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IEEE Transactions on Cybernetics

Qingpeng Zhang, Lu Zhong, Siyang Gao, Xiaoming Li
There are multiple modes for human immunodeficiency virus (HIV) transmissions, each of which is usually associated with a certain key population (e.g., needle sharing among people who inject drugs). Recent field studies revealed the merging trend of multiple key populations, making HIV intervention difficult because of the existence of multiple transmission modes in such complex multiplex social networks. In this paper, we aim to address this challenge by developing a multiplex social network framework to capture the multimode transmission across two key populations...
July 16, 2018: IEEE Transactions on Cybernetics
Junni Zou, Liwan Huang, Xiaofeng Gao, Hongkai Xiong
The cognitive radio technique allows secondary users (SUs) to share the spectrum with primary users (PUs) in an exclusive or opportunistic manner. This paper studies spectrum pricing conducted by spectrum owners, that is, primary operators (POs), and SU decision-making strategies for three kinds of duopoly markets. The single-band exclusive use market considers two POs with each providing a single band dedicated to SUs. A pre-emptive resume priority (PRP) M/M/1 queueing model is presented, based on which SUs decide to join which PO and which queue...
July 16, 2018: IEEE Transactions on Cybernetics
Xinli Shi, Jinde Cao, Guanghui Wen, Matjaz Perc
In this paper, some efficient criteria for finite-time consensus of a class of nonsmooth opinion dynamics over a digraph are established. The lower and upper bounds on the finite settling time are obtained based respectively on the maximal and minimal cut capacity of the digraph. By using tools of the nonsmooth theory and algebraic graph theory, the Carathéodory and Filippov solutions of nonsmooth opinion dynamics are analyzed and compared in detail. In the sense of Filippov solutions, the dynamic consensus is demonstrated without a leader and the finite-time bipartite consensus is also investigated in a signed digraph correspondingly...
July 16, 2018: IEEE Transactions on Cybernetics
Yunlong Yu, Zhong Ji, Jichang Guo, Zhongfei Zhang
Zero-shot learning (ZSL) is typically achieved by resorting to a class semantic embedding space to transfer the knowledge from the seen classes to unseen ones. Capturing the common semantic characteristics between the visual modality and the class semantic modality (e.g., attributes or word vector) is a key to the success of ZSL. In this paper, we propose a novel encoder-decoder approach, namely latent space encoding (LSE), to connect the semantic relations of different modalities. Instead of requiring a projection function to transfer information across different modalities like most previous work, LSE performs the interactions of different modalities via a feature aware latent space, which is learned in an implicit way...
July 16, 2018: IEEE Transactions on Cybernetics
Peng Dong, Zhongliang Jing, Henry Leung, Kai Shen, Minzhe Li
The traditional consensus-based filters are widely used in distributed sensor networks. However, they suffer from divergence when outliers occur. This paper proposes a robust consensus nonlinear information filter for distributed state estimation with measurement outliers. Unlike the Gaussian assumption in traditional consensus filers, the measurement of each sensor node is modeled here as a multivariate Student-t process with unknown parameters of the sufficient statistic. The variational Bayesian method is employed to jointly estimate the state and the parameters...
July 16, 2018: IEEE Transactions on Cybernetics
Zhanshan Wang, Nannan Rong, Huaguang Zhang
This paper investigates the finite-time decentralized control problem for interconnected systems with discontinuous interconnections. By using the interval type-2 Takagi-Sugeno (IT2 T-S) fuzzy model, a unified IT2 T-S fuzzy interconnected system is provided, in which the global system is described as a fuzzy blending of local subsystems under IF-THEN rules. In addition, based on the differential inclusion theory, the solutions of such discontinuous system are defined in the sense of Filippov. In order to stabilize the considered system in finite time, several decentralized discontinuous state feedback controllers are proposed...
July 16, 2018: IEEE Transactions on Cybernetics
Shaobo Lin, Jinshan Zeng
This paper proposes a new learning system of low computational cost, called fast polynomial kernel learning (FPL), based on regularized least squares with polynomial kernel and subsampling. The almost optimal learning rate as well as the feasibility verifications including the subsampling mechanism and solvability of FPL are provided in the framework of learning theory. Our theoretical assertions are verified by numerous toy simulations and real data applications. The studies in this paper show that FPL can reduce the computational burden of kernel methods without sacrificing its generalization ability very much...
July 13, 2018: IEEE Transactions on Cybernetics
Chang-Qin Huang, Ji-Kai Chen, Yan Pan, Han-Jiang Lai, Jian Yin, Qiong-Hao Huang
This paper considers a problem of landmark point detection in clothes, which is important and valuable for clothing industry. A novel method for landmark localization has been proposed, which is based on a deep end-to-end architecture using prior of key point associations. With the estimated landmark points as input, a deep network has been proposed to predict clothing categories and attributes. A systematic design of the proposed detecting system is implemented by using deep learning techniques and a large-scale clothes dataset containing 145,000 upper-body clothing images with landmark annotations...
July 12, 2018: IEEE Transactions on Cybernetics
Huaguang Zhang, Ji Han, Yingchun Wang, He Jiang
This paper studies the H∞ consensus problem for linear heterogeneous discrete-time multiagent systems (MASs). For a special kind of nonlinear matrix inequality, a novel method is provided to make these inequalities turn into some equivalent conditions that can be solved by utilizing the LMI toolbox. According to this method, a necessary and sufficient condition of H∞ consensus for linear heterogeneous discrete-time MAS with output feedback control scheme and a corresponding iterative algorithm are proposed, respectively...
July 12, 2018: IEEE Transactions on Cybernetics
Lixin Tang, Xianpeng Wang, Zhiming Dong
Due to the simple but effective search framework, differential evolution (DE) has achieved successful applications in multiobjective optimization problems. However, most of the previous research on the multiobjective DE (MODE) focused on the design of control strategies of parameters and mutation operators for a given population at each generation, and ignored that the given population might have a bad distribution in the objective space. Therefore, this paper proposes a new variant of MODE in which a reference axis vicinity mechanism (RAVM) is developed to restore the good distribution of the given population and maintain its convergence before the evolution (i...
July 12, 2018: IEEE Transactions on Cybernetics
Qiulei Dong, Hong Wang
How to implement an effective factorization for nonrigid structure from motion (NRSFM) has attracted much attention in recent years. A straightforward factorization scheme is to multilinearly solve NRSFM in an alternating manner, where each of the unknown variables in NRSFM is updated by fixing the others at each iteration. However, recent works show that most existing multilinear factorization (MLF) methods achieve poorer performances than some state-of-the-art sequential factorization methods. In this paper, we reinvestigate the MLF scheme for improving factorization accuracy, and first propose an MLF method with the only low-rank prior for NRSFM in the presence of missing data...
July 9, 2018: IEEE Transactions on Cybernetics
Xingguang Peng, Yaochu Jin, Handing Wang
Cooperative coevolutionary (CC) algorithms decompose a problem into several subcomponents and optimize them separately. Such a divide-and-conquer strategy makes CC algorithms potentially well suited for large-scale optimization. However, decomposition may be inaccurate, resulting in a wrong division of the interacting decision variables into different subcomponents and thereby a loss of important information about the topology of the overall fitness landscape. In this paper, we suggest an idea that concurrently searches for multiple optima and uses them as informative representatives to be exchanged among subcomponents for compensation...
July 6, 2018: IEEE Transactions on Cybernetics
Le Ou-Yang, Xiao-Fei Zhang, Xing-Ming Zhao, Debby D Wang, Fu Lee Wang, Baiying Lei, Hong Yan
Graphical models have been widely used to learn the conditional dependence structures among random variables. In many controlled experiments, such as the studies of disease or drug effectiveness, learning the structural changes of graphical models under two different conditions is of great importance. However, most existing graphical models are developed for estimating a single graph and based on a tacit assumption that there is no missing relevant variables, which wastes the common information provided by multiple heterogeneous data sets and underestimates the influence of latent/unobserved relevant variables...
July 6, 2018: IEEE Transactions on Cybernetics
Yangming Zhou, Jin-Kao Hao, Fred Glover
Critical node problems (CNPs) involve finding a set of critical nodes from a graph whose removal results in optimizing a predefined measure over the residual graph. As useful models for a variety of practical applications, these problems are computationally challenging. In this paper, we study the classic CNP and introduce an effective memetic algorithm for solving CNP. The proposed algorithm combines a double backbone-based crossover operator (to generate promising offspring solutions), a component-based neighborhood search procedure (to find high-quality local optima), and a rank-based pool updating strategy (to guarantee a healthy population)...
July 4, 2018: IEEE Transactions on Cybernetics
Xu Jin
Most works on iterative learning control (ILC) assume identical reference trajectories for the system state over the iteration domain. This fundamental assumption may not always hold in practice, where the desired trajectories or control objectives may be iteration dependent. In this paper, we relax this fundamental assumption, by introducing a new way of modifying the reference trajectories. The concept of modifier functions has been introduced for the first time in the ILC literature. This proposed approach is also a unified framework that can handle other common types of initial conditions in ILC...
July 3, 2018: IEEE Transactions on Cybernetics
Chang Liu, Wenguan Wang, Jianbing Shen, Ling Shao
We present an unsupervised segmentation framework for stereo videos using stereoscopic trajectories. The proposed stereo trajectory shows favorable properties for modeling the long-term motion information through the whole sequence and explicitly capturing the corresponding relationships between two stereo views. The stereo prior is important for inferring the desired object and guarantees the consistent spatial-temporal segmentation, which contributes to an enjoyable stereo experience. We start by deriving stereo trajectories from left and right views simultaneously, which are represented via a graph structure...
July 2, 2018: IEEE Transactions on Cybernetics
Liang Feng, Lei Zhou, Jinghui Zhong, Abhishek Gupta, Yew-Soon Ong, Kay-Chen Tan, A K Qin
Evolutionary multitasking (EMT) is an emerging research topic in the field of evolutionary computation. In contrast to the traditional single-task evolutionary search, EMT conducts evolutionary search on multiple tasks simultaneously. It aims to improve convergence characteristics across multiple optimization problems at once by seamlessly transferring knowledge among them. Due to the efficacy of EMT, it has attracted lots of research attentions and several EMT algorithms have been proposed in the literature...
July 2, 2018: IEEE Transactions on Cybernetics
Yuan Yuan, Zidong Wang, Peng Zhang, Hongjian Liu
In this paper, the near-optimal resilient control strategy design problem is investigated for a class of discrete time-varying system in simultaneous presence of stochastic communication protocols (SCPs), gain perturbations, state saturations, and additive nonlinearities. In the sensor-to-controller network, only one sensor is permitted to get access to the communication media so as to avoid possible data collisions. Described by a Markov chain, the SCP is employed to determine which sensor should obtain the access to the network at a certain time...
July 2, 2018: IEEE Transactions on Cybernetics
Jack Gaston, Ji Ming, Danny Crookes
Many approaches to unconstrained face identification exploit small patches which are unaffected by distortions outside their locality. A larger area usually contains more discriminative information, but may be unidentifiable due to local appearance changes across its area, given limited training data. We propose a novel block-based approach, as a complement to existing patch-based approaches, to exploit the greater discriminative information in larger areas, while maintaining robustness to limited training data...
June 28, 2018: IEEE Transactions on Cybernetics
Chong-Xiao Shi, Guang-Hong Yang
This paper studies the fault tolerant synchronization control for a class of derivative coupled complex dynamical networks (CDNs). Different from the existing results, each subsystem model is assumed to be completely unknown and the coupling terms are mismatched with the control input. Within this framework, a novel model-free fault tolerant controller is designed. Under the proposed control law, the synchronization errors of CDNs are proved to asymptotically converge to zero, which means that the synchronization is successfully achieved...
June 28, 2018: IEEE Transactions on Cybernetics
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