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  • Multiple Intentional Delays Can Facilitate Fast Consensus and Noise Reduction in a Multiagent System
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-02-21
    Adrián Ramírez; Rifat Sipahi

    We investigate the use of a derivative-free control scheme called the multiple-delay proportional-retarded (PR) protocol to achieve fast consensus in a large-scale multiagent system. The delays are intentionally introduced in the PR with the purpose of creating derivative-like controllers that rely only on position measurements, thus mitigating undesirable noise effects. The main result places the spectral abscissa of the consensus dynamics at a desired locus and also achieves a user-defined spectral ``separation,'' both of which directly influence convergence to consensus. Design rules and limitations arising from analytical derivations to achieve these results are laid out. Case studies are provided to demonstrate the concepts.

    更新日期:2018-02-22
  • Predictor-Based Extended-State-Observer Design for Consensus of MASs With Delays and Disturbances
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-02-14
    Chunyan Wang; Zongyu Zuo; Zhenqiang Qi; Zhengtao Ding

    In this paper, we study output feedback leader-follower consensus problem for multiagent systems subject to external disturbances and time delays in both input and output. First, we consider the linear case and a novel predictor-based extended state observer is designed for each follower with relative output information of the neighboring agents. Then, leader-follower consensus protocols are proposed which can compensate the delays and disturbances efficiently. In particular, the proposed observer and controller do not contain any integral term of the past control input and hence are easy to implement. Consensus analysis is put in the framework of Lyapunov-Krasovskii functionals and sufficient conditions are derived to guarantee that the consensus errors converge to zero asymptotically. Then, the results are extended to nonlinear multiagent systems with nonlinear disturbances. Finally, the validity of the proposed design is demonstrated through a numerical example of network-connected unmanned aerial vehicles.

    更新日期:2018-02-14
  • Multivariate Chaotic Time Series Online Prediction Based on Improved Kernel Recursive Least Squares Algorithm
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-02-14
    Min Han; Shuhui Zhang; Meiling Xu; Tie Qiu; Ning Wang

    Kernel recursive least squares (KRLS) is a kind of kernel methods, which has attracted wide attention in the research of time series online prediction. It has low computational complexity and updates in a recursive form. However, as data size increases, computational complexity of calculating kernel inverse matrix will raise. And it has some difficulties in accommodating time-varying environments. Therefore, we have presented an improved KRLS algorithm for multivariate chaotic time series online prediction. Approximate linear dependency, dynamic adjustment, and coherence criterion are combined with quantization to form our improved KRLS algorithm. In the process of online prediction, it can bring computational efficiency up and adjust weights adaptively in time-varying environments. Moreover, Lorenz chaotic time series, El Nino-Southern Oscillation indexes chaotic time series, yearly sunspots and runoff of the Yellow River chaotic time series online prediction are presented to prove the effectiveness of our proposed algorithm.

    更新日期:2018-02-14
  • Opinion Dynamics in the Presence of Increasing Agreement Pressure
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-02-13
    Justin Semonsen; Christopher Griffin; Anna Squicciarini; Sarah Rajtmajer

    In this paper, we study a model of agent consensus in a social network in the presence increasing interagent influence, i.e., increasing peer pressure. Each agent in the social network has a distinct social stress function given by a weighted sum of internal and external behavioral pressures. We assume a weighted average update rule consistent with the classic DeGroot model and prove conditions under which a connected group of agents converge to a fixed opinion distribution, and under which conditions the group reaches consensus. We show that the update rule converges to gradient descent and explain its transient and asymptotic convergence properties. Through simulation, we study the rate of convergence on a scale-free network.

    更新日期:2018-02-14
  • Prescribed Performance for Bipartite Tracking Control of Nonlinear Multiagent Systems With Hysteresis Input Uncertainties
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-02-13
    Tao Yu; Lei Ma; Hongwei Zhang

    This paper studies bipartite tracking problem of nonlinear multiagent systems over signed directed graphs. Each following agent is modeled by a higher-order nonlinear system in strict-feedback form with unknown dynamics and hysteresis input uncertainty. Both distributed state feedback and output feedback control laws are proposed to achieve bipartite tracking confined by the prescribed performance bounds. The proposed approximation-free distributed controllers only utilize error variables incorporating with performance bound functions, which lead to a low-complexity control algorithm. Moreover, the proposed control laws guarantee that all signals of the closed-loop system are uniformly ultimately bounded.

    更新日期:2018-02-14
  • User Participation in Collaborative Filtering-Based Recommendation Systems: A Game Theoretic Approach
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-02-13
    Lei Xu; Chunxiao Jiang; Yan Chen; Yong Ren; K. J. Ray Liu

    Collaborative filtering is widely used in recommendation systems. A user can get high-quality recommendations only when both the user himself/herself and other users actively participate, i.e., provide sufficient ratings. However, due to the rating cost, rational users tend to provide as few ratings as possible. Therefore, there exists a tradeoff between the rating cost and the recommendation quality. In this paper, we model the interactions among users as a game in satisfaction form and study the corresponding equilibrium, namely satisfaction equilibrium (SE). Considering that accumulated ratings are used for generating recommendations, we design a behavior rule which allows users to achieve an SE via iteratively rating items. We theoretically analyze under what conditions an SE can be learned via the behavior rule. Experimental results on Jester and MovieLens data sets confirm the analysis and demonstrate that, if all users have moderate expectations for recommendation quality and satisfied users are willing to provide more ratings, then all users can get satisfying recommendations without providing many ratings. The SE analysis of the proposed game in this paper is helpful for designing mechanisms to encourage user participation.

    更新日期:2018-02-14
  • A Hybrid Intelligent Approach for Co-Scheduling of Cascaded Locks With Multiple Chambers
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-02-12
    Bin Ji; Xiaohui Yuan; Yanbin Yuan

    A complex and typical scheduling problem in waterway transportation: co-scheduling of cascaded locks with multiple chambers (CCLM) is studied. Based on in-depth analysis of the problem properties, the CCLM is handled by separating it into three interconnected subproblems, each with a simpler structure and higher flexibility to be handled. The outer layer and inner layer concerns the sum of lockage number and ship placement, respectively. The interlayer as a connection bridge between the other two refers to lockage direction combination and timetable optimization which is a high-dimensional mixed integer optimization problem. To solve the CCLM problem, a hybrid approach based on iteration which mainly combines quantum inspired binary gravitational search algorithm and modified moth-flame optimization algorithm is proposed. In addition, two different scheduling rules which are usually concerned in practice, the area utilization maximization and first-come-first-served (FCFS) rule, are also tested in the CCLM problem. Experiments are conducted on instances that are extracted from real world data. The scheduling and comparison results verify that the CCLM problem can be well handled by the proposed method.

    更新日期:2018-02-13
  • Analysis and Pinning Control for Output Synchronization and H∞ Output Synchronization of Multiweighted Complex Networks
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-02-12
    Jin-Liang Wang; Zhen Qin; Huai-Ning Wu; Tingwen Huang; Pu-Chong Wei

    The output synchronization and H∞ output synchronization problems for multiweighted complex network are discussed in this paper. First, we analyze the output synchronization of multiweighted complex network by exploiting Lyapunov functional and Barbalat's lemma. In addition, some nodes- and edges-based pinning control strategies are developed to ensure the output synchronization of multiweighted complex network. Similarly, the H∞ output synchronization problem of multiweighted complex network is also discussed. Finally, two numerical examples are presented to verify the correctness of the obtained results.

    更新日期:2018-02-13
  • Neural Networks-Based Adaptive Control for Nonlinear State Constrained Systems With Input Delay
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-02-12
    Da-Peng Li; Yan-Jun Liu; Shaocheng Tong; C. L. Philip Chen; Dong-Juan Li

    This paper addresses the problem of adaptive tracking control for a class of strict-feedback nonlinear state constrained systems with input delay. To alleviate the major challenges caused by the appearances of full state constraints and input delay, an appropriate barrier Lyapunov function and an opportune backstepping design are used to avoid the constraint violation, and the Pade approximation and an intermediate variable are employed to eliminate the effect of the input delay. Neural networks are employed to estimate unknown functions in the design procedure. It is proven that the closed-loop signals are semiglobal uniformly ultimately bounded, and the tracking error converges to a compact set of the origin, as well as the states remain within a bounded interval. The simulation studies are given to illustrate the effectiveness of the proposed control strategy in this paper.

    更新日期:2018-02-13
  • Decentralized Fault Prognosis of Discrete-Event Systems Using State-Estimate-Based Protocols
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-02-12
    Xiang Yin; Zhaojian Li

    We investigate the problem of decentralized fault prognosis in the context of discrete-event systems. In this problem, the system is monitored by a set of local agents; each of them sends its local information to a coordinator in order to issue a fault alarm before the occurrence of fault. Two new decentralized protocols are proposed by exploiting the state-estimate of each local agent. For each protocol, a necessary and sufficient condition for its correctness is proposed; they are termed as positive state-estimate-prognosability and negative state-estimate prognosability. Verification algorithms for the necessary and sufficient conditions are also provided. We show that the proposed new protocols are incomparable with any of the existing protocols in the literature. Therefore, they provide new opportunities for correctly predicting the fault when all existing protocols fail.

    更新日期:2018-02-13
  • Generalized State Estimation for Markovian Coupled Networks Under Round-Robin Protocol and Redundant Channels
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-02-12
    Hao Shen; Shicheng Huo; Jinde Cao; Tingwen Huang

    In this paper, the problem of generalized state estimation for an array of Markovian coupled networks under the round-Robin protocol (RRP) and redundant channels is investigated by using an extended dissipative property. The randomly varying coupling of the networks under consideration is governed by a Markov chain. With the aid of using the RRP, the transmission order of nodes is availably orchestrated. In this case, the probability of occurrence data collisions through a shared constrained network may be reduced. The redundant channels are also used in the signal transmission to deal with the frangibility of networks caused by a single channel in the networks. The network induced phenomena, that is, randomly occurring packet dropouts and randomly occurring quantization are fully considered. The main purpose of the research is to find a desired estimator design approach such that the extended (Ω₁,Ω₂,Ω₃) - ɣ-stochastic dissipativity property of the estimation error system is guaranteed. In terms of the Lyapunov-Krasovskii methodology, the Kronecker product and an improved matrix decoupling approach, sufficient conditions for such an addressed problem are established by means of handling some convex optimization problems. Finally, the serviceability of the proposed method is explained by providing an illustrated example.

    更新日期:2018-02-13
  • Nonrigid Point Set Registration by Preserving Local Connectivity
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2017-08-03
    Lifei Bai; Xianqiang Yang; Huijun Gao

    This paper is concerned with the nonrigid point set registration problem and a probability-based registration algorithm with local connectivity preservation is proposed. A unified formulation for point set registration problem is introduced and the derived energy function is composed of three parts, distance measurement item, transformation constraint item, and correspondence constraint item. In order to preserve the local structure of point set, the definitions of ${k}$ -connected neighbors and connectivity matrix are given and the local connectivity constraint is constructed as a weighted least square error item. The point set registration problem is formulated in the expectation-maximization algorithm scheme and the optimal spatial transformation and correspondence matrix are estimated simultaneously. The effectiveness of the proposed method is verified by applying the method to synthetic point sets and real scenarios of hand shapes and surface-mount technology components.

    更新日期:2018-02-10
  • R₁-2-DPCA and Face Recognition
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 
    Quanxue Gao; Sai Xu; Fang Chen; Chris Ding; Xinbo Gao; Yunsong Li

    2-D principal component analysis (2-DPCA) is one of the successful dimensionality reduction approaches for image classification and representation. However, 2-DPCA is not robust to outliers. To tackle this problem, we present an efficient robust method, namely R₁-2-DPCA for feature extraction. R₁-2-DPCA aims to seek the projection matrix such that the projected data have the maximum variance, which is measured by R₁-norm. Compared with most existing robust 2-DPCA methods, our model is not only robust to outliers but also helps encode discriminant information. Accordingly, we develop a nongreedy iterative algorithm, which has not only a closed-form solution in each iteration but also a good convergence, to solve our model. Moreover, to further improve classification performance, we employ nuclear norm as the distance metric in the classification phase. Extensive experiments on several face databases illustrate that our proposed method is superior to most existing robust 2-DPCA methods.

    更新日期:2018-02-09
  • 3-D Fully Convolutional Networks for Multimodal Isointense Infant Brain Image Segmentation
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 
    Dong Nie; Li Wang; Ehsan Adeli; Cuijin Lao; Weili Lin; Dinggang Shen

    Accurate segmentation of infant brain images into different regions of interest is one of the most important fundamental steps in studying early brain development. In the isointense phase (approximately 6-8 months of age), white matter and gray matter exhibit similar levels of intensities in magnetic resonance (MR) images, due to the ongoing myelination and maturation. This results in extremely low tissue contrast and thus makes tissue segmentation very challenging. Existing methods for tissue segmentation in this isointense phase usually employ patch-based sparse labeling on single modality. To address the challenge, we propose a novel 3-D multimodal fully convolutional network (FCN) architecture for segmentation of isointense phase brain MR images. Specifically, we extend the conventional FCN architectures from 2-D to 3-D, and, rather than directly using FCN, we intuitively integrate coarse (naturally high-resolution) and dense (highly semantic) feature maps to better model tiny tissue regions, in addition, we further propose a transformation module to better connect the aggregating layers; we also propose a fusion module to better serve the fusion of feature maps. We compare the performance of our approach with several baseline and state-of-the-art methods on two sets of isointense phase brain images. The comparison results show that our proposed 3-D multimodal FCN model outperforms all previous methods by a large margin in terms of segmentation accuracy. In addition, the proposed framework also achieves faster segmentation results compared to all other methods. Our experiments further demonstrate that: 1) carefully integrating coarse and dense feature maps can considerably improve the segmentation performance; 2) batch normalization can speed up the convergence of the networks, especially when hierarchical feature aggregations occur; and 3) integrating multimodal information can further boost the segmentation performance.

    更新日期:2018-02-09
  • Nyström Approximated Temporally Constrained Multisimilarity Spectral Clustering Approach for Movie Scene Detection
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2017-02-07
    Rameswar Panda; Sanjay K. Kuanar; Ananda S. Chowdhury

    Movie scene detection has emerged as an important problem in present day multimedia applications. Since a movie typically consists of huge amount of video data with widespread content variations, detecting a movie scene has become extremely challenging. In this paper, we propose a fast yet accurate solution for movie scene detection using Nyström approximated multisimilarity spectral clustering with a temporal integrity constraint. We use multiple similarity matrices to model the wide content variations typically present in any movie dataset. Nyström approximation is employed to reduce the high computational cost of constructing multiple similarity measures. The temporal integrity constraint captures the inherent temporal cohesion of the movie shots. Experiments on five movie datasets from different genres clearly demonstrate the superiority of the proposed solution over the state-of-the-art methods.

    更新日期:2018-02-08
  • Degeneration Recognizing Clonal Selection Algorithm for Multimodal Optimization
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2017-02-09
    Nan Xu; Yongsheng Ding; Lihong Ren; Kuangrong Hao

    In this paper, a computing speed improvement for the clonal selection algorithm (CSA) is proposed based on a degeneration recognizing (DR) method. The degeneration recognizing clonal selection algorithm (DR-CSA) is designed for solving complex engineering multimodal optimization problems. On each iteration of CSA, there is a large amount of eliminated solutions which are usually neglected. But these solutions do contain the knowledge of the nonoptimal area. By storing and utilizing these data, the DR-CSA is aimed to identify part of the new population as degenerated and eliminate them before the evaluation operation, so that a number of evaluation times can be avoided. This pre-elimination operation is able to save computing time because the evaluation is the main reason for the time cost in the complex engineering optimization problem. Experiments on both test function and a real-world engineering optimization problem (wet spinning coagulating process) are conducted. The results show that the proposed DR-CSA is as accurate as regular CSA and is effective in reducing a considerable amount of computing time.

    更新日期:2018-02-08
  • Object Discovery via Cohesion Measurement
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2017-02-16
    Guanjun Guo; Hanzi Wang; Wan-Lei Zhao; Yan Yan; Xuelong Li

    Color and intensity are two important components in an image. Usually, groups of image pixels, which are similar in color or intensity, are an informative representation for an object. They are therefore particularly suitable for computer vision tasks, such as saliency detection and object proposal generation. However, image pixels, which share a similar real-world color, may be quite different since colors are often distorted by intensity. In this paper, we reinvestigate the affinity matrices originally used in image segmentation methods based on spectral clustering. A new affinity matrix, which is robust to color distortions, is formulated for object discovery. Moreover, a cohesion measurement (CM) for object regions is also derived based on the formulated affinity matrix. Based on the new CM, a novel object discovery method is proposed to discover objects latent in an image by utilizing the eigenvectors of the affinity matrix. Then we apply the proposed method to both saliency detection and object proposal generation. Experimental results on several evaluation benchmarks demonstrate that the proposed CM-based method has achieved promising performance for these two tasks.

    更新日期:2018-02-08
  • Joint Feature Selection and Classification for Multilabel Learning
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2017-02-14
    Jun Huang; Guorong Li; Qingming Huang; Xindong Wu

    Multilabel learning deals with examples having multiple class labels simultaneously. It has been applied to a variety of applications, such as text categorization and image annotation. A large number of algorithms have been proposed for multilabel learning, most of which concentrate on multilabel classification problems and only a few of them are feature selection algorithms. Current multilabel classification models are mainly built on a single data representation composed of all the features which are shared by all the class labels. Since each class label might be decided by some specific features of its own, and the problems of classification and feature selection are often addressed independently, in this paper, we propose a novel method which can perform joint feature selection and classification for multilabel learning, named JFSC. Different from many existing methods, JFSC learns both shared features and label-specific features by considering pairwise label correlations, and builds the multilabel classifier on the learned low-dimensional data representations simultaneously. A comparative study with state-of-the-art approaches manifests a competitive performance of our proposed method both in classification and feature selection for multilabel learning.

    更新日期:2018-02-08
  • Fast Variable Structure Stochastic Automaton for Discovering and Tracking Spatiotemporal Event Patterns
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2017-04-05
    Junqi Zhang; Yuheng Wang; Cheng Wang; MengChu Zhou

    Discovering and tracking spatiotemporal event patterns have many applications. For example, in a smart-home project, a set of spatiotemporal pattern learning automata are used to monitor a user’s repetitive activities, by which the home’s automaticity can be promoted while some of his/her burdens can be reduced. Existing algorithms for spatiotemporal event pattern recognition in dynamic noisy environment are based on fixed structure stochastic automata whose state transition function is fixed and predesigned to guarantee their immunity to noise. However, such design is conservative because it needs continuous and identical feedbacks to converge, thus leading to its very low convergence rate. In many real-life applications, such as ambient assisted living, consecutive nonoccurrences of an elder resident’s routine activities should be treated with an alert as quickly as possible. On the other hand, no alert should be output even for some occurrences in order to diminish the effects caused by noise. Clearly, confronting a pattern’s change, slow speed and low accuracy may degrade a user’s life security. This paper proposes a fast and accurate leaning automaton based on variable structure stochastic automata to satisfy the realistic requirements for both speed and accuracy. Bias toward alert is necessary for elder residents while the existing method can only support the bias toward “no alert.” This paper introduces a method to allow bias toward alert or no alert to meet a user’s specific bias requirement. Experimental results show its better performance than the state-of-the-art methods.

    更新日期:2018-02-08
  • Compositional Model-Based Sketch Generator in Facial Entertainment
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2017-02-14
    Mingjin Zhang; Jie Li; Nannan Wang; Xinbo Gao

    Face sketch synthesis (FSS) plays an important role in facial entertainment, which includes face sketch morphing among two styles, multiview FSS and face sketch expression manipulation. For facial entertainment, most existing FSS methods generate sketches with over-smoothing effects, i.e., fine details are suppressed more or less. In this paper, we propose a face sketch generator based on the compositional model to handle this issue. It decomposes a face into different components instead of patches as before, and each component has several candidate templates. Multilevel B-spline approximation is utilized to delicately polish the chosen templates of all components. To fuse these components, Poisson blending is employed instead of the weighted average operator. The proposed compositional method crucially reduces the high frequency loss and improves the synthesis performance in comparison to the state-of-the-art methods. Experiments on face sketch morphing, expression manipulation, and multiview FSS, make further efforts to demonstrate the effectiveness of the proposed method.

    更新日期:2018-02-08
  • View-Based 3-D Model Retrieval: A Benchmark
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2017-02-15
    An-An Liu; Wei-Zhi Nie; Yue Gao; Yu-Ting Su

    View-based 3-D model retrieval is one of the most important techniques in numerous applications of computer vision. While many methods have been proposed in recent years, to the best of our knowledge, there is no benchmark to evaluate the state-of-the-art methods. To tackle this problem, we systematically investigate and evaluate the related methods by: 1) proposing a clique graph-based method and 2) reimplementing six representative methods. Moreover, we concurrently evaluate both hand-crafted visual features and deep features on four popular datasets (NTU60, NTU216, PSB, and ETH) and one challenging real-world multiview model dataset (MV-RED) prepared by our group with various evaluation criteria to understand how these algorithms perform. By quantitatively analyzing the performances, we discover the graph matching-based method with deep features, especially the clique graph matching algorithm with convolutional neural networks features, can usually outperform the others. We further discuss the future research directions in this field.

    更新日期:2018-02-08
  • A Generic Deep-Learning-Based Approach for Automated Surface Inspection
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2017-02-24
    Ruoxu Ren; Terence Hung; Kay Chen Tan

    Automated surface inspection (ASI) is a challenging task in industry, as collecting training dataset is usually costly and related methods are highly dataset-dependent. In this paper, a generic approach that requires small training data for ASI is proposed. First, this approach builds classifier on the features of image patches, where the features are transferred from a pretrained deep learning network. Next, pixel-wise prediction is obtained by convolving the trained classifier over input image. An experiment on three public and one industrial data set is carried out. The experiment involves two tasks: 1) image classification and 2) defect segmentation. The results of proposed algorithm are compared against several best benchmarks in literature. In the classification tasks, the proposed method improves accuracy by 0.66%–25.50%. In the segmentation tasks, the proposed method reduces error escape rates by 6.00%–19.00% in three defect types and improves accuracies by 2.29%–9.86% in all seven defect types. In addition, the proposed method achieves 0.0% error escape rate in the segmentation task of industrial data.

    更新日期:2018-02-08
  • Distributed Coordination for Optimal Energy Generation and Distribution in Cyber-Physical Energy Networks
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2017-02-23
    Hyo-Sung Ahn; Byeong-Yeon Kim; Young-Hun Lim; Byung-Hun Lee; Kwang-Kyo Oh

    This paper proposes three coordination laws for optimal energy generation and distribution in energy network, which is composed of physical flow layer and cyber communication layer. The physical energy flows through the physical layer; but all the energies are coordinated to generate and flow by distributed coordination algorithms on the basis of communication information. First, distributed energy generation and energy distribution laws are proposed in a decoupled manner without considering the interactive characteristics between the energy generation and energy distribution. Second, a joint coordination law to treat the energy generation and energy distribution in a coupled manner taking account of the interactive characteristics is designed. Third, to handle over- or less-energy generation cases, an energy distribution law for networks with batteries is designed. The coordination laws proposed in this paper are fully distributed in the sense that they are decided optimally only using relative information among neighboring nodes. Through numerical simulations, the validity of the proposed distributed coordination laws is illustrated.

    更新日期:2018-02-08
  • Greedy Criterion in Orthogonal Greedy Learning
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2017-02-23
    Lin Xu; Shaobo Lin; Jinshan Zeng; Xia Liu; Yi Fang; Zongben Xu

    Orthogonal greedy learning (OGL) is a stepwise learning scheme that starts with selecting a new atom from a specified dictionary via the steepest gradient descent (SGD) and then builds the estimator through orthogonal projection. In this paper, we found that SGD is not the unique greedy criterion and introduced a new greedy criterion, called as “ $ {\delta }$ -greedy threshold” for learning. Based on this new greedy criterion, we derived a straightforward termination rule for OGL. Our theoretical study shows that the new learning scheme can achieve the existing (almost) optimal learning rate of OGL. Numerical experiments are also provided to support that this new scheme can achieve almost optimal generalization performance while requiring less computation than OGL.

    更新日期:2018-02-08
  • A Regularization Approach for Instance-Based Superset Label Learning
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2017-02-24
    Chen Gong; Tongliang Liu; Yuanyan Tang; Jian Yang; Jie Yang; Dacheng Tao

    Different from the traditional supervised learning in which each training example has only one explicit label, superset label learning (SLL) refers to the problem that a training example can be associated with a set of candidate labels, and only one of them is correct. Existing SLL methods are either regularization-based or instance-based, and the latter of which has achieved state-of-the-art performance. This is because the latest instance-based methods contain an explicit disambiguation operation that accurately picks up the groundtruth label of each training example from its ambiguous candidate labels. However, such disambiguation operation does not fully consider the mutually exclusive relationship among different candidate labels, so the disambiguated labels are usually generated in a nondiscriminative way, which is unfavorable for the instance-based methods to obtain satisfactory performance. To address this defect, we develop a novel regularization approach for instance-based superset label (RegISL) learning so that our instance-based method also inherits the good discriminative ability possessed by the regularization scheme. Specifically, we employ a graph to represent the training set, and require the examples that are adjacent on the graph to obtain similar labels. More importantly, a discrimination term is proposed to enlarge the gap of values between possible labels and unlikely labels for every training example. As a result, the intrinsic constraints among different candidate labels are deployed, and the disambiguated labels generated by RegISL are more discriminative and accurate than those output by existing instance-based algorithms. The experimental results on various tasks convincingly demonstrate the superiority of our RegISL to other typical SLL methods in terms of both training accuracy and test accuracy.

    更新日期:2018-02-08
  • A Self-Adaptive Sleep/Wake-Up Scheduling Approach for Wireless Sensor Networks
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2017-03-03
    Dayong Ye; Minjie Zhang au

    Sleep/wake-up scheduling is one of the fundamental problems in wireless sensor networks, since the energy of sensor nodes is limited and they are usually unrechargeable. The purpose of sleep/wake-up scheduling is to save the energy of each node by keeping nodes in sleep mode as long as possible (without sacrificing packet delivery efficiency) and thereby maximizing their lifetime. In this paper, a self-adaptive sleep/wake-up scheduling approach is proposed. Unlike most existing studies that use the duty cycling technique, which incurs a tradeoff between packet delivery delay and energy saving, the proposed approach, which does not us duty cycling, avoids such a tradeoff. The proposed approach, based on the reinforcement learning technique, enables each node to autonomously decide its own operation mode (sleep, listen, or transmission) in each time slot in a decentralized manner. Simulation results demonstrate the good performance of the proposed approach in various circumstances.

    更新日期:2018-02-08
  • Optimizing Evaluation Metrics for Multitask Learning via the Alternating Direction Method of Multipliers
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2017-02-27
    Ge-Yang Ke; Yan Pan; Jian Yin; Chang-Qin Huang

    Multitask learning (MTL) aims to improve the generalization performance of multiple tasks by exploiting the shared factors among them. Various metrics (e.g., $F$ -score, area under the ROC curve) are used to evaluate the performances of MTL methods. Most existing MTL methods try to minimize either the misclassified errors for classification or the mean squared errors for regression. In this paper, we propose a method to directly optimize the evaluation metrics for a large family of MTL problems. The formulation of MTL that directly optimizes evaluation metrics is the combination of two parts: 1) a regularizer defined on the weight matrix over all tasks, in order to capture the relatedness of these tasks and 2) a sum of multiple structured hinge losses, each corresponding to a surrogate of some evaluation metric on one task. This formulation is challenging in optimization because both of its parts are nonsmooth. To tackle this issue, we propose a novel optimization procedure based on the alternating direction scheme of multipliers, where we decompose the whole optimization problem into a subproblem corresponding to the regularizer and another subproblem corresponding to the structured hinge losses. For a large family of MTL problems, the first subproblem has closed-form solutions. To solve the second subproblem, we propose an efficient primal-dual algorithm via coordinate ascent. Extensive evaluation results demonstrate that, in a large family of MTL problems, the proposed MTL method of directly optimization evaluation metrics has superior performance gains against the corresponding baseline methods.

    更新日期:2018-02-08
  • Event-Based Variance-Constrained ${\mathcal {H}}_{\infty }$ Filtering for Stochastic Parameter Systems Over Sensor Networks With Successive Missing Measurements
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2017-03-06
    Licheng Wang; Zidong Wang; Qing-Long Han; Guoliang Wei

    This paper is concerned with the distributed ${\mathcal {H}}_{\infty }$ filtering problem for a class of discrete time-varying stochastic parameter systems with error variance constraints over a sensor network where the sensor outputs are subject to successive missing measurements. The phenomenon of the successive missing measurements for each sensor is modeled via a sequence of mutually independent random variables obeying the Bernoulli binary distribution law. To reduce the frequency of unnecessary data transmission and alleviate the communication burden, an event-triggered mechanism is introduced for the sensor node such that only some vitally important data is transmitted to its neighboring sensors when specific events occur. The objective of the problem addressed is to design a time-varying filter such that both the ${\mathcal {H}}_{\infty }$ requirements and the variance constraints are guaranteed over a given finite-horizon against the random parameter matrices, successive missing measurements, and stochastic noises. By recurring to stochastic analysis techniques, sufficient conditions are established to ensure the existence of the time-varying filters whose gain matrices are then explicitly characterized in term of the solutions to a series of recursive matrix inequalities. A numerical simulation example is provided to illustrate the effectiveness of the developed event-triggered distributed filter design strategy.

    更新日期:2018-02-08
  • Design and Validation of a Virtual Player for Studying Interpersonal Coordination in the Mirror Game
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2017-03-07
    Chao Zhai; Francesco Alderisio; Piotr Słowiński; Krasimira Tsaneva-Atanasova; Mario di Bernardo

    The mirror game has been recently proposed as a simple, yet powerful paradigm for studying interpersonal interactions. It has been suggested that a virtual partner able to play the game with human subjects can be an effective tool to affect the underlying neural processes needed to establish the necessary connections between the players, and also to provide new clinical interventions for rehabilitation of patients suffering from social disorders. Inspired by the motor processes of the central nervous system (CNS) and the musculoskeletal system in the human body, in this paper we develop a novel interactive cognitive architecture based on nonlinear control theory to drive a virtual player (VP) to play the mirror game with a human player (HP) in different configurations. Specifically, we consider two cases: 1) the VP acts as leader and 2) the VP acts as follower. The crucial problem is to design a feedback control architecture capable of imitating and following or leading an HP in a joint action task. The movement of the end-effector of the VP is modeled by means of a feedback controlled Haken–Kelso–Bunz (HKB) oscillator, which is coupled with the observed motion of the HP measured in real time. To this aim, two types of control algorithms (adaptive control and optimal control) are used and implemented on the HKB model so that the VP can generate a human-like motion while satisfying certain kinematic constraints. A proof of convergence of the control algorithms is presented together with an extensive numerical and experimental validation of their effectiveness. A comparison with other existing designs is also discussed, showing the flexibility and the advantages of our control-based approach.

    更新日期:2018-02-08
  • Constrained Superpixel Tracking
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2017-03-09
    Lijun Wang; Huchuan Lu; Ming-Hsuan Yang

    In this paper, we propose a constrained graph labeling algorithm for visual tracking where nodes denote superpixels and edges encode the underlying spatial, temporal, and appearance fitness constraints. First, the spatial smoothness constraint, based on a transductive learning method, is enforced to leverage the latent manifold structure in feature space by investigating unlabeled superpixels in the current frame. Second, the appearance fitness constraint, which measures the probability of a superpixel being contained in the target region, is developed to incrementally induce a long-term appearance model. Third, the temporal smoothness constraint is proposed to construct a short-term appearance model of the target, which handles the drastic appearance change between consecutive frames. All these three constraints are incorporated in the proposed graph labeling algorithm such that induction and transduction, short- and long-term appearance models are combined, respectively. The foreground regions inferred by the proposed graph labeling method are used to guide the tracking process. Tracking results, in turn, facilitate more accurate online update by filtering out potential contaminated training samples. Both quantitative and qualitative evaluations on challenging tracking data sets show that the proposed constrained tracking algorithm performs favorably against the state-of-the-art methods.

    更新日期:2018-02-08
  • The Overcomplete Dictionary-Based Directional Estimation Model and Nonconvex Reconstruction Methods
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2017-03-10
    Leping Lin; Fang Liu; Licheng Jiao; Shuyuan Yang; Hongxia Hao

    In this paper, it is proposed the directional estimation model on the overcomplete dictionary, which bridges the compressed measurements of the image blocks and the directional structures of the dictionary. In the model, it is established the analytical method to estimate the structure type of a block as either smooth, single-oriented, or multioriented. Furthermore, the structures of each type of blocks are described by the structured subdictionaries. Then based on the obtained estimations and the constrains on the sparse dictionaries, the original image will be estimated. To verify the model, the nonconvex methods are designed for compressed sensing. Specifically, the greedy pursuit-based methods are established to search the subdictionaries obtained by the model, which achieve better local structural estimation than the methods without the directional estimation. More importantly, it is proposed the nonconvex image reconstruction method with direction-guided dictionaries and evolutionary searching strategies (NR_DG), where the evolutionary searching strategies are delicately designed for each type of the blocks based on the directional estimation. By the experimental results, it is shown that the NR_DG method performs better than the available two-stage evolutionary reconstruction method.

    更新日期:2018-02-08
  • Denoising Hyperspectral Image With Non-i.i.d. Noise Structure
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2017-07-27
    Yang Chen; Xiangyong Cao; Qian Zhao; Deyu Meng; Zongben Xu

    Hyperspectral image (HSI) denoising has been attracting much research attention in remote sensing area due to its importance in improving the HSI qualities. The existing HSI denoising methods mainly focus on specific spectral and spatial prior knowledge in HSIs, and share a common underlying assumption that the embedded noise in HSI is independent and identically distributed (i.i.d.). In real scenarios, however, the noise existed in a natural HSI is always with much more complicated non-i.i.d. statistical structures and the under-estimation to this noise complexity often tends to evidently degenerate the robustness of current methods. To alleviate this issue, this paper attempts the first effort to model the HSI noise using a non-i.i.d. mixture of Gaussians (NMoGs) noise assumption, which finely accords with the noise characteristics possessed by a natural HSI and thus is capable of adapting various practical noise shapes. Then we integrate such noise modeling strategy into the low-rank matrix factorization (LRMF) model and propose an NMoG-LRMF model in the Bayesian framework. A variational Bayes algorithm is then designed to infer the posterior of the proposed model. As substantiated by our experiments implemented on synthetic and real noisy HSIs, the proposed method performs more robust beyond the state-of-the-arts.

    更新日期:2018-02-08
  • Active Learning of Regular Expressions for Entity Extraction
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2017-03-24
    Alberto Bartoli; Andrea De Lorenzo; Eric Medvet; Fabiano Tarlao

    We consider the automatic synthesis of an entity extractor, in the form of a regular expression , from examples of the desired extractions in an unstructured text stream. This is a long-standing problem for which many different approaches have been proposed, which all require the preliminary construction of a large dataset fully annotated by the user. In this paper, we propose an active learning approach aimed at minimizing the user annotation effort: the user annotates only one desired extraction and then merely answers extraction queries generated by the system. During the learning process, the system digs into the input text for selecting the most appropriate extraction query to be submitted to the user in order to improve the current extractor. We construct candidate solutions with genetic programming (GP) and select queries with a form of querying-by-committee, i.e., based on a measure of disagreement within the best candidate solutions. All the components of our system are carefully tailored to the peculiarities of active learning with GP and of entity extraction from unstructured text. We evaluate our proposal in depth, on a number of challenging datasets and based on a realistic estimate of the user effort involved in answering each single query. The results demonstrate high accuracy with significant savings in terms of computational effort, annotated characters, and execution time over a state-of-the-art baseline.

    更新日期:2018-02-08
  • Semantically Enhanced Online Configuration of Feedback Control Schemes
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2017-03-31
    Georgios M. Milis; Christos G. Panayiotou; Marios M. Polycarpou

    Recent progress toward the realization of the “Internet of Things” has improved the ability of physical and soft/cyber entities to operate effectively within large-scale, heterogeneous systems. It is important that such capacity be accompanied by feedback control capabilities sufficient to ensure that the overall systems behave according to their specifications and meet their functional objectives. To achieve this, such systems require new architectures that facilitate the online deployment, composition, interoperability, and scalability of control system components. Most current control systems lack scalability and interoperability because their design is based on a fixed configuration of specific components, with knowledge of their individual characteristics only implicitly passed through the design. This paper addresses the need for flexibility when replacing components or installing new components, which might occur when an existing component is upgraded or when a new application requires a new component, without the need to readjust or redesign the overall system. A semantically enhanced feedback control architecture is introduced for a class of systems, aimed at accommodating new components into a closed-loop control framework by exploiting the semantic inference capabilities of an ontology-based knowledge model. This architecture supports continuous operation of the control system, a crucial property for large-scale systems for which interruptions have negative impact on key performance metrics that may include human comfort and welfare or economy costs. A case-study example from the smart buildings domain is used to illustrate the proposed architecture and semantic inference mechanisms.

    更新日期:2018-02-08
  • Body Joint Guided 3-D Deep Convolutional Descriptors for Action Recognition
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2017-12-07
    Congqi Cao; Yifan Zhang; Chunjie Zhang; Hanqing Lu

    3-D convolutional neural networks (3-D CNNs) have been established as a powerful tool to simultaneously learn features from both spatial and temporal dimensions, which is suitable to be applied to video-based action recognition. In this paper, we propose not to directly use the activations of fully connected layers of a 3-D CNN as the video feature, but to use selective convolutional layer activations to form a discriminative descriptor for video. It pools the feature on the convolutional layers under the guidance of body joint positions. Two schemes of mapping body joints into convolutional feature maps for pooling are discussed. The body joint positions can be obtained from any off-the-shelf skeleton estimation algorithm. The helpfulness of the body joint guided feature pooling with inaccurate skeleton estimation is systematically evaluated. To make it end-to-end and do not rely on any sophisticated body joint detection algorithm, we further propose a two-stream bilinear model which can learn the guidance from the body joints and capture the spatio-temporal features simultaneously. In this model, the body joint guided feature pooling is conveniently formulated as a bilinear product operation. Experimental results on three real-world datasets demonstrate the effectiveness of body joint guided pooling which achieves promising performance.

    更新日期:2018-02-08
  • EmotionMeter: A Multimodal Framework for Recognizing Human Emotions
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-02-07
    Wei-Long Zheng; Wei Liu; Yifei Lu; Bao-Liang Lu; Andrzej Cichocki

    In this paper, we present a multimodal emotion recognition framework called EmotionMeter that combines brain waves and eye movements. To increase the feasibility and wearability of EmotionMeter in real-world applications, we design a six-electrode placement above the ears to collect electroencephalography (EEG) signals. We combine EEG and eye movements for integrating the internal cognitive states and external subconscious behaviors of users to improve the recognition accuracy of EmotionMeter. The experimental results demonstrate that modality fusion with multimodal deep neural networks can significantly enhance the performance compared with a single modality, and the best mean accuracy of 85.11% is achieved for four emotions (happy, sad, fear, and neutral). We explore the complementary characteristics of EEG and eye movements for their representational capacities and identify that EEG has the advantage of classifying happy emotion, whereas eye movements outperform EEG in recognizing fear emotion. To investigate the stability of EmotionMeter over time, each subject performs the experiments three times on different days. EmotionMeter obtains a mean recognition accuracy of 72.39% across sessions with the six-electrode EEG and eye movement features. These experimental results demonstrate the effectiveness of EmotionMeter within and between sessions.

    更新日期:2018-02-08
  • A New Varying-Parameter Recurrent Neural-Network for Online Solution of Time-Varying Sylvester Equation
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-02-07
    Zhijun Zhang; Lunan Zheng; Jian Weng; Yijun Mao; Wei Lu; Lin Xiao

    Solving Sylvester equation is a common algebraic problem in mathematics and control theory. Different from the traditional fixed-parameter recurrent neural networks, such as gradient-based recurrent neural networks or Zhang neural networks, a novel varying-parameter recurrent neural network, [called varying-parameter convergent-differential neural network (VP-CDNN)] is proposed in this paper for obtaining the online solution to the time-varying Sylvester equation. With time passing by, this kind of new varying-parameter neural network can achieve super-exponential performance. Computer simulation comparisons between the fixed-parameter neural networks and the proposed VP-CDNN via using different kinds of activation functions demonstrate that the proposed VP-CDNN has better convergence and robustness properties.

    更新日期:2018-02-08
  • Fixed-Time Leader-Follower Output Feedback Consensus for Second-Order Multiagent Systems
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-02-07
    Bailing Tian; Hanchen Lu; Zongyu Zuo; Wen Yang

    This paper addresses the fixed-time leader-follower consensus problem for second-order multiagent systems without velocity measurement. A new continuous fixed-time distributed observer-based consensus protocol is developed to achieve consensus in a bounded finite time fully independent of initial condition. A rigorous stability proof of the multiagent systems by output feedback control is presented based on the bi-limit homogeneity and the Lyapunov technique. Finally, the efficiency of the proposed methodology is illustrated by numerical simulation.

    更新日期:2018-02-08
  • Sampled-Data Control for the Synchronization of Boolean Control Networks
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-02-05
    Yang Liu; Liyun Tong; Jungang Lou; Jianquan Lu; Jinde Cao

    In this paper, we investigate the sampled-data state feedback control (SDSFC) for the synchronization of Boolean control networks (BCNs) under the configuration of drive-response coupling. Necessary and sufficient conditions for the complete synchronization of BCNs are obtained by the algebraic representations of logical dynamics. Based on the analysis of the sampling periods, we establish an algorithm to guarantee the synchronization of drive-response coupled BCNs by SDSFC. An example is given to illustrate the significance of the obtained results.

    更新日期:2018-02-07
  • Fast Large-Scale Spectral Clustering via Explicit Feature Mapping
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-02-05
    Li He; Nilanjan Ray; Yisheng Guan; Hong Zhang

    We propose an efficient spectral clustering method for large-scale data. The main idea in our method consists of employing random Fourier features to explicitly represent data in kernel space. The complexity of spectral clustering thus is shown lower than existing Nyström approximations on large-scale data. With m training points from a total of n data points, Nyström method requires O(nmd+m³+nm²) operations, where d is the input dimension. In contrast, our proposed method requires O(nDd+D³+n'D²), where n' is the number of data points needed until convergence and D is the kernel mapped dimension. In large-scale datasets where n' << n hold true, our explicitly mapping method can significantly speed up eigenvector approximation and benefit prediction speed in spectral clustering. For instance, on MNIST (60,000 data points), the proposed method is similar in clustering accuracy to Nyström methods while its speed is twice as fast as Nyström.

    更新日期:2018-02-06
  • A Two-Phase Meta-Heuristic for Multiobjective Flexible Job Shop Scheduling Problem With Total Energy Consumption Threshold
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-02-02
    Deming Lei; Ming Li; Ling Wang

    Flexible job shop scheduling problem (FJSP) has been extensively considered; however, multiobjective FJSP with energy consumption threshold is seldom investigated, the goal of which is to minimize makespan and total tardiness under the constraint that total energy consumption does not exceed a given threshold. Energy constraint is not always met and the threshold is difficult to be decided in advance. These features make it more difficult to solve the problem. In this paper, a two-phase meta-heuristic (TPM) based on imperialist competitive algorithm (ICA) and variable neighborhood search (VNS) is proposed. In the first phase, the problem is converted into FJSP with makespan, total tardiness and total energy consumption and the new FJSP is solved by an ICA, which uses some new methods to build initial empires and do imperialist competition. In the second phase, new strategies are provided for comparing solutions and updating the nondominated set of the first phase and a VNS is used for the original problem. The current solution of VNS is periodically replaced with member of the set $Ω$ to improve solution quality. An energy consumption threshold is obtained by optimization. Extensive experiments are conducted to test the performance of TPM finally. The computational results show that TPM is a very competitive algorithm for the considered FJSP.

    更新日期:2018-02-04
  • A Novel Deep Learning-Based Collaborative Filtering Model for Recommendation System
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-02-02
    Mingsheng Fu; Hong Qu; Zhang Yi; Li Lu; Yongsheng Liu

    The collaborative filtering (CF) based models are capable of grasping the interaction or correlation of users and items under consideration. However, existing CF-based methods can only grasp single type of relation, such as restricted Boltzmann machine which distinctly seize the correlation of user-user or item-item relation. On the other hand, matrix factorization explicitly captures the interaction between them. To overcome these setbacks in CF-based methods, we propose a novel deep learning method which imitates an effective intelligent recommendation by understanding the users and items beforehand. In the initial stage, corresponding low-dimensional vectors of users and items are learned separately, which embeds the semantic information reflecting the user-user and item-item correlation. During the prediction stage, a feed-forward neural networks is employed to simulate the interaction between user and item, where the corresponding pretrained representational vectors are taken as inputs of the neural networks. Several experiments based on two benchmark datasets (MovieLens 1M and MovieLens 10M) are carried out to verify the effectiveness of the proposed method, and the result shows that our model outperforms previous methods that used feed-forward neural networks by a significant margin and performs very comparably with state-of-the-art methods on both datasets.

    更新日期:2018-02-04
  • Increasing Interpretation of Web Topic Detection via Prototype Learning From Sparse Poisson Deconvolution
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-02-02
    Junbiao Pang; Anjing Hu; Qingming Huang; Qi Tian; Baocai Yin

    Organizing webpages into interesting topics is one of the key steps to understand the trends from multimodal Web data. The sparse, noisy, and less-constrained user-generated content results in inefficient feature representations. These descriptors unavoidably cause that a detected topic still contains a certain number of the false detected webpages, which further make a topic be less coherent, less interpretable, and less useful. In this paper, we address this problem from a viewpoint interpreting a topic by its prototypes, and present a two-step approach to achieve this goal. Following the detection-by-ranking approach, a sparse Poisson deconvolution is proposed to learn the intratopic similarities between webpages. To find the prototypes, leveraging the intratopic similarities, top-k diverse yet representative prototype webpages are identified from a submodularity function. Experimental results not only show the improved accuracies for the Web topic detection task, but also increase the interpretation of a topic by its prototypes on two public datasets.

    更新日期:2018-02-04
  • Multiagent Rendezvous With Shortest Distance to Convex Regions With Empty Intersection: Algorithms and Experiments
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-02-02
    Peng Lin; Wei Ren; Hao Wang; Ubaid M. Al-Saggaf

    This paper presents both algorithms and experimental results to solve a distributed rendezvous problem with shortest distance to convex regions. In a multiagent network, each agent is assigned to a certain convex region and has information about only its own region. All these regions might not have an intersection. Through local interaction with their neighbors, multiple agents collectively rendezvous at an optimal location that is a priori unknown to each agent and has the shortest total squared distance to these regions. First, a distributed time-varying algorithm is introduced, where a corresponding condition is given to guarantee that all agents rendezvous at the optimal location asymptotically for bounded convex regions. Then a distributed tracking algorithm combined with a distributed estimation algorithm is proposed. It is first shown that for general possibly unbounded convex regions, all agents rendezvous in finite time and then collectively slide to the optimal location asymptotically. Then it is shown that for convex regions with certain projection compressibility, all agents collectively rendezvous at the optimal location in finite time, even when the regions are time varying. The algorithms are experimentally implemented on multiple ground robots to illustrate the obtained theoretical results.

    更新日期:2018-02-04
  • Reach-Avoid Games With Two Defenders and One Attacker: An Analytical Approach
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-02-02
    Rui Yan; Zongying Shi; Yisheng Zhong

    This paper considers a reach-avoid game on a rectangular domain with two defenders and one attacker. The attacker aims to reach a specified edge of the game domain boundary, while the defenders strive to prevent that by capturing the attacker. First, we are concerned with the barrier, which is the boundary of the reach-avoid set, splitting the state space into two disjoint parts: 1) defender dominance region (DDR) and 2) attacker dominance region (ADR). For the initial states lying in the DDR, there exists a strategy for the defenders to intercept the attacker regardless of the attacker's best effort, while for the initial states lying in the ADR, the attacker can always find a successful attack strategy. We propose an attack region method to construct the barrier analytically by employing Voronoi diagram and Apollonius circle for two kinds of speed ratios. Then, by taking practical payoff functions into considerations, we present optimal strategies for the players when their initial states lie in their winning regions, and show that the ADR is divided into several parts corresponding to different strategies for the players. Numerical approaches, which suffer from inherent inaccuracy, have already been utilized for multiplayer reach-avoid games, but computational complexity complicates solving such games and consequently hinders efficient on-line applications. However, this method can obtain the exact formulation of the barrier and is applicable for real-time updates.

    更新日期:2018-02-04
  • Game-Based Memetic Algorithm to the Vertex Cover of Networks
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-01-31
    Jianshe Wu; Xing Shen; Kui Jiao

    The minimum vertex cover (MVC) is a well-known combinatorial optimization problem. A game-based memetic algorithm (GMA-MVC) is provided, in which the local search is an asynchronous updating snowdrift game and the global search is an evolutionary algorithm (EA). The game-based local search can implement (k,l)-exchanges for various numbers of k and l to remove k vertices from and add l vertices into the solution set, thus is much better than the previous (1,0)-exchange. Beyond that, the proposed local search is able to deal with the constraint, such that the crossover operator can be very simple and efficient. Degree-based initialization method is also provided which is much better than the previous uniform random initialization. Each individual of the GMA-MVC is designed as a snowdrift game state of the network. Each vertex is treated as an intelligent agent playing the snowdrift game with its neighbors, which is the local refinement process. The game is designed such that its strict Nash equilibrium (SNE) is always a vertex cover of the network. Most of the SNEs are only local optima of the problem. Then an EA is employed to guide the game to escape from those local optimal Nash equilibriums to reach a better Nash equilibrium. From comparison with the state of the art algorithms in experiments on various networks, the proposed algorithm always obtains the best solutions.

    更新日期:2018-02-01
  • Heterogeneous Ensemble-Based Infill Criterion for Evolutionary Multiobjective Optimization of Expensive Problems
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-01-31
    Dan Guo; Yaochu Jin; Jinliang Ding; Tianyou Chai

    Gaussian processes (GPs) are the most popular model used in surrogate-assisted evolutionary optimization of computationally expensive problems, mainly because GPs are able to measure the uncertainty of the estimated fitness values, based on which certain infill sampling criteria can be used to guide the search and update the surrogate model. However, the computation time for constructing GPs may become excessively long when the number of training samples increases, which makes it inappropriate to use them as surrogates in evolutionary optimization. To address this issue, this paper proposes to use ensembles as surrogates and infill criteria for model management in evolutionary optimization. A heterogeneous ensemble consisting of a least square support vector machine and two radial basis function networks is constructed to enhance the reliability of ensembles for uncertainty estimation. In addition to the original decision variables, a selected subset of the decision variables and a set of transformed variables are used as inputs of the heterogeneous ensemble to further promote the diversity of the ensemble. The proposed heterogeneous ensemble is compared with a GP and a homogeneous ensemble for infill sampling criteria in evolutionary multiobjective optimization. Experimental results demonstrate that the heterogeneous ensemble is competitive in performance compared with GPs and much more scalable in computational complexity to the increase in search dimension.

    更新日期:2018-02-01
  • Finite-Time Formation Control of Under-Actuated Ships Using Nonlinear Sliding Mode Control
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-01-31
    Tieshan Li; Rong Zhao; C. L. Philip Chen; Liyou Fang; Cheng Liu

    A novel nonlinear sliding mode control approach dealing with the formation control of under-actuated ships is presented in this paper. To avoid the singularity problem, state space of the system is partitioned into two regions, with one region bounded for terminal sliding mode control and its complement singular for that. And a linear auxiliary sliding mode controller is designed for system trajectories starting from the complement region. With the application of nonlinear sliding mode control approach and finite-time stability theory, a distributed controller is designed for individual under-actuated ship to achieve the given formation pattern within a finite time. Finally, two simulation examples are provided to verify the effectiveness and performance of the proposed approach.

    更新日期:2018-02-01
  • Data-Driven Distributed Output Consensus Control for Partially Observable Multiagent Systems
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-01-30
    He Jiang; Haibo He

    This paper is concerned with a class of optimal output consensus control problems for discrete linear multiagent systems with the partially observable system state. Since the optimal control policy depends on the full system state which is not accessible for a partially observable system, traditionally, distributed observers are employed to recover the system state. However, in many situations, the accurate model of a real-world dynamical system might be difficult to obtain, which makes the observer design infeasible. Furthermore, the optimal consensus control policy cannot be analytically solved without system functions. To overcome these challenges, we propose a data-driven adaptive dynamic programming approach that does not require the complete system inner state. The key idea is to use the input and output sequence as an equivalent representation of the underlying state. Based on this representation, an adaptive dynamic programming algorithm is developed to generate the optimal control policy. For the implementation of this algorithm, we design a neural network-based actor-critic structure to approximate the local performance indices and the control polices. Two numerical simulations are used to demonstrate the effectiveness of our method.

    更新日期:2018-01-31
  • Tooth-Marked Tongue Recognition Using Multiple Instance Learning and CNN Features
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-01-30
    Xiaoqiang Li; Yin Zhang; Qing Cui; Xiaoming Yi; Yi Zhang

    Tooth-marked tongue or crenated tongue can provide valuable diagnostic information for traditional Chinese Medicine doctors. However, tooth-marked tongue recognition is challenging. The characteristics of different tongues are multiform and have a great amount of variations, such as different colors, different shapes, and different types of teeth marks. The regions of teeth mark only appear along the lateral borders. Most existing methods make use of concave regions information to classify the tooth-marked tongue which leads to inconstant performance when the region of teeth mark is not concave. In this paper, we try to solve these problems by proposing a three-stage approach which first makes use of concavity information to propose the suspected regions, then use a convolutional neural network to extract deep features and at last use a multiple-instance classifier to make the final decision. Experimental results demonstrate the effectiveness of the proposed method.

    更新日期:2018-01-31
  • Transductive Zero-Shot Learning With a Self-Training Dictionary Approach
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-01-30
    Yunlong Yu; Zhong Ji; Xi Li; Jichang Guo; Zhongfei Zhang; Haibin Ling; Fei Wu

    As an important and challenging problem in computer vision, zero-shot learning (ZSL) aims at automatically recognizing the instances from unseen object classes without training data. To address this problem, ZSL is usually carried out in the following two aspects: 1) capturing the domain distribution connections between seen classes data and unseen classes data and 2) modeling the semantic interactions between the image feature space and the label embedding space. Motivated by these observations, we propose a bidirectional mapping-based semantic relationship modeling scheme that seeks for cross-modal knowledge transfer by simultaneously projecting the image features and label embeddings into a common latent space. Namely, we have a bidirectional connection relationship that takes place from the image feature space to the latent space as well as from the label embedding space to the latent space. To deal with the domain shift problem, we further present a transductive learning approach that formulates the class prediction problem in an iterative refining process, where the object classification capacity is progressively reinforced through bootstrapping-based model updating over highly reliable instances. Experimental results on four benchmark datasets (animal with attribute, Caltech-UCSD Bird2011, aPascal-aYahoo, and SUN) demonstrate the effectiveness of the proposed approach against the state-of-the-art approaches.

    更新日期:2018-01-31
  • Distributed Adaptive Event-Triggered Fault-Tolerant Consensus of Multiagent Systems With General Linear Dynamics
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-01-30
    Dan Ye; Meng-Meng Chen; Hai-Jiao Yang

    In this paper, the distributed adaptive event-triggered fault-tolerant consensus of general linear multiagent systems (MASs) is considered. First, in order to deal with multiplicative fault, a distributed event-triggered consensus protocol is designed. Using distributed adaptive online updating strategies, the computation of the minimum eigenvalue of Laplacian matrix is avoided. Second, some adaptive parameters are introduced in trigger function to improve the self-regulation ability of event-triggered mechanism. The new trigger threshold is both state-dependent and time-dependent, which is independent of the number of agents. Then sufficient conditions are derived to guarantee the leaderless and leader-following consensus. On the basis of this, the results are extended to the case of actuator saturation. It is proved the Zeno-behavior of considered event-triggered mechanism is avoided. At last, the effectiveness of the proposed methods are demonstrated by three simulation examples.

    更新日期:2018-01-31
  • Prescribed-Time Consensus and Containment Control of Networked Multiagent Systems
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-01-30
    Yujuan Wang; Yongduan Song; David J. Hill; Miroslav Krstic

    In this paper, we present a new prescribed-time distributed control method for consensus and containment of networked multiple systems. Different from both regular finite-time control (where the finite settling time is not uniform in initial conditions) and the fixed-time control (where the settling time cannot be preassigned arbitrarily), the proposed one is built upon a novel scaling function, resulting in prespecifiable convergence time (the settling time can be preassigned as needed within any physically allowable range). Furthermore, the developed control scheme not only ensures that all the agents reach the average consensus in prescribed finite time under undirected connected topology, but also ensures that all the agents reach a prescribed-time consensus with the root's state being the group decision value under the directed topology containing a spanning tree with the root as the leader. In addition, we extend the result to prescribed-time containment control involving multiple leaders under directed communication topology. Numerical examples are provided to verify the effectiveness and the superiority of the proposed control.

    更新日期:2018-01-31
  • Spatial-Temporal Recurrent Neural Network for Emotion Recognition
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-01-30
    Tong Zhang; Wenming Zheng; Zhen Cui; Yuan Zong; Yang Li

    In this paper, we propose a novel deep learning framework, called spatial-temporal recurrent neural network (STRNN), to integrate the feature learning from both spatial and temporal information of signal sources into a unified spatial-temporal dependency model. In STRNN, to capture those spatially co-occurrent variations of human emotions, a multidirectional recurrent neural network (RNN) layer is employed to capture long-range contextual cues by traversing the spatial regions of each temporal slice along different directions. Then a bi-directional temporal RNN layer is further used to learn the discriminative features characterizing the temporal dependencies of the sequences, where sequences are produced from the spatial RNN layer. To further select those salient regions with more discriminative ability for emotion recognition, we impose sparse projection onto those hidden states of spatial and temporal domains to improve the model discriminant ability. Consequently, the proposed two-layer RNN model provides an effective way to make use of both spatial and temporal dependencies of the input signals for emotion recognition. Experimental results on the public emotion datasets of electroencephalogram and facial expression demonstrate the proposed STRNN method is more competitive over those state-of-the-art methods.

    更新日期:2018-01-31
  • Event-Triggered Coordination for Formation Tracking Control in Constrained Space With Limited Communication
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-01-30
    Xiaomei Liu; Shuzhi Sam Ge; Cher-Hiang Goh; Yanan Li

    In this paper, the formation tracking control is studied for a multiagent system (MAS) with communication limitations. The objective is to control a group of agents to track a desired trajectory while maintaining a given formation in nonomniscient constrained space. The role switching triggered by the detection of unexpected spatial constraints facilitates efficiency of event-triggered control in communication bandwidth, energy consumption, and processor usage. A coordination mechanism is proposed based on a novel role ``coordinator'' to indirectly spread environmental information among the whole communication network and form a feedback link from followers to the leader to guarantee the formation keeping. A formation scaling factor is introduced to scale up or scale down the given formation size in the case that the region is impassable for MAS with the original formation size. Controllers for the leader and followers are designed and the adaptation law is developed for the formation scaling factor. The conditions for asymptotic stability of MAS are discussed based on the Lyapunov theory. Simulation results are presented to illustrate the performance of proposed approaches.

    更新日期:2018-01-31
  • Dimension Reduction for Non-Gaussian Data by Adaptive Discriminative Analysis
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-01-24
    Tingjin Luo; Chenping Hou; Feiping Nie; Dongyun Yi

    High-dimensional non-Gaussian data are ubiquitous in many real applications. Face recognition is a typical example of such scenarios. The sampled face images of each person in the original data space are more closely located to each other than to those of the same individuals due to the changes of various conditions like illumination, pose variation, and facial expression. They are often non-Gaussian and differentiating the importance of each data point has been recognized as an effective approach to process the high-dimensional non-Gaussian data. In this paper, to embed non-Gaussian data well, we propose a novel unified framework named adaptive discriminative analysis (ADA), which combines the sample's importance measurement and subspace learning in a unified framework. Therefore, our ADA can preserve the within-class local structure and learn the discriminative transformation functions simultaneously by minimizing the distances of the projected samples within the same classes while maximizing the between-class separability. Meanwhile, an efficient method is developed to solve our formulated problem. Comprehensive analyses, including convergence behavior and parameter determination, together with the relationship to other related approaches, are as well presented. Systematical experiments are conducted to understand the work of our proposed ADA. Promising experimental results on various types of real-world benchmark data sets are provided to examine the effectiveness of our algorithm. Furthermore, we have also evaluated our method in face recognition. They all validate the effectiveness of our method on processing the high-dimensional non-Gaussian data.

    更新日期:2018-01-25
  • Auxiliary Demographic Information Assisted Age Estimation With Cascaded Structure
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-01-23
    Jun Wan; Zichang Tan; Zhen Lei; Guodong Guo; Stan Z. Li

    Owing to the variations including both intrinsic and extrinsic factors, age estimation remains a challenging problem. In this paper, five cascaded structure frameworks are proposed for age estimation based on convolutional neural networks. All frameworks are learned and guided by auxiliary demographic information, since other demographic information (i.e., gender and race) is beneficial for age prediction. Each cascaded structure framework is embodied in a parent network and several subnetworks. For example, one of the applied framework is a gender classifier trained by gender information, and then two subnetworks are trained by the male and female samples, respectively. Furthermore, we use the features extracted from the cascaded structure frameworks with Gaussian process regression that can boost the performance further for age estimation. Experimental results on the MORPH II and CACD datasets have gained superior performances compared to the state-of-the-art methods. The mean absolute error is significantly reduced from 3.63 to 2.93 years under the same test protocol on the MORPH II dataset.

    更新日期:2018-01-24
  • Granular Data Aggregation: An Adaptive Principle of the Justifiable Granularity Approach
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-01-23
    Dan Wang; Witold Pedrycz; Zhiwu Li

    The design of information granules assumes a central position in the discipline of Granular Computing and its applications. The principle of justifiable granularity offers a conceptually and algorithmically attractive way of designing information granule completed on a basis of some experimental evidence (especially present in the form of numeric data). This paper builds upon the existing principle and presents its significant generalization, referred here as an adaptive principle of justifiable information granularity. The method supports a granular data aggregation producing an optimal information granule (with the optimality expressed in terms of the criteria of coverage and specificity commonly used when characterizing quality of information granules). The flexibility of the method stems from an introduction of the adaptive weighting scheme of the data leading to a vector of weights used in the construction of the optimal information granule. A detailed design procedure is provided along with the required optimization vehicle (realized with the aid of the population-based optimization techniques, such as particle swarm optimization and differential evolution). Two direct application areas in which the principle becomes of direct usage include prediction of time series and prediction of spatial data. In both cases, it is advocated that the results formed by the principle are reflective of the precision (quality) of the prediction process.

    更新日期:2018-01-24
  • An Interclass Margin Maximization Learning Algorithm for Evolving Spiking Neural Network
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-01-23
    Shirin Dora; Suresh Sundaram; Narasimhan Sundararajan

    This paper presents a new learning algorithm developed for a three layered spiking neural network for pattern classification problems. The learning algorithm maximizes the interclass margin and is referred to as the two stage margin maximization spiking neural network (TMM-SNN). In the structure learning stage, the learning algorithm completely evolves the hidden layer neurons in the first epoch. Further, TMM-SNN updates the weights of the hidden neurons for multiple epochs using the newly developed normalized membrane potential learning rule such that the interclass margins (based on the response of hidden neurons) are maximized. The normalized membrane potential learning rule considers both the local information in the spike train generated by a presynaptic neuron and the existing knowledge (synaptic weights) stored in the network to update the synaptic weights. After the first stage, the number of hidden neurons and their parameters are not updated. In the output weights learning stage, TMM-SNN updates the weights of the output layer neurons for multiple epochs to maximize the interclass margins (based on the response of output neurons). Performance of TMM-SNN is evaluated using ten benchmark data sets from the UCI machine learning repository. Statistical performance comparison of TMM-SNN with other existing learning algorithms for SNNs is conducted using the nonparametric Friedman test followed by a pairwise comparison using the Fisher's least significant difference method. The results clearly indicate that TMM-SNN achieves better generalization performance in comparison to other algorithms.

    更新日期:2018-01-24
  • Adaptive Fuzzy Containment Control of Nonlinear Systems With Unmeasurable States
    IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-01-23
    Wei Wang; Shaocheng Tong; Dan Wang

    The adaptive fuzzy containment control problem is discussed for high-order systems with unknown nonlinear dynamics and unmeasurable states guided by multiple dynamic leaders. A high gain observer is introduced to reconstruct the system states. Then, utilizing fuzzy logic systems to model followers' dynamics, an observer-based adaptive fuzzy containment control approach is presented using only the relative position of the neighbors. It is shown that the uniformly ultimately bounded containment control is realized under the condition that, each follower can obtain the information from at least one leader through a directed path. As an extension, an observer-based containment control with prescribed performance is developed, which guarantees the relative position error to be bounded by a specified bound. The obtained theoretical results are validated by simulation examples.

    更新日期:2018-01-24
Some contents have been Reproduced with permission of the American Chemical Society.
Some contents have been Reproduced by permission of The Royal Society of Chemistry.
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