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IEEE Transactions on Cybernetics

Xian-Ming Zhang, Qing-Long Han, Zhigang Zeng
This paper is concerned with global asymptotic stability of delayed neural networks. Notice that a Bessel-Legendre inequality plays a key role in deriving less conservative stability criteria for delayed neural networks. However, this inequality is in the form of Legendre polynomials and the integral interval is fixed on . As a result, the application scope of the Bessel-Legendre inequality is limited. This paper aims to develop the Bessel-Legendre inequality method so that less conservative stability criteria are expected...
May 2018: IEEE Transactions on Cybernetics
Liyuan Li, Qianli Xu, Tian Gan, Cheston Tan, Joo-Hwee Lim
Social working memory (SWM) plays an important role in navigating social interactions. Inspired by studies in psychology, neuroscience, cognitive science, and machine learning, we propose a probabilistic model of SWM to mimic human social intelligence for personal information retrieval (IR) in social interactions. First, we establish a semantic hierarchy as social long-term memory to encode personal information. Next, we propose a semantic Bayesian network as the SWM, which integrates the cognitive functions of accessibility and self-regulation...
May 2018: IEEE Transactions on Cybernetics
Lei Ding, Qing-Long Han, Xiaohua Ge, Xian-Ming Zhang
Event-triggered consensus of multiagent systems (MASs) has attracted tremendous attention from both theoretical and practical perspectives due to the fact that it enables all agents eventually to reach an agreement upon a common quantity of interest while significantly alleviating utilization of communication and computation resources. This paper aims to provide an overview of recent advances in event-triggered consensus of MASs. First, a basic framework of multiagent event-triggered operational mechanisms is established...
April 2018: IEEE Transactions on Cybernetics
Kangkang Sun, Shuai Sui, Shaocheng Tong
This paper considers the optimal decentralized fuzzy adaptive control design problem for a class of interconnected large-scale nonlinear systems in strict feedback form and with unknown nonlinear functions. The fuzzy logic systems are introduced to learn the unknown dynamics and cost functions, respectively, and a state estimator is developed. By applying the state estimator and the backstepping recursive design algorithm, a decentralized feedforward controller is established. By using the backstepping decentralized feedforward control scheme, the considered interconnected large-scale nonlinear system in strict feedback form is changed into an equivalent affine large-scale nonlinear system...
April 2018: IEEE Transactions on Cybernetics
Licheng Liu, C L Philip Chen, Shutao Li, Yuan Yan Tang, Long Chen
Recently, locality-constrained linear coding (LLC) has been drawn great attentions and been widely used in image processing and computer vision tasks. However, the conventional LLC model is always fragile to outliers. In this paper, we present a robust locality-constrained bi-layer representation model to simultaneously hallucinate the face images and suppress noise and outliers with the assistant of a group of training samples. The proposed scheme is not only able to capture the nonlinear manifold structure but also robust to outliers by incorporating a weight vector into the objective function to subtly tune the contribution of each pixel offered in the objective...
April 2018: IEEE Transactions on Cybernetics
Changhao Sun
Toward the global optimality of the SET K-COVER problem in wireless sensor networks, we view each sensor node as a rational player and propose a time variant log-linear learning algorithm (TVLLA) that relies on local information only. By defining the local utility as the normalized area covered by one node alone, we formulate the problem as a spatial potential game. The resulting optimal Nash equilibria correspond to the optimal partition. Such equilibria are obtained by designing a time varying parameter that approaches infinity with time...
April 2018: IEEE Transactions on Cybernetics
Xiaoge Zhang, Sankaran Mahadevan
Finding an equilibrium state of the traffic assignment plays a significant role in the design of transportation networks. We adapt the path finding mathematical model of slime mold Physarum polycephalum to solve the traffic equilibrium assignment problem. We make three contributions in this paper. First, we propose a generalized Physarum model to solve the shortest path problem in directed and asymmetric graphs. Second, we extend it further to resolve the network design problem with multiple source nodes and sink nodes...
April 2018: IEEE Transactions on Cybernetics
Yao Sui, Guanghui Wang, Li Zhang
This paper presents a novel visual tracking approach to correlation filter learning toward peak strength of correlation response. Previous methods leverage all features of the target and the immediate background to learn a correlation filter. Some features, however, may be distractive to tracking, like those from occlusion and local deformation, resulting in unstable tracking performance. This paper aims at solving this issue and proposes a novel algorithm to learn the correlation filter. The proposed approach, by imposing an elastic net constraint on the filter, can adaptively eliminate those distractive features in the correlation filtering...
April 2018: IEEE Transactions on Cybernetics
Qiuping Jiang, Feng Shao, Weisi Lin, Gangyi Jiang
The goal of image retargeting is to adapt source images to target displays with different sizes and aspect ratios. Different retargeting operators create different retargeted images, and a key problem is to evaluate the performance of each retargeting operator. Subjective evaluation is most reliable, but it is cumbersome and labor-consuming, and more importantly, it is hard to be embedded into online optimization systems. This paper focuses on exploring the effectiveness of sparse representation for objective image retargeting quality assessment...
April 2018: IEEE Transactions on Cybernetics
Kim Schouten, Onne van der Weijde, Flavius Frasincar, Rommert Dekker
Using online consumer reviews as electronic word of mouth to assist purchase-decision making has become increasingly popular. The Web provides an extensive source of consumer reviews, but one can hardly read all reviews to obtain a fair evaluation of a product or service. A text processing framework that can summarize reviews, would therefore be desirable. A subtask to be performed by such a framework would be to find the general aspect categories addressed in review sentences, for which this paper presents two methods...
April 2018: IEEE Transactions on Cybernetics
Xin Luo, MengChu Zhou, Shuai Li, YunNi Xia, Zhu-Hong You, QingSheng Zhu, Hareton Leung
Generating highly accurate predictions for missing quality-of-service (QoS) data is an important issue. Latent factor (LF)-based QoS-predictors have proven to be effective in dealing with it. However, they are based on first-order solvers that cannot well address their target problem that is inherently bilinear and nonconvex, thereby leaving a significant opportunity for accuracy improvement. This paper proposes to incorporate an efficient second-order solver into them to raise their accuracy. To do so, we adopt the principle of Hessian-free optimization and successfully avoid the direct manipulation of a Hessian matrix, by employing the efficiently obtainable product between its Gauss-Newton approximation and an arbitrary vector...
April 2018: IEEE Transactions on Cybernetics
Yuanqing Wu, Renquan Lu, Peng Shi, Hongye Su, Zheng-Guang Wu
In this paper, we investigate the synchronization for heterogeneous network subject to event-triggering communication. The designed controller for each node includes reference generator (RG) and regulator. The predicted value of relative information between intermittent communication can significantly reduce the transmitted information. Based on the event triggering strategy and time-dependent threshold, all RGs can exponentially track the target trajectory. Then by the action of regulator, each node synchronizes with its RG...
April 2018: IEEE Transactions on Cybernetics
Kasper van der El, Daan M Pool, Marinus Rene M van Paassen, Max Mulder
This paper investigates how humans use a previewed target trajectory for control in tracking tasks with various controlled element dynamics. The human's hypothesized "near" and "far" control mechanisms are first analyzed offline in simulations with a quasi-linear model. Second, human control behavior is quantified by fitting the same model to measurements from a human-in-the-loop experiment, where subjects tracked identical target trajectories with a pursuit and a preview display, each with gain, single-, and double-integrator controlled element dynamics...
April 2018: IEEE Transactions on Cybernetics
Ankita Mandal, Pradipta Maji
One of the main problems associated with high dimensional multimodal real life data sets is how to extract relevant and significant features. In this regard, a fast and robust feature extraction algorithm, termed as FaRoC, is proposed, integrating judiciously the merits of canonical correlation analysis (CCA) and rough sets. The proposed method extracts new features sequentially from two multidimensional data sets by maximizing their relevance with respect to class label and significance with respect to already-extracted features...
April 2018: IEEE Transactions on Cybernetics
Tingrui Han, Zhiyun Lin, Ronghao Zheng, Minyue Fu
This paper investigates two formation control problems for a leader-follower network in 3-D. One is called the formation marching control problem, the objective of which is to steer the agents to maintain a target formation shape while moving with the synchronized velocity. The other one is called the formation rotating control problem, whose goal is to drive the agents to rotate around a common axis with a target formation. For the above two problems, we consider directed and switching sensing topologies while the communication is assumed to be bidirectional and switching...
April 2018: IEEE Transactions on Cybernetics
Aida Brankovic, Alessandro Falsone, Maria Prandini, Luigi Piroddi
We here introduce a novel classification approach adopted from the nonlinear model identification framework, which jointly addresses the feature selection (FS) and classifier design tasks. The classifier is constructed as a polynomial expansion of the original features and a selection process is applied to find the relevant model terms. The selection method progressively refines a probability distribution defined on the model structure space, by extracting sample models from the current distribution and using the aggregate information obtained from the evaluation of the population of models to reinforce the probability of extracting the most important terms...
April 2018: IEEE Transactions on Cybernetics
Sen Jia, Linlin Shen, Jiasong Zhu, Qingquan Li
As manual labeling is very difficult and time-consuming, the labeled samples used to train a supervised classifier are generally limited, which become one of the biggest challenge for hyperspectral imagery classification. In order to tackle this issue, a recent trend is to exploit the structure information of materials, as which reflects the region homogeneity in the spatial domain and offers an invaluable complement to the spectral information. In this respect, 3-D Gabor wavelets have been introduced to extract joint spectral-spatial features for hyperspectral images...
April 2018: IEEE Transactions on Cybernetics
Leo Yu Zhang, Yuansheng Liu, Fabio Pareschi, Yushu Zhang, Kwok-Wo Wong, Riccardo Rovatti, Gianluca Setti
The need for fast and strong image cryptosystems motivates researchers to develop new techniques to apply traditional cryptographic primitives in order to exploit the intrinsic features of digital images. One of the most popular and mature technique is the use of complex dynamic phenomena, including chaotic orbits and quantum walks, to generate the required key stream. In this paper, under the assumption of plaintext attacks we investigate the security of a classic diffusion mechanism (and of its variants) used as the core cryptographic primitive in some image cryptosystems based on the aforementioned complex dynamic phenomena...
April 2018: IEEE Transactions on Cybernetics
Qin Zou, Lihao Ni, Qian Wang, Qingquan Li, Song Wang
Gait has been considered as a promising and unique biometric for person identification. Traditionally, gait data are collected using either color sensors, such as a CCD camera, depth sensors, such as a Microsoft Kinect, or inertial sensors, such as an accelerometer. However, a single type of sensors may only capture part of the dynamic gait features and make the gait recognition sensitive to complex covariate conditions, leading to fragile gait-based person identification systems. In this paper, we propose to combine all three types of sensors for gait data collection and gait recognition, which can be used for important identification applications, such as identity recognition to access a restricted building or area...
April 2018: IEEE Transactions on Cybernetics
Yu-Feng Yu, Chuan-Xian Ren, Dao-Qing Dai, Ke-Kun Huang
Local feature descriptor plays a key role in different image classification applications. Some of these methods such as local binary pattern and image gradient orientations have been proven effective to some extent. However, such traditional descriptors which only utilize single-type features, are deficient to capture the edges and orientations information and intrinsic structure information of images. In this paper, we propose a kernel embedding multiorientation local pattern (MOLP) to address this problem...
April 2018: IEEE Transactions on Cybernetics
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