Mathematical Problems in Engineering The latest articles from Hindawi Publishing Corporation © 2014 , Hindawi Publishing Corporation . All rights reserved. A Joint Optimization of Momentum Item and Levenberg-Marquardt Algorithm to Level Up the BPNN’s Generalization Ability Wed, 16 Apr 2014 17:24:13 +0000 Back propagation neural network (BPNN) as a kind of artificial neural network is widely used in pattern recognition and trend prediction. For standard BPNN, it has many drawbacks such as trapping into local optima, oscillation, and long training time. Because training the standard BPNN is based on gradient descent method, and the learning rate is fixed. Momentum item and Levenberg-Marquardt (LM) algorithm are two ways to adjust the weights among the neurons and improve the BPNN’s performance. However, there is still much space to improve the two algorithms. The hybrid optimization of damping factor of LM and the dynamic momentum item is proposed in this paper. The improved BPNN is validated by Fisher Iris data and wine data. Then, it is used to predict the visit_spend. The database is provided by Dunnhumby's Shopper Challenge. Compared with the other two improved BPNNs, the proposed method gets a better performance. Therefore, the proposed method can be used to do the pattern recognition and time series prediction more effectively. Lei Xiao, Xiaohui Chen, and Xinghui Zhang Copyright © 2014 Lei Xiao et al. All rights reserved. Wind Turbine Gearbox Fault Diagnosis Method Based on Riemannian Manifold Wed, 16 Apr 2014 17:19:46 +0000 As multivariate time series problems widely exist in social production and life, fault diagnosis method has provided people with a lot of valuable information in the finance, hydrology, meteorology, earthquake, video surveillance, medical science, and other fields. In order to find faults in time sequence quickly and efficiently, this paper presents a multivariate time series processing method based on Riemannian manifold. This method is based on the sliding window and uses the covariance matrix as a descriptor of the time sequence. Riemannian distance is used as the similarity measure and the statistical process control diagram is applied to detect the abnormity of multivariate time series. And the visualization of the covariance matrix distribution is used to detect the abnormity of mechanical equipment, leading to realize the fault diagnosis. With wind turbine gearbox faults as the experiment object, the fault diagnosis method is verified and the results show that the method is reasonable and effective. Shoubin Wang, Xiaogang Sun, and Chengwei Li Copyright © 2014 Shoubin Wang et al. All rights reserved. A Novel Improved ELM Algorithm for a RealIndustrial Application Wed, 16 Apr 2014 16:10:49 +0000 It is well known that the feedforward neural networks meet numbers of difficulties in the applications because of its slow learning speed. The extreme learning machine (ELM) is a new single hidden layer feedforward neural network method aiming at improving the training speed. Nowadays ELM algorithm has received wide application with its good generalization performance under fast learning speed. However, there are still several problems needed to be solved in ELM. In this paper, a new improved ELM algorithm named R-ELM is proposed to handle the multicollinear problem appearing in calculation of the ELM algorithm. The proposed algorithm is employed in bearing fault detection using stator current monitoring. Simulative results show that R-ELM algorithm has better stability and generalization performance compared with the original ELM and the other neural network methods. Hai-Gang Zhang, Sen Zhang, and Yi-Xin Yin Copyright © 2014 Hai-Gang Zhang et al. All rights reserved. Wind Turbine Pitch Control and Load Mitigation Using an Adaptive Approach Wed, 16 Apr 2014 14:21:43 +0000 We present an application of adaptive output feedback control design to wind turbine collective pitch control and load mitigation. Our main objective is the design of an output feedback controller without wind speed estimation, ensuring that the generator speed tracks the reference trajectory with robustness to uncertain parameters and time-varying disturbances (mainly the uniform wind disturbance across the wind turbine rotor). The wind turbine model CART (controls advanced research turbine) developed by the national renewable energy laboratory (NREL) is used to validate the performance of the proposed adaptive controller using the FAST (fatigue, aerodynamics, structures, and turbulence) code. A comparative study is also conducted between the proposed controller and the most popular methods in practice: gain scheduling PI (GSPI) controls and disturbance accommodating control (DAC) methods. The results show better performance of output feedback controller over the other two methods. Moreover, based on the FAST software and LQR analysis in the reference model selection of adaptive controller, tradeoff can be achieved between control performance and loads mitigation. Danyong Li, Yongduan Song, Wenchuan Cai, Peng Li, and Hamid R. Karimi Copyright © 2014 Danyong Li et al. All rights reserved. The Research on Modeling and Simulation of TFE Polymerization Process Wed, 16 Apr 2014 14:15:13 +0000 PTFE (polytetrafluoroethylene) is the fluorinated straight-chain polymer, made by the polymerization of tetrafluoroethylene monomer; it is used widely because of its excellent performance and can be obtained by the polymerization of body, solutions, suspensions, and emulsions. But only the last two are the main ways. This research paper makes simulation based on Polymer Plus. It uses the emulsion polymerization method at background to carry out a semibatch reactor system. Upon the actual production conditions, simulation process under the steady state conditions is used to analyze the effects of the changes on operating conditions; the corresponding dynamic model is created to analyze the impact of the changes of conditions on the entire system. Moreover, the amount of APS which plays an important part in this reaction is discussed for getting the most suitable amount of initiator. Because of less research work on this job, it is so difficult to find the related data from the literature. Therefore, this research will have a great significance for the process of the tetrafluoroethylene emulsion polymerization in the future. Jing Gao Sun and Qin Cao Copyright © 2014 Jing Gao Sun and Qin Cao. All rights reserved. Improved Artificial Bee Colony Algorithm and Its Application in LQR Controller Optimization Wed, 16 Apr 2014 14:12:42 +0000 In order to get the optimal performance of controller and improve the design efficiency, artificial bee colony (ABC) algorithm as a metaheuristic approach which is inspired by the collective foraging behavior of honey bee swarms is considered for optimal linear quadratic regulator (LQR) design in this paper. Furthermore, for accelerating the convergence speed and enhancing the diversities of population of the traditional ABC algorithm, improved solution searching approach is proposed creatively. The proposed approach refers to the procedure of differential mutation in differential evolutionary (DE) algorithm and produces uniform distributed food sources in employed bee phase to avoid local optimal solution. Meanwhile, during the onlooker bees searching stage where the solution search area has been narrowed by employed bees, new solutions are generated around the solution with higher fitness value to keep the fitness values increasing monotonously. The improved ABC algorithm is applied to the optimization of LQR controller for the circular-rail double inverted pendulum system, and the simulation results show the effect on the proposed optimization problem. Haiquan Wang, Lei Liao, Dongyun Wang, Shengjun Wen, and Mingcong Deng Copyright © 2014 Haiquan Wang et al. All rights reserved. Almost Sure Asymptotical Adaptive Synchronization for Neutral-Type Neural Networks with Stochastic Perturbation and Markovian Switching Wed, 16 Apr 2014 12:59:31 +0000 The problem of almost sure (a.s.) asymptotic adaptive synchronization for neutral-type neural networks with stochastic perturbation and Markovian switching is researched. Firstly, we proposed a new criterion of a.s. asymptotic stability for a general neutral-type stochastic differential equation which extends the existing results. Secondly, based upon this stability criterion, by making use of Lyapunov functional method and designing an adaptive controller, we obtained a condition of a.s. asymptotic adaptive synchronization for neutral-type neural networks with stochastic perturbation and Markovian switching. The synchronization condition is expressed as linear matrix inequality which can be easily solved by Matlab. Finally, we introduced a numerical example to illustrate the effectiveness of the method and result obtained in this paper. Wuneng Zhou, Xueqing Yang, Jun Yang, Anding Dai, and Huashan Liu Copyright © 2014 Wuneng Zhou et al. All rights reserved. Stability Analysis of Fractional-Order Nonlinear Systems with Delay Wed, 16 Apr 2014 12:55:49 +0000 Stability analysis of fractional-order nonlinear systems with delay is studied. We propose the definition of Mittag-Leffler stability of time-delay system and introduce the fractional Lyapunov direct method by using properties of Mittag-Leffler function and Laplace transform. Then some new sufficient conditions ensuring asymptotical stability of fractional-order nonlinear system with delay are proposed firstly. And the application of Riemann-Liouville fractional-order systems is extended by the fractional comparison principle and the Caputo fractional-order systems. Numerical simulations of an example demonstrate the universality and the effectiveness of the proposed method. Yu Wang and Tianzeng Li Copyright © 2014 Yu Wang and Tianzeng Li. All rights reserved. Allocating Tradable Emissions Permits Based on the Proportional Allocation Concept to Achieve a Low-Carbon Economy Wed, 16 Apr 2014 11:53:49 +0000 A key issue within the emissions trading system is how tradable emissions permits (TEPs) are initially allocated among a set of entities. This study proposes an approach based on the proportional allocation concept to allocate TEPs among a set of decision making units (DMUs). We firstly deduce a TEP allocation set based on the rule that the TEPs allocated to DMUs should be proportional to their environmental contribution. We then obtain the allocation intervals of DMUs from the set, expressing the allocation as the convex combination between the upper and the lower bound. Finally, we define the satisfaction degree as the coefficient of the convex combination, and propose an algorithm based on the max-min fairness of satisfaction degrees to obtain a unique TEP allocation plan. To illustrate our approach, we provide the example of how TEPs are allocated among 30 provincial administrative regions in China. Our findings indicate that our allocation method can be helpful for achieving a saving in energy consumption and reducing emissions. In addition, from the data envelopment analysis perspective, the TEP allocation set can ensure that both each individual DMU and the organization as a whole become efficient under a common set of variable weights. Qianzhi Dai, Yongjun Li, Qiwei Xie, and Liang Liang Copyright © 2014 Qianzhi Dai et al. All rights reserved. Portfolio Selection with Subsistence Consumption Constraints and CARA Utility Wed, 16 Apr 2014 11:51:34 +0000 We consider the optimal consumption and portfolio choice problem with constant absolute risk aversion (CARA) utility and a subsistence consumption constraint. A subsistence consumption constraint means there exists a positive constant minimum level for the agent’s optimal consumption. We use the dynamic programming approach to solve the optimization problem and also give the verification theorem. We illustrate the effects of the subsistence consumption constraint on the optimal consumption and portfolio choice rules by the numerical results. Gyoocheol Shim and Yong Hyun Shin Copyright © 2014 Gyoocheol Shim and Yong Hyun Shin. All rights reserved. The Pressure Drop Model of Liquid Flow with Wall Mass Transfer in Horizontal Wellbore with Perforated Completion Wed, 16 Apr 2014 11:50:59 +0000 The fluids in horizontal wells can exhibit complicated flow behaviors with wall mass transfer, partly due to the interaction between the main flow and the radial influx along the wellbore and the completion parameters used. This paper presents a novel regression model established based on the experiment data retrieved from the available literatures to determine the apparent friction factor for a single phase wellbore flow. The proposed model has the potential to be readily applicable to different perforation parameters, such as shot phasing and shot density. Compared with other models in the same practical example which is offered by Ouyang et al., the model of this paper to calculate the wellbore pressure is applicable and reasonable. This new model can be easily incorporated into reservoir simulators or analytical reservoir and horizontal wellbore inflow coupling models. Ping Yue, Zhimin Du, Xiaofan Chen, and Chao Tang Copyright © 2014 Ping Yue et al. All rights reserved. Strongly Secure Certificateless Signature Scheme Supporting Batch Verification Wed, 16 Apr 2014 09:47:52 +0000 We propose a strongly secure certificateless signature scheme supporting batch verification, which makes it possible for a verifier to verify a set of signatures more efficiently than verifying them one by one. In an identity-based digital signature scheme, private key generator (PKG) knows each user's signing key, so it can generate a signature which is indistinguishable from the signature generated by the user. This is a serious problem because the property of signature nonrepudiation will not be achieved. In our proposed scheme, it is impossible for PKG to produce a signature which is indistinguishable from any signature produced by a user. Compared with existing signature schemes with batch verification, although our proposed scheme is not the most efficient one, it achieves Girault's level-3 security, while the others have Girault's level-1 or level-2 security only. We also formally prove that the proposed scheme is unforgeable and satisfies Girault's level-3 security based on hard problems. Chun-I Fan, Pei-Hsiu Ho, and Yi-Feng Tseng Copyright © 2014 Chun-I Fan et al. All rights reserved. EEG Eye State Identification Using Incremental Attribute Learning with Time-Series Classification Wed, 16 Apr 2014 09:33:32 +0000 Eye state identification is a kind of common time-series classification problem which is also a hot spot in recent research. Electroencephalography (EEG) is widely used in eye state classification to detect human's cognition state. Previous research has validated the feasibility of machine learning and statistical approaches for EEG eye state classification. This paper aims to propose a novel approach for EEG eye state identification using incremental attribute learning (IAL) based on neural networks. IAL is a novel machine learning strategy which gradually imports and trains features one by one. Previous studies have verified that such an approach is applicable for solving a number of pattern recognition problems. However, in these previous works, little research on IAL focused on its application to time-series problems. Therefore, it is still unknown whether IAL can be employed to cope with time-series problems like EEG eye state classification. Experimental results in this study demonstrates that, with proper feature extraction and feature ordering, IAL can not only efficiently cope with time-series classification problems, but also exhibit better classification performance in terms of classification error rates in comparison with conventional and some other approaches. Ting Wang, Sheng-Uei Guan, Ka Lok Man, and T. O. Ting Copyright © 2014 Ting Wang et al. All rights reserved. Modeling the Dynamics of Shanghai Interbank Offered Rate Based on Single-Factor Short Rate Processes Wed, 16 Apr 2014 08:04:42 +0000 Using the Shanghai Interbank Offered Rate data of overnight, 1 week, 2 week and 1 month, this paper provides a comparative analysis of some popular one-factor short rate models, including the Merton model, the geometric Brownian model, the Vasicek model, the Cox-Ingersoll-Ross model, and the mean-reversion jump-diffusion model. The parameter estimation and the model selection of these single-factor short interest rate models are investigated. We document that the most successful model in capturing the Shanghai Interbank Offered Rate is the mean-reversion jump-diffusion model. Xili Zhang Copyright © 2014 Xili Zhang. All rights reserved. Dual PD Control Regulation with Nonlinear Compensation for a Ball and Plate System Wed, 16 Apr 2014 07:14:24 +0000 The normal proportional derivative (PD) control is modified to a new dual form for the regulation of a ball and plate system. First, to analyze this controller, a novel complete nonlinear model of the ball and plate system is obtained. Second, an asymptotic stable dual PD control with a nonlinear compensation is developed. Finally, the experimental results of ball and plate system are provided to verify the effectiveness of the proposed methodology. Sergio Galvan-Colmenares, Marco A. Moreno-Armendáriz, José de Jesús Rubio, Floriberto Ortíz-Rodriguez, Wen Yu, and Carlos F. Aguilar-Ibáñez Copyright © 2014 Sergio Galvan-Colmenares et al. All rights reserved. A Data-Driven Reliability Estimation Approach for Phased-Mission Systems Tue, 15 Apr 2014 13:24:49 +0000 We attempt to address the issues associated with reliability estimation for phased-mission systems (PMS) and present a novel data-driven approach to achieve reliability estimation for PMS using the condition monitoring information and degradation data of such system under dynamic operating scenario. In this sense, this paper differs from the existing methods only considering the static scenario without using the real-time information, which aims to estimate the reliability for a population but not for an individual. In the presented approach, to establish a linkage between the historical data and real-time information of the individual PMS, we adopt a stochastic filtering model to model the phase duration and obtain the updated estimation of the mission time by Bayesian law at each phase. At the meanwhile, the lifetime of PMS is estimated from degradation data, which are modeled by an adaptive Brownian motion. As such, the mission reliability can be real time obtained through the estimated distribution of the mission time in conjunction with the estimated lifetime distribution. We demonstrate the usefulness of the developed approach via a numerical example. Hua-Feng He, Juan Li, Qing-Hua Zhang, and Guoxi Sun Copyright © 2014 Hua-Feng He et al. All rights reserved. A Network DEA Model with Super Efficiency and Undesirable Outputs: An Application to Bank Efficiency in China Tue, 15 Apr 2014 12:19:41 +0000 There are two typical subprocesses in bank production—deposit generation and loan generation. Aiming to open the black box of input-output production of banks and provide comprehensive and accurate assessment on the efficiency of each stage, this paper proposes a two-stage network model with bad outputs and supper efficiency (US-NSBM). Empirical comparisons show that the US-NSBM may be promising and practical for taking the nonperforming loans into account and being able to rank all samples. Applying it to measure the efficiency of Chinese commercial banks from 2008 to 2012, this paper explores the characteristics of overall and divisional efficiency, as well as the determinants of them. Some interesting results are discovered. The polarization of efficiency occurs in the bank level and deposit generation, yet does not in the loan generation. Five hypotheses work as expected in the bank level, but not all of them are supported in the stage level. Our results extend and complement some earlier empirical publications in the bank level. Jianhuan Huang, Juanjuan Chen, and Zhujia Yin Copyright © 2014 Jianhuan Huang et al. All rights reserved. D-FNN Based Modeling and BP Neural Network Decoupling Control of PVC Stripping Process Tue, 15 Apr 2014 11:32:29 +0000 PVC stripping process is a kind of complicated industrial process with characteristics of highly nonlinear and time varying. Aiming at the problem of establishing the accurate mathematics model due to the multivariable coupling and big time delay, the dynamic fuzzy neural network (D-FNN) is adopted to establish the PVC stripping process model based on the actual process operation datum. Then, the PVC stripping process is decoupled by the distributed neural network decoupling module to obtain two single-input-single-output (SISO) subsystems (slurry flow to top tower temperature and steam flow to bottom tower temperature). Finally, the PID controller based on BP neural networks is used to control the decoupled PVC stripper system. Simulation results show the effectiveness of the proposed integrated intelligent control method. Shu-zhi Gao, Jing Yang, and Jie-sheng Wang Copyright © 2014 Shu-zhi Gao et al. All rights reserved. Local Community Detection in Complex Networks Based on Maximum Cliques Extension Tue, 15 Apr 2014 00:00:00 +0000 Detecting local community structure in complex networks is an appealing problem that has attracted increasing attention in various domains. However, most of the current local community detection algorithms, on one hand, are influenced by the state of the source node and, on the other hand, cannot effectively identify the multiple communities linked with the overlapping nodes. We proposed a novel local community detection algorithm based on maximum clique extension called LCD-MC. The proposed method firstly finds the set of all the maximum cliques containing the source node and initializes them as the starting local communities; then, it extends each unclassified local community by greedy optimization until a certain objective is satisfied; finally, the expected local communities will be obtained until all maximum cliques are assigned into a community. An empirical evaluation using both synthetic and real datasets demonstrates that our algorithm has a superior performance to some of the state-of-the-art approaches. Meng Fanrong, Zhu Mu, Zhou Yong, and Zhou Ranran Copyright © 2014 Meng Fanrong et al. All rights reserved. Signal Processing for Digital Beamforming FMCW SAR Tue, 15 Apr 2014 00:00:00 +0000 According to the limitations of single channel Frequency Modulation Continuous Wave (FMCW) Synthetic Aperture Radar (SAR), Digital Beamforming (DBF) technology is introduced to improve system performance. Combined with multiple receive apertures, DBF FMCW SAR can obtain high resolution in low pulse repetition frequency, which can increase the processing gain and decrease the sampling frequency. The received signal model of DBF FMCW SAR is derived. The continuous antenna motion which is the main characteristic of FMCW SAR received signal is taken into account in the whole signal processing. The detailed imaging diagram of DBF FMCW SAR is given. A reference system is also demonstrated in the paper by comparing with a single channel FMCW SAR. The validity of the presented diagram is demonstrated with a point target simulation results. Qin Xin, Zhihong Jiang, Pu Cheng, and Mi He Copyright © 2014 Qin Xin et al. All rights reserved. Optimized Measurement Matrix Design Using Spatiotemporal Chaos for CS-MIMO Radar Tue, 15 Apr 2014 00:00:00 +0000 We investigate the possibility of utilizing the chaotic dynamic system for the measurement matrix design in the CS-MIMO radar system. The CS-MIMO radar achieves better detection performance than conventional MIMO radar with fewer measurements. For exactly recovering from compressed measurements, we should carefully design the measurement matrix to make the sensing matrix satisfy the restricted isometry property (RIP). A Gaussian random measurement matrix (GRMM), typically used in CS problems, is not satisfied for on-line optimization and the low coherence with the basis matrix corresponding to the MIMO radar scenario can not be well guaranteed. An optimized measurement matrix design method applying the two-dimensional spatiotemporal chaos is proposed in this paper. It incorporates the optimization criterion which restricts the coherence of the sensing matrix and singular value decomposition (SVD) for the optimization process. By varying the initial state of the spatiotemporal chaos and optimizing each spatiotemporal chaotic measurement matrix (SCMM), we can finally obtain the optimized measurement matrix. Its simulation results show that the optimized SCMM can highly reduce the coherence of the sensing matrix and improve the DOA estimation accuracy for the CS-MIMO radar. Zhenni Peng, Gong Zhang, Jindong Zhang, and De Ben Copyright © 2014 Zhenni Peng et al. All rights reserved. Coupled Model of Artificial Neural Network and Grey Model for Tendency Prediction of Labor Turnover Tue, 15 Apr 2014 00:00:00 +0000 The tendency of labor turnover in the Chinese enterprise shows the characteristics of seasonal fluctuations and irregular distribution of various factors, especially the Chinese traditional social and cultural characteristics. In this paper, we present a coupled model for the tendency prediction of labor turnover. In the model, a time series of tendency prediction of labor turnover was expressed as trend item and its random item. Trend item of tendency prediction of labor turnover is predicted using Grey theory. Random item of trend item is calculated by artificial neural network model (ANN). A case study is presented by the data of 24 months in a Chinese matured enterprise. The model uses the advantages of “accumulative generation” of a Grey prediction method, which weakens the original sequence of random disturbance factors and increases the regularity of data. It also takes full advantage of the ANN model approximation performance, which has a capacity to solve economic problems rapidly, describes the nonlinear relationship easily, and avoids the defects of Grey theory. Yueru Ma and Lijun Peng Copyright © 2014 Yueru Ma and Lijun Peng. All rights reserved. An Improved Interacting Multiple Model Algorithm Used in Aircraft Tracking Mon, 14 Apr 2014 15:52:36 +0000 There are some problems in traditional interacting multiple model algorithms (IMM) when used in target tracking systems. For instance, the mode transition matrix is inaccurate and cannot be determined when the sojourn times are not known. To solve these problems, an optimal mode transition matrix IMM (OMTM-IMM) algorithm is proposed in this paper. The linear minimum variance theory is used to calculate the mode transition matrix which depends on the continuous system state rather than the sojourn times in this algorithm. Moreover, the correlation of the subfilter is considered; hence the covariance matrices are utilized to compute mode transition matrix. In this algorithm, the model probability is defined as a diagonal matrix which is combined with the filters outputs; thus the effects produced by each state can be distinguished. Finally, to verify the superiority of the new algorithm, the theoretical proof and simulation results are given. They show that the OMTM-IMM algorithm can improve the tracking accuracy and can be utilized in the complex environment. Wei dong Zhou, Jia nan Cai, Long Sun, and Chen Shen Copyright © 2014 Wei dong Zhou et al. All rights reserved. Processing Centroids of Smearing Star Image of Star Sensor Mon, 14 Apr 2014 13:13:52 +0000 A novel method was presented for increasing the accuracy of subpixel centroid estimation for smearing star image. Model of the smearing trajectory of smearing star was built. It helped to study the analytical form of the errors, caused by image smearing, for centroid estimation. In the algorithm, the errors were estimated with accuracy and used to revise the centroid processed by CoM (centre of mass). Simulations have been run to study the effect of angular rates, integration time, and actual position of star on the accuracy of centroid estimation. Results were presented which suggested that the proposed algorithm had a precision better than 1/10 of a pixel when the angular rate was up to 3.0 deg/s. Yufu Liao, Enhai Liu, Jianyong Zhong, and Hui Zhang Copyright © 2014 Yufu Liao et al. All rights reserved. Freeway Traffic Density and On-Ramp Queue Control via ILC Approach Mon, 14 Apr 2014 13:11:58 +0000 A new queue length information fused iterative learning control approach (QLIF-ILC) is presented for freeway traffic ramp metering to achieve a better performance by utilizing the error information of the on-ramp queue length. The QLIF-ILC consists of two parts, where the iterative feedforward part updates the control input signal by learning from the past control data in previous trials, and the current feedback part utilizes the tracking error of the current learning iteration to stabilize the controlled plant. These two parts are combined in a complementary manner to enhance the robustness of the proposed QLIF-ILC. A systematic approach is developed to analyze the convergence and robustness of the proposed learning scheme. The simulation results are further given to demonstrate the effectiveness of the proposed QLIF-ILC. Ronghu Chi, Mengze Li, Zhongsheng Hou, Xiangpeng Liu, and Zhaoxu Yu Copyright © 2014 Ronghu Chi et al. All rights reserved. Robust Stabilization of Nonlinear Systems with Uncertain Varying Control Coefficient Mon, 14 Apr 2014 12:24:07 +0000 This paper investigates the stabilization problem for a class of nonlinear systems, whose control coefficient is uncertain and varies continuously in value and sign. The study emphasizes the development of a robust control that consists of a modified Nussbaum function to tackle the uncertain varying control coefficient. By such a method, the finite-time escape phenomenon has been prevented when the control coefficient is crossing zero and varying its sign. The proposed control guarantees the asymptotic stabilization of the system and boundedness of all closed-loop signals. The control performance is illustrated by a numerical simulation. Zaiyue Yang, C. W. Chan, and Yiwen Wang Copyright © 2014 Zaiyue Yang et al. All rights reserved. Probabilistic and Nonprobabilistic Sensitivity Analyses of Uncertain Parameters Mon, 14 Apr 2014 11:10:54 +0000 Parameter sensitivity analyses have been widely applied to industrial problems for evaluating parameter significance, effects on responses, uncertainty influence, and so forth. In the interest of simple implementation and computational efficiency, this study has developed two sensitivity analysis methods corresponding to the situations with or without sufficient probability information. The probabilistic method is established with the aid of the stochastic response surface and the mathematical derivation proves that the coefficients of first-order items embody the parameter main effects on the response. Simultaneously, a nonprobabilistic interval analysis based method is brought forward for the circumstance when the parameter probability distributions are unknown. The two methods have been verified against a numerical beam example with their accuracy compared to that of a traditional variance-based method. The analysis results have demonstrated the reliability and accuracy of the developed methods. And their suitability for different situations has also been discussed. Sheng-En Fang, Qiu-Hu Zhang, Bao Zhang, and Xiao-Hua Zhang Copyright © 2014 Sheng-En Fang et al. All rights reserved. Direct Numerical Simulation and Large Eddy Simulation on a Turbulent Wall-Bounded Flow Using Lattice Boltzmann Method and Multiple GPUs Mon, 14 Apr 2014 10:50:46 +0000 Direct numerical simulation (DNS) and large eddy simulation (LES) were performed on the wall-bounded flow at using lattice Boltzmann method (LBM) and multiple GPUs (Graphic Processing Units). In the DNS, 8 K20M GPUs were adopted. The maximum number of meshes is , which results in the nondimensional mesh size of for the whole solution domain. It took 24 hours for GPU-LBM solver to simulate LBM steps. The aspect ratio of resolution domain was tested to obtain accurate results for DNS. As a result, both the mean velocity and turbulent variables, such as Reynolds stress and velocity fluctuations, perfectly agree with the results of Kim et al. (1987) when the aspect ratios in streamwise and spanwise directions are 8 and 2, respectively. As for the LES, the local grid refinement technique was tested and then used. Using grids and Smagorinsky constant , good results were obtained. The ability and validity of LBM on simulating turbulent flow were verified. Xian Wang, Yanqin Shangguan, Naoyuki Onodera, Hiromichi Kobayashi, and Takayuki Aoki Copyright © 2014 Xian Wang et al. All rights reserved. Advanced Control and Optimization with Applications to Complex Automotive Systems Mon, 14 Apr 2014 09:18:25 +0000 Hui Zhang, Hamid Reza Karimi, Xinjie Zhang, and Junmin Wang Copyright © 2014 Hui Zhang et al. All rights reserved. STP-LWE: A Variant of Learning with Error for a Flexible Encryption Mon, 14 Apr 2014 09:15:01 +0000 We construct a flexible lattice based scheme based on semitensor product learning with errors (STP-LWE), which is a variant of learning with errors problem. We have proved that STP-LWE is hard when LWE is hard. Our scheme is proved to be secure against indistinguishable chosen message attacks, and it can achieve a balance between the security and efficiency in the hierarchical encryption systems. In addition, our scheme is almost as efficient as the dual encryption in GPV08. Bo Gao, Yanfeng Shi, Chunli Yang, Lixiang Li, Licheng Wang, and Yixian Yang Copyright © 2014 Bo Gao et al. All rights reserved.