Mathematical Problems in Engineering The latest articles from Hindawi Publishing Corporation © 2014 , Hindawi Publishing Corporation . All rights reserved. Generalizations of Refined Hölder’s Inequalities and Their Applications Thu, 30 Oct 2014 11:47:49 +0000 We present several new generalized versions of refined Hölder’s inequalities proposed by Tian and Hu. And then we obtained some new generalized and sharp versions of Hölder’s inequalities. As the applications, the obtained results are used to improve Aczél-Popoviciu type inequality and Aczél-Vasić-Pečarić inequality. Jingfeng Tian and Wen-Li Wang Copyright © 2014 Jingfeng Tian and Wen-Li Wang. All rights reserved. Vibration Analysis of Collecting Electrodes by means of the Hybrid Finite Element Method Thu, 30 Oct 2014 10:16:45 +0000 The paper presents a hybrid finite element method of shell modeling in order to model collecting electrodes of electrostatic precipitators. The method uses the finite element method to reflect elastic features and the rigid finite element method in order to model mass features of the body. A model of dust removal systems of an electrostatic precipitator is presented. The system consists of two beams which are modeled by means of the rigid finite element method and a system of collecting shells modeled by means of the hybrid finite element method. The paper discusses both the procedure of deriving the equations of motion and the results of numerical simulations carried out in order to analyze vibrations of the whole system. Experimental verification of the model is also presented. I. Adamiec-Wójcik, J. Awrejcewicz, A. Nowak, and S. Wojciech Copyright © 2014 I. Adamiec-Wójcik et al. All rights reserved. Time Series Outlier Detection Based on Sliding Window Prediction Thu, 30 Oct 2014 09:51:25 +0000 In order to detect outliers in hydrological time series data for improving data quality and decision-making quality related to design, operation, and management of water resources, this research develops a time series outlier detection method for hydrologic data that can be used to identify data that deviate from historical patterns. The method first built a forecasting model on the history data and then used it to predict future values. Anomalies are assumed to take place if the observed values fall outside a given prediction confidence interval (PCI), which can be calculated by the predicted value and confidence coefficient. The use of PCI as threshold is mainly on the fact that it considers the uncertainty in the data series parameters in the forecasting model to address the suitable threshold selection problem. The method performs fast, incremental evaluation of data as it becomes available, scales to large quantities of data, and requires no preclassification of anomalies. Experiments with different hydrologic real-world time series showed that the proposed methods are fast and correctly identify abnormal data and can be used for hydrologic time series analysis. Yufeng Yu, Yuelong Zhu, Shijin Li, and Dingsheng Wan Copyright © 2014 Yufeng Yu et al. All rights reserved. Spatial-Temporal ARX Modeling and Optimization for Polymer Flooding Thu, 30 Oct 2014 09:41:22 +0000 A new polymer flooding model based on spatial-temporal decomposition and autoregressive model with external input (ARX) (STDARX model) is proposed. Karhunen-Loeve (K-L) decomposition is used to model the two-dimensional state parameters of reservoir (such as water saturation, pressure, and grid concentration). The polymer injection concentration and time coefficient got from the decomposition are taken as the input and output information. After being identified by least square method, the time iterative ARX models of all state variables are obtained, we build the ARX model among pressure, water saturation, grid concentration, and moisture content of production well, and identify it with recursive least-squares (RLS) method. After combining the above two models, we get the STDARX model of polymer flooding. The accuracy is proved by model with four injection wells and nine production wells through data which is obtained from mechanism model. In order to enhance the polymer flooding oil recovery when oil price is changing, iterative dynamic programming (IDP) is applied to optimize the STDARX model, to get the optimal injection of production scheme. Yulei Ge, Shurong Li, Songlin Lu, Peng Chang, and Yang Lei Copyright © 2014 Yulei Ge et al. All rights reserved. Research on Routing Algorithm Based on Limitation Arrangement Principle in Mathematics Thu, 30 Oct 2014 06:50:16 +0000 Since the research on information consistency of the whole network under OSPF protocol has been insufficient in recent years, an algorithm based on limitation arrangement principle for routing decision is proposed and it is a permutation and combination problem in mathematical area. The most fundamental function of this algorithm is to accomplish the information consistency of the whole network at a relatively fast speed. Firstly, limitation arrangement principle algorithm is proposed and proved. Secondly, LAP routing algorithm in single link network and LAP routing algorithm in single link network with multiloops are designed. Finally, simulation experiments are worked by VC6.0 and NS2, which proves that LAPSN algorithm and LAPSNM algorithm can solve the problem of information consistency of the whole network under OSPF protocol and LAPSNM algorithm is superior to Dijkstra algorithm. Jianhui Lv, Xingwei Wang, and Min Huang Copyright © 2014 Jianhui Lv et al. All rights reserved. Convergence of Gossip Algorithms for Consensus in Wireless Sensor Networks with Intermittent Links and Mobile Nodes Wed, 29 Oct 2014 00:00:00 +0000 We study the convergence of pairwise gossip algorithms and broadcast gossip algorithms for consensus with intermittent links and mobile nodes. By nonnegative matrix theory and ergodicity coefficient theory, we prove gossip algorithms surely converge as long as the graph is partitionally weakly connected which, in comparison with existing analysis, is the weakest condition and can be satisfied for most networks. In addition we characterize the supremum for the mean squared error of convergence as a function associated with the initial states and the number of nodes. Furthermore, on the condition that the graph is partitionally strongly connected, the rate of convergence is proved to be exponential and governed by the second largest eigenvalue of expected coefficient matrix. For partitionally strongly connected digraphs, simulation results illustrate that gossip algorithms actually converge, and broadcast gossip algorithms can converge faster than pairwise gossip algorithms at the cost of larger error of convergence. Shaochuan Wu, Jiayan Zhang, Yuguan Hou, and Xu Bai Copyright © 2014 Shaochuan Wu et al. All rights reserved. An Efficient Solution for Hierarchical Access Control Problem in Cloud Environment Tue, 28 Oct 2014 11:41:55 +0000 The time-bound hierarchical key assignment scheme provides a cryptographic solution for the access control problem in distributed systems (e.g., Pay-TV and cloud computing applications). Most time-bound hierarchical key assignment schemes can be divided into two types: adopting tamper-resistant devices and utilizing public values. Despite the fact that adopting tamper-resistant devices can easily resist to collusion attacks, utilizing public values is much cheaper and more suitable for cloud environment. In this paper, we proposed a new time-bound hierarchical key assignment scheme, which can effectively defeat the collusion attack. Besides, the proposed scheme utilizes public values instead of tamper-resistant devices, which will restrict user’s convenience. Compared with the previous works, our scheme requires fewer public values and has better performance. Bing-Zhe He, Chien-Ming Chen, Tsu-Yang Wu, and Hung-Min Sun Copyright © 2014 Bing-Zhe He et al. All rights reserved. Hybrid Artificial Bee Colony Algorithm and Particle Swarm Search for Global Optimization Tue, 28 Oct 2014 11:31:54 +0000 Artificial bee colony (ABC) algorithm is one of the most recent swarm intelligence based algorithms, which has been shown to be competitive to other population-based algorithms. However, there is still an insufficiency in ABC regarding its solution search equation, which is good at exploration but poor at exploitation. To overcome this problem, we propose a novel artificial bee colony algorithm based on particle swarm search mechanism. In this algorithm, for improving the convergence speed, the initial population is generated by using good point set theory rather than random selection firstly. Secondly, in order to enhance the exploitation ability, the employed bee, onlookers, and scouts utilize the mechanism of PSO to search new candidate solutions. Finally, for further improving the searching ability, the chaotic search operator is adopted in the best solution of the current iteration. Our algorithm is tested on some well-known benchmark functions and compared with other algorithms. Results show that our algorithm has good performance. Wang Chun-Feng, Liu Kui, and Shen Pei-Ping Copyright © 2014 Wang Chun-Feng et al. All rights reserved. An Endogenous Project Performance Evaluation Approach Based on Random Forests and IN-PROMETHEE II Methods Tue, 28 Oct 2014 06:34:12 +0000 In order to identify the best or poorest alternative project by an overall ranking result in the scenario of assessing multiple infrastructure projects, multicriteria decision aid methods need to be incorporated into evaluating project performance. Most previous methods for assessing infrastructure project performance may not be applicable to frequent cases with numerous evaluation criteria but inadequate observation data. This paper proposed an objective performance evaluation approach from annual field-survey data through Random Forests and IN-PROMETHEE II methods together. Random Forests method is employed to predict performance values under selected criteria as the single-valued performance scores. IN-PROMETHEE II method is further developed to quantify the preference index among different projects under each criterion. By calculating a weighted average of single-criterion preference index, the multicriteria preference index can be obtained to determine the ultimate ranking of alternative projects. A comprehensive empirical study reveals that this approach is able to successfully avoid subjective bias. It is helpful in tracing decisive factors of project performance for practical projects in multicriteria cases. The analysis results have proved that the proposed method can be widely used in performance evaluation of complicated infrastructure projects. Na Xie, Chenglong Chu, Xiaoye Tian, and Lei Wang Copyright © 2014 Na Xie et al. All rights reserved. Log-Aesthetic Magnetic Curves and Their Application for CAD Systems Mon, 27 Oct 2014 13:49:46 +0000 Curves are the building blocks of shapes and designs in computer aided geometric design (CAGD). It is important to ensure these curves are both visually and geometrically aesthetic to meet the high aesthetic need for successful product marketing. Recently, magnetic curves that have been proposed for computer graphics purposes are a particle tracing technique that generates a wide variety of curves and spirals under the influence of a magnetic field. The contributions of this paper are threefold, where the first part reformulates magnetic curves in the form of log-aesthetic curve (LAC) denoting it as log-aesthetic magnetic curves (LMC) and log-aesthetic magnetic space curves (LMSC), the second part elucidates vital properties of LMCs, and the final part proposes G2 LMC design for CAD applications. The final section shows two examples of LMC surface generation along with its zebra maps. LMC holds great potential in overcoming the weaknesses found in current interactive LAC mechanism where matching a single segment with G2 Hermite data is still a cumbersome task. Mei Seen Wo, R. U. Gobithaasan, and Kenjiro T. Miura Copyright © 2014 Mei Seen Wo et al. All rights reserved. Polynomial Least Squares Method for the Solution of Nonlinear Volterra-Fredholm Integral Equations Mon, 27 Oct 2014 13:48:13 +0000 The present paper presents the application of the polynomial least squares method to nonlinear integral equations of the mixed Volterra-Fredholm type. For this type of equations, accurate approximate polynomial solutions are obtained in a straightforward manner and numerical examples are given to illustrate the validity and the applicability of the method. A comparison with previous results is also presented and it emphasizes the accuracy of the method. Bogdan Căruntu and Constantin Bota Copyright © 2014 Bogdan Căruntu and Constantin Bota. All rights reserved. Nonsingular Fast Terminal Sliding Mode Control with Extended State Observer and Tracking Differentiator for Uncertain Nonlinear Systems Mon, 27 Oct 2014 13:46:04 +0000 A continuous nonsingular fast terminal sliding mode (NFTSM) control scheme with the extended state observer (ESO) and the tracking differentiator (TD) is proposed for second-order uncertain SISO nonlinear systems. The system’s disturbances and states can be estimated by introducing the ESO, then the disturbances are compensated effectively, and the ideal transient process of the system can be arranged based on TD to provide the target tracking signal and its high-order derivatives. The proposed controller obtains finite-time convergence property and keeps good robustness of sliding mode control (SMC) for disturbances. Moreover, compared with conventional SMC, the proposed control law is continuous and no chattering phenomenon exists. The property of system stability is guaranteed by Lyapunov stability theory. The simulation results show that the proposed method can be employed to shorten the system reaching time, improve the system tracking precision, and suppress the system chattering and the input noise. The proposed control method is finally applied for the rotating control problem of theodolite servo system. Zhenxin He, Chuntong Liu, Ying Zhan, Hongcai Li, Xianxiang Huang, and Zhili Zhang Copyright © 2014 Zhenxin He et al. All rights reserved. Using the Efficient Frontier to Obtain the Best Solution for the Storage Location Assignment Problem Mon, 27 Oct 2014 09:09:49 +0000 The main variables that influence the efficiency of a warehouse are the use of space and the order picking distance. In the literature, there are proposals to add the costs with space and order picking in order to evaluate each alternative for storage location assignment. However, there were problems with the adoption of this methodology, including difficulties in determining the costs and tradeoffs between them. These difficulties can result in solutions that are suboptimal. Based on these facts, this paper proposes a class-based storage process and storage location assignment by a cube-per-order index (COI) that analyzes the space required and the total order picking distance by Pareto-optimal calculations. The efficient frontier possibilities allow the reduction of the set of alternatives, and the DM can analyze only the alternatives on efficient frontier. Marcele Elisa Fontana and Cristiano Alexandre Virgínio Cavalcante Copyright © 2014 Marcele Elisa Fontana and Cristiano Alexandre Virgínio Cavalcante. All rights reserved. A Novel Method Based on Oblique Projection Technology for Mixed Sources Estimation Thu, 23 Oct 2014 12:40:28 +0000 Reducing the computational complexity of the near-field sources and far-field sources localization algorithms has been considered as a serious problem in the field of array signal processing. A novel algorithm caring for mixed sources location estimation based on oblique projection is proposed in this paper. The sources are estimated at two different stages and the sensor noise power is estimated and eliminated from the covariance which improve the accuracy of the estimation of mixed sources. Using the idea of compress, the range information of near-field sources is obtained by searching the partial area instead of the whole Fresnel area which can reduce the processing time. Compared with the traditional algorithms, the proposed algorithm has the lower computation complexity and has the ability to solve the two closed-spaced sources with high resolution and accuracy. The duplication of range estimation is also avoided. Finally, simulation results are provided to demonstrate the performance of the proposed method. Weijian Si, Xiaolin Li, Yilin Jiang, and Liangtian Wan Copyright © 2014 Weijian Si et al. All rights reserved. Selection Ideal Coal Suppliers of Thermal Power Plants Using the Matter-Element Extension Model with Integrated Empowerment Method for Sustainability Thu, 23 Oct 2014 11:50:36 +0000 In order to reduce thermal power generation cost and improve its market competitiveness, considering fuel quality, cost, creditworthiness, and sustainable development capacity factors, this paper established the evaluation system for coal supplier selection of thermal power and put forward the coal supplier selection strategies for thermal power based on integrated empowering and ideal matter-element extension models. On the one hand, the integrated empowering model can overcome the limitations of subjective and objective methods to determine weights, better balance subjective, and objective information. On the other hand, since the evaluation results of the traditional element extension model may fall into the same class and only get part of the order results, in order to overcome this shortcoming, the idealistic matter-element extension model is constructed. It selects the ideal positive and negative matter-elements classical field and uses the closeness degree to replace traditional maximum degree of membership criterion and calculates the positive or negative distance between the matter-element to be evaluated and the ideal matter-element; then it can get the full order results of the evaluation schemes. Simulated and compared with the TOPSIS method, Romania selection method, and PROMETHEE method, numerical example results show that the method put forward by this paper is effective and reliable. Zhongfu Tan, Liwei Ju, Xiaobao Yu, Huijuan Zhang, and Chao Yu Copyright © 2014 Zhongfu Tan et al. All rights reserved. An Efficient Hierarchy Algorithm for Community Detection in Complex Networks Thu, 23 Oct 2014 09:16:26 +0000 Community structure is one of the most fundamental and important topology characteristics of complex networks. The research on community structure has wide applications and is very important for analyzing the topology structure, understanding the functions, finding the hidden properties, and forecasting the time-varying of the networks. This paper analyzes some related algorithms and proposes a new algorithm—CN agglomerative algorithm based on graph theory and the local connectedness of network to find communities in network. We show this algorithm is distributed and polynomial; meanwhile the simulations show it is accurate and fine-grained. Furthermore, we modify this algorithm to get one modified CN algorithm and apply it to dynamic complex networks, and the simulations also verify that the modified CN algorithm has high accuracy too. Lili Zhang, Qing Ye, Yehong Shao, Chenming Li, and Hongmin Gao Copyright © 2014 Lili Zhang et al. All rights reserved. Modelling of Lime Kiln Using Subspace Method with New Order Selection Criterion Thu, 23 Oct 2014 06:20:37 +0000 This paper is taking actual control demand of rotary kiln as background and builds a calcining belt state space model using PO-Moesp subspace method. A novel order-delay double parameters error criterion (ODC) is presented to reduce the modeling order. The proposed subspace order identification method takes into account the influence of order and delay on model error criterion simultaneously. For the introduction of the delay factors, the order is reduced dramatically in the system modeling. Also, in the data processing part sliding-window method is adopted for stripping delay factor from historical data. For this, the parameters can be changed flexibly. Some practical problems in industrial kiln process modeling are also solved. Finally, it is applied to an industrial kiln case. Li Zhang, Chengjin Zhang, Qingyang Xu, and Chaoyang Wang Copyright © 2014 Li Zhang et al. All rights reserved. Vehicle Velocity and Roll Angle Estimation with Road and Friction Adaptation for Four-Wheel Independent Drive Electric Vehicle Wed, 22 Oct 2014 00:00:00 +0000 Vehicle velocity and roll angle are important information for active safety control systems of four-wheel independent drive electric vehicle. In order to obtain robustness estimation of vehicle velocity and roll angle, a novel method is proposed based on vehicle dynamics and the measurement information provided by the sensors equipped in modern cars. The method is robust with respect to different road and friction conditions. Firstly, the dynamic characteristics of four-wheel independent drive electric vehicle are analyzed, and a four-degree-of-freedom nonlinear dynamic model of vehicle and a tire longitudinal dynamic equation are established. The relationship between the longitudinal and lateral friction forces is derived based on Dugoff tire model. The unknown input reconstruction technique of sliding mode observer is used to achieve longitudinal tire friction force estimation. A simple observer is designed for the estimation of the roll angle of the vehicle. And then using the relationship, the estimated longitudinal friction forces and roll angle, a sliding mode observer for vehicle velocity estimation is provided, which does not need to know the tire-road friction coefficient and road angles. Finally, the proposed method is evaluated experimentally under a variety of maneuvers and road conditions. Linhui Zhao and Zhiyuan Liu Copyright © 2014 Linhui Zhao and Zhiyuan Liu. All rights reserved. Random Forest Based Coarse Locating and KPCA Feature Extraction for Indoor Positioning System Wed, 22 Oct 2014 00:00:00 +0000 With the fast developing of mobile terminals, positioning techniques based on fingerprinting method draw attention from many researchers even world famous companies. To conquer some shortcomings of the existing fingerprinting systems and further improve the system performance, on the one hand, in the paper, we propose a coarse positioning method based on random forest, which is able to customize several subregions, and classify test point to the region with an outstanding accuracy compared with some typical clustering algorithms. On the other hand, through the mathematical analysis in engineering, the proposed kernel principal component analysis algorithm is applied for radio map processing, which may provide better robustness and adaptability compared with linear feature extraction methods and manifold learning technique. We build both theoretical model and real environment for verifying the feasibility and reliability. The experimental results show that the proposed indoor positioning system could achieve 99% coarse locating accuracy and enhance 15% fine positioning accuracy on average in a strong noisy environment compared with some typical fingerprinting based methods. Yun Mo, Zhongzhao Zhang, Yang Lu, Weixiao Meng, and Gul Agha Copyright © 2014 Yun Mo et al. All rights reserved. Variational Level Set Method for Two-Stage Image Segmentation Based on Morphological Gradients Tue, 21 Oct 2014 13:48:09 +0000 We use variational level set method and transition region extraction techniques to achieve image segmentation task. The proposed scheme is done by two steps. We first develop a novel algorithm to extract transition region based on the morphological gradient. After this, we integrate the transition region into a variational level set framework and develop a novel geometric active contour model, which include an external energy based on transition region and fractional order edge indicator function. The external energy is used to drive the zero level set toward the desired image features, such as object boundaries. Due to this external energy, the proposed model allows for more flexible initialization. The fractional order edge indicator function is incorporated into the length regularization term to diminish the influence of noise. Moreover, internal energy is added into the proposed model to penalize the deviation of the level set function from a signed distance function. The results evolution of the level set function is the gradient flow that minimizes the overall energy functional. The proposed model has been applied to both synthetic and real images with promising results. Zemin Ren Copyright © 2014 Zemin Ren. All rights reserved. An Efficient Approach for Solving Mesh Optimization Problems Using Newton’s Method Tue, 21 Oct 2014 13:45:04 +0000 We present an efficient approach for solving various mesh optimization problems. Our approach is based on Newton’s method, which uses both first-order (gradient) and second-order (Hessian) derivatives of the nonlinear objective function. The volume and surface mesh optimization algorithms are developed such that mesh validity and surface constraints are satisfied. We also propose several Hessian modification methods when the Hessian matrix is not positive definite. We demonstrate our approach by comparing our method with nonlinear conjugate gradient and steepest descent methods in terms of both efficiency and mesh quality. Jibum Kim Copyright © 2014 Jibum Kim. All rights reserved. Three-Dimensional Stability Analysis of a Homogeneous Slope Reinforced with Micropiles Tue, 21 Oct 2014 09:46:33 +0000 Micropiles are widely used to reinforce slopes due to their successful performance and fast construction. In this study, a simple nonlinear method is proposed to analyze the stability of a homogeneous slope reinforced with micropiles. This method is based on shear strength reduction technique, in which the soil behavior is described using the nonassociated Mohr-Coulomb criterion and micropiles are modeled as 3D pile elements. A series of slope stability analyses is performed to investigate the coupled mechanism of micropile system, and the optimum of pile position, depth of embedment, and length of truncation are analyzed. Results show that the position of micropile system plays an important role not only in the calculation of the safety factor, but also in locating the failure surface, which demonstrates the dominating coupled effect exists between micropiles and slope. The critical embedment depth of the micropile is about 2 times the length of micropile above the critical slip surface, and the micropiles flexure rather than rotation becomes increasingly prevalent as the depth of micropiles embedment increases. Truncation of micropiles may improve the capacity of the micropile system, and the largest truncation length of micropile is about 1/4 depth of critical slip surface in this study. Shu-Wei Sun, Wei Wang, and Fu Zhao Copyright © 2014 Shu-Wei Sun et al. All rights reserved. Classification Identification of Acoustic Emission Signals from Underground Metal Mine Rock by ICIMF Classifier Tue, 21 Oct 2014 09:46:15 +0000 To overcome the drawback that fuzzy classifier was sensitive to noises and outliers, Mamdani fuzzy classifier based on improved chaos immune algorithm was developed, in which bilateral Gaussian membership function parameters were set as constraint conditions and the indexes of fuzzy classification effectiveness and number of correct samples of fuzzy classification as the subgoal of fitness function. Moreover, Iris database was used for simulation experiment, classification, and recognition of acoustic emission signals and interference signals from stope wall rock of underground metal mines. The results showed that Mamdani fuzzy classifier based on improved chaos immune algorithm could effectively improve the prediction accuracy of classification of data sets with noises and outliers and the classification accuracy of acoustic emission signal and interference signal from stope wall rock of underground metal mines was 90.00%. It was obvious that the improved chaos immune Mamdani fuzzy (ICIMF) classifier was useful for accurate diagnosis of acoustic emission signal and interference signal from stope wall rock of underground metal mines. Hongyan Zuo, Zhouquan Luo, and Chao Wu Copyright © 2014 Hongyan Zuo et al. All rights reserved. Accurate Object Recognition with Assembling Appearance and Motion Information Tue, 21 Oct 2014 09:25:06 +0000 How to effectively detect object and accurately give out its visible parts is a major challenge for object detection. In this paper we propose an explicit occlusion model through integrating appearance and motion information. The model combines together two parts: part-level object detection with single frame and object occlusion estimation with continuous frames. It breaks through the performance bottleneck caused by lack of information and effectively improves object detection rate under severe occlusion. Through reevaluating the semantic parts, the detecting performance of partial object detectors is largely enhanced. The explicit model enables the partial detectors to have the capability of occlusion estimation. By discarding the geometric representation in rigid single-angle perspective and applying effective pattern of objective shape, our proposed approaches greatly improve the performance and robustness of similarity measurement. For validating the performance of proposed methods, we designed a comparative experiment on challenging pedestrian frame sequences database. The experimental results on challenging pedestrian frame sequence demonstrate that, compared to the traditional algorithms, the methods proposed in this paper have significantly improved the detection rate for severe occlusion. Furthermore, it also can achieve better localization of semantic parts and estimation of occluding. Yongxin Chang, Huapeng Yu, Zhiyong Xu, Jing Zhang, and Chunming Gao Copyright © 2014 Yongxin Chang et al. All rights reserved. A Greedy Multistage Convex Relaxation Algorithm Applied to Structured Group Sparse Reconstruction Problems Based on Iterative Support Detection Tue, 21 Oct 2014 09:02:43 +0000 We propose a new effective algorithm for recovering a group sparse signal from very limited observations or measured data. As we know that a better reconstruction quality can be achieved when encoding more structural information besides sparsity, the commonly employed -regularization incorporating the prior grouping information has a better performance than the plain -regularized models as expected. In this paper we make a further use of the prior grouping information as well as possibly other prior information by considering a weighted model. Specifically, we propose a multistage convex relaxation procedure to alternatively estimate weights and solve the resulted weighted problem. The procedure of estimating weights makes better use of the prior grouping information and is implemented based on the iterative support detection (Wang and Yin, 2010). Comprehensive numerical experiments show that our approach brings significant recovery enhancements compared with the plain model, solved via the alternating direction method (ADM) (Deng et al., 2013), either in noiseless or in noisy environments. Liangtian He and Yilun Wang Copyright © 2014 Liangtian He and Yilun Wang. All rights reserved. Green Clustering Implementation Based on DPS-MOPSO Tue, 21 Oct 2014 06:40:47 +0000 A green clustering implementation is proposed to be as the first method in the framework of an energy-efficient strategy for centralized enterprise high-density WLANs. Traditionally, to maintain the network coverage, all of the APs within the WLAN have to be powered on. Nevertheless, the new algorithm can power off a large proportion of APs while the coverage is maintained as the always-on counterpart. The proposed algorithm is composed of two parallel and concurrent procedures, which are the faster procedure based on -means and the more accurate procedure based on Dynamic Population Size Multiple Objective Particle Swarm Optimization (DPS-MOPSO). To implement green clustering efficiently and accurately, dynamic population size and mutational operators are introduced as complements for the classical MOPSO. In addition to the function of AP selection, the new green clustering algorithm has another new function as the reference and guidance for AP deployment. This paper also presents simulations in scenarios modeled with ray-tracing method and FDTD technique, and the results show that about 67% up to 90% of energy consumption can be saved while the original network coverage is maintained during periods when few users are online or when the traffic load is low. Yang Lu, Xuezhi Tan, Yun Mo, and Lin Ma Copyright © 2014 Yang Lu et al. All rights reserved. Effective Volumetric Feature Modeling and Coarse Correspondence via Improved 3DSIFT and Spectral Matching Mon, 20 Oct 2014 07:03:50 +0000 This paper presents a nonrigid coarse correspondence computation algorithm for volumetric images. Our matching algorithm first extracts then correlates image features based on a revised and improved 3DSIFT (I3DSIFT) algorithm. With a scale-related keypoint reorientation and descriptor construction, this feature correlation is less sensitive to image rotation and scaling. Then, we present an improved spectral matching (ISM) algorithm on correlated features to obtain a one-to-one mapping between corresponded features. One can effectively extend this feature correspondence to dense correspondence between volume images. Our algorithm can benefit nonrigid volumetric image registration in many tasks such as motion modeling in medical image analysis and processing. Peizhi Chen and Xin Li Copyright © 2014 Peizhi Chen and Xin Li. All rights reserved. Multiscale Probability Transformation of Basic Probability Assignment Mon, 20 Oct 2014 06:41:43 +0000 Decision making is still an open issue in the application of Dempster-Shafer evidence theory. A lot of works have been presented for it. In the transferable belief model (TBM), pignistic probabilities based on the basic probability assignments are used for decision making. In this paper, multiscale probability transformation of basic probability assignment based on the belief function and the plausibility function is proposed, which is a generalization of the pignistic probability transformation. In the multiscale probability function, a factor q based on the Tsallis entropy is used to make the multiscale probabilities diversified. An example showing that the multiscale probability transformation is more reasonable in the decision making is given. Meizhu Li, Xi Lu, Qi Zhang, and Yong Deng Copyright © 2014 Meizhu Li et al. All rights reserved. Identification of Nonlinear Dynamic Systems Using Hammerstein-Type Neural Network Sun, 19 Oct 2014 11:14:42 +0000 Hammerstein model has been popularly applied to identify the nonlinear systems. In this paper, a Hammerstein-type neural network (HTNN) is derived to formulate the well-known Hammerstein model. The HTNN consists of a nonlinear static gain in cascade with a linear dynamic part. First, the Lipschitz criterion for order determination is derived. Second, the backpropagation algorithm for updating the network weights is presented, and the stability analysis is also drawn. Finally, simulation results show that HTNN identification approach demonstrated identification performances. Hongshan Yu, Jinzhu Peng, and Yandong Tang Copyright © 2014 Hongshan Yu et al. All rights reserved. Facial Expression Recognition Based on Discriminant Neighborhood Preserving Nonnegative Tensor Factorization and ELM Sun, 19 Oct 2014 11:13:47 +0000 A novel facial expression recognition algorithm based on discriminant neighborhood preserving nonnegative tensor factorization (DNPNTF) and extreme learning machine (ELM) is proposed. A discriminant constraint is adopted according to the manifold learning and graph embedding theory. The constraint is useful to exploit the spatial neighborhood structure and the prior defined discriminant properties. The obtained parts-based representations by our algorithm vary smoothly along the geodesics of the data manifold and have good discriminant property. To guarantee the convergence, the project gradient method is used for optimization. Then features extracted by DNPNTF are fed into ELM which is a training method for the single hidden layer feed-forward networks (SLFNs). Experimental results on JAFFE database and Cohn-Kanade database demonstrate that our proposed algorithm could extract effective features and have good performance in facial expression recognition. Gaoyun An, Shuai Liu, Yi Jin, Qiuqi Ruan, and Shan Lu Copyright © 2014 Gaoyun An et al. All rights reserved.