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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
Congqi Cao, Yifan Zhang, Chunjie Zhang, Hanqing Lu
3-D convolutional neural networks (3-D CNNs) have been established as a powerful tool to simultaneously learn features from both spatial and temporal dimensions, which is suitable to be applied to video-based action recognition. In this paper, we propose not to directly use the activations of fully connected layers of a 3-D CNN as the video feature, but to use selective convolutional layer activations to form a discriminative descriptor for video. It pools the feature on the convolutional layers under the guidance of body joint positions...
March 2018: IEEE Transactions on Cybernetics
Shuming Wang, Witold Pedrycz
In this paper, we consider a generic class of adaptive optimization problems under uncertainty, and develop a data-driven paradigm of adaptive probabilistic robust optimization (APRO) in a robust and computationally tractable manner. The paradigm comprises two phases: 1) bilayer information granulation (IG), which involves the data-mining techniques and nested decomposition of convex sets that establish and restructure the knowledge from data and 2) robustization and optimization over the restructured knowledge by the IG, which forms the APRO model...
February 2018: IEEE Transactions on Cybernetics
Jinna Li, Bahare Kiumarsi, Tianyou Chai, Frank L Lewis, Jialu Fan
Industrial flow lines are composed of unit processes operating on a fast time scale and performance measurements known as operational indices measured at a slower time scale. This paper presents a model-free optimal solution to a class of two time-scale industrial processes using off-policy reinforcement learning (RL). First, the lower-layer unit process control loop with a fast sampling period and the upper-layer operational index dynamics at a slow time scale are modeled. Second, a general optimal operational control problem is formulated to optimally prescribe the set-points for the unit industrial process...
December 2017: IEEE Transactions on Cybernetics
Partha Pratim Kundu, Sushmita Mitra
A novel similarity-based feature selection algorithm is developed, using the concept of distance correlation. A feature subset is selected in terms of this similarity measure between pairs of features, without assuming any underlying distribution of the data. The pair-wise similarity is then employed, in a message passing framework, to select a set of exemplars features involving minimum redundancy and reduced parameter tuning. The algorithm does not need an exhaustive traversal of the search space. The methodology is next extended to handle large data, using an inherent property of distance correlation...
December 2017: IEEE Transactions on Cybernetics
Gen Yang, Sebastien Destercke, Marie-Helene Masson
Indeterminate classifiers are cautious models able to predict more than one class in case of high uncertainty. A problem that arises when using such classifiers is how to evaluate their performances. This problem has already been considered in the case where all prediction errors have equivalent costs (that we will refer as the "0/1 costs" or accuracy setting). The purpose of this paper is to study the case of generic cost functions. We provide some properties that the costs of indeterminate predictions could or should follow, and review existing proposals in the light of those properties...
December 2017: IEEE Transactions on Cybernetics
Honghao Chang, Zuren Feng, Zhigang Ren
Many complex networks have been shown to have community structures. Detecting those structures is very important for understanding the organization and function of networks. Because this problem is NP-hard, it is appropriate to resort to evolutionary algorithms. Chemical reaction optimization (CRO) is a novel evolutionary algorithm inspired by the interactions among molecules during chemical reactions. In this paper, we propose a CRO variant named dual-representation CRO (DCRO) to address the community detection problem...
December 2017: IEEE Transactions on Cybernetics
Sen Bong Gee, Kay Chen Tan, Cesare Alippi
Evolutionary multiobjective optimization in dynamic environments is a challenging task, as it requires the optimization algorithm converging to a time-variant Pareto optimal front. This paper proposes a dynamic multiobjective optimization algorithm which utilizes an inverse model set to guide the search toward promising decision regions. In order to reduce the number of fitness evalutions for change detection purpose, a two-stage change detection test is proposed which uses the inverse model set to check potential changes in the objective function landscape...
December 2017: IEEE Transactions on Cybernetics
Joost Venrooij, Max Mulder, Mark Mulder, David A Abbink, Marinus M van Paassen, Frans C T van der Helm, Heinrich H Bulthoff
Biodynamic feedthrough (BDFT) refers to the feedthrough of vehicle accelerations through the human body, leading to involuntary control device inputs. BDFT impairs control performance in a large range of vehicles under various circumstances. Research shows that BDFT strongly depends on adaptations in the neuromuscular admittance dynamics of the human body. This paper proposes a model-based approach of BDFT mitigation that accounts for these neuromuscular adaptations. The method was tested, as proof-of-concept, in an experiment where participants inside a motion simulator controlled a simulated vehicle through a virtual tunnel...
December 2017: IEEE Transactions on Cybernetics
Ran Cheng, Yaochu Jin, Markus Olhofer, Bernhard Sendhoff
The interests in multiobjective and many-objective optimization have been rapidly increasing in the evolutionary computation community. However, most studies on multiobjective and many-objective optimization are limited to small-scale problems, despite the fact that many real-world multiobjective and many-objective optimization problems may involve a large number of decision variables. As has been evident in the history of evolutionary optimization, the development of evolutionary algorithms (EAs) for solving a particular type of optimization problems has undergone a co-evolution with the development of test problems...
December 2017: IEEE Transactions on Cybernetics
Sankar Kumar Pal, Debarati Bhunia Chakraborty
A new method of adaptive rule generation in granular computing framework is described based on rough rule base and granular flow graph, and applied for video tracking. In the process, several new concepts and operations are introduced, and methodologies formulated with superior performance. The flow graph enables in defining an intelligent technique for rule base adaptation where its characteristics in mapping the relevance of attributes and rules in decision-making system are exploited. Two new features, namely, expected flow graph and mutual dependency between flow graphs are defined to make the flow graph applicable in the tasks of both training and validation...
December 2017: IEEE Transactions on Cybernetics
Lorenzo Sabattini, Cristian Secchi, Cesare Fantuzzi
In this paper, we propose a novel methodology for achieving complex dynamic behaviors in multirobot systems. In particular, we consider a multirobot system partitioned into two subgroups: 1) dependent and 2) independent robots. Independent robots are utilized as a control input, and their motion is controlled in such a way that the dependent robots solve a tracking problem, that is following arbitrarily defined setpoint trajectories, in a coordinated manner. The control strategy proposed in this paper explicitly addresses the collision avoidance problem, utilizing a null space-based behavioral approach: this leads to combining, in a non conflicting manner, the tracking control law with a collision avoidance strategy...
December 2017: IEEE Transactions on Cybernetics
Jiuwen Cao, Wei Wang, Jianzhong Wang, Ruirong Wang
Excavation equipment recognition attracts increasing attentions in recent years due to its significance in underground pipeline network protection and civil construction management. In this paper, a novel classification algorithm based on acoustics processing is proposed for four representative excavation equipments. New acoustic statistical features, namely, the short frame energy ratio, concentration of spectrum amplitude ratio, truncated energy range, and interval of pulse are first developed to characterize acoustic signals...
December 2017: IEEE Transactions on Cybernetics
Zijia Lin, Guiguang Ding, Jungong Han, Jianmin Wang
For efficiently retrieving nearest neighbors from large-scale multiview data, recently hashing methods are widely investigated, which can substantially improve query speeds. In this paper, we propose an effective probability-based semantics-preserving hashing (SePH) method to tackle the problem of cross-view retrieval. Considering the semantic consistency between views, SePH generates one unified hash code for all observed views of any instance. For training, SePH first transforms the given semantic affinities of training data into a probability distribution, and aims to approximate it with another one in Hamming space, via minimizing their Kullback-Leibler divergence...
December 2017: IEEE Transactions on Cybernetics
Yanwei Pang, Jiale Cao, Xuelong Li
Cascaded AdaBoost classifier is a well-known efficient object detection algorithm. The cascade structure has many parameters to be determined. Most of existing cascade learning algorithms are designed by assigning detection rate and false positive rate to each stage either dynamically or statically. Their objective functions are not directly related to minimum computation cost. These algorithms are not guaranteed to have optimal solution in the sense of minimizing computation cost. On the assumption that a strong classifier is given, in this paper, we propose an optimal cascade learning algorithm (iCascade) which iteratively partitions the strong classifiers into two parts until predefined number of stages are generated...
December 2017: IEEE Transactions on Cybernetics
Xiubin Zhu, Witold Pedrycz, Zhiwu Li
Granular computing (GrC) has emerged as a unified conceptual and processing framework. Information granules are fundamental constructs that permeate concepts and models of GrC. This paper is concerned with a design of a collection of meaningful, easily interpretable ellipsoidal information granules with the use of the principle of justifiable granularity by taking into consideration reconstruction abilities of the designed information granules. The principle of justifiable granularity supports designing of information granules based on numeric or granular evidence, and aims to achieve a compromise between justifiability and specificity of the information granules to be constructed...
December 2017: IEEE Transactions on Cybernetics
Qi Kang, XiaoShuang Chen, SiSi Li, MengChu Zhou
Under-sampling is a popular data preprocessing method in dealing with class imbalance problems, with the purposes of balancing datasets to achieve a high classification rate and avoiding the bias toward majority class examples. It always uses full minority data in a training dataset. However, some noisy minority examples may reduce the performance of classifiers. In this paper, a new under-sampling scheme is proposed by incorporating a noise filter before executing resampling. In order to verify the efficiency, this scheme is implemented based on four popular under-sampling methods, i...
December 2017: IEEE Transactions on Cybernetics
Wanyuan Wang, Jiuchuan Jiang, Bo An, Yichuan Jiang, Bing Chen
Crowdsourcing has become a popular service computing paradigm for requesters to integrate the ubiquitous human-intelligence services for tasks that are difficult for computers but trivial for humans. This paper focuses on crowdsourcing complex tasks by team formation in social networks (SNs) where a requester connects to a large number of workers. A good indicator of efficient team collaboration is the social connection among workers. Most previous social team formation approaches, however, either assume that the requester can maintain information of all workers and can directly communicate with them to build teams, or assume that the workers are cooperative and be willing to join the specific team built by the requester, both of which are impractical in many real situations...
December 2017: IEEE Transactions on Cybernetics
Filippo Bistaffa, Nicola Bombieri, Alessandro Farinelli
Bucket elimination (BE) is a framework that encompasses several algorithms, including belief propagation (BP) and variable elimination for constraint optimization problems (COPs). BE has significant computational requirements that can be addressed by using graphics processing units (GPUs) to parallelize its fundamental operations, i.e., composition and marginalization, which operate on functions represented by large tables. We propose a novel approach to parallelize these operations with GPUs, which optimizes the table layout so to achieve better performance in terms of increased speedup and scalability...
November 2017: IEEE Transactions on Cybernetics
Xinyi Le, Sijie Chen, Zheng Yan, Juntong Xi
In this paper, a distributed neurodynamic approach is proposed for constrained convex optimization. The objective function is a sum of local convex subproblems, whereas the constraints of these subproblems are coupled. Each local objective function is minimized individually with the proposed neurodynamic optimization approach. Through information exchange between connected neighbors only, all nodes can reach consensus on the Lagrange multipliers of all global equality and inequality constraints, and the decision variables converge to the global optimum in a distributed manner...
October 18, 2017: IEEE Transactions on Cybernetics
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