Mathematical Problems in Engineering The latest articles from Hindawi Publishing Corporation © 2016 , Hindawi Publishing Corporation . All rights reserved. Projective Invariants from Multiple Images: A Direct and Linear Method Thu, 28 Apr 2016 15:46:36 +0000 The projective reconstruction of 3D structures from 2D images is a central problem in computer vision. Existing methods for this problem are usually nonlinear or indirect. In the previous direct methods, we usually have to solve a system of nonlinear equations. They are very complicated and hard to implement. The previous linear indirect methods are usually imprecise. This paper presents a linear and direct method to derive projective structures of 3D points from their 2D images. Algorithms to compute projective invariants from two images, three images, and four images are given. The method is clear, simple, and easy to implement. For the first time in the literature, we present explicit linear formulas to solve this problem. Mathematica codes are provided to demonstrate the correctness of the formulas. Yuanbin Wang, Xingwei Wang, and Bin Zhang Copyright © 2016 Yuanbin Wang et al. All rights reserved. Balancing Lexicographic Multi-Objective Assembly Lines with Multi-Manned Stations Thu, 28 Apr 2016 14:32:35 +0000 In a multi-manned assembly line, tasks of the same workpiece can be executed simultaneously by different workers working in the same station. This line has significant advantages over a simple assembly line such as shorter line length, less work-in-process, smaller installation space, and less product flow time. In many realistic line balancing situations, there are usually more than one objective conflicting with each other. This paper presents a preemptive goal programming model and some heuristic methods based on variable neighborhood search approach for multi-objective assembly line balancing problems with multi-manned stations. Three different objectives are considered, minimizing the total number of multi-manned stations as the primary objective, minimizing the total number of workers as the secondary objective, and smoothing the number of workers at stations as the tertiary objective. A set of test instances taken from the literature is solved to compare the performance of all methods, and results are presented. Talip Kellegöz Copyright © 2016 Talip Kellegöz. All rights reserved. Thermal Equilibrium Dynamic Control Based on DPWM Dual-Mode Modulation of High Power NPC Three-Level Inverter Thu, 28 Apr 2016 14:17:43 +0000 In some special applications of NPC three-level inverters, such as mine hoist, there exist special conditions of overloading during the whole hoisting process and large overload in starting stage, during which the power-loss calculation of power devices and thermal control are important factors affecting the thermal stability of inverters. The principles of SVPWM and DPWM were described in this paper firstly, based on which the dynamic power losses of the two modulations of hoist in single period were calculated. Secondly, a thermal equilibrium dynamic control based on DPMW dual-mode modulation was proposed, which can switch the modulation dynamically according to the change of dynamic power loss to realize dynamic control of power loss and thermal equilibrium of inverter. Finally, simulation and experiment prove the effectiveness of the proposed strategy. Shi-Zhou Xu and Feng-You He Copyright © 2016 Shi-Zhou Xu and Feng-You He. All rights reserved. Design of a Load Torque Based Control Strategy for Improving Electric Tractor Motor Energy Conversion Efficiency Thu, 28 Apr 2016 14:16:30 +0000 In order to improve the electrical conversion efficiency of an electric tractor motor, a load torque based control strategy (LTCS) is designed in this paper by using a particle swarm optimization algorithm (PSO). By mathematically modeling electric-mechanical performance and theoretical energy waste of the electric motor, as well as the transmission characteristics of the drivetrain, the objective function, control relationship, and analytical platform are established. Torque and rotation speed of the motor’s output shaft are defined as manipulated variables. LTCS searches the working points corresponding to the best energy conversion efficiency via PSO to control the running status of the electric motor and uses logic and fuzzy rules to fit the search initialization for load torque fluctuation. After using different plowing forces to imitate all the common tillage forces, the simulation of traction experiment is conducted, which proves that LTCS can make the tractor use electrical power efficiently and maintain agricultural applicability on farmland conditions. It provides a novel method of fabricating a more efficient electric motor used in the traction of an off-road vehicle. Mengnan Liu, Liyou Xu, and Zhili Zhou Copyright © 2016 Mengnan Liu et al. All rights reserved. Maximum Matchings of a Digraph Based on the Largest Geometric Multiplicity Thu, 28 Apr 2016 12:54:36 +0000 Matching theory is one of the most forefront issues of graph theory. Based on the largest geometric multiplicity, we develop an efficient approach to identify maximum matchings in a digraph. For a given digraph, it has been proved that the number of maximum matched nodes has close relationship with the largest geometric multiplicity of the transpose of the adjacency matrix. Moreover, through fundamental column transformations, we can obtain the matched nodes and related matching edges. In particular, when a digraph contains a cycle factor, the largest geometric multiplicity is equal to one. In this case, the maximum matching is a perfect matching and each node in the digraph is a matched node. The method is validated by an example. Yunyun Yang and Gang Xie Copyright © 2016 Yunyun Yang and Gang Xie. All rights reserved. Two-Stage Multiobjective Optimization for Emergency Supplies Allocation Problem under Integrated Uncertainty Thu, 28 Apr 2016 12:47:28 +0000 This paper proposes a new two-stage optimization method for emergency supplies allocation problem with multisupplier, multiaffected area, multirelief, and multivehicle. The triplet of supply, demand, and the availability of path is unknown prior to the extraordinary event and is descriptive with fuzzy random variable. Considering the fairness, timeliness, and economical efficiency, a multiobjective expected value model is built for facility location, vehicle routing, and supply allocation decisions. The goals of proposed model aim to minimize the proportion of demand nonsatisfied and response time of emergency reliefs and the total cost of the whole process. When the demand and the availability of path are discrete, the expected values in the objective functions are converted into their equivalent forms. When the supply amount is continuous, the equilibrium chance in the constraint is transformed to its equivalent one. To overcome the computational difficulty caused by multiple objectives, a goal programming model is formulated to obtain a compromise solution. Finally, an example is presented to illustrate the validity of the proposed model and the effectiveness of the solution method. Xuejie Bai Copyright © 2016 Xuejie Bai. All rights reserved. Intention-Aware Autonomous Driving Decision-Making in an Uncontrolled Intersection Thu, 28 Apr 2016 08:23:08 +0000 Autonomous vehicles need to perform social accepted behaviors in complex urban scenarios including human-driven vehicles with uncertain intentions. This leads to many difficult decision-making problems, such as deciding a lane change maneuver and generating policies to pass through intersections. In this paper, we propose an intention-aware decision-making algorithm to solve this challenging problem in an uncontrolled intersection scenario. In order to consider uncertain intentions, we first develop a continuous hidden Markov model to predict both the high-level motion intention (e.g., turn right, turn left, and go straight) and the low level interaction intentions (e.g., yield status for related vehicles). Then a partially observable Markov decision process (POMDP) is built to model the general decision-making framework. Due to the difficulty in solving POMDP, we use proper assumptions and approximations to simplify this problem. A human-like policy generation mechanism is used to generate the possible candidates. Human-driven vehicles’ future motion model is proposed to be applied in state transition process and the intention is updated during each prediction time step. The reward function, which considers the driving safety, traffic laws, time efficiency, and so forth, is designed to calculate the optimal policy. Finally, our method is evaluated in simulation with PreScan software and a driving simulator. The experiments show that our method could lead autonomous vehicle to pass through uncontrolled intersections safely and efficiently. Weilong Song, Guangming Xiong, and Huiyan Chen Copyright © 2016 Weilong Song et al. All rights reserved. Research on Friction Compensation Control for Electric Power Steering System Wed, 27 Apr 2016 09:51:24 +0000 A novel friction compensation control method is proposed to compensate both the dynamic and static friction torque of steering system. The change of EPS assist torque under fixed amplitude friction compensation torque can cause the diver’s steering feeling fuzzy. That is due to the fact that the friction torque felt by driver varies with EPS assist gain. Therefore, a further modified friction compensation control method is proposed based on EPS assist gain to make the driver have similar friction feeling. Finally, computer simulation and vehicle test are performed to verify the effectiveness of adaptation method in the proposed controller. Test results indicate that the proposed controller improved the driver’s steering performance. Shaosong Li, Jiafei Niu, Gaojian Cui, Zhixin Yu, and Ren Sheng Copyright © 2016 Shaosong Li et al. All rights reserved. Glowworm Swarm Optimization and Its Application to Blind Signal Separation Wed, 27 Apr 2016 09:46:15 +0000 Traditional optimization algorithms for blind signal separation (BSS) are mainly based on the gradient, which requires the objective function to be continuous and differentiable, so the applications of these algorithms are very limited. Moreover, these algorithms have problems with the convergence speed and accuracy. To overcome these drawbacks, this paper presents a modified glowworm swarm optimization (MGSO) algorithm based on a novel step adjustment rule and then applies MGSO to BSS. Taking kurtosis of the mixed signals as the objective function of BSS, MGSO-BSS succeeds in separating the mixed signals in Matlab environment. The simulation results prove that MGSO is more effective in capturing the global optimum of the objective function of the BSS algorithm and has faster convergence speed and higher accuracy, compared with particle swarm optimization (PSO) and GSO. Zhucheng Li and Xianglin Huang Copyright © 2016 Zhucheng Li and Xianglin Huang. All rights reserved. Multiple Attribute Decision Making Based on Cross-Evaluation with Uncertain Decision Parameters Wed, 27 Apr 2016 08:51:18 +0000 Multiple attribute decision making (MADM) problem is one of the most common and popular research fields in the theory of decision science. A variety of methods have been proposed to deal with such problems. Nevertheless, many of them assumed that attribute weights are determined by different types of additional preference information which will result in subjective decision making. In order to solve such problems, in this paper, we propose a novel MADM approach based on cross-evaluation with uncertain parameters. Specifically, the proposed approach assumes that all attribute weights are uncertain. It can overcome the drawback in prior research that the alternatives’ ranking may be determined by a single attribute with an overestimated weight. In addition, the proposed method can also balance the mean and deviation of each alternative’s cross-evaluation score to guarantee the stability of evaluation. Then, this method is extended to a more generalized situation where the attribute values are also uncertain. Finally, we illustrate the applicability of the proposed method by revisiting two reported studies and by a case study on the selection of community service companies in the city of Hefei in China. Tao Ding, Liang Liang, Min Yang, and Huaqing Wu Copyright © 2016 Tao Ding et al. All rights reserved. An Effective Strategy to Build Up a Balanced Test Suite for Spectrum-Based Fault Localization Wed, 27 Apr 2016 07:11:01 +0000 During past decades, many automated software faults diagnosis techniques including Spectrum-Based Fault Localization (SBFL) have been proposed to improve the efficiency of software debugging activity. In the field of SBFL, suspiciousness calculation is closely related to the number of failed and passed test cases. Studies have shown that the ratio of the number of failed and passed test case has more significant impact on the accuracy of SBFL than the total number of test cases, and a balanced test suite is more beneficial to improving the accuracy of SBFL. Based on theoretical analysis, we proposed an PNF (Passed test cases, Not execute Faulty statement) strategy to reduce test suite and build up a more balanced one for SBFL, which can be used in regression testing. We evaluated the strategy making experiments using the Siemens program and Space program. Experiments indicated that our PNF strategy can be used to construct a new test suite effectively. Compared with the original test suite, the new one has smaller size (average 90% test case was reduced in experiments) and more balanced ratio of failed test cases to passed test cases, while it has the same statement coverage and fault localization accuracy. Ning Li, Rui Wang, Yu-li Tian, and Wei Zheng Copyright © 2016 Ning Li et al. All rights reserved. Global Quantitative Sensitivity Analysis and Compensation of Geometric Errors of CNC Machine Tool Tue, 26 Apr 2016 16:28:34 +0000 A quantitative analysis to identify the key geometric error elements and their coupling is the prerequisite and foundation for improving the precision of machine tools. The purpose of this paper is to identify key geometric error elements and compensate for geometric errors accordingly. The geometric error model of three-axis machine tool is built on the basis of multibody system theory; and the quantitative global sensitivity analysis (GSA) model of geometric error elements is constructed by using extended Fourier amplitude sensitivity test method. The crucial geometric errors are identified; and stochastic characteristics of geometric errors are taken into consideration in the formulation of building up the compensation strategy. The validity of geometric error compensation based on sensitivity analysis is verified on a high-precision three-axis machine tool with open CNC system. The experimental results show that the average compensation rates along the , , and directions are 59.8%, 65.5%, and 73.5%, respectively. The methods of sensitivity analysis and geometric errors compensation presented in this paper are suitable for identifying the key geometric errors and improving the precision of CNC machine tools effectively. Shijie Guo, Dongsheng Zhang, and Yang Xi Copyright © 2016 Shijie Guo et al. All rights reserved. Denoising and Trend Terms Elimination Algorithm of Accelerometer Signals Tue, 26 Apr 2016 16:27:54 +0000 Acceleration-based displacement measurement approach is often used to measure the polish rod displacement in the oilfield pumping well. Random noises and trend terms of the accelerometer signals are the main factors that affect the measuring accuracy. In this paper, an efficient online learning algorithm is proposed to improve the measurement precision of polish rod displacement in the oilfield pumping well. To remove the random noises and eliminate the trend term of accelerometer signals, the ARIMA model and its parameters are firstly derived by using the obtained data of time series of acceleration sensor signals. Secondly, the period of the accelerometer signals is estimated through the Rife-Jane frequency estimation approach based on Fast Fourier Transform. With the obtained model and parameters, the random noises are removed by employing the Kalman filtering algorithm. The quadratic integration of the period is calculated to obtain the polish rod displacement. Moreover, the windowed recursive least squares algorithm is implemented to eliminate the trend terms. The simulation results demonstrate that the proposed online learning algorithm is able to remove the random noises and trend terms effectively and greatly improves the measurement accuracy of the displacement. Peng Zhang, Jing Chang, Boyang Qu, and Qifeng Zhao Copyright © 2016 Peng Zhang et al. All rights reserved. Fuzzy Controllers for a Gantry Crane System with Experimental Verifications Tue, 26 Apr 2016 16:19:44 +0000 The control problem of gantry cranes has attracted the attention of many researchers because of the various applications of these cranes in the industry. In this paper we propose two fuzzy controllers to control the position of the cart of a gantry crane while suppressing the swing angle of the payload. Firstly, we propose a dual PD fuzzy controller where the parameters of each PD controller change as the cart moves toward its desired position, while maintaining a small swing angle of the payload. This controller uses two fuzzy subsystems. Then, we propose a fuzzy controller which is based on heuristics. The rules of this controller are obtained taking into account the knowledge of an experienced crane operator. This controller is unique in that it uses only one fuzzy system to achieve the control objective. The validity of the designed controllers is tested through extensive MATLAB simulations as well as experimental results on a laboratory gantry crane apparatus. The simulation results as well as the experimental results indicate that the proposed fuzzy controllers work well. Moreover, the simulation and the experimental results demonstrate the robustness of the proposed control schemes against output disturbances as well as against uncertainty in some of the parameters of the crane. Naif B. Almutairi and Mohamed Zribi Copyright © 2016 Naif B. Almutairi and Mohamed Zribi. All rights reserved. Lateral Drift Behavior Analysis in Mixed Bicycle Traffic: A Cellular Automaton Model Approach Tue, 26 Apr 2016 14:39:38 +0000 Bicycle movements are always associated with lateral drifts. However, the impacts of lateral drift behavior, as well as variable lateral clearance maintaining behavior due to the variation of drift intensity, on mixed bicycle flow are not clear. This paper establishes a new cellular automata model to study typical characteristics of mixed bicycle traffic induced by lateral drift and its accompanying behavior. Based on derived positive correlation between passing speed and drift speed through survey, the occurrence probability of lateral drift and the degree of maintained lateral clearance are both introduced in accordance with the variance of passing speed. Then, in whole density region, firm conformity between simulation results and actual survey data is reached, which has seldom been achieved in previous studies. It is further verified that speed distortions in intermediate and high density region induced by assumption of constant lateral clearance requirements can be revised by introducing its variability characteristics. In addition, two contrastive impacts of lateral drift behavior are observed. That is, it causes speed fluctuation in low density while alleviating the speed fluctuation in relatively high density. These results are expected to be helpful to improve the simulation performance of mixed bicycle flow as well as depict more realistic vehicle-bicycle conflicts and so on. Xue Feng, Xi-fu Wang, and Dong-fan Xie Copyright © 2016 Xue Feng et al. All rights reserved. Analytical and Numerical Study on Magnetoconvection Stagnation-Point Flow in a Porous Medium with Chemical Reaction, Radiation, and Slip Effects Tue, 26 Apr 2016 12:49:17 +0000 We investigate the effects of slip and radiation on magnetoconvection flow of a chemically reacting fluid near a stagnation-point towards a vertical plate embedded in a porous medium analytically and numerically. The governing partial differential equations are diminished into the coupled ordinary differential equations by similarity transformations. Then they are solved analytically by homotopy analysis method and solved numerically by shooting method with RK fourth-order method. In this study, the analytical and numerical results are compared for many combinations of parameters. The rates of heat and mass transfer are calculated. The velocity profile near the plate overshoots on increasing the slip parameter. The concentration and temperature are decreasing on increasing the slip parameter. H. Niranjan, S. Sivasankaran, and M. Bhuvaneswari Copyright © 2016 H. Niranjan et al. All rights reserved. A Unified Factors Analysis Framework for Discriminative Feature Extraction and Object Recognition Tue, 26 Apr 2016 09:51:13 +0000 Various methods for feature extraction and dimensionality reduction have been proposed in recent decades, including supervised and unsupervised methods and linear and nonlinear methods. Despite the different motivations of these methods, we present in this paper a general formulation known as factor analysis to unify them within a common framework. During factor analysis, an object can be seen as being comprised of content and style factors, and the objective of feature extraction and dimensionality reduction is to obtain the content factor without style factor. There are two vital steps in factor analysis framework; one is the design of factor separating objective function, including the design of partition and weight matrix, and the other is the design of space mapping function. In this paper, classical Linear Discriminant Analysis (LDA) and Locality Preserving Projection (LPP) algorithms are improved based on factor analysis framework, and LDA based on factor analysis (FA-LDA) and LPP based on factor analysis (FA-LPP) are proposed. Experimental results show the superiority of our proposed approach in classification performance compared to classical LDA and LPP algorithms. Ningbo Hao, Jie Yang, Haibin Liao, and Wenhua Dai Copyright © 2016 Ningbo Hao et al. All rights reserved. The Design and Its Application in Secure Communication and Image Encryption of a New Lorenz-Like System with Varying Parameter Tue, 26 Apr 2016 09:39:32 +0000 A new Lorenz-like chaotic system with varying parameter is proposed by adding a state feedback function. The structure of the new designed system is simple and has more complex dynamic behaviors. The chaos behavior of the new system is studied by theoretical analysis and numerical simulation. And the bifurcation diagram shows a chaos-cycle-chaos evolution when the new parameter changes. Then a new synchronization scheme by a single state variable drive is given based on the new system and a chaotic parameter modulation digital secure communication system is also constructed. The results of simulation demonstrate that the new proposed system could be well applied in secure communication. Otherwise, based on the new system, the encryption and decryption of image could be achieved also. Lilian Huang, Donghai Shi, and Jie Gao Copyright © 2016 Lilian Huang et al. All rights reserved. Two-Dimensional Far Field Source Locating Method with Nonprior Velocity Tue, 26 Apr 2016 07:33:11 +0000 Relative position of seismic source and sensors has great influence on locating accuracy, particularly in far field conditions, and the accuracy will decrease seriously due to limited calculation precision and prior velocity error. In order to improve the locating accuracy of far field sources by isometric placed sensors in a straight line, a new locating method with nonprior velocity is proposed. After exhaustive research, this paper states that the hyperbola which is used for locating will be very close to its asymptote when seismic source locates in far field of sensors; therefore, the locating problem with prior velocity is equivalent to solving linear equations and the problem with nonprior velocity is equivalent to a nonlinear optimization problem with respect to the unknown velocity. And then, this paper proposed a new locating method based on a one-variable objective function with respect to the unknown velocity. Numerical experiments show that the proposed method has faster convergence speed, higher accuracy, and better stability. Qing Chen, Xiaowen Liu, Juanjuan Li, Manyi Wang, and Peng Liu Copyright © 2016 Qing Chen et al. All rights reserved. Transfer Learning for Collaborative Filtering Using a Psychometrics Model Tue, 26 Apr 2016 06:19:28 +0000 In a real e-commerce website, usually only a small number of users will give ratings to the items they purchased, and this can lead to the very sparse user-item rating data. The data sparsity issue will greatly limit the recommendation performance of most recommendation algorithms. However, a user may register accounts in many e-commerce websites. If such users’ historical purchasing data on these websites can be integrated, the recommendation performance could be improved. But it is difficult to align the users and items between these websites, and thus how to effectively borrow the users’ rating data of one website (source domain) to help improve the recommendation performance of another website (target domain) is very challenging. To this end, this paper extended the traditional one-dimensional psychometrics model to multidimension. The extended model can effectively capture users’ multiple interests. Based on this multidimensional psychometrics model, we further propose a novel transfer learning algorithm. It can effectively transfer users’ rating preferences from the source domain to the target domain. Experimental results show that the proposed method can significantly improve the recommendation performance. Haijun Zhang, Bo Zhang, Zhoujun Li, Guicheng Shen, and Liping Tian Copyright © 2016 Haijun Zhang et al. All rights reserved. Serviceability Assessment for Cascading Failures in Water Distribution Network under Seismic Scenario Sun, 24 Apr 2016 15:37:40 +0000 The stability of water service is a hot point in industrial production, public safety, and academic research. The paper establishes a service evaluation model for the water distribution network (WDN). The serviceability is measured in three aspects: (1) the functionality of structural components under disaster environment; (2) the recognition of cascading failure process; and (3) the calculation of system reliability. The node and edge failures in WDN are interrelated under seismic excitations. The cascading failure process is provided with the balance of water supply and demand. The matrix-based system reliability (MSR) method is used to represent the system events and calculate the nonfailure probability. An example is used to illustrate the proposed method. The cascading failure processes with different node failures are simulated. The serviceability is analyzed. The critical node can be identified. The result shows that the aged network has a greater influence on the system service under seismic scenario. The maintenance could improve the antidisaster ability of WDN. Priority should be given to controlling the time between the initial failure and the first secondary failure, for taking postdisaster emergency measures within this time period can largely cut down the spread of cascade effect in the whole WDN. Qing Shuang, Yisheng Liu, Jing Liu, and Qigang Chen Copyright © 2016 Qing Shuang et al. All rights reserved. Conditional Random Fields for Image Labeling Sun, 24 Apr 2016 15:31:37 +0000 With the rapid development and application of CRFs (Conditional Random Fields) in computer vision, many researchers have made some outstanding progress in this domain because CRFs solve the classical version of the label bias problem with respect to MEMMs (maximum entropy Markov models) and HMMs (hidden Markov models). This paper reviews the research development and status of object recognition with CRFs and especially introduces two main discrete optimization methods for image labeling with CRFs: graph cut and mean field approximation. This paper describes graph cut briefly while it introduces mean field approximation more detailedly which has a substantial speed of inference and is researched popularly in recent years. Tong Liu, Xiutian Huang, and Jianshe Ma Copyright © 2016 Tong Liu et al. All rights reserved. Novel Damage Detection Techniques for Structural Health Monitoring Using a Hybrid Sensor Sun, 24 Apr 2016 15:30:30 +0000 This study presents a technique for detecting fatigue cracks based on a hybrid sensor monitoring system consisting of a combination of intelligent coating monitoring (ICM) and piezoelectric transducer (PZT) sensors. An experimental procedure using this hybrid sensor system was designed to monitor the cracks generated by fatigue testing in plate structures. A probability of detection (POD) model that quantifies the reliability of damage detection for a specific sensor or the nondestructive testing (NDT) method was used to evaluate the weight factor for the ICM and PZT sensors. To estimate the uncertainty of model parameters in this study, the Bayesian method was employed. Realistic data from fatigue testing was used to validate the overall method, and the results show that the novel damage detection technique using a hybrid sensor can quantify fatigue cracks more accurately than results obtained by conventional sensor methods. Dengjiang Wang, Jingjing He, Banglin Dong, Xiaopeng Liu, and Weifang Zhang Copyright © 2016 Dengjiang Wang et al. All rights reserved. Multistage Warning Indicators of Concrete Dam under Influences of Random Factors Sun, 24 Apr 2016 13:11:10 +0000 Warning indicators are required for the real-time monitoring of the service conditions of dams to ensure safe and normal operations. Warnings are traditionally targeted at some “single point deformation” by deformation measuring points of concrete dam, and scientific warning theory on “overall deformation” measured is nonexistent. Furthermore, the influences of random factors are not considered. In this paper, the overall deformation of the dam was seen as a deformation system of single interactional observation points with different contribution degrees. The spatial deformation entropy, which describes the overall deformation, was established and the fuzziness indicator that measures the influence of complex random factors on monitoring values according to cloud theory was constructed. On this basis, multistage warning indicators of “spatial deformation” that consider fuzziness and randomness were determined. Analysis showed that the change law of information entropy of the dam’ overall deformation is identical to the real change law of the dam; thus, it reflects the real deformation state of the dam. Moreover, the identified warning indicators improved the warning ability of concrete dams. Guang Yang and Meng Yang Copyright © 2016 Guang Yang and Meng Yang. All rights reserved. Maximum Temperature and Relaxation Time in Wet Surface Grinding for a General Heat Flux Profile Sun, 24 Apr 2016 12:49:02 +0000 We solve the boundary-value problem of the heat transfer modeling in wet surface grinding, considering a constant heat transfer coefficient over the workpiece surface and a general heat flux profile within the friction zone between wheel and workpiece. We particularize this general solution to the most common heat flux profiles reported in the literature, that is, constant, linear, parabolic, and triangular. For these cases, we propose a fast method for the numerical computation of maximum temperature, in order to avoid the thermal damage of the workpiece. Also, we provide a very efficient method for the numerical evaluation of the transient regime duration (relaxation time). J. L. González-Santander Copyright © 2016 J. L. González-Santander. All rights reserved. Three-Dimensional Short-Term Prediction Model of Dissolved Oxygen Content Based on PSO-BPANN Algorithm Coupled with Kriging Interpolation Sun, 24 Apr 2016 09:44:15 +0000 Dissolved oxygen (DO) content is a significant aspect of water quality in aquaculture. Prediction of dissolved oxygen may timely avoid the financial loss caused by inappropriate dissolved oxygen content and three-dimensional prediction can achieve more accurate and overall guidance. Therefore, this study presents a three-dimensional short-term prediction model of dissolved oxygen in crab aquaculture ponds based on back propagation artificial neural network (BPANN) optimized by particle swarm optimization (PSO), which coupled with Kriging method. In this model, wavelet analysis is adopted for denoising, BPANN optimized by PSO is utilized for data analysis and one-dimensional prediction, and Kriging method is used for three-dimensional prediction. Compared with traditional one-dimensional prediction model, three-dimensional model has more real reaction of dissolved oxygen content in crab growth environment. In particular, the merits of PSO are evaluated against genetic algorithm (GA). The root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) for PSO model are 0.136445, 0.90534, and 0.15384, respectively, while for the GA model the values are 2.04184, 1.18316, and 0.21014, respectively. Furthermore, results of cross validation experiment show that the average error of this model is 0.0705 (mg/L). Consequently, this study suggests that the prediction model operates in a satisfactory manner. Yingyi Chen, Jing Xu, Huihui Yu, Zhumi Zhen, and Daoliang Li Copyright © 2016 Yingyi Chen et al. All rights reserved. An Integrated Multiechelon Logistics Model with Uncertain Delivery Lead Time and Quality Unreliability Sun, 24 Apr 2016 09:14:39 +0000 Nowadays, in order to achieve advantages in supply chain management, how to keep inventory in adequate level and how to enhance customer service level are two critical practices for decision makers. Generally, uncertain lead time and defective products have much to do with inventory and service level. Therefore, this study mainly aims at developing a multiechelon integrated just-in-time inventory model with uncertain lead time and imperfect quality to enhance the benefits of the logistics model. In addition, the Ant Colony Algorithm (ACA) is established to determine the optimal solutions. Moreover, based on our proposed model and analysis, the ACA is more efficient than Particle Swarm Optimization (PSO) and Lingo in SMEIJI model. An example is provided in this study to illustrate how production run and defective rate have an effect on system costs. Finally, the results of our research could provide some managerial insights which support decision makers in real-world operations. Ming-Feng Yang, Yi Lin, Li Hsing Ho, and Wei Feng Kao Copyright © 2016 Ming-Feng Yang et al. All rights reserved. Fault Localization Analysis Based on Deep Neural Network Sun, 24 Apr 2016 07:54:19 +0000 With software’s increasing scale and complexity, software failure is inevitable. To date, although many kinds of software fault localization methods have been proposed and have had respective achievements, they also have limitations. In particular, for fault localization techniques based on machine learning, the models available in literatures are all shallow architecture algorithms. Having shortcomings like the restricted ability to express complex functions under limited amount of sample data and restricted generalization ability for intricate problems, the faults cannot be analyzed accurately via those methods. To that end, we propose a fault localization method based on deep neural network (DNN). This approach is capable of achieving the complex function approximation and attaining distributed representation for input data by learning a deep nonlinear network structure. It also shows a strong capability of learning representation from a small sized training dataset. Our DNN-based model is trained utilizing the coverage data and the results of test cases as input and we further locate the faults by testing the trained model using the virtual test suite. This paper conducts experiments on the Siemens suite and Space program. The results demonstrate that our DNN-based fault localization technique outperforms other fault localization methods like BPNN, Tarantula, and so forth. Wei Zheng, Desheng Hu, and Jing Wang Copyright © 2016 Wei Zheng et al. All rights reserved. Optimal Replenishment Policy for Weibull-Distributed Deteriorating Items with Trapezoidal Demand Rate and Partial Backlogging Thu, 21 Apr 2016 12:54:08 +0000 An inventory model for Weibull-distributed deteriorating items is considered so as to minimize the total cost per unit time in this paper. The model starts with shortage, allowed partial backlogging, and trapezoidal demand rate. By analyzing the model, an efficient solution procedure is proposed to determine the optimal replenishment and the optimal order quantity and the average total costs are also obtained. Finally, numerical examples are provided to illustrate the theoretical results and a sensitivity analysis of the major parameters with respect to the stability of optimal solution is also carried out. Lianxia Zhao Copyright © 2016 Lianxia Zhao. All rights reserved. A Hierarchical Load Balancing Strategy Considering Communication Delay Overhead for Large Distributed Computing Systems Thu, 21 Apr 2016 12:46:27 +0000 Load balancing technology can effectively exploit potential enormous compute power available on distributed systems and achieve scalability. Communication delay overhead on distributed system, which is time-varying and is usually ignored or assumed to be deterministic for traditional load balancing strategies, can greatly degrade the load balancing performance. Considering communication delay overhead and its time-varying feature, a hierarchical load balancing strategy based on generalized neural network (HLBSGNN) is presented for large distributed systems. The novelty of the HLBSGNN is threefold: () the hierarchy with optimized communication is employed to reduce load balancing overhead for large distributed computing systems, () node computation rate and communication delay randomness imposed by the communication medium are considered, and () communication and migration overheads are optimized via forecasting delay. Comparisons with traditional strategies, such as centralized, distributed, and random delay strategies, indicate that the HLBSGNN is more effective and efficient. Jixiang Yang, Ling Ling, and Haibin Liu Copyright © 2016 Jixiang Yang et al. All rights reserved.