayton Copyright © 2015 J. D. Clayton. All rights reserved. Distributed Node-to-Node Consensus of Multiagent Systems with Delayed Nonlinear Dynamics and Intermittent Communications Thu, 08 Oct 2015 13:00:32 +0000 The paper is concerned with the problem of distributed node-to-node consensus of multiagent systems with delayed nonlinear dynamics and communication constraints. A new kind of consensus protocol based only on the intermittent measurements of neighboring agents is proposed to make each follower track the corresponding leader asymptotically. Based on the Lyapunov stability theory and M-matrix theory, some novel and simple criteria are derived for node-to-node consensus of multiagent systems. It is shown that consensus can be reached if the communication time duration is larger than the corresponding threshold value. Finally, a numerical example is provided to demonstrate the effectiveness of the obtained theoretical results. Kexin Jia, Hongjie Li, Wenqiang Zheng, Qinyou Mou, and Jiajun Shao Copyright © 2015 Kexin Jia et al. All rights reserved. A Dependent Insurance Risk Model with Surrender and Investment under the Thinning Process Thu, 08 Oct 2015 12:49:17 +0000 A dependent insurance risk model with surrender and investment under the thinning process is discussed, where the arrival of the policies follows a compound Poisson-Geometric process, and the occurrences of the claim and surrender happen as the -thinning process and the -thinning process of the arrival process, respectively. By the martingale theory, the properties of the surplus process, adjustment coefficient equation, the upper bound of ruin probability, and explicit expression of ruin probability are obtained. Moreover, we also get the Laplace transformation, the expectation, and the variance of the time when the surplus reaches a given level for the first time. Finally, various trends of the upper bound of ruin probability and the expectation and the variance of the time when the surplus reaches a given level for the first time are simulated analytically along with changing the investment size, investment interest rates, claim rate, and surrender rate. Wenguang Yu and Yujuan Huang Copyright © 2015 Wenguang Yu and Yujuan Huang. All rights reserved. Thermal Error Modeling Method for a CNC Machine Tool Feed Drive System Thu, 08 Oct 2015 12:20:11 +0000 The disadvantages of the common current thermal error modeling methods for CNC machine tool feed drive systems were analyzed, such as the requirement of many temperature sensors to reach high accuracy and poor applicability of different moving states. A new robust modeling method based on the heat transfer theory is proposed, and the procedure of the thermal tests for a feed drive system is presented. Multiple regression method and robust modeling method based on the heat transfer theory were, respectively, used to establish a thermal error model, and a pointer automatic optimizer was used to optimize the parameters in the robust model. A compensation simulation was conducted under five different moving states using these two modeling methods, and the advantages of the robust modeling method were proved. Finally, the compensation effect of the robust modeling method was verified under a random moving state on a vertical machining center. Kuo Liu, Mingjia Sun, Yuliang Wu, and Tiejun Zhu Copyright © 2015 Kuo Liu et al. All rights reserved. Prediction Method of the Fuel Consumption of Wheel Loaders in the V-Type Loading Cycle Thu, 08 Oct 2015 12:06:32 +0000 Wheel loaders in the V-type loading cycle are characterized by complicated loading conditions, nonlinear power-train system, and time-variable engine power distribution. Therefore, it is difficult to predict the fuel consumption of wheel loaders in the V-type loading cycle. The static matching methods cannot provide fuel consumption prediction for the loading cycle. In this paper, the prediction method and model of the fuel consumption for wheel loaders in the V-type loading cycle were proposed. Firstly, the hydraulic system data were tested when a wheel loader loaded three different materials in a typical V-type loading cycle. Secondly, the tested data were filtered by the 8th-order Butterworth filter and the dimensionless power deduction equations of hydraulic power system for loading three different materials were obtained by Gaussian and linear fitting based on the filtered data in the loading cycle. Finally, fuel consumption was obtained with the compiling dynamic calculation program as well as input parameters of tested vehicle speed, throttle parameter, and the dimensionless equation. The simulation results agreed well with experiment results. Dynamic calculation program is applicable to calculate loading economy and can provide academic guidance for wheel loader’s design and optimization. Wenxing Ma, Yubo Zhang, Chunbao Liu, and Songlin Wang Copyright © 2015 Wenxing Ma et al. All rights reserved. Design and Analytical Analysis of a Novel DBA Algorithm with Dual-Polling Tables in EPON Thu, 08 Oct 2015 09:31:26 +0000 Ethernet passive optical network is a point-to-multipoint structure, and an effective media access protocol should be designed for collision avoidance and bandwidth allocation. Most previous researches have aimed to solve the problem using dynamic bandwidth allocation (DBA) algorithms with polling-based schemes. Unfortunately, idle channel time among the polling cycles cannot be overcome efficiently. In this paper, a dual-polling DBA (DP-DBA) algorithm which consists of an interpolling mechanism and an intrapolling mechanism is proposed. The interpolling mechanism helps eliminate the idle time problem while the intrapolling part aggregates the unused bandwidth of the light-loaded users and those due to nondefragmentation of the Ethernet frames of the general users. We also evaluate the performance of the DP-DBA algorithm by a comparison with the interleaved polling with adaptive cycle time (IPACT), the dynamic bandwidth allocation with a modified grant table generation algorithm (DBA2), the Double-Phase Polling (DPP), and the adaptive DBA algorithm with sorting report messages (Sort-DBA) schemes with simulation results. For greater realism with regard to general applications, we also simulate the asymmetric traffic loads at the optical network units (ONUs).The results show that DP-DBA outperforms the other DBA schemes under asymmetric load conditions. Jiunn-Ru Lai, Hsin-Yi Huang, Wen-Ping Chen, Luke K. Wang, and Ming-Yuan Cho Copyright © 2015 Jiunn-Ru Lai et al. All rights reserved. FTC with Dynamic Virtual Actuators: Characterization via Dynamic Output Controllers and Approach Thu, 08 Oct 2015 08:35:16 +0000 The paper presents new conditions, adequate in design of dynamic virtual actuators and utilizable in fault-tolerant control structures (FTC) for continuous-time linear systems, which are stabilizable by dynamic output controllers. Taking into account disturbance conditions and changes of variables in FTC after virtual actuator activation and applying the nominal control scheme relating to the biproper dynamic output controller of prescribed order, the design conditions are outlined in terms of the linear matrix inequalities within the enhanced bounded real lemma forms. Using a free tuning parameter in design, and with suitable choice of the controller order, the approach provides the way to obtain acceptable dynamics of the closed-loop system after activation of the dynamic virtual actuator. Dušan Krokavec, Anna Filasová, and Vladimír Serbák Copyright © 2015 Dušan Krokavec et al. All rights reserved. Time-Varying Scheme for Noncentralized Model Predictive Control of Large-Scale Systems Thu, 08 Oct 2015 08:30:23 +0000 The noncentralized model predictive control (NC-MPC) framework in this paper refers to any distributed, hierarchical, or decentralized model predictive controller (or a combination of them) the structure of which can change over time and the control actions of which are not obtained based on a centralized computation. Within this framework, we propose suitable online methods to decide which information is shared and how this information is used between the different local predictive controllers operating in a decentralized, distributed, and/or hierarchical way. Evaluating all the possible structures of the NC-MPC controller leads to a combinatorial optimization problem. Therefore, we also propose heuristic reduction methods, to keep the number of NC-MPC problems tractable to be solved. To show the benefits of the proposed framework, a case study of a set of coupled water tanks is presented. Alfredo Núñez, Carlos Ocampo-Martinez, José María Maestre, and Bart De Schutter Copyright © 2015 Alfredo Núñez et al. All rights reserved. Dynamic Inventory and Pricing Policy in a Periodic-Review Inventory System with Finite Ordering Capacity and Price Adjustment Cost Thu, 08 Oct 2015 08:29:01 +0000 We consider a dynamic inventory control and pricing optimization problem in a periodic-review inventory system with price adjustment cost. Each order occurs with a fixed ordering cost; the ordering quantity is capacitated. We consider a sequential decision problem, where the firm first chooses the ordering quantity and then the sale price to maximize the expected total discounted profit over the sale horizon. We show that the optimal inventory control is partially characterized by a policy in four regions, and the optimal pricing policy is dependent on the inventory level after the replenishment decision. We present some numerical examples to explore the effects of various parameters on the optimal pricing and replenishment policy. Baimei Yang, Chunyan Gao, Na Liu, and Liang Xu Copyright © 2015 Baimei Yang et al. All rights reserved. Risk and Resilience Analysis of Complex Network Systems Considering Cascading Failure and Recovery Strategy Based on Coupled Map Lattices Thu, 08 Oct 2015 08:13:55 +0000 Risk and resilience are important and challenging issues in complex network systems since a single failure may trigger a whole collapse of the systems due to cascading effect. New theories, models, and methods are urgently demanded to deal with this challenge. In this paper, a coupled map lattices (CML) based approach is adopted to analyze the risk of cascading process in Watts-Strogatz (WS) small-world network and Barabási and Albert (BA) scale-free network, respectively. Then, to achieve an effective and robust system and provide guidance in countering the cascading failure, a modified CML model with recovery strategy factor is proposed. Numerical simulations are put forward based on small-world CML and scale-free CML. The simulation results reveal that appropriate recovery strategies would significantly improve the resilience of networks. Fuchun Ren, Tingdi Zhao, and Hongli Wang Copyright © 2015 Fuchun Ren et al. All rights reserved. Damage Localization of an Offshore Platform considering Temperature Variations Thu, 08 Oct 2015 07:10:17 +0000 Modal parameters are sensitive indicators of structural damages. However, these modal parameters are sensitive not only to damage, but also to the environmental variations. Development of vibration based damage detection methodology which is robust to environmental variation is essentially important for the structural safety. The present paper utilizes a recently developed modal strain energy decomposition (MSED) method to localize the damage of an offshore structure. A progress of the present paper is to take the temperature variation into consideration and Monte Carlo simulation is introduced to investigate the effect of temperature variation on the robustness of damage localization. Numerical study is conducted on an offshore platform structure considering the temperature variation. Several damage cases, including single and double damage scenarios, are included to investigate the damage localization algorithm. Results indicate that the MSED algorithm is able to detect the damage despite the temperature variations. Shuqing Wang, Min Zhang, and Huajun Li Copyright © 2015 Shuqing Wang et al. All rights reserved. Terminal Sliding Mode Control with Adaptive Law for Uncertain Nonlinear System Wed, 07 Oct 2015 13:12:41 +0000 A novel nonsingular terminal sliding mode controller is proposed for a second-order system with unmodeled dynamics uncertainties and external disturbances. We need not achieve the knowledge for boundaries of uncertainties and external disturbances in advance. The adaptive control gains are obtained to estimate the uncertain parameters and external disturbances which are unknown but bounded. The closed loop system stability is ensured with robustness and adaptation by the Lyapunov stability theorem in finite time. An illustrative example of second-order nonlinear system with unmodeled dynamics and external disturbances is given to demonstrate the effectiveness of the presented scheme. Zhanshan Zhao, Jing Zhang, Liankun Sun, and Dakun Zhang Copyright © 2015 Zhanshan Zhao et al. All rights reserved. A Cooperative -Learning Path Planning Algorithm for Origin-Destination Pairs in Urban Road Networks Wed, 07 Oct 2015 12:05:20 +0000 As an important part of intelligent transportation systems, path planning algorithms have been extensively studied in the literature. Most of existing studies are focused on the global optimization of paths to find the optimal path between Origin-Destination (OD) pairs. However, in urban road networks, the optimal path may not be always available when some unknown emergent events occur on the path. Thus a more practical method is to calculate several suboptimal paths instead of finding only one optimal path. In this paper, a cooperative -learning path planning algorithm is proposed to seek a suboptimal multipath set for OD pairs in urban road networks. The road model is abstracted to the form that -learning can be applied firstly. Then the gray prediction algorithm is combined into -learning to find the suboptimal paths with reliable constraints. Simulation results are provided to show the effectiveness of the proposed algorithm. Xiaoyong Zhang, Heng Li, Jun Peng, and Weirong Liu Copyright © 2015 Xiaoyong Zhang et al. All rights reserved. A Random Parameter Model for Continuous-Time Mean-Variance Asset-Liability Management Wed, 07 Oct 2015 11:52:33 +0000 We consider a continuous-time mean-variance asset-liability management problem in a market with random market parameters; that is, interest rate, appreciation rates, and volatility rates are considered to be stochastic processes. By using the theories of stochastic linear-quadratic (LQ) optimal control and backward stochastic differential equations (BSDEs), we tackle this problem and derive optimal investment strategies as well as the mean-variance efficient frontier analytically in terms of the solution of BSDEs. We find that the efficient frontier is still a parabola in a market with random parameters. Comparing with the existing results, we also find that the liability does not affect the feasibility of the mean-variance portfolio selection problem. However, in an incomplete market with random parameters, the liability can not be fully hedged. Hui-qiang Ma, Meng Wu, and Nan-jing Huang Copyright © 2015 Hui-qiang Ma et al. All rights reserved. Generalized Serre Problem over Elementary Divisor Rings Wed, 07 Oct 2015 11:06:18 +0000 Matrix factorization has been widely investigated in the past years due to its fundamental importance in several areas of engineering. This paper investigates completion and zero prime factorization of matrices over elementary divisor rings (EDR). The Serre problem and Lin-Bose problems are generalized to EDR and are completely solved. Licui Zheng, Jinwang Liu, and Weijun Liu Copyright © 2015 Licui Zheng et al. All rights reserved. A Robust Method for Speech Emotion Recognition Based on Infinite Student’s -Mixture Model Wed, 07 Oct 2015 10:14:09 +0000 Speech emotion classification method, proposed in this paper, is based on Student’s -mixture model with infinite component number (iSMM) and can directly conduct effective recognition for various kinds of speech emotion samples. Compared with the traditional GMM (Gaussian mixture model), speech emotion model based on Student’s -mixture can effectively handle speech sample outliers that exist in the emotion feature space. Moreover, -mixture model could keep robust to atypical emotion test data. In allusion to the high data complexity caused by high-dimensional space and the problem of insufficient training samples, a global latent space is joined to emotion model. Such an approach makes the number of components divided infinite and forms an iSMM emotion model, which can automatically determine the best number of components with lower complexity to complete various kinds of emotion characteristics data classification. Conducted over one spontaneous (FAU Aibo Emotion Corpus) and two acting (DES and EMO-DB) universal speech emotion databases which have high-dimensional feature samples and diversiform data distributions, the iSMM maintains better recognition performance than the comparisons. Thus, the effectiveness and generalization to the high-dimensional data and the outliers are verified. Hereby, the iSMM emotion model is verified as a robust method with the validity and generalization to outliers and high-dimensional emotion characters. Xinran Zhang, Huawei Tao, Cheng Zha, Xinzhou Xu, and Li Zhao Copyright © 2015 Xinran Zhang et al. All rights reserved. Decision Making Methods and Applications in Civil Engineering Wed, 07 Oct 2015 09:37:22 +0000 Jurgita Antucheviciene, Zdeněk Kala, Mohamed Marzouk, and Egidijus Rytas Vaidogas Copyright © 2015 Jurgita Antucheviciene et al. All rights reserved. Tracking Control for Switched Cascade Nonlinear Systems Wed, 07 Oct 2015 09:17:54 +0000 The issue of output tracking for switched cascade nonlinear systems is discussed in this paper, where not all the linear parts of subsystems are stabilizable. The conditions of the solvability for the issue are given by virtue of the structural characteristics of the systems and the average dwell time method, in which the total activation time for stabilizable subsystems is longer than that for the unstabilizable subsystems. At last, a simulation example is used to demonstrate the validity and advantages of the proposed approach. Xiaoxiao Dong, Linyan Chang, Fangfang Wu, and Na Hu Copyright © 2015 Xiaoxiao Dong et al. All rights reserved. Coordinated Stability Control of Wind-Thermal Hybrid AC/DC Power System Wed, 07 Oct 2015 08:31:55 +0000 The wind-thermal hybrid power transmission will someday be the main form of transmitting wind power in China but such transmission mode is poor in system stability. In this paper, a coordinated stability control strategy is proposed to improve the system stability. Firstly, the mathematical model of doubly fed wind farms and DC power transmission system is established. The rapid power controllability of large-scale wind farms is discussed based on DFIG model and wide-field optical fiber delay feature. Secondly, low frequency oscillation and power-angle stability are analyzed and discussed under the hybrid transmission mode of a conventional power plant with wind farms. A coordinated control strategy for the wind-thermal hybrid AC/DC power system is proposed and an experimental prototype is made. Finally, real time simulation modeling is set up through Real Time Digital Simulator (RTDS), including wind power system and synchronous generator system and DC power transmission system. The experimental prototype is connected with RTDS for joint debugging. Joint debugging result shows that, under the coordinated control strategy, the experimental prototype is conductive to enhance the grid damping and effectively prevents the grid from occurring low frequency oscillation. It can also increase the transient power-angle stability of a power system. Zhiqing Yao, Zhenghang Hao, Zhuo Chen, and Zhiguo Yan Copyright © 2015 Zhiqing Yao et al. All rights reserved. Bias Modeling for Distantly Supervised Relation Extraction Wed, 07 Oct 2015 08:24:31 +0000 Distant supervision (DS) automatically annotates free text with relation mentions from existing knowledge bases (KBs), providing a way to alleviate the problem of insufficient training data for relation extraction in natural language processing (NLP). However, the heuristic annotation process does not guarantee the correctness of the generated labels, promoting a hot research issue on how to efficiently make use of the noisy training data. In this paper, we model two types of biases to reduce noise: (1) bias-dist to model the relative distance between points (instances) and classes (relation centers); (2) bias-reward to model the possibility of each heuristically generated label being incorrect. Based on the biases, we propose three noise tolerant models: MIML-dist, MIML-dist-classify, and MIML-reward, building on top of a state-of-the-art distantly supervised learning algorithm. Experimental evaluations compared with three landmark methods on the KBP dataset validate the effectiveness of the proposed methods. Yang Xiang, Yaoyun Zhang, Xiaolong Wang, Yang Qin, and Wenying Han Copyright © 2015 Yang Xiang et al. All rights reserved. Incremental Discriminant Analysis on Interval-Valued Parameters for Emitter Identification Wed, 07 Oct 2015 08:23:06 +0000 Emitter identification has been widely recognized as one crucial issue for communication, electronic reconnaissance, and radar intelligence analysis. However, the measurements of emitter signal parameters typically take the form of uncertain intervals rather than precise values. In addition, the measurements are generally accumulated dynamically and continuously. As a result, one imminent task has become how to carry out discriminant analysis of interval-valued parameters incrementally for emitter identification. Existing machine learning approaches for interval-valued data analysis are unfit for this purpose as they generally assume a uniform distribution and are usually restricted to static data analysis. To address the above problems, we bring forward an incremental discriminant analysis method on interval-valued parameters (IDAIP) for emitter identification. Extensive experiments on both synthetic and real-life data sets have validated the efficiency and effectiveness of our method. Xin Xu, Zhaohua Xiong, and Wei Wang Copyright © 2015 Xin Xu et al. All rights reserved. A Fault-Tolerant Filtering Algorithm for SINS/DVL/MCP Integrated Navigation System Wed, 07 Oct 2015 08:21:01 +0000 The Kalman filter (KF), which recursively generates a relatively optimal estimate of underlying system state based upon a series of observed measurements, has been widely used in integrated navigation system. Due to its dependence on the accuracy of system model and reliability of observation data, the precision of KF will degrade or even diverge, when using inaccurate model or trustless data set. In this paper, a fault-tolerant adaptive Kalman filter (FTAKF) algorithm for the integrated navigation system composed of a strapdown inertial navigation system (SINS), a Doppler velocity log (DVL), and a magnetic compass (MCP) is proposed. The evolutionary artificial neural networks (EANN) are used in self-learning and training of the intelligent data fusion algorithm. The proposed algorithm can significantly outperform the traditional KF in providing estimation continuously with higher accuracy and smoothing the KF outputs when observation data are inaccurate or unavailable for a short period. The experiments of the prototype verify the effectiveness of the proposed method. Xiaosu Xu, Peijuan Li, and Jian-juan Liu Copyright © 2015 Xiaosu Xu et al. All rights reserved. Manifold Learning with Self-Organizing Mapping for Feature Extraction of Nonlinear Faults in Rotating Machinery Wed, 07 Oct 2015 08:19:14 +0000 A new method for extracting the low-dimensional feature automatically with self-organization mapping manifold is proposed for the detection of rotating mechanical nonlinear faults (such as rubbing, pedestal looseness). Under the phase space reconstructed by single vibration signal, the self-organization mapping (SOM) with expectation maximization iteration algorithm is used to divide the local neighborhoods adaptively without manual intervention. After that, the local tangent space alignment algorithm is adopted to compress the high-dimensional phase space into low-dimensional feature space. The proposed method takes advantages of the manifold learning in low-dimensional feature extraction and adaptive neighborhood construction of SOM and can extract intrinsic fault features of interest in two dimensional projection space. To evaluate the performance of the proposed method, the Lorenz system was simulated and rotation machinery with nonlinear faults was obtained for test purposes. Compared with the holospectrum approaches, the results reveal that the proposed method is superior in identifying faults and effective for rotating machinery condition monitoring. Lin Liang, Fei Liu, Maolin Li, and Guanghua Xu Copyright © 2015 Lin Liang et al. All rights reserved. Automatic Classification of Remote Sensing Images Using Multiple Classifier Systems Wed, 07 Oct 2015 08:17:33 +0000 It is a challenge to obtain accurate result in remote sensing images classification, which is affected by many factors. In this paper, aiming at correctly identifying land use types reflec ted in remote sensing images, support vector machine, maximum likelihood classifier, backpropagation neural network, fuzzy c-means, and minimum distance classifier were combined to construct three multiple classifier systems (MCSs). Two MCSs were implemented, namely, comparative major voting (CMV) and Bayesian average (BA). One method called WA-AHP was proposed, which introduced analytic hierarchy process into MCS. Classification results of base classifiers and MCSs were compared with the ground truth map. Accuracy indicators were computed and receiver operating characteristic curves were illustrated, so as to evaluate the performance of MCSs. Experimental results show that employing MCSs can increase classification accuracy significantly, compared with base classifiers. From the accuracy evaluation result and visual check, the best MCS is WA-AHP with overall accuracy of 94.2%, which overmatches BA and rivals CMV in this paper. The producer’s accuracy of each land use type proves the good performance of WA-AHP. Therefore, we can draw the conclusion that MCS is superior to base classifiers in remote sensing image classification, and WA-AHP is an efficient MCS. Bin Yang, Chunxiang Cao, Ying Xing, and Xiaowen Li Copyright © 2015 Bin Yang et al. All rights reserved. Locating High-Impedance Fault Section in Electric Power Systems Using Wavelet Transform, -Means, Genetic Algorithms, and Support Vector Machine Wed, 07 Oct 2015 08:16:02 +0000 High-impedance faults (HIFs) caused by downed conductors in electric power systems are in general difficult to be detected using traditional protection relays due to small fault currents. The energized downed conductor can pose a safety risk to the public and cause a fire hazard. This paper presents a new method for locating the line (feeder) section of the HIF with the help of limited measurements in electric power systems. The discrete wavelet transform is used to extract the features of transients caused by HIFs. A modified -means algorithm associated with genetic algorithms is then utilized to determine the placement of measurement facilities. The signal energies attained by wavelet coefficients serve as inputs to the support vector machine for locating the HIF line section. The simulation results obtained from an 18-busbar distribution system show the applicability of the proposed method. Ying-Yi Hong and Wei-Shun Huang Copyright © 2015 Ying-Yi Hong and Wei-Shun Huang. All rights reserved. Fault Diagnosis of Supervision and Homogenization Distance Based on Local Linear Embedding Algorithm Wed, 07 Oct 2015 07:44:40 +0000 In view of the problems of uneven distribution of reality fault samples and dimension reduction effect of locally linear embedding (LLE) algorithm which is easily affected by neighboring points, an improved local linear embedding algorithm of homogenization distance (HLLE) is developed. The method makes the overall distribution of sample points tend to be homogenization and reduces the influence of neighboring points using homogenization distance instead of the traditional Euclidean distance. It is helpful to choose effective neighboring points to construct weight matrix for dimension reduction. Because the fault recognition performance improvement of HLLE is limited and unstable, the paper further proposes a new local linear embedding algorithm of supervision and homogenization distance (SHLLE) by adding the supervised learning mechanism. On the basis of homogenization distance, supervised learning increases the category information of sample points so that the same category of sample points will be gathered and the heterogeneous category of sample points will be scattered. It effectively improves the performance of fault diagnosis and maintains stability at the same time. A comparison of the methods mentioned above was made by simulation experiment with rotor system fault diagnosis, and the results show that SHLLE algorithm has superior fault recognition performance. Guangbin Wang, Jun Luo, Yilin He, and Qinyi Chen Copyright © 2015 Guangbin Wang et al. All rights reserved. A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications Wed, 07 Oct 2015 07:07:32 +0000 Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms. Yudong Zhang, Shuihua Wang, and Genlin Ji Copyright © 2015 Yudong Zhang et al. All rights reserved. Efficient and Enhanced Diffusion of Vector Field for Active Contour Model Wed, 07 Oct 2015 06:53:59 +0000 Gradient vector flow (GVF) is an important external force field for active contour models. Various vector fields based on GVF have been proposed. However, these vector fields are obtained with many iterations and have difficulty in capturing the whole image area. On the other hand, the ability to converge to deep and complex concavity with these vector fields is also needed to improve. In this paper, by analyzing the diffusion equation of GVF, a normalized set is defined and a dynamically normalized constraint of vector fields is used for efficient diffusion, which makes the edge vector diffusing rapidly to the entire image region. In order to improve the ability to converge to concavity, an enhanced diffusion term is integrated into the original energy functional. With the dynamically normalized constraint and enhanced diffusion term, new vector fields of EDGVF (efficient and enhanced diffusion for GVF) and EDNGVF (efficient and enhanced diffusion of NGVF) are obtained. Experimental results demonstrate that vector fields with proposed method capture the entire image and are obtained with less iterations and computational times. In particular, EDNGVF greatly improves the ability to converge to concavity. Guoqi Liu, Lin Sun, and Shangwang Liu Copyright © 2015 Guoqi Liu et al. All rights reserved. A New Image Denoising Method by Combining WT with ICA Mon, 05 Oct 2015 14:11:07 +0000 In order to improve the image denoising ability, the wavelet transform (WT) and independent component analysis (ICA) are both introduced into image denoising in this paper. Although these two algorithms have their own advantages in image denoising, they are unable to reduce noises completely, which makes it difficult to achieve ideal effect. Therefore, a new image denoising method is proposed based on the combination of WT with ICA (WT-ICA). For verifying the WT-ICA denoising method, we adopt four image denoising methods for comparison: median filtering (MF), wavelet soft thresholding (WST), ICA, and WT-ICA. From the experimental results, it is shown that WT-ICA can significantly reduce noises and get lower-noise image. Moreover, the average of WT-ICA denoising image’s peak signal to noise ratio (PSNR) is improved by 20.54% compared with noisy image and 11.68% compared with the classical WST denoising image, which demonstrates its advantage. From the performance of texture and edge detection, denoising image by WT-ICA is closer to the original image. Therefore, the new method has its unique advantage in image denoising, which lays a solid foundation for the realization of further image processing task. Chengzhi Ruan, Dean Zhao, Weikuan Jia, Chen Chen, Yu Chen, and Xiaoyang Liu Copyright © 2015 Chengzhi Ruan et al. All rights reserved.