Mathematical Problems in Engineering The latest articles from Hindawi Publishing Corporation © 2014 , Hindawi Publishing Corporation . 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. 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. 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. 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. 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. Working Characteristics of a Mechanical Insufflation-Exsufflation Device Wed, 23 Jul 2014 00:00:00 +0000 Secretions of ventilated patients must be cleared efficiently and timely; to improve the secretion clearance efficiency of an insufflation-exsufflation device (IL-IE device) and lay a foundation for the optimization of the IL-IE device, a mathematical model of the ventilation system with the IL-IE device is set up. Through the experimental and simulation research on the ventilation system, it can be concluded that, firstly, the mathematical model is proved to be authentic and reliable. Secondly, with the deposition of secretion or an increase in the respiratory compliance, the peak exsufflation airflow may be reduced. Thirdly, with a decrease in the suction pressure, the peak exsufflation airflow of the ventilated lung may rise proportionally, but the minimum pressure in the ventilated lung may descend proportionally. To improve the efficiency of the secretion clearance but not to injure the ventilated patient, the suction pressure can be elevated properly. Last, increasing the inspiratory positive airway pressure (IPAP) is a method to improve the secretion clearance efficiency. This research lays a foundation for improving the secretion clearance efficiency of the IL-IE device. Yan Shi, Shuai Ren, Maolin Cai, and Weiqing Xu Copyright © 2014 Yan Shi 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. Further Application of Surface Capturing Method and Cartesian Cut Cell Mesh on Hydroelastic Water-Entry Problems of Free-Falling Elastic Wedge Wed, 23 Jul 2014 00:00:00 +0000 In order to study the interactions between fluid and elastic structure (such as marine lifeboat falling down and ship), this paper presents a new CFD method on hydroelastic water-entry problem of free-falling elastic wedge, which can more conveniently handle moving solid boundaries. In the CFD solver, a surface capturing method and the Cartesian cut cell mesh are employed to deal with the moving free surface and solid boundaries, respectively. On the other hand, in structural analysis, the finite element method and lath-beam structural model are introduced to calculate the elastic response. Furthermore, based on the current CFD and structural solver, a particular data transfer method and coupling strategy are presented for the fluid-structure interaction. Finally, by comparing numerical results with experimental data, the present method is validated to be available and feasible for hydroelastic water-entry problem and further successfully adopted to analyze the motion characteristics of free-falling elastic wedge. Wen-hua Wang, Yi Huang, and Yan-ying Wang Copyright © 2014 Wen-hua Wang et al. All rights reserved. Extended Finite Element Method for Predicting Productivity of Multifractured Horizontal Wells Wed, 23 Jul 2014 00:00:00 +0000 Based on the theory of the extended finite element method (XFEM), which was first proposed by Moës for dealing with the problem characterized by discontinuities, an extended finite element model for predicting productivity of multifractured horizontal well has been established. The model couples four main porous flow regimes, including fluid flow in the away-from-wellbore region of reservoir matrix, radial flow in the near-wellbore region of reservoir matrix, linear flow in the away-from-wellbore region of fracture, and radial flow in the near-wellbore region of fracture by considering mass transfer between fracture and matrix. The method to introduce the interior well boundary condition into the XFEM is proposed, and therefore the model can be highly adaptable to the complex and asymmetrical physical conditions. Case studies indicate that this kind of multiflow problems can be solved with high accuracy by the use of the XFEM. Youshi Jiang, Jinzhou Zhao, Yongming Li, Hu Jia, and Liehui Zhang Copyright © 2014 Youshi Jiang et al. All rights reserved. A Transient Queuing Model for Analyzing and Optimizing Gate Congestion of Railway Container Terminals Wed, 23 Jul 2014 00:00:00 +0000 As the significant connection between the external and internal of the railway container terminal, the operation performance of the gate system plays an important role in the entire system. So the gate congestion will bring many losses to the railway container terminal, even the entire railway container freight system. In this paper, the queue length and the average waiting time of the railway container terminal gate system, as well as the optimal number of service channels during the different time period, are investigated. An transient queuing model is developed based on the distribution of the arrival time interval and the service time; besides the transient solutions are acquired by the equally likely combinations (ELC) heuristic method. Then the model is integrated into an optimization framework to obtain the optimal operation schemes. Finally, some computational experiments are conducted for model validation, sensitivity testing, and system optimization. Experimental results indicate that the model can provide the accurate reflection to the operation situation of the railway container terminal gate system, and the approach can yield the optimal number of service channels within the reasonable computation time. Ming Zeng, Wenming Cheng, and Peng Guo Copyright © 2014 Ming Zeng et al. All rights reserved. The Drainage Consolidation Modeling of Sand Drain in Red Mud Tailing and Analysis on the Change Law of the Pore Water Pressure Tue, 22 Jul 2014 11:40:16 +0000 In order to prevent the occurring of dam failure and leakage, sand-well drainages systems were designed and constructed in red mud tailing. It is critical to focus on the change law of the pore water pressure. The calculation model of single well drainage pore water pressure was established. The pore water pressure differential equation was deduced and the analytical solution of differential equation using Bessel function and Laplace transform was given out. The impact of parameters such as diameter , separation distance , loading rate , and coefficient of consolidation in the function on the pore water pressure is analyzed by control variable method. This research is significant and has great reference for preventing red mud tailings leakage and the follow-up studies on the tailings stability. Chuan-sheng Wu Copyright © 2014 Chuan-sheng Wu. All rights reserved. A Hybrid Optimized Weighted Minimum Spanning Tree for the Shortest Intrapath Selection in Wireless Sensor Network Tue, 22 Jul 2014 11:36:03 +0000 Wireless sensor network (WSN) consists of sensor nodes that need energy efficient routing techniques as they have limited battery power, computing, and storage resources. WSN routing protocols should enable reliable multihop communication with energy constraints. Clustering is an effective way to reduce overheads and when this is aided by effective resource allocation, it results in reduced energy consumption. In this work, a novel hybrid evolutionary algorithm called Bee Algorithm-Simulated Annealing Weighted Minimal Spanning Tree (BASA-WMST) routing is proposed in which randomly deployed sensor nodes are split into the best possible number of independent clusters with cluster head and optimal route. The former gathers data from sensors belonging to the cluster, forwarding them to the sink. The shortest intrapath selection for the cluster is selected using Weighted Minimum Spanning Tree (WMST). The proposed algorithm computes the distance-based Minimum Spanning Tree (MST) of the weighted graph for the multihop network. The weights are dynamically changed based on the energy level of each sensor during route selection and optimized using the proposed bee algorithm simulated annealing algorithm. Saravanan Matheswaran and Muthusamy Madheswaran Copyright © 2014 Saravanan Matheswaran and Muthusamy Madheswaran. All rights reserved. Hybrid Taguchi DNA Swarm Intelligence for Optimal Inverse Kinematics Redundancy Resolution of Six-DOF Humanoid Robot Arms Tue, 22 Jul 2014 11:34:33 +0000 This paper presents a hybrid Taguchi deoxyribonucleic acid (DNA) swarm intelligence for solving the inverse kinematics redundancy problem of six degree-of-freedom (DOF) humanoid robot arms. The inverse kinematics problem of the multi-DOF humanoid robot arm is redundant and has no general closed-form solutions or analytical solutions. The optimal joint configurations are obtained by minimizing the predefined performance index in DNA algorithm for real-world humanoid robotics application. The Taguchi method is employed to determine the DNA parameters to search for the joint solutions of the six-DOF robot arms more efficiently. This approach circumvents the disadvantage of time-consuming tuning procedure in conventional DNA computing. Simulation results are conducted to illustrate the effectiveness and merit of the proposed methods. This Taguchi-based DNA (TDNA) solver outperforms the conventional solvers, such as geometric solver, Jacobian-based solver, genetic algorithm (GA) solver and ant, colony optimization (ACO) solver. Hsu-Chih Huang, Sendren Sheng-Dong Xu, and Huan-Shiuan Hsu Copyright © 2014 Hsu-Chih Huang et al. All rights reserved. Intelligent Mechanical Fault Diagnosis Based on Multiwavelet Adaptive Threshold Denoising and MPSO Tue, 22 Jul 2014 11:19:44 +0000 The condition diagnosis of rotating machinery depends largely on the feature analysis of vibration signals measured for the condition diagnosis. However, the signals measured from rotating machinery usually are nonstationary and nonlinear and contain noise. The useful fault features are hidden in the heavy background noise. In this paper, a novel fault diagnosis method for rotating machinery based on multiwavelet adaptive threshold denoising and mutation particle swarm optimization (MPSO) is proposed. Geronimo, Hardin, and Massopust (GHM) multiwavelet is employed for extracting weak fault features under background noise, and the method of adaptively selecting appropriate threshold for multiwavelet with energy ratio of multiwavelet coefficient is presented. The six nondimensional symptom parameters (SPs) in the frequency domain are defined to reflect the features of the vibration signals measured in each state. Detection index (DI) using statistical theory has been also defined to evaluate the sensitiveness of SP for condition diagnosis. MPSO algorithm with adaptive inertia weight adjustment and particle mutation is proposed for condition identification. MPSO algorithm effectively solves local optimum and premature convergence problems of conventional particle swarm optimization (PSO) algorithm. It can provide a more accurate estimate on fault diagnosis. Practical examples of fault diagnosis for rolling element bearings are given to verify the effectiveness of the proposed method. Hao Sun, Ke Li, Huaqing Wang, Peng Chen, and Yi Cao Copyright © 2014 Hao Sun et al. All rights reserved. A Stochastic Integer Programming Model for Minimizing Cost in the Use of Rain Water Collectors for Firefighting Tue, 22 Jul 2014 10:57:21 +0000 In this paper we propose a stochastic integer programming optimization model to determine the optimal location and number of rain water collectors (RWCs) for forest firefighting. The objective is to minimize expected total cost to control forest fires. The model is tested using a real case and several additional realistic scenarios. The impact on the solution of varying the limit on the number of RWCs, the RWC water capacity, the aircraft capacity, the water demands, and the aircraft operating cost is explored. Some observations are that the objective value improves with larger RWCs and with the use of aircraft with greater capacity. Luis A. Rivera-Morales, Neale R. Smith, Mario G. Manzano, and David Garza-Ramírez Copyright © 2014 Luis A. Rivera-Morales et al. All rights reserved. Grey Accumulation Generation Relational Analysis Model for Nonequidistance Unequal-Length Sequences and Its Application Tue, 22 Jul 2014 10:23:04 +0000 As research is required on nonequidistance unequal-length sequences, so grey accumulation generation relational analysis model based on grey exponential law (AGRA) for nonequidistance unequal-length (NDUL) sequences is put forward in this paper. The original data is accumulated generation firstly and the generation sequences are simulated. Then the generation rate is established as the ratio of the tangent slope and the mean of the simulation function. Furthermore, the dynamic similarity of change trend of the original time sequences is characterized by the proximity of generation rate sequences. Meanwhile, properties of AGRA model for nonequidistance unequal-length sequences are discussed. The new relational analysis model is available for equal interval sequences, nonequidistance sequences, sequences which have relationship before transformation and sequences which have relationship after accumulation; therefore, the AGRA model has expanded the scope of application of grey relational analysis. Lastly, factors which affect the amount of passenger cars in China are sorted using AGRA model for NDUL sequences. This application is presented to illustrate the effectiveness and practicality of the proposed model. Xuemei Li, Yaoguo Dang, Song Ding, and Juan Zhang Copyright © 2014 Xuemei Li et al. All rights reserved. Intuitionistic Trapezoidal Fuzzy Multiple Criteria Group Decision Making Method Based on Binary Relation Tue, 22 Jul 2014 10:18:41 +0000 The aim of this paper is to develop a methodology for intuitionistic trapezoidal fuzzy multiple criteria group decision making problems based on binary relation. Firstly, the similarity measure between two vectors based on binary relation is defined, which can be utilized to aggregate preference information. Some desirable properties of the similarity measure based on fuzzy binary relation are also studied. Then, a methodology for fuzzy multiple criteria group decision making is proposed, in which the criteria values are in the terms of intuitionistic trapezoidal fuzzy numbers (ITFNs). Simple and exact formulas are also proposed to determine the vector of the aggregation and group set. According to the weighted expected values of group set, it is easy to rank the alternatives and select the best one. Finally, we apply the proposed method and the Cosine similarity measure method to a numerical example; the numerical results show that our method is effective and practical. Liyuan Zhang, Tao Li, and Xuanhua Xu Copyright © 2014 Liyuan Zhang et al. All rights reserved. Temperature Control via Affine Nonlinear Systems for Intermediate Point of Supercritical Once-Through Boiler Units Tue, 22 Jul 2014 09:43:08 +0000 For the operation of the supercritical once-through boiler generation units, the control of the temperature at intermediate point (IPT) is highly significant. IPT is the steam temperature at the outlet of the separator. Currently, PID control algorithms are widely adopted for the IPT control. However, PID cannot achieve the optimal performances as the units’ dynamic characteristic changes at different working points due to the severe nonlinearity. To address the problem, a new control algorithm using affine nonlinear system is adopted for a 600 MW unit in this paper. In order to establish the model of IPT via affine nonlinear system, the simplified mechanism equations on the evaporation zone and steam separator of the unit are established. Then, the feedback linearizing control law can be obtained. Full range simulations with the load varying from 100% to 30% are conducted. To verify the effectiveness of the proposed control algorithm, the performance of the new method is compared with the results of the PID control. The feed-water flow disturbances are considered in simulations of both of the two control methods. The comparison shows the new method has a better performance with a quicker response time and a smaller overshoot, which demonstrates the potential improvement for the supercritical once-through boiler generation unit control. Hong Zhou, Changkun Liu, Zhi-Wei Liu, and Wenshan Hu Copyright © 2014 Hong Zhou et al. All rights reserved. OL-DEC-MDP Model for Multiagent Online Scheduling with a Time-Dependent Probability of Success Tue, 22 Jul 2014 08:15:25 +0000 Focusing on the on-line multiagent scheduling problem, this paper considers the time-dependent probability of success and processing duration and proposes an OL-DEC-MDP (opportunity loss-decentralized Markov Decision Processes) model to include opportunity loss into scheduling decision to improve overall performance. The success probability of job processing as well as the process duration is dependent on the time at which the processing is started. The probability of completing the assigned job by an agent would be higher when the process is started earlier, but the opportunity loss could also be high due to the longer engaging duration. As a result, OL-DEC-MDP model introduces a reward function considering the opportunity loss, which is estimated based on the prediction of the upcoming jobs by a sampling method on the job arrival. Heuristic strategies are introduced in computing the best starting time for an incoming job by each agent, and an incoming job will always be scheduled to the agent with the highest reward among all agents with their best starting policies. The simulation experiments show that the OL-DEC-MDP model will improve the overall scheduling performance compared with models not considering opportunity loss in heavy-loading environment. Cheng Zhu, Jiangfeng Luo, Weiming Zhang, and Zhong Liu Copyright © 2014 Cheng Zhu et al. All rights reserved.