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IEEE Transactions on Cybernetics

Xiubin Zhu, Witold Pedrycz, Zhiwu Li
Granular computing (GrC) has emerged as a unified conceptual and processing framework. Information granules are fundamental constructs that permeate concepts and models of GrC. This paper is concerned with a design of a collection of meaningful, easily interpretable ellipsoidal information granules with the use of the principle of justifiable granularity by taking into consideration reconstruction abilities of the designed information granules. The principle of justifiable granularity supports designing of information granules based on numeric or granular evidence, and aims to achieve a compromise between justifiability and specificity of the information granules to be constructed...
October 12, 2016: IEEE Transactions on Cybernetics
Qi Kang, XiaoShuang Chen, SiSi Li, MengChu Zhou
Under-sampling is a popular data preprocessing method in dealing with class imbalance problems, with the purposes of balancing datasets to achieve a high classification rate and avoiding the bias toward majority class examples. It always uses full minority data in a training dataset. However, some noisy minority examples may reduce the performance of classifiers. In this paper, a new under-sampling scheme is proposed by incorporating a noise filter before executing resampling. In order to verify the efficiency, this scheme is implemented based on four popular under-sampling methods, i...
October 12, 2016: IEEE Transactions on Cybernetics
Shengnan Wang, Chunguang Li
In this paper, we consider a distributed robust optimization (DRO) problem, where multiple agents in a networked system cooperatively minimize a global convex objective function with respect to a global variable under the global constraints. The objective function can be represented by a sum of local objective functions. The global constraints contain some uncertain parameters which are partially known, and can be characterized by some inequality constraints. After problem transformation, we adopt the Lagrangian primal-dual method to solve this problem...
October 11, 2016: IEEE Transactions on Cybernetics
Jiahu Qin, Weiming Fu, Wei Xing Zheng, Huijun Gao
The bipartite consensus problem for a group of homogeneous generic linear agents with input saturation under directed interaction topology is examined. It is established that if each agent is asymptotically null controllable with bounded controls and the interaction topology described by a signed digraph is structurally balanced and contains a spanning tree, then the semi-global bipartite consensus can be achieved for the linear multiagent system by a linear feedback controller with the control gain being designed via the low gain feedback technique...
October 11, 2016: IEEE Transactions on Cybernetics
Li Qiu, Yang Shi, Jianfei Pan, Bo Zhang, Gang Xu
This paper investigates the collaborative tracking control for dual linear switched reluctance machines (LSRMs) over a communication network with random time delays. Considering the spatio-temporal constraint relationship of the dual LSRMs in complex industrial processes, the collaborative tracking control scheme is proposed based on the networked motion control method. The stability conditions and the controller design method for the networked dual LSRMs are obtained from the two motors relative position error by using Lyapunov theory and delay systems approach...
October 11, 2016: IEEE Transactions on Cybernetics
Guoxing Wen, C L Philip Chen, Yan-Jun Liu, Zhi Liu
Compared with the existing neural network (NN) or fuzzy logic system (FLS) based adaptive consensus methods, the proposed approach can greatly alleviate the computation burden because it needs only to update a few adaptive parameters online. In the multiagent agreement control, the system uncertainties derive from the unknown nonlinear dynamics are counteracted by employing the adaptive NNs; the state delays are compensated by designing a Lyapunov-Krasovskii functional. Finally, based on Lyapunov stability theory, it is demonstrated that the proposed consensus scheme can steer a multiagent system synchronizing to the predefined reference signals...
October 11, 2016: IEEE Transactions on Cybernetics
Ping Jiang, Yongqiang Cheng, Xiaonian Wang, Zuren Feng
In a complex environment, simultaneous object recognition and tracking has been one of the challenging topics in computer vision and robotics. Current approaches are usually fragile due to spurious feature matching and local convergence for pose determination. Once a failure happens, these approaches lack a mechanism to recover automatically. In this paper, data-driven unfalsified control is proposed for solving this problem in visual servoing. It recognizes a target through matching image features with a 3-D model and then tracks them through dynamic visual servoing...
October 5, 2016: IEEE Transactions on Cybernetics
Lantao Xing, Changyun Wen, Fanghong Guo, Zhitao Liu, Hongye Su
In this paper, we propose a new distributed event-trigger consensus protocol for linear multiagent systems with external disturbances. Two consensus problems are considered: one is a leader-follower case and the other is a nonleader case. Different from the existing results, our proposed scheme enables each agent to decide when to transmit its state signals to its neighbors such that continuous communication between neighboring agents is avoided. Clearly, this can largely decrease the communication burden of the whole communication network...
October 4, 2016: IEEE Transactions on Cybernetics
Jiuwen Cao, Wei Wang, Jianzhong Wang, Ruirong Wang
Excavation equipment recognition attracts increasing attentions in recent years due to its significance in underground pipeline network protection and civil construction management. In this paper, a novel classification algorithm based on acoustics processing is proposed for four representative excavation equipments. New acoustic statistical features, namely, the short frame energy ratio, concentration of spectrum amplitude ratio, truncated energy range, and interval of pulse are first developed to characterize acoustic signals...
September 30, 2016: IEEE Transactions on Cybernetics
Shangwen Chen, Xianyong Fang, Jianbing Shen, Linbo Wang, Ling Shao
Existing distance measurement methods either require multiple images and special photographing poses or only measure the height with a special view configuration. We propose a novel image-based method that can measure various types of distance from single image captured by a smart mobile device. The embedded accelerometer is used to determine the view orientation of the device. Consequently, pixels can be back-projected to the ground, thanks to the efficient calibration method using two known distances. Then the distance in pixel is transformed to a real distance in centimeter with a linear model parameterized by the magnification ratio...
September 29, 2016: IEEE Transactions on Cybernetics
Mohammad Eshaghnezhad, Sohrab Effati, Amin Mansoori
In this paper, a neurodynamic model is given to solve nonlinear pseudo-monotone projection equation. Under pseudo-monotonicity condition and Lipschitz continuous condition, the projection neurodynamic model is proved to be stable in the sense of Lyapunov, globally convergent, globally asymptotically stable, and globally exponentially stable. Also, we show that, our new neurodynamic model is effective to solve the nonconvex optimization problems. Moreover, since monotonicity is a special case of pseudo-monotonicity and also since a co-coercive mapping is Lipschitz continuous and monotone, and a strongly pseudo-monotone mapping is pseudo-monotone, the neurodynamic model can be applied to solve a broader classes of constrained optimization problems related to variational inequalities, pseudo-convex optimization problem, linear and nonlinear complementarity problems, and linear and convex quadratic programming problems...
September 29, 2016: IEEE Transactions on Cybernetics
Chengzhi Wu, Bo Qi, Chunlin Chen, Daoyi Dong
Robust control design for quantum unitary transformations has been recognized as a fundamental and challenging task in the development of quantum information processing due to unavoidable decoherence or operational errors in the experimental implementation of quantum operations. In this paper, we extend the systematic methodology of sampling-based learning control (SLC) approach with a gradient flow algorithm for the design of robust quantum unitary transformations. The SLC approach first uses a "training" process to find an optimal control strategy robust against certain ranges of uncertainties...
September 29, 2016: IEEE Transactions on Cybernetics
Brajesh Kumar, Onkar Dikshit
In this paper, a spectral-spatial classification framework based on probabilistic relaxation labeling using compatibility coefficients is proposed for hyperspectral images. It is a two-stage classifier that uses maximum a posteriori (MAP) estimation to maximize posterior probabilities of classification map obtained in first stage to incorporate spatial information for better classification accuracy. Two different forms of compatibility coefficients based on correlation and mutual information are used for MAP estimation...
September 29, 2016: IEEE Transactions on Cybernetics
Haiyu Song, Wen-An Zhang, Li Yu, Bo Chen
In this paper, we consider a periodic estimation problem in sensor networks with a shared communication channel. The transmission constraint is inevitable in a single-channel-based sensor network if the sensors are heterogeneous or deployed far away from each other. A novel stochastic competitive transmission strategy is presented to deal with the transmission constraint, such that the sensors communicate with the fusion center (FC) in a strict asynchronous manner. A periodic mixed storage strategy combing the zero-input and the hold-input mechanisms is presented to describe periodic updating of the stored information in the sensors' buffers...
September 29, 2016: IEEE Transactions on Cybernetics
Zijia Lin, Guiguang Ding, Jungong Han, Jianmin Wang
For efficiently retrieving nearest neighbors from large-scale multiview data, recently hashing methods are widely investigated, which can substantially improve query speeds. In this paper, we propose an effective probability-based semantics-preserving hashing (SePH) method to tackle the problem of cross-view retrieval. Considering the semantic consistency between views, SePH generates one unified hash code for all observed views of any instance. For training, SePH first transforms the given semantic affinities of training data into a probability distribution, and aims to approximate it with another one in Hamming space, via minimizing their Kullback-Leibler divergence...
September 29, 2016: IEEE Transactions on Cybernetics
Partha Pratim Kundu, Sushmita Mitra
A novel similarity-based feature selection algorithm is developed, using the concept of distance correlation. A feature subset is selected in terms of this similarity measure between pairs of features, without assuming any underlying distribution of the data. The pair-wise similarity is then employed, in a message passing framework, to select a set of exemplars features involving minimum redundancy and reduced parameter tuning. The algorithm does not need an exhaustive traversal of the search space. The methodology is next extended to handle large data, using an inherent property of distance correlation...
September 28, 2016: IEEE Transactions on Cybernetics
Gen Yang, Sebastien Destercke, Marie-Helene Masson
Indeterminate classifiers are cautious models able to predict more than one class in case of high uncertainty. A problem that arises when using such classifiers is how to evaluate their performances. This problem has already been considered in the case where all prediction errors have equivalent costs (that we will refer as the ''0/1 costs'' or accuracy setting). The purpose of this paper is to study the case of generic cost functions. We provide some properties that the costs of indeterminate predictions could or should follow, and review existing proposals in the light of those properties...
September 28, 2016: IEEE Transactions on Cybernetics
Honghao Chang, Zuren Feng, Zhigang Ren
Many complex networks have been shown to have community structures. Detecting those structures is very important for understanding the organization and function of networks. Because this problem is NP-hard, it is appropriate to resort to evolutionary algorithms. Chemical reaction optimization (CRO) is a novel evolutionary algorithm inspired by the interactions among molecules during chemical reactions. In this paper, we propose a CRO variant named dual-representation CRO (DCRO) to address the community detection problem...
September 23, 2016: IEEE Transactions on Cybernetics
Qingshan Liu, Jing Yang, Kaihua Zhang, Yi Wu
Recently, the compressive tracking (CT) method has attracted much attention due to its high efficiency, but it cannot well deal with the large scale target appearance variations due to its data-independent random projection matrix that results in less discriminative features. To address this issue, in this paper, we propose an adaptive CT approach, which selects the most discriminative features to design an effective appearance model. Our method significantly improves CT in three aspects. First, the most discriminative features are selected via an online vector boosting method...
September 19, 2016: IEEE Transactions on Cybernetics
Giorgio Biagetti, Paolo Crippa, Laura Falaschetti, Simone Orcioni, Claudio Turchetti
Speaker identification plays a crucial role in biometric person identification as systems based on human speech are increasingly used for the recognition of people. Mel frequency cepstral coefficients (MFCCs) have been widely adopted for decades in speech processing to capture the speech-specific characteristics with a reduced dimensionality. However, although their ability to decorrelate the vocal source and the vocal tract filter make them suitable for speech recognition, they greatly mitigate the speaker variability, a specific characteristic that distinguishes different speakers...
September 19, 2016: IEEE Transactions on Cybernetics
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