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IEEE Transactions on Cybernetics

Rashad S Oreifej, Rawad Al-Haddad, Ramtin Zand, Rizwan A Ashraf, Ronald F DeMara
A resilient system design problem is formulated as the quantification of uncommitted reconfigurable resources required for a system of components to survive its lifetime within mission availability specifications. We show that this survivability metric can be calculated according to the residual functionality obtained from pools of dynamically configurable elements constituting the amorphous resource pool (ARP). The ARP is depleted based on the failure rate to replenish the functionality lost in a reconfigurable fabric due to the occurrence of permanent faults during the mission lifetime...
March 21, 2017: IEEE Transactions on Cybernetics
Wei Li
This paper considers the designated convergence rate (DCR) (or the designated convergence margin) problems of consensus or flocking of coupled double-integrator agents. The DCR problems are more valuable for systems design than just convergence or stability conditions. The system setting in this paper is general, i.e., the velocity coupling and position coupling (VCPC) between agents, respectively, are set to be general and nonequal (up to rescaling), together with distinct damping and stiffness gains for the VCPC, respectively...
March 14, 2017: IEEE Transactions on Cybernetics
Leping Lin, Fang Liu, Licheng Jiao, Shuyuan Yang, Hongxia Hao
In this paper, it is proposed the directional estimation model on the overcomplete dictionary, which bridges the compressed measurements of the image blocks and the directional structures of the dictionary. In the model, it is established the analytical method to estimate the structure type of a block as either smooth, single-oriented, or multioriented. Furthermore, the structures of each type of blocks are described by the structured subdictionaries. Then based on the obtained estimations and the constrains on the sparse dictionaries, the original image will be estimated...
March 10, 2017: IEEE Transactions on Cybernetics
Yue Li, Lu Liu, Gang Feng
This paper studies the adaptive finite-time stabilization problem for a class of nonlinear systems described by Takagi-Sugeno (T-S) fuzzy dynamic models with parametric uncertainties. A novel adaptive state feedback control scheme for the T-S fuzzy systems is proposed, and the scheme is developed based on finite-time Lyapunov theorem and adaptive backstepping-like method. Augmented dynamics are introduced in the design of finite-time stabilization controllers to construct suitable finite-time Lyapunov functions...
March 7, 2017: IEEE Transactions on Cybernetics
Chao Zhai, Francesco Alderisio, Piotr Slowinski, Krasimira Tsaneva-Atanasova, Mario di Bernardo
The mirror game has been recently proposed as a simple, yet powerful paradigm for studying interpersonal interactions. It has been suggested that a virtual partner able to play the game with human subjects can be an effective tool to affect the underlying neural processes needed to establish the necessary connections between the players, and also to provide new clinical interventions for rehabilitation of patients suffering from social disorders. Inspired by the motor processes of the central nervous system (CNS) and the musculoskeletal system in the human body, in this paper we develop a novel interactive cognitive architecture based on nonlinear control theory to drive a virtual player (VP) to play the mirror game with a human player (HP) in different configurations...
March 7, 2017: IEEE Transactions on Cybernetics
Licheng Wang, Zidong Wang, Qing-Long Han, Guoliang Wei
This paper is concerned with the distributed H∞ filtering problem for a class of discrete time-varying stochastic parameter systems with error variance constraints over a sensor network where the sensor outputs are subject to successive missing measurements. The phenomenon of the successive missing measurements for each sensor is modeled via a sequence of mutually independent random variables obeying the Bernoulli binary distribution law. To reduce the frequency of unnecessary data transmission and alleviate the communication burden, an event-triggered mechanism is introduced for the sensor node such that only some vitally important data is transmitted to its neighboring sensors when specific events occur...
March 6, 2017: IEEE Transactions on Cybernetics
Ronghua Shang, Wenbing Wang, Rustam Stolkin, Licheng Jiao
Feature selection is an important approach for reducing the dimension of high-dimensional data. In recent years, many feature selection algorithms have been proposed, but most of them only exploit information from the data space. They often neglect useful information contained in the feature space, and do not make full use of the characteristics of the data. To overcome this problem, this paper proposes a new unsupervised feature selection algorithm, called non-negative spectral learning and sparse regression-based dual-graph regularized feature selection (NSSRD)...
March 6, 2017: IEEE Transactions on Cybernetics
Yue Long, Ju H Park, Dan Ye
This paper addresses a novel transmission-dependent fault detection and isolation (FDI) scheme in finite frequency domain for networked control systems with consideration of limited communication capacity, which includes multiple transmission intervals and delays, media accessing constraints as well as packet losses. By focusing on these phenomena, a switched stochastic system with multistochastic parameters is first modeled to represent the network-induced features. Then, with the aid of finite frequency stochastic performance index and geometric analysis method, a novel FDI scheme is proposed in finite frequency domain...
March 3, 2017: IEEE Transactions on Cybernetics
Dayong Ye, Minjie Zhang
Sleep/wake-up scheduling is one of the fundamental problems in wireless sensor networks, since the energy of sensor nodes is limited and they are usually unrechargeable. The purpose of sleep/wake-up scheduling is to save the energy of each node by keeping nodes in sleep mode as long as possible (without sacrificing packet delivery efficiency) and thereby maximizing their lifetime. In this paper, a self-adaptive sleep/wake-up scheduling approach is proposed. Unlike most existing studies that use the duty cycling technique, which incurs a tradeoff between packet delivery delay and energy saving, the proposed approach, which does not us duty cycling, avoids such a tradeoff...
March 3, 2017: IEEE Transactions on Cybernetics
Chen Gong, Tongliang Liu, Yuanyan Tang, Jian Yang, Jie Yang, Dacheng Tao
Different from the traditional supervised learning in which each training example has only one explicit label, superset label learning (SLL) refers to the problem that a training example can be associated with a set of candidate labels, and only one of them is correct. Existing SLL methods are either regularization-based or instance-based, and the latter of which has achieved state-of-the-art performance. This is because the latest instance-based methods contain an explicit disambiguation operation that accurately picks up the groundtruth label of each training example from its ambiguous candidate labels...
February 24, 2017: IEEE Transactions on Cybernetics
Bin Ji, Xiaohui Yuan, Yanbin Yuan
Continuous berth allocation problem (BAPC) is a major optimization problem in transportation engineering. It mainly aims at minimizing the port stay time of ships by optimally scheduling ships to the berthing areas along quays while satisfying several practical constraints. Most of the previous literatures handle the BAPC by heuristics with different constraint handling strategies as it is proved NP-hard. In this paper, we transform the constrained single-objective BAPC (SBAPC) model into unconstrained multiobjective BAPC (MBAPC) model by converting the constraint violation as another objective, which is known as the multiobjective optimization (MOO) constraint handling technique...
February 24, 2017: IEEE Transactions on Cybernetics
Ruoxu Ren, Terence Hung, Kay Chen Tan
Automated surface inspection (ASI) is a challenging task in industry, as collecting training dataset is usually costly and related methods are highly dataset-dependent. In this paper, a generic approach that requires small training data for ASI is proposed. First, this approach builds classifier on the features of image patches, where the features are transferred from a pretrained deep learning network. Next, pixel-wise prediction is obtained by convolving the trained classifier over input image. An experiment on three public and one industrial data set is carried out...
February 24, 2017: IEEE Transactions on Cybernetics
Lin Xu, Shaobo Lin, Jinshan Zeng, Xia Liu, Yi Fang, Zongben Xu
Orthogonal greedy learning (OGL) is a stepwise learning scheme that starts with selecting a new atom from a specified dictionary via the steepest gradient descent (SGD) and then builds the estimator through orthogonal projection. In this paper, we found that SGD is not the unique greedy criterion and introduced a new greedy criterion, called as ''δ-greedy threshold'' for learning. Based on this new greedy criterion, we derived a straightforward termination rule for OGL. Our theoretical study shows that the new learning scheme can achieve the existing (almost) optimal learning rate of OGL...
February 23, 2017: IEEE Transactions on Cybernetics
Hyo-Sung Ahn, Byeong-Yeon Kim, Young-Hun Lim, Byung-Hun Lee, Kwang-Kyo Oh
This paper proposes three coordination laws for optimal energy generation and distribution in energy network, which is composed of physical flow layer and cyber communication layer. The physical energy flows through the physical layer; but all the energies are coordinated to generate and flow by distributed coordination algorithms on the basis of communication information. First, distributed energy generation and energy distribution laws are proposed in a decoupled manner without considering the interactive characteristics between the energy generation and energy distribution...
February 23, 2017: IEEE Transactions on Cybernetics
Changzhu Zhang, Jinfei Hu, Jianbin Qiu, Qijun Chen
Due to the unavailability of full state variables in many control systems, this paper is concerned with the design of reliable observer-based output feedback controller for a class of network-based Takagi-Sugeno fuzzy systems with actuator failures. In order to better allocate network resources under the case that the sensor nodes are physically distributed, the decentralized event triggering communication scheme is adopted such that each sensor node is capable to determine the transmission of its local measurement information independently...
February 23, 2017: IEEE Transactions on Cybernetics
Huanqing Wang, Peter Xiaoping Liu, Peng Shi
This paper is concerned with the observer-based fuzzy output-feedback control for stochastic nonlinear multiple time-delay systems. On the basis of the consistent form of virtual input signals and increasing characteristics of the system upper bound functions, a variable splitting technique is employed to surmount the difficulty occurred in the nonlower-triangular form. In the controller design procedure, a state observer is first designed, and then an adaptive fuzzy output-feedback control method is presented by combining backstepping design together with fuzzy systems' universal approximation capability...
February 21, 2017: IEEE Transactions on Cybernetics
Sujata Pal, Barun Kumar Saha, Sudip Misra
In cooperative communication, a set of players forming a coalition ensures communal behavior among themselves by helping one another in message forwarding. Opportunistic mobile networks (OMNs) require multihop communications for transferring messages from the source to the destination nodes. However, noncooperative nodes only forward their own messages to others, and drop others' messages upon receiving them. So, the message delivery overhead increases in OMN. For minimizing the overhead and maximizing the delivery rate, we propose two coalition-based cooperative schemes: 1) simple coalition formation (SCF) and 2) overlapping coalition formation (OCF) game...
February 21, 2017: IEEE Transactions on Cybernetics
Minnan Luo, Xiaojun Chang, Liqiang Nie, Yi Yang, Alexander G Hauptmann, Qinghua Zheng
Video semantic recognition usually suffers from the curse of dimensionality and the absence of enough high-quality labeled instances, thus semisupervised feature selection gains increasing attentions for its efficiency and comprehensibility. Most of the previous methods assume that videos with close distance (neighbors) have similar labels and characterize the intrinsic local structure through a predetermined graph of both labeled and unlabeled data. However, besides the parameter tuning problem underlying the construction of the graph, the affinity measurement in the original feature space usually suffers from the curse of dimensionality...
February 20, 2017: IEEE Transactions on Cybernetics
Soroush Haeri, Ljiljana Trajkovic
Network virtualization helps overcome shortcomings of the current Internet architecture. The virtualized network architecture enables coexistence of multiple virtual networks (VNs) on an existing physical infrastructure. VN embedding (VNE) problem, which deals with the embedding of VN components onto a physical network, is known to be NP-hard. In this paper, we propose two VNE algorithms: MaVEn-M and MaVEn-S. MaVEn-M employs the multicommodity flow algorithm for virtual link mapping while MaVEn-S uses the shortest-path algorithm...
February 20, 2017: 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
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