Mathematical Problems in Engineering The latest articles from Hindawi Publishing Corporation © 2014 , Hindawi Publishing Corporation . All rights reserved. Optimization Method for Girder of Wind Turbine Blade Thu, 24 Jul 2014 14:01:47 +0000 This paper presents a recently developed numerical multidisciplinary optimization method for design of wind turbine blade. The objective was the highest possible blade weight under specified atmospheric conditions, determined by the design giving girder layer and location parameter. Wind turbine blade on box-section beams girder is calculated by ply thickness, main girder and trailing edge. In this study, a realistic 30 m blade from a 1.2 MW wind turbine model of blade girder parameters is established. The optimization evolves a structure which transforms along the length of the blade, changing from a design with spar caps at the maximum thickness and a trailing edge mass to a design with spar caps toward the tip. In addition, the cross-section structural properties and the modal characteristics of a 62 m rotor blade were predicted by the developed beam finite element. In summary, these findings indicate that the conventional structural layout of a wind turbine blade is suboptimal under the static load conditions, suggesting an opportunity to reduce blade weight and cost. Yuqiao Zheng, Rongzhen Zhao, and Hong Liu Copyright © 2014 Yuqiao Zheng et al. All rights reserved. EVD Dualdating Based Online Subspace Learning Thu, 24 Jul 2014 12:26:33 +0000 Conventional incremental PCA methods usually only discuss the situation of adding samples. In this paper, we consider two different cases: deleting samples and simultaneously adding and deleting samples. To avoid the NP-hard problem of downdating SVD without right singular vectors and specific position information, we choose to use EVD instead of SVD, which is used by most IPCA methods. First, we propose an EVD updating and downdating algorithm, called EVD dualdating, which permits simultaneous arbitrary adding and deleting operation, via transforming the EVD of the covariance matrix into a SVD updating problem plus an EVD of a small autocorrelation matrix. A comprehensive analysis is delivered to express the essence, expansibility, and computation complexity of EVD dualdating. A mathematical theorem proves that if the whole data matrix satisfies the low-rank-plus-shift structure, EVD dualdating is an optimal rank-k estimator under the sequential environment. A selection method based on eigenvalues is presented to determine the optimal rank k of the subspace. Then, we propose three incremental/decremental PCA methods: EVDD-IPCA, EVDD-DPCA, and EVDD-IDPCA, which are adaptive to the varying mean. Finally, plenty of comparative experiments demonstrate that EVDD-based methods outperform conventional incremental/decremental PCA methods in both efficiency and accuracy. Bo Jin, Zhongliang Jing, and Haitao Zhao Copyright © 2014 Bo Jin et al. All rights reserved. Optimal Finite-Time State Estimation for Discrete-Time Switched Systems under Switching Frequency Constraint Thu, 24 Jul 2014 12:09:15 +0000 The state estimation problem for a class of switched linear systems which only switches in some short interval is addressed. Besides the asymptotic stability of error dynamics, the boundness of error state is a significant issue for short-time switched systems. By introducing the concept of finite-time stability, the state estimation procedure is formulated to determine appropriate observer gains ensuring the error dynamics is finite-time stable in the short-time switching intervals of interest. Optimal finite-time observers are designed through iterative algorithms to minimize the bound of error state, in the cases with and without disturbances. Particularly, when the total activation time is known, a less conservative result can be derived and an optimization problem can be solved with the help of the genetic algorithm. A numerical example is provided to illustrate the theoretical findings in this paper. Lin Du, Weiming Xiang, and Yongchi Zhao Copyright © 2014 Lin Du et al. All rights reserved. Effects of Chemical Reactions on Unsteady Free Convective and Mass Transfer Flow from a Vertical Cone with Heat Generation/Absorption in the Presence of VWT/VWC Thu, 24 Jul 2014 11:55:45 +0000 A mathematical model for the effects of chemical reaction and heat generation/absorption on unsteady laminar free convective flow with heat and mass transfer over an incompressible viscous fluid past a vertical permeable cone with nonuniform surface temperature and concentration is considered here. The dimensionless governing boundary layer equations of the flow that are transient, coupled, and nonlinear partial differential equations are solved by an efficient, accurate, and unconditionally stable finite difference scheme of Crank-Nicholson type. The velocity, temperature, and concentration profiles have been studied for various parameters, namely, chemical reaction parameter , the heat generation and absorption parameter , Schmidt number Sc, Prandtl number , buoyancy ratio parameter , surface temperature power law exponent , and surface concentration power law exponent . The local as well as average skin friction, Nusselt number, and Sherwood number are discussed and analyzed graphically. The present results are compared with available results in open literature and are found to be in excellent agreement. Bapuji Pullepu, P. Sambath, and K. K. Viswanathan Copyright © 2014 Bapuji Pullepu et al. All rights reserved. Expert System for Competences Evaluation 360° Feedback Using Fuzzy Logic Thu, 24 Jul 2014 11:53:08 +0000 Performance evaluation (PE) is a process that estimates the employee overall performance during a given period, and it is a common function carried out inside modern companies. PE is important because it is an instrument that encourages employees, organizational areas, and the whole company to have an appropriate behavior and continuous improvement. In addition, PE is useful in decision making about personnel allocation, productivity bonuses, incentives, promotions, disciplinary measures, and dismissals. There are many performance evaluation methods; however, none is universal and common to all companies. This paper proposes an expert performance evaluation system based on a fuzzy logic model, with competences 360° feedback oriented to human behavior. This model uses linguistic labels and adjustable numerical values to represent ambiguous concepts, such as imprecision and subjectivity. The model was validated in the administrative department of a real Mexican manufacturing company, where final results and conclusions show the fuzzy logic method advantages in comparison with traditional 360° performance evaluation methodologies. Alberto Alfonso Aguilar Lasserre, Marina Violeta Lafarja Solabac, Roberto Hernandez-Torres, Rubén Posada-Gomez, Ulises Juárez-Martínez, and Gregorio Fernández Lambert Copyright © 2014 Alberto Alfonso Aguilar Lasserre et al. All rights reserved. OrclassWeb: A Tool Based on the Classification Methodology ORCLASS from Verbal Decision Analysis Framework Thu, 24 Jul 2014 11:45:24 +0000 The decision making is present in every activity of the human world, either in simple day-by-day problems or in complex situations inside of an organization. Sometimes emotions and reasons become hard to separate; therefore decision support methods were created to help decision makers to make complex decisions, and Decision Support Systems (DSS) were created to aid the application of such methods. The paper presents the development of a new tool, which reproduces the procedure to apply the Verbal Decision Analysis (VDA) methodology ORCLASS. The tool, called OrclassWeb, is software that supports the process of the mentioned DSS method and the paper provides proof of concepts, that which presents its reliability with ORCLASS. Thais Cristina Sampaio Machado, Plácido Rogerio Pinheiro, and Isabelle Tamanini Copyright © 2014 Thais Cristina Sampaio Machado et al. All rights reserved. A Review on the Modified Finite Point Method Thu, 24 Jul 2014 11:26:45 +0000 The objective of this paper is to make a review on recent advancements of the modified finite point method, named MFPM hereafter. This MFPM method is developed for solving general partial differential equations. Benchmark examples of employing this method to solve Laplace, Poisson, convection-diffusion, Helmholtz, mild-slope, and extended mild-slope equations are verified and then illustrated in fluid flow problems. Application of MFPM to numerical generation of orthogonal grids, which is governed by Laplace equation, is also demonstrated. Nan-Jing Wu, Boe Shiun Chen, and Ting-Kuei Tsay Copyright © 2014 Nan-Jing Wu et al. All rights reserved. Stochastic Signal Processing for Sound Environment System with Decibel Evaluation and Energy Observation Thu, 24 Jul 2014 11:17:47 +0000 In real sound environment system, a specific signal shows various types of probability distribution, and the observation data are usually contaminated by external noise (e.g., background noise) of non-Gaussian distribution type. Furthermore, there potentially exist various nonlinear correlations in addition to the linear correlation between input and output time series. Consequently, often the system input and output relationship in the real phenomenon cannot be represented by a simple model using only the linear correlation and lower order statistics. In this study, complex sound environment systems difficult to analyze by using usual structural method are considered. By introducing an estimation method of the system parameters reflecting correlation information for conditional probability distribution under existence of the external noise, a prediction method of output response probability for sound environment systems is theoretically proposed in a suitable form for the additive property of energy variable and the evaluation in decibel scale. The effectiveness of the proposed stochastic signal processing method is experimentally confirmed by applying it to the observed data in sound environment systems. Akira Ikuta and Hisako Orimoto Copyright © 2014 Akira Ikuta and Hisako Orimoto. All rights reserved. Discrete Fractional COSHAD Transform and Its Application Thu, 24 Jul 2014 09:24:46 +0000 In recent years, there has been a renewed interest in finding methods to construct orthogonal transforms. This interest is driven by the large number of applications of the orthogonal transforms in image analysis and compression, especially for colour images. Inspired by this motivation, this paper first introduces a new orthogonal transform known as a discrete fractional COSHAD (FrCOSHAD) using the Kronecker product of eigenvectors and the eigenvalues of the COSHAD kernel functions. Next, this study discusses the properties of the FrCOSHAD kernel function, such as angle additivity. Using the algebra of quaternions, the study presents quaternion COSHAD/FrCOSHAD transforms to represent colour images in a holistic manner. This paper also develops an inverse polynomial reconstruction method (IPRM) in the discrete COSHAD/FrCOSHAD domains. This method can effectively recover a piecewise smooth signal from the finite set of its COSHAD/FrCOSHAD coefficients, with high accuracy. The convergence theorem has proved that the partial sum of COSHAD provides a spectrally accurate approximation to the underlying piecewise smooth signal. The experimental results verify the numerical stability and accuracy of the proposed methods. Hongqing Zhu, Zhiguo Gui, Yu Zhu, and Zhihua Chen Copyright © 2014 Hongqing Zhu et al. All rights reserved. Edgeworth Expansion Based Model for the Convolutional Noise pdf Thu, 24 Jul 2014 09:00:53 +0000 Recently, the Edgeworth expansion up to order 4 was used to represent the convolutional noise probability density function (pdf) in the conditional expectation calculations where the source pdf was modeled with the maximum entropy density approximation technique. However, the applied Lagrange multipliers were not the appropriate ones for the chosen model for the convolutional noise pdf. In this paper we use the Edgeworth expansion up to order 4 and up to order 6 to model the convolutional noise pdf. We derive the appropriate Lagrange multipliers, thus obtaining new closed-form approximated expressions for the conditional expectation and mean square error (MSE) as a byproduct. Simulation results indicate hardly any equalization improvement with Edgeworth expansion up to order 4 when using optimal Lagrange multipliers over a nonoptimal set. In addition, there is no justification for using the Edgeworth expansion up to order 6 over the Edgeworth expansion up to order 4 for the 16QAM and easy channel case. However, Edgeworth expansion up to order 6 leads to improved equalization performance compared to the Edgeworth expansion up to order 4 for the 16QAM and hard channel case as well as for the case where the 64QAM is sent via an easy channel. Yonatan Rivlin and Monika Pinchas Copyright © 2014 Yonatan Rivlin and Monika Pinchas. All rights reserved. Cubic Bezier Curve Approach for Automated Offline Signature Verification with Intrusion Identification Thu, 24 Jul 2014 08:17:46 +0000 Authentication is a process of identifying person’s rights over a system. Many authentication types are used in various systems, wherein biometrics authentication systems are of a special concern. Signature verification is a basic biometric authentication technique used widely. The signature matching algorithm uses image correlation and graph matching technique which provides false rejection or acceptance. We proposed a model to compare knowledge from signature. Intrusion in the signature repository system results in copy of the signature that leads to false acceptance. Our approach uses a Bezier curve algorithm to identify the curve points and uses the behaviors of the signature for verification. An analyzing mobile agent is used to identify the input signature parameters and compare them with reference signature repository. It identifies duplication of signature over intrusion and rejects it. Experiments are conducted on a database with thousands of signature images from various sources and the results are favorable. Arun Vijayaragavan, J. Visumathi, and K. L. Shunmuganathan Copyright © 2014 Arun Vijayaragavan et al. All rights reserved. An Automatic High Efficient Method for Dish Concentrator Alignment Thu, 24 Jul 2014 07:21:26 +0000 Alignment of dish concentrator is a key factor to the performance of solar energy system. We propose a new method for the alignment of faceted solar dish concentrator. The isosceles triangle configuration of facet’s footholds determines a fixed relation between light spot displacements and foothold movements, which allows an automatic determination of the amount of adjustments. Tests on a 25 kW Stirling Energy System dish concentrator verify the feasibility, accuracy, and efficiency of our method. Yong Wang, Song Li, Jinshan Xu, Yijiang Wang, Xu Cheng, Changgui Gu, Shengyong Chen, and Bin Wan Copyright © 2014 Yong Wang et al. All rights reserved. Availability Allocation of Networked Systems Using Markov Model and Heuristics Algorithm Thu, 24 Jul 2014 07:19:33 +0000 It is a common practice to allocate the system availability goal to reliability and maintainability goals of components in the early design phase. However, the networked system availability is difficult to be allocated due to its complex topology and multiple down states. To solve these problems, a practical availability allocation method is proposed. Network reliability algebraic methods are used to derive the availability expression of the networked topology on the system level, and Markov model is introduced to determine that on the component level. A heuristic algorithm is proposed to obtain the reliability and maintainability allocation values of components. The principles applied in the AGREE reliability allocation method, proposed by the Advisory Group on Reliability of Electronic Equipment, and failure rate-based maintainability allocation method persist in our allocation method. A series system is used to verify the new algorithm, and the result shows that the allocation based on the heuristic algorithm is quite accurate compared to the traditional one. Moreover, our case study of a signaling system number 7 shows that the proposed allocation method is quite efficient for networked systems. Ruiying Li, Xiaoxi Liu, and Ning Huang Copyright © 2014 Ruiying Li et al. All rights reserved. Mathematical Simulation of Heat and Mass Transfer Processes at the Ignition of Liquid Fuel by Concentrated Flux of Radiation Thu, 24 Jul 2014 00:00:00 +0000 The physical and forecasting mathematical models of heat and mass transfer with phase transformations and chemical reactions under heating and following ignition of typical liquid fuel by using concentrated flow of radiation were developed. The influence scales of energy absorption process by means of gas-vapor mixture and liquid on ignition characteristics were established. The ignition delay time dependencies on the concentrated luminous power and radius of its coverage were determined. Olga V. Vysokomornaya, Genii V. Kuznetsov, and Pavel A. Strizhak Copyright © 2014 Olga V. Vysokomornaya et al. All rights reserved. A Novel Clustering Model Based on Set Pair Analysis for the Energy Consumption Forecast in China Thu, 24 Jul 2014 00:00:00 +0000 The energy consumption forecast is important for the decision-making of national economic and energy policies. But it is a complex and uncertainty system problem affected by the outer environment and various uncertainty factors. Herein, a novel clustering model based on set pair analysis (SPA) was introduced to analyze and predict energy consumption. The annual dynamic relative indicator (DRI) of historical energy consumption was adopted to conduct a cluster analysis with Fisher’s optimal partition method. Combined with indicator weights, group centroids of DRIs for influence factors were transferred into aggregating connection numbers in order to interpret uncertainty by identity-discrepancy-contrary (IDC) analysis. Moreover, a forecasting model based on similarity to group centroid was discussed to forecast energy consumption of a certain year on the basis of measured values of influence factors. Finally, a case study predicting China’s future energy consumption as well as comparison with the grey method was conducted to confirm the reliability and validity of the model. The results indicate that the method presented here is more feasible and easier to use and can interpret certainty and uncertainty of development speed of energy consumption and influence factors as a whole. Mingwu Wang, Dongfang Wei, Jian Li, Hui Jiang, and Juliang Jin Copyright © 2014 Mingwu Wang et al. All rights reserved. Predicting Click-Through Rates of New Advertisements Based on the Bayesian Network Thu, 24 Jul 2014 00:00:00 +0000 Most classical search engines choose and rank advertisements (ads) based on their click-through rates (CTRs). To predict an ad’s CTR, historical click information is frequently concerned. To accurately predict the CTR of the new ads is challenging and critical for real world applications, since we do not have plentiful historical data about these ads. Adopting Bayesian network (BN) as the effective framework for representing and inferring dependencies and uncertainties among variables, in this paper, we establish a BN-based model to predict the CTRs of new ads. First, we built a Bayesian network of the keywords that are used to describe the ads in a certain domain, called keyword BN and abbreviated as KBN. Second, we proposed an algorithm for approximate inferences of the KBN to find similar keywords with those that describe the new ads. Finally based on the similar keywords, we obtain the similar ads and then calculate the CTR of the new ad by using the CTRs of the ads that are similar with the new ad. Experimental results show the efficiency and accuracy of our method. Zhipeng Fang, Kun Yue, Jixian Zhang, Dehai Zhang, and Weiyi Liu Copyright © 2014 Zhipeng Fang et al. All rights reserved. Application of CFD, Taguchi Method, and ANOVA Technique to Optimize Combustion and Emissions in a Light Duty Diesel Engine Thu, 24 Jul 2014 00:00:00 +0000 Some previous research results have shown that EGR (exhaust gas recirculation) rate, pilot fuel quantity, and main injection timing closely associated with engine emissions and fuel consumption. In order to understand the combined effect of EGR rate, pilot fuel quantity, and main injection timing on the (oxides of nitrogen), soot, and ISFC (indicated specific fuel consumption), in this study, CFD (computational fluid dynamics) simulation together with the Taguchi method and the ANOVA (analysis of variance) technique was applied as an effective research tool. At first, simulation model on combustion and emissions of a light duty diesel engine at original baseline condition was developed and the model was validated by test. At last, a confirmation experiment with the best combination of factors and levels was implemented. The study results indicated that EGR is the most influencing factor on . In case of soot emission and ISFC, the greatest influence parameter is main injection timing. For all objectives, pilot fuel quantity is an insignificant factor. Furthermore, the engine with optimized combination reduces by at least 70% for , 20% in soot formation, and 1% for ISFC, in contrast to original baseline engine. Senlin Xiao, Wanchen Sun, Jiakun Du, and Guoliang Li Copyright © 2014 Senlin Xiao et al. All rights reserved. Improving the Bin Packing Heuristic through Grammatical Evolution Based on Swarm Intelligence Thu, 24 Jul 2014 00:00:00 +0000 In recent years Grammatical Evolution (GE) has been used as a representation of Genetic Programming (GP) which has been applied to many optimization problems such as symbolic regression, classification, Boolean functions, constructed problems, and algorithmic problems. GE can use a diversity of searching strategies including Swarm Intelligence (SI). Particle Swarm Optimisation (PSO) is an algorithm of SI that has two main problems: premature convergence and poor diversity. Particle Evolutionary Swarm Optimization (PESO) is a recent and novel algorithm which is also part of SI. PESO uses two perturbations to avoid PSO’s problems. In this paper we propose using PESO and PSO in the frame of GE as strategies to generate heuristics that solve the Bin Packing Problem (BPP); it is possible however to apply this methodology to other kinds of problems using another Grammar designed for that problem. A comparison between PESO, PSO, and BPP’s heuristics is performed through the nonparametric Friedman test. The main contribution of this paper is proposing a Grammar to generate online and offline heuristics depending on the test instance trying to improve the heuristics generated by other grammars and humans; it also proposes a way to implement different algorithms as search strategies in GE like PESO to obtain better results than those obtained by PSO. Marco Aurelio Sotelo-Figueroa, Héctor José Puga Soberanes, Juan Martín Carpio, Héctor J. Fraire Huacuja, Laura Cruz Reyes, and Jorge Alberto Soria-Alcaraz Copyright © 2014 Marco Aurelio Sotelo-Figueroa et al. All rights reserved. An Improved Genetic Algorithm for Single-Machine Inverse Scheduling Problem Wed, 23 Jul 2014 13:43:34 +0000 The goal of the scheduling is to arrange operations on suitable machines with optimal sequence for corresponding objectives. In order to meet market requirements, scheduling systems must own enough flexibility against uncertain events. These events can change production status or processing parameters, even causing the original schedule to no longer be optimal or even to be infeasible. Traditional scheduling strategies, however, cannot cope with these cases. Therefore, a new idea of scheduling called inverse scheduling has been proposed. In this paper, the inverse scheduling with weighted completion time (SMISP) is considered in a single-machine shop environment. In this paper, an improved genetic algorithm (IGA) with a local searching strategy is proposed. To improve the performance of IGA, efficient encoding scheme, fitness evaluation mechanism, feasible initialization methods, and a local search procedure have been employed in the paper. Because of the local improving method, the proposed IGA can balance its exploration ability and exploitation ability. We adopt 27 instances to verify the effectiveness of the proposed algorithm. The experimental results illustrated that the proposed algorithm can generate satisfactory solutions. This approach also has been applied to solve the scheduling problem in the real Chinese shipyard and can bring some benefits. Jianhui Mou, Xinyu Li, Liang Gao, Chao Lu, and Guohui Zhang Copyright © 2014 Jianhui Mou et al. All rights reserved. A Framing Link Based Tabu Search Algorithm for Large-Scale Multidepot Vehicle Routing Problems Wed, 23 Jul 2014 11:48:45 +0000 A framing link (FL) based tabu search algorithm is proposed in this paper for a large-scale multidepot vehicle routing problem (LSMDVRP). Framing links are generated during continuous great optimization of current solutions and then taken as skeletons so as to improve optimal seeking ability, speed up the process of optimization, and obtain better results. Based on the comparison between pre- and postmutation routes in the current solution, different parts are extracted. In the current optimization period, links involved in the optimal solution are regarded as candidates to the FL base. Multiple optimization periods exist in the whole algorithm, and there are several potential FLs in each period. If the update condition is satisfied, the FL base is updated, new FLs are added into the current route, and the next period starts. Through adjusting the borderline of multidepot sharing area with dynamic parameters, the authors define candidate selection principles for three kinds of customer connections, respectively. Link split and the roulette approach are employed to choose FLs. 18 LSMDVRP instances in three groups are studied and new optimal solution values for nine of them are obtained, with higher computation speed and reliability. Xuhao Zhang, Shiquan Zhong, Yiliu Liu, and Xuelian Wang Copyright © 2014 Xuhao Zhang et al. All rights reserved. Classification of Earthquake-Induced Damage for R/C Slab Column Frames Using Multiclass SVM and Its Combination with MLP Neural Network Wed, 23 Jul 2014 11:48:28 +0000 Nonlinear time history analysis (NTHA) is an important engineering method in order to evaluate the seismic vulnerability of buildings under earthquake loads. However, it is time consuming and requires complex calculations and a high memory machine. In this study, two networks were used for damage classification: multiclass support vector machine (M-SVM) and combination of multilayer perceptron neural network with M-SVM (MM-SVM). In order to collect data, three frames of R/C slab column frame buildings with wide beams in slab were considered. For NTHA, twenty different ground motion records were selected and scaled to ten different levels of peak ground acceleration (PGA). Thus, 600 obtained data from the numerical simulations were applied to M-SVM and MM-SVM in order to predict the global damage classification of samples based on park and Ang damage index. Amongst the four different kernel tricks, the Gaussian function was determined as an efficient kernel trick using the maximum total accuracy method of test data. By comparing the obtained results from M-SVM and MM-SVM, the total classification accuracy of MM-SVM is more than M-SVM and it is accurate and reliable for global damage classification of R/C slab column frames. Furthermore, the proposed combined model is able to classify the classes with low members. Ali Kia and Serhan Sensoy Copyright © 2014 Ali Kia and Serhan Sensoy. All rights reserved. Due Date Single Machine Scheduling Problems with Nonlinear Deterioration and Learning Effects and Past Sequence Dependent Setup Times Wed, 23 Jul 2014 11:47:42 +0000 We present some problems against due dates with nonlinear learning and deterioration effects and past sequence dependent setup times. In this study, two effects (learning and deterioration) are used for the same processing time. The processing time of a job is shorter if it is scheduled later, rather than in the sequence. This phenomenon is known in the literature as a “learning effect.” On the other hand, in many realistic scheduling settings, a job processed later consumes more time than the same job processed earlier—this is known as scheduling with deteriorating jobs. In the past sequence dependent setup times approach, the setup time of a job is proportionate to the sum of processing times of the jobs already scheduled. In this study, we demonstrated that some problems with due dates remain polynomially solvable. However, for some other problems, we concentrated on finding polynomially solves under their special cases. Hüseyin Ceylan Copyright © 2014 Hüseyin Ceylan. All rights reserved. Artificial Fish Swarm Algorithm-Based Particle Filter for Li-Ion Battery Life Prediction Wed, 23 Jul 2014 10:04:26 +0000 An intelligent online prognostic approach is proposed for predicting the remaining useful life (RUL) of lithium-ion (Li-ion) batteries based on artificial fish swarm algorithm (AFSA) and particle filter (PF), which is an integrated approach combining model-based method with data-driven method. The parameters, used in the empirical model which is based on the capacity fade trends of Li-ion batteries, are identified dependent on the tracking ability of PF. AFSA-PF aims to improve the performance of the basic PF. By driving the prior particles to the domain with high likelihood, AFSA-PF allows global optimization, prevents particle degeneracy, thereby improving particle distribution and increasing prediction accuracy and algorithm convergence. Data provided by NASA are used to verify this approach and compare it with basic PF and regularized PF. AFSA-PF is shown to be more accurate and precise. Ye Tian, Chen Lu, Zili Wang, and Laifa Tao Copyright © 2014 Ye Tian et al. All rights reserved. Distributed Model Predictive Control over Multiple Groups of Vehicles in Highway Intelligent Space for Large Scale System Wed, 23 Jul 2014 09:23:39 +0000 The paper presents the three time warning distances for solving the large scale system of multiple groups of vehicles safety driving characteristics towards highway tunnel environment based on distributed model prediction control approach. Generally speaking, the system includes two parts. First, multiple vehicles are divided into multiple groups. Meanwhile, the distributed model predictive control approach is proposed to calculate the information framework of each group. Each group of optimization performance considers the local optimization and the neighboring subgroup of optimization characteristics, which could ensure the global optimization performance. Second, the three time warning distances are studied based on the basic principles used for highway intelligent space (HIS) and the information framework concept is proposed according to the multiple groups of vehicles. The math model is built to avoid the chain avoidance of vehicles. The results demonstrate that the proposed highway intelligent space method could effectively ensure driving safety of multiple groups of vehicles under the environment of fog, rain, or snow. Tang Xiaofeng, Gao Feng, Xu Guoyan, Ding Nenggen, Cai Yao, and Liu Jian Xing Copyright © 2014 Tang Xiaofeng et al. All rights reserved. A Differential Evolution with Two Mutation Strategies and a Selection Based on an Improved Constraint-Handling Technique for Bilevel Programming Problems Wed, 23 Jul 2014 08:39:25 +0000 Two mutation operators are used in the differential evolution algorithm to improve the diversity of population. An improved constraint-handling technique based on a comparison mechanism is presented, and then it is combined with the selection operator in the differential evolution algorithm to fulfill constraint handling and selection simultaneously. A differential evolution with two mutation strategies and a selection based on this improved constraint-handling technique is developed to solve bilevel programming problems. The simulation results on some linear and nonlinear bilevel programming problems show the effectiveness and efficiency of the proposed algorithm. Hong Li and Li Zhang Copyright © 2014 Hong Li and Li Zhang. All rights reserved. Displacement Prediction of Tunnel Surrounding Rock: A Comparison of Support Vector Machine and Artificial Neural Network Wed, 23 Jul 2014 07:24:51 +0000 Displacement prediction of tunnel surrounding rock plays an important role in safety monitoring and quality control tunnel construction. In this paper, two methodologies, support vector machines (SVM) and artificial neural network (ANN), are introduced to predict tunnel surrounding rock displacement. Then the two modes are texted with the data of Fangtianchong tunnel, respectively. The comparative results show that solutions gained by SVM seem to be more robust with a smaller standard error compared to ANN. Generally, the comparison between artificial neural network (ANN) and SVM shows that SVM has a higher accuracy prediction than ANN. Results also show that SVM seems to be a powerful tool for tunnel surrounding rock displacement prediction. Qingdong Wu, Bo Yan, Chao Zhang, Lu Wang, Guobao Ning, and B. Yu Copyright © 2014 Qingdong Wu et al. All rights reserved. An Improved Path-Generating Regulator for Two-Wheeled Robots to Track the Circle/Arc Passage Wed, 23 Jul 2014 07:12:14 +0000 The improved path-generating regulator (PGR) is proposed to path track the circle/arc passage for two-wheeled robots. The PGR, which is a control method for robots so as to orient its heading toward the tangential direction of one of the curves belonging to the family of path functions, is applied to navigation problem originally. Driving environments for robots are usually roads, streets, paths, passages, and ridges. These tracks can be seen as they consist of straight lines and arcs. In the case of small interval, arc can be regarded as straight line approximately; therefore we extended the PGR to drive the robot move along circle/arc passage based on the theory that PGR to track the straight passage. In addition, the adjustable look-ahead method is proposed to improve the robot trajectory convergence property to the target circle/arc. The effectiveness is proved through MATLAB simulations on both the comparisons with the PGR and the improved PGR with adjustable look-ahead method. The results of numerical simulations show that the adjustable look-ahead method has better convergence property and stronger capacity of resisting disturbance. Jun Dai, Naohiko Hanajima, Toshiharu Kazama, and Akihiko Takashima Copyright © 2014 Jun Dai et al. All rights reserved. Model of Wagons’ Placing-In and Taking-Out Problem in a Railway Station and Its Heuristic Algorithm Wed, 23 Jul 2014 07:11:29 +0000 Placing-in and taking-out wagons timely can decrease wagons’ dwell time in railway stations, improve the efficiency of railway transportation, and reduce the cost of goods transportation. We took the locomotive running times between goods operation sites as weights, so the wagons’ placing-in and taking-out problem could be regarded as a single machine scheduling problem, , which could be transformed into the shortest circle problem in a Hamilton graph whose relaxation problem was an assignment problem. We used a Hungarian algorithm to calculate the optimal solution of the assignment problem. Then we applied a broken circle and connection method, whose computational complexity was , to find the available satisfactory order of wagons’ placing-in and taking-out. Complex problems, such as placing-in and transferring combined, taking-out and transferring combined, placing-in and taking-out combined, or placing-in, transferring, and taking-out combined, could also be resolved with the extended algorithm. A representative instance was given to illustrate the reliability and efficiency of our results. Chuijiang Guo and Dingyou Lei Copyright © 2014 Chuijiang Guo and Dingyou Lei. All rights reserved. Quantum-Inspired Evolutionary Algorithm for Continuous Space Optimization Based on Multiple Chains Encoding Method of Quantum Bits Wed, 23 Jul 2014 06:26:21 +0000 This study proposes a novel quantum evolutionary algorithm called four-chain quantum-inspired evolutionary algorithm (FCQIEA) based on the four gene chains encoding method. In FCQIEA, a chromosome comprises four gene chains to expand the search space effectively and promote the evolutionary rate. Different parameters, including rotational angle and mutation probability, have been analyzed for better optimization. Performance comparison with other quantum-inspired evolutionary algorithms (QIEAs), evolutionary algorithms, and different chains of QIEA demonstrates the effectiveness and efficiency of FCQIEA. Rui Zhang, Zhiteng Wang, and Hongjun Zhang Copyright © 2014 Rui Zhang et al. All rights reserved. An Analytical Model for Fatigue Crack Propagation Prediction with Overload Effect Wed, 23 Jul 2014 00:00:00 +0000 In this paper a theoretical model was developed to predict the fatigue crack growth behavior under the constant amplitude loading with single overload. In the proposed model, crack growth retardation was accounted for by using crack closure and plastic zone. The virtual crack annealing model modified by Bauschinger effect was used to calculate the crack closure level in the outside of retardation effect region. And the Dugdale plastic zone model was employed to estimate the size of retardation effect region. A sophisticated equation was developed to calculate the crack closure variation during the retardation area. Model validation was performed in D16 aluminum alloy and 350WT steel specimens subjected to constant amplitude load with single or multiple overloads. The predictions of the proposed model were contrasted with experimental data, and fairly good agreements were observed. Shan Jiang, Wei Zhang, Xiaoyang Li, and Fuqiang Sun Copyright © 2014 Shan Jiang et al. All rights reserved.