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IEEE Transactions on Cybernetics

Wensen Feng, Peng Qiao, Yunjin Chen
The degradation of the acquired signal by Poisson noise is a common problem for various imaging applications, such as medical imaging, night vision, and microscopy. Up to now, many state-of-the-art Poisson denoising techniques mainly concentrate on achieving utmost performance, with little consideration for the computation efficiency. Therefore, in this paper we aim to propose an efficient Poisson denoising model with both high computational efficiency and recovery quality. To this end, we exploit the newly developed trainable nonlinear reaction diffusion (TNRD) model which has proven an extremely fast image restoration approach with performance surpassing recent state-of-the-arts...
June 20, 2017: IEEE Transactions on Cybernetics
Xiaoming Zhang, Senzhang Wang, Zhoujun Li, Shuai Ma
Landmark retrieval is to return a set of images with their landmarks similar to those of the query images. Existing studies on landmark retrieval focus on exploiting the geometries of landmarks for visual similarity matches. However, the visual content of social images is of large diversity in many landmarks, and also some images share common patterns over different landmarks. On the other side, it has been observed that social images usually contain multimodal contents, i.e., visual content and text tags, and each landmark has the unique characteristic of both visual content and text content...
June 20, 2017: IEEE Transactions on Cybernetics
Sheng Xin Zhang, Shao Yong Zheng, Li Ming Zheng
Differential evolution (DE) is recognized as a simple but powerful algorithm in the family of evolutionary algorithms. Over the past two decades, many advanced DE variants with significantly improved performance have been proposed. However, the variants may only achieve the best performance on a certain type of functions. Moreover, a specific optimizer may not always be suitable for the whole optimization process. To overcome these weaknesses, this paper proposes a multiple variants coordination (MVC) framework with two mechanisms, namely, the multiple variants adaptive selecting mechanism and the multiple variants adaptive solutions preserving mechanisms (MV-APM)...
June 19, 2017: IEEE Transactions on Cybernetics
Shintami C Hidayati, Chuang-Wen You, Wen-Huang Cheng, Kai-Lung Hua
According to the theory of clothing design, the genres of clothes can be recognized based on a set of visually differentiable style elements, which exhibit salient features of visual appearance and reflect high-level fashion styles for better describing clothing genres. Instead of using less-discriminative low-level features or ambiguous keywords to identify clothing genres, we proposed a novel approach for automatically classifying clothing genres based on the visually differentiable style elements. A set of style elements, that are crucial for recognizing specific visual styles of clothing genres, were identified based on the clothing design theory...
June 19, 2017: IEEE Transactions on Cybernetics
Marco Crocco, Samuele Martelli, Andrea Trucco, Andrea Zunino, Vittorio Murino
A novel method is proposed for generic target tracking by audio measurements from a microphone array. To cope with noisy environments characterized by persistent and high energy interfering sources, a classification map (CM) based on spectral signatures is calculated by means of a machine learning algorithm. Next, the CM is combined with the acoustic map, describing the spatial distribution of sound energy, in order to obtain a cleaned joint map in which contributions from the disturbing sources are removed...
June 13, 2017: IEEE Transactions on Cybernetics
Lei Zhang, Hebin Pan, Yansen Su, Xingyi Zhang, Yunyun Niu
Designing multiobjective evolutionary algorithms (MOEAs) for community detection in complex networks has attracted much attention of researchers recently. However, most of the existing methods focus on addressing the task of nonoverlapping community detection, where each node must belong to one and only one community. In fact, communities are often overlapped with each other in many real-world networks, thus it is necessary to design overlapping community detection algorithms. To this end, this paper proposes a mixed representation-based MOEA (MR-MOEA) for overlapping community detection...
June 13, 2017: IEEE Transactions on Cybernetics
Tongjia Zheng, Cong Wang
Based on the notion of persistent excitation (PE), a deterministic learning theory is recently proposed for RBF network-based identification of nonlinear systems. In this paper, we study the relationship between the PE levels, the structures of RBF networks and the performance of deterministic learning. Specifically, given a state trajectory generated from a nonlinear dynamical system, we investigate how to construct the RBF networks in order to guarantee sufficient PE levels (especially the level of excitation) for deterministic learning...
June 12, 2017: IEEE Transactions on Cybernetics
Qingling Zhu, Qiuzhen Lin, Weineng Chen, Ka-Chun Wong, Carlos A Coello Coello, Jianqiang Li, Jianyong Chen, Jun Zhang
The selection of swarm leaders (i.e., the personal best and global best), is important in the design of a multiobjective particle swarm optimization (MOPSO) algorithm. Such leaders are expected to effectively guide the swarm to approach the true Pareto optimal front. In this paper, we present a novel external archive-guided MOPSO algorithm (AgMOPSO), where the leaders for velocity update are all selected from the external archive. In our algorithm, multiobjective optimization problems (MOPs) are transformed into a set of subproblems using a decomposition approach, and then each particle is assigned accordingly to optimize each subproblem...
June 12, 2017: IEEE Transactions on Cybernetics
Da-Peng Li, Dong-Juan Li, Yan-Jun Liu, Shaocheng Tong, C L Philip Chen
This paper deals with the tracking control problem for a class of nonlinear multiple input multiple output unknown time-varying delay systems with full state constraints. To overcome the challenges which cause by the appearances of the unknown time-varying delays and full-state constraints simultaneously in the systems, an adaptive control method is presented for such systems for the first time. The appropriate Lyapunov-Krasovskii functions and a separation technique are employed to eliminate the effect of unknown time-varying delays...
June 12, 2017: IEEE Transactions on Cybernetics
Xiao-Gen Zhou, Gui-Jun Zhang
In differential evolution (DE), different strategies applied in different evolutionary stages may be more effective than a single strategy used in the entire evolutionary process. However, it is not trivial to appropriately determine the evolutionary stage. In this paper, we present an abstract convex underestimation-assisted multistage DE. In the proposed algorithm, the underestimation is calculated through the supporting vectors of some neighboring individuals. Based on the variation of the average underestimation error (UE), the evolutionary process is divided into three stages...
June 8, 2017: IEEE Transactions on Cybernetics
Zhunga Liu, Quan Pan, Jean Dezert, Jun-Wei Han, You He
Classifier fusion is an efficient strategy to improve the classification performance for the complex pattern recognition problem. In practice, the multiple classifiers to combine can have different reliabilities and the proper reliability evaluation plays an important role in the fusion process for getting the best classification performance. We propose a new method for classifier fusion with contextual reliability evaluation (CF-CRE) based on inner reliability and relative reliability concepts. The inner reliability, represented by a matrix, characterizes the probability of the object belonging to one class when it is classified to another class...
June 8, 2017: IEEE Transactions on Cybernetics
Boda Ning, Qing-Long Han, Zongyu Zuo, Jiong Jin, Jinchuan Zheng
This paper is concerned with the collective behaviors of robots beyond the nearest neighbor rules, i.e., dispersion and flocking, when robots interact with others by applying an acute angle test (AAT)-based interaction rule. Different from a conventional nearest neighbor rule or its variations, the AAT-based interaction rule allows interactions with some far-neighbors and excludes unnecessary nearest neighbors. The resulting dispersion and flocking hold the advantages of scalability, connectivity, robustness, and effective area coverage...
June 8, 2017: IEEE Transactions on Cybernetics
Wenying Xu, Zidong Wang, Daniel W C Ho
This paper is concerned with the finite-horizon H∞ consensus problem for a class of discrete time-varying multiagent systems with external disturbances and missing measurements. To improve the communication reliability, redundant channels are introduced and the corresponding protocol is constructed for the information transmission over redundant channels. An event-triggered scheme is adopted to determine whether the information of agents should be transmitted to their neighbors. Subsequently, an observer-type event-triggered control protocol is proposed based on the latest received neighbors' information...
June 6, 2017: IEEE Transactions on Cybernetics
Zhi-Hui Zhang, Guang-Hong Yang
This paper is concerned with the fault isolation problem for discrete-time fuzzy interconnected systems with unknown interconnections. First, for each subsystem, a fault isolation interval observer is constructed by taking into account the bounds of the unknown interconnections and subsystem disturbances. Then, l₁ and H∞ performances are introduced to improve the tightness of the residual intervals and the sensitivity to their own faults, respectively. Furthermore, the fault isolation decision is made by determining whether the zero value is excluded from each residual interval...
June 6, 2017: IEEE Transactions on Cybernetics
Siqi Wang, Qiang Liu, En Zhu, Jianping Yin, Wentao Zhao
One-class classification (OCC) models a set of target data from one class to detect outliers. OCC approaches like one-class support vector machine (OCSVM) and support vector data description (SVDD) have wide practical applications. Recently, one-class extreme learning machine (OCELM), which inherits the fast learning speed of original ELM and achieves equivalent or higher data description performance than OCSVM and SVDD, is proposed as a promising alternative. However, OCELM faces the same thorny parameter selection problem as OCSVM and SVDD...
June 5, 2017: IEEE Transactions on Cybernetics
Jiuxiang Dong, Yue Wu, Guang-Hong Yang
This paper is concerned with the fault isolation problem for T-S fuzzy systems with sensor faults. With the help of a set theoretic description of T-S fuzzy models, a new fault isolation scheme is proposed. It consists of a set of fuzzy observers and each of them corresponds to a specified sensor, where the antecedent and consequent parts of the observer are independent on the sensor output. Different from the existing approaches, the premise variables, which do not depend on the specified sensor output but depend on the other sensor outputs, are used in the proposed observer, which has the potential to lead to a better fault isolation performance...
June 5, 2017: IEEE Transactions on Cybernetics
Yamin Wang, Xiaoping Li, Ruben Ruiz, Shaochun Sui
The mixed no-wait flowshop problem with both wait and no-wait constraints has many potential real-life applications. The problem can be regarded as a generalization of the traditional permutation flowshop and the no-wait flowshop. In this paper, we study, for the first time, this scheduling setting with makespan minimization. We first propose a mathematical model and then we design a speed-up makespan calculation procedure. By introducing a varying number of destructed jobs, a modified iterated greedy algorithm is proposed for the considered problem which consists of four components: 1) initialization solution construction; 2) destruction; 3) reconstruction; and 4) local search...
June 5, 2017: IEEE Transactions on Cybernetics
Hazem El-Alfy, Ikuhisa Mitsugami, Yasushi Yagi
Gait is a commonly used biometric for human recognition. Its main advantage relies on its ability to identify people at distances at which other biometrics fail. In this paper, we develop a new approach for gait recognition that combines the distance transform with curvatures of local contours. We call our gait feature template the normal distance map. Our method encodes both body shapes and boundary curvatures into a novel feature descriptor that is more robust than existing gait representations. We evaluate our approach on the widely used and challenging USF and CASIA-B datasets...
June 5, 2017: IEEE Transactions on Cybernetics
Yao Guo, Youfu Li, Zhanpeng Shao
The motion behaviors of a rigid body can be characterized by a six degrees of freedom motion trajectory, which contains the 3-D position vectors of a reference point on the rigid body and 3-D rotations of this rigid body over time. This paper devises a rotation and relative velocity (RRV) descriptor by exploring the local translational and rotational invariants of rigid body motion trajectories, which is insensitive to noise, invariant to rigid transformation and scale. The RRV descriptor is then applied to characterize motions of a human body skeleton modeled as articulated interconnections of multiple rigid bodies...
May 29, 2017: IEEE Transactions on Cybernetics
Xinghu Wang, Yiguang Hong, Peng Yi, Haibo Ji, Yu Kang
In this paper, a distributed optimization problem is studied for continuous-time multiagent systems with unknown-frequency disturbances. A distributed gradient-based control is proposed for the agents to achieve the optimal consensus with estimating unknown frequencies and rejecting the bounded disturbance in the semi-global sense. Based on convex optimization analysis and adaptive internal model approach, the exact optimization solution can be obtained for the multiagent system disturbed by exogenous disturbances with uncertain parameters...
May 24, 2017: IEEE Transactions on Cybernetics
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