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IEEE Transactions on Cybernetics

Chuan-Ke Zhang, Yong He, Lin Jiang, Qing-Guo Wang, Min Wu
This paper is concerned with the stability analysis of discrete-time neural networks with a time-varying delay. Assessment of the effect of time delays on system stability requires suitable delay-dependent stability criteria. This paper aims to develop new stability criteria for reduction of conservatism without much increase of computational burden. An extended reciprocally convex matrix inequality is developed to replace the popular reciprocally convex combination lemma (RCCL). It has potential to reduce the conservatism of the RCCL-based criteria without introducing any extra decision variable due to its advantage of reduced estimation gap using the same decision variables...
February 17, 2017: IEEE Transactions on Cybernetics
Junchi Yan, Changsheng Li, Yin Li, Guitao Cao
This paper addresses the problem of hypergraph matching using higher-order affinity information. We propose a solver that iteratively updates the solution in the discrete domain by linear assignment approximation. The proposed method is guaranteed to converge to a stationary discrete solution and avoids the annealing procedure and ad-hoc post binarization step that are required in several previous methods. Specifically, we start with a simple iterative discrete gradient assignment solver. This solver can be trapped in an m-circle sequence under moderate conditions, where m is the order of the graph matching problem...
February 17, 2017: IEEE Transactions on Cybernetics
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...
February 16, 2017: IEEE Transactions on Cybernetics
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...
February 15, 2017: IEEE Transactions on Cybernetics
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...
February 14, 2017: IEEE Transactions on Cybernetics
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...
February 14, 2017: IEEE Transactions on Cybernetics
Guofeng Mei, Xiaoqun Wu, Yingfei Wang, Mi Hu, Jun-An Lu, Guanrong Chen
The coexistence of multiple types of interactions within social, technological, and biological networks has motivated the study of the multilayer nature of real-world networks. Meanwhile, identifying network structures from dynamical observations is an essential issue pervading over the current research on complex networks. This paper addresses the problem of structure identification for multilayer networks, which is an important topic but involves a challenging inverse problem. To clearly reveal the formalism, the simplest two-layer network model is considered and a new approach to identifying the structure of one layer is proposed...
February 13, 2017: IEEE Transactions on Cybernetics
Pau Rodriguez, Guillem Cucurull, Jordi Gonalez, Josep M Gonfaus, Kamal Nasrollahi, Thomas B Moeslund, F Xavier Roca
Pain is an unpleasant feeling that has been shown to be an important factor for the recovery of patients. Since this is costly in human resources and difficult to do objectively, there is the need for automatic systems to measure it. In this paper, contrary to current state-of-the-art techniques in pain assessment, which are based on facial features only, we suggest that the performance can be enhanced by feeding the raw frames to deep learning models, outperforming the latest state-of-the-art results while also directly facing the problem of imbalanced data...
February 9, 2017: IEEE Transactions on Cybernetics
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...
February 9, 2017: IEEE Transactions on Cybernetics
Chao Yang, Hongbo Liu, Sean McLoone, C L Philip Chen, Xindong Wu
A comprehensive knowledge system reveals the intangible insights hidden in an information system by integrating information from multiple data sources in a synthetical manner. In this paper, we present a variable precision reduction theory, underpinned by two new concepts: 1) distribution tables and 2) genealogical binary trees. Sufficient and necessary conditions to extract comprehensive knowledge from a given information system are also presented and proven. A complete variable precision reduction algorithm is proposed, in which we introduce four important strategies, namely, distribution table abstracting, attribute rank dynamic updating, hierarchical binary classifying, and genealogical tree pruning...
February 8, 2017: IEEE Transactions on Cybernetics
Cristiano Cervellera, Danilo Maccio
Approximate dynamic programming (ADP) is the standard tool to solve Markovian decision problems under general hypotheses on the system and the cost equations. It is known that one of the key issues of the procedure is how to generate an efficient sampling of the state space, needed for the approximation of the value function, in order to cope with the well-known phenomenon of the curse of dimensionality. The most common approaches in the literature are either aimed at a uniform covering of the state space or driven by the actual evolution of the system trajectories...
February 7, 2017: IEEE Transactions on Cybernetics
Chen Peng, Shaodong Ma, Xiangpeng Xie
This paper addresses the problem of an event-triggered non-parallel distribution compensation (PDC) control for networked Takagi-Sugeno (T-S) fuzzy systems, under consideration of the limited data transmission bandwidth and the imperfect premise matching membership functions. First, a unified event-triggered T-S fuzzy model is provided, in which: 1) a fuzzy observer with the imperfect premise matching is constructed to estimate the unmeasurable states of the studied system; 2) a fuzzy controller is designed following the same premise as the observer; and 3) an output-based event-triggering transmission scheme is designed to economize the restricted network resources...
February 7, 2017: IEEE Transactions on Cybernetics
Qunfeng Liu, Wei-Neng Chen, Jeremiah D Deng, Tianlong Gu, Huaxiang Zhang, Zhengtao Yu, Jun Zhang
The popular performance profiles and data profiles for benchmarking deterministic optimization algorithms are extended to benchmark stochastic algorithms for global optimization problems. A general confidence interval is employed to replace the significance test, which is popular in traditional benchmarking methods but suffering more and more criticisms. Through computing confidence bounds of the general confidence interval and visualizing them with performance profiles and (or) data profiles, our benchmarking method can be used to compare stochastic optimization algorithms by graphs...
February 7, 2017: IEEE Transactions on Cybernetics
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...
February 7, 2017: IEEE Transactions on Cybernetics
Justin R Klotz, Serhat Obuz, Zhen Kan, Warren E Dixon
A decentralized controller is designed for leader-based synchronization of communication-delayed networked agents. The agents have heterogeneous dynamics modeled by uncertain, nonlinear Euler-Lagrange equations of motion affected by heterogeneous, unknown, exogenous disturbances. The developed controller requires only one-hop (delayed) communication from network neighbors and the communication delays are assumed to be heterogeneous, uncertain, and time-varying. Each agent uses an estimate of communication delay to provide feedback of estimated recent tracking error...
February 7, 2017: IEEE Transactions on Cybernetics
Hyun Duck Choi, Choon Ki Ahn, Hamid Reza Karimi, Myo Taeg Lim
This paper studies delay-dependent exponential dissipative and l₂-l∞ filtering problems for discrete-time switched neural networks (DSNNs) including time-delayed states. By introducing a novel discrete-time inequality, which is a discrete-time version of the continuous-time Wirtinger-type inequality, we establish new sets of linear matrix inequality (LMI) criteria such that discrete-time filtering error systems are exponentially stable with guaranteed performances in the exponential dissipative and l₂-l∞ senses...
January 31, 2017: IEEE Transactions on Cybernetics
Sumin Zhang, Zhengming Ma, Hengliang Tan
Among the representative algorithms of manifold learning, Hessian locally linear embedding (HLLE) and local tangent space alignment (LTSA) algorithms haven been regarded as two different algorithms. However, in practice, the effects of these two algorithms are very similar and LTSA performs better than HLLE in some applications. This paper tries to account for this phenomenon from a mathematical point of view. There are only two differences between HLLE and LTSA. First, LTSA includes a data point into its neighborhood, while HLLE does not...
January 31, 2017: IEEE Transactions on Cybernetics
Yafeng Li, Changchun Hua, Shuangshuang Wu, Xinping Guan
In this paper, we study the problem of output feedback distributed containment control for a class of high-order nonlinear multiagent systems under a fixed undirected graph and a fixed directed graph, respectively. Only the output signals of the systems can be measured. The novel reduced order dynamic gain observer is constructed to estimate the unmeasured state variables of the system with the less conservative condition on nonlinear terms than traditional Lipschitz one. Via the backstepping method, output feedback distributed nonlinear controllers for the followers are designed...
January 31, 2017: IEEE Transactions on Cybernetics
Bin Xu, Fuchun Sun
This paper addresses the dynamic surface control of uncertain nonlinear systems on the basis of composite intelligent learning and disturbance observer in presence of unknown system nonlinearity and time-varying disturbance. The serial-parallel estimation model with intelligent approximation and disturbance estimation is built to obtain the prediction error and in this way the composite law for weights updating is constructed. The nonlinear disturbance observer is developed using intelligent approximation information while the disturbance estimation is guaranteed to converge to a bounded compact set...
January 31, 2017: IEEE Transactions on Cybernetics
Xiao Zeng, Hua Huang, Chun Qi
The quality of training data is very important for learning-based facial image super-resolution (SR). The more similarity between training data and testing input is, the better SR results we can have. To generate a better training set of low/high resolution training facial images for a particular testing input, this paper is the first work that proposes expanding the training data for improving facial image SR. To this end, observing that facial images are highly structured, we propose three constraints, i...
January 31, 2017: IEEE Transactions on Cybernetics
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