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Communications in Computer and Information Science 2014年 461卷 268-277页

作者： Li, Xiangchen Chen, X. Wang, Wei School of Automation Huazhong University of Science and Technology Wuhan China Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Wuhan China

ISBN: (纸本)9783662452820

As a sociological phenomenon, rumor spreading has been widely researched by sociologists and other fields’ scholars. How do people generate those strange thinking about the rumor? This paper, from artificial intelligence view, focuses on an algorithm based on Hierarchical Temporal Memory (HTM) to simulate human cognitive process of generating rumors from individual heterogeneous living experience. Comparison with classical Bayesian Networks, our algorithm could effectively and really simulate human cognitive process of rumors, and in accordance with some typical psychology effects proved by sociologists and psychologists. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Bayesian networks

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Tissue P Systems with Look-ahead Mode

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Chinese Journal of Electronics 2014年第1期23卷 81-86页

作者： JIANG Yun SONG Tao ZHANG Zheng School of Computer Science and Information Engineering Chongqing Technology and Business University Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Department of Control Science and Engineering Huazhong University of Science and Technology

Tissue P systems are a class of distributed and parallel computing models inspired from inter-cellular communication and cooperation between cells. In this work, a variant of tissue P system, named tissue P system with look-ahead mode, is discussed for decreasing the inherent non-determinism of tissue P systems and helping implementing tissue P systems on computers. Such systems are proved to be universal by simulating register machine, and they are also proved to be able to efficiently solve computationally hard problems by means of a spacetime tradeoff, which is illustrated with a polynomial solution to 3-coloring problem.

关键词： Tissue P system Look-ahead mode Recursively enumerable language Cell separation 3-Coloring

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AN ADAPTIVE MEMBRANE ALGORITHM FOR SOLVING COMBINATORIAL OPTIMIZATION PROBLEMS

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Acta Mathematica Scientia 2014年第5期34卷 1377-1394页

作者：贺娟娟肖建华邵泽辉 Key Laboratory of Image Processing and Intelligent Control School of AutomationHuazhong University of Science and Technology The Research Center of Logistics Nankai University School of Information Science and Technology Chengdu University

Membrane algorithms （MAs）, which inherit from P systems, constitute a new parallel and distribute framework for approximate computation. In the paper, a membrane algorithm is proposed with the improvement that the involved parameters can be adaptively chosen. In the algorithm, some membranes can evolve dynamically during the computing process to specify the values of the requested parameters. The new algorithm is tested on a well-known combinatorial optimization problem, the travelling salesman problem. The em-pirical evidence suggests that the proposed approach is efficient and reliable when dealing with 11 benchmark instances, particularly obtaining the best of the known solutions in eight instances. Compared with the genetic algorithm, simulated annealing algorithm, neural net-work and a fine-tuned non-adaptive membrane algorithm, our algorithm performs better than them. In practice, to design the airline network that minimize the total routing cost on the CAB data with twenty-five US cities, we can quickly obtain high quality solutions using our algorithm.

关键词： membrane algorithm adaptation travelling salesman problem

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Cell resistance slope combined with LVQ neural network for prediction of anode effect

Cell resistance slope combined with LVQ neural network for p...

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International Conference on intelligent control and Information processing (ICICIP)

作者： Kaibo Zhou Zhikai Lin Dengzhi Yu Bin Cao Ziqian Wang Sihai Guo School of Automation Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Wuhan China Guiyang Aluminum Magnesium Design and Research Institute Guiyang China School of Automation Wuhan University of Technology Wuhan China

It is an important part in aluminum electrolysis production to control the anode effect (AE). Since there are some shortcomings in traditional methods of anode effect prediction in aluminum electrolysis, this paper combined two methods, the slope of cell resistance and learning vector quantization (LVQ) neural network, to predict anode effect. First of all, the first prediction of anode effect will be conducted based on the slope of cell resistance. Afterwards, the inaccurate data are supposed to be re-predicted. The second prediction consists of two steps, one is to estimate the power spectrum from the signal of cell resistance by means of periodogram, the other is to re-predict the anode effect with the LVQ neural network, since the energy of frequency bands are served as the input feature variables of neural network, so as to raise the accuracy of prediction. It turned out that the success rate of ten-minute in advance prediction for anode effect can be above 85%, though just cell resistance signal is studied.

关键词： Resistance Decision support systems Neural networks Spectral analysis Data acquisition Vector quantization

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Global coordinated tracking of multi-agent systems with disturbance uncertainties via bounded control inputs

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Nonlinear Dynamics 2015年第4期82卷 2059-2068页

作者： Su, Housheng Chen, Michael Z. Q. Wang, Xiaofan School of Automation Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan China Department of Mechanical Engineering The University of Hong Kong Pokfulam Hong Kong Department of Automation Shanghai Jiao Tong University Shanghai China

This paper investigates the problem of global coordinated tracking of a multi-agent system with input additive uncertainties and disturbances via bounded control inputs. Scheduled low-and-high gain feedback-based distributed coordinated tracking protocols are developed. It is shown that, under the assumptions that each agent is asymptotically null controllable with bounded controls and the network is connected, global coordinated tracking of the multi-agent system can be achieved. We finally show some numerical simulations to verify and illustrate the theoretical results.

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Self-triggered Based Semi-global Consensus Tracking of Multi-agent Systems with Input Saturation

Self-triggered Based Semi-global Consensus Tracking of Multi...

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第三十四届中国控制会议

作者： Xiaoling Wang Housheng Su Jiwei Li Xiaofan Wang Department of Automation Shanghai Jiaotong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 P. R. China School of Automation Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Luoyu Road 1037 Wuhan 430074 P. R. China

ISBN: (纸本)9781467374439

Self-triggered control is a technique which can determine the next sampling instant by virtue of the last sampling information. Combining this technique and the low-gain feedback one, we investigate the consensus tracking of general linear multi-agent systems with input saturation and updating delays in this paper. Specific formulations of heterogenous updating delays and heterogenous sampling periods are acquired to guarantee the semi-global consensus tracking of multi-agent systems. Numerical simulations are provided to verify the main results.

关键词： Multi-agent Systems Consensus Tracking Self-triggered Technique Input Saturation Low-gain Feedback Multi-agent systems sampling time tracking

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Motion control and fall detection of intelligent cane robot

Springer Tracts in Advanced Robotics

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Springer Tracts in Advanced Robotics 2015年 106卷 317-337页

作者： Fukuda, Toshio Huang, Jian Di, Pei Sekiyama, Kosuge Key Laboratory of Image Processing and Intelligent Control School of Automation Huazhong University of Science and Technology No. 1037 Luoyu Road Wuhan Hubei430074 China Department of Micro-Nano Systems Engineering Nagoya University Furo-cho Chikusa-ku Nagoya464-8603 Japan

An intelligent cane robot was designed for aiding the elderly and handicapped people walking. The robot consists of a stick, a group of sensors and an omni-directional basis driven by three Swedish wheels. Multiple sensors were used to recognize the user’s "walking intention", which is quantitatively described by a new concept called intentional direction (ITD). Based on the guidance of filtered ITD, a novel intentionbased admittance motion control (IBAC) scheme was proposed for the cane robot. The experiment results demonstrated that the user feels more natural and comfortable when walking with the assistance of cane robot controlled by the IBAC strategy. To detect the fall of user, a detection method based on Dubois possibility theory was proposed using the combined sensor information from force sensors and a laser ranger finder (LRF) fixed on the cane robot. The human fall model was represented in a two-dimensional space, where the relative position between the Zero- Moment-Point (ZMP) and the center of support triangle was utilized as a significant feature. The effectiveness of proposed fall detection method was also confirmed by experiments. © Springer International Publishing Switzerland 2015.

关键词： Motion control

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Discovering distinctive action parts for action recognition

Discovering distinctive action parts for action recognition

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作者： Chen, Feifei Sang, Nong Gao, Changxin Kuang, Xiaoqin Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Automation Huazhong University of Science and Technology China

ISBN: (纸本)9781479957514

Recent methods based on mid-level visual concepts have shown promising capability in human action recognition field. Automatically discovering semantic entities such as parts for an action class remains challenging. In this paper, we focus on discovering distinctive action parts for recognition of human actions by learning and selecting a small number of discriminative part detectors directly from training videos. We initially train a large collection of candidate Exemplar-LDA detectors from clusters obtained by clustering spatiotemporal patches in whitened space. A novel Coverage-Entropy curve is proposed as a means of measuring the representative and discriminative capabilities of part detectors, and used to select a set of compact and meaningful detectors out of the vast candidates. By integrating these mined detectors into 'bag of parts' representation, our approach demonstrates state-of-the-art performance on the UCF50 dataset. © 2014 IEEE.

关键词： Semantics

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Small universal simple spiking neural P systems with weights

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Science China(Information Sciences) 2014年第9期57卷 19-29页

作者： ZENG XiangXiang PAN LinQiang PREZ-JIMNEZ Mario J. Key Laboratory of Image Processing and Intelligent Control School of AutomationHuazhong University of Science and Technology Department of Computer Science and Artificial Intelligence University of SevillaAvda. Reina Mercedes s/nSevilla 41012Spain

Spiking neural P systems with weights(WSN P systems,for short)are a new variant of spiking neural P systems,where the rules of a neuron are enabled when the potential of that neuron equals a given *** is known that WSN P systems are universal by simulating register ***,in these universal systems,no bound is considered on the number of neurons and *** this work,a restricted variant of WSN P systems is considered,called simple WSN P systems,where each neuron has only one *** complexity parameter,the number of neurons,to construct a universal simple WSN P system is *** is proved that there is a universal simple WSN P system with 48 neurons for computing functions;as generator of sets of numbers,there is an almost simple(that is,each neuron has only one rule except that one neuron has two rules)and universal WSN P system with 45 neurons.

关键词： bio-inspired computing membrane computing P system spiking neural P system universal computing device

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Spiking neural P systems with astrocytes using the rules in the exhaustive mode

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8961卷 274-283页

作者： Kong, Yuan Zhao, Dongming Key Laboratory of Image Processing and Intelligent Control School of Automation Huazhong University of Science and Technology WuhanHubei430074 China School of Automation Wuhan University of Technology WuhanHubei430070 China

Spiking neural P systems (SN P systems, for short) are a class of distributed parallel computing devices inspired by the way neurons communicate by means of electrical impulses or spikes. SN P systems with astrocytes are a new variant of SN P systems, where astrocytes are introduced to control the amount of spikes passing along synapses. In this work, we investigate the computation power of SN P systems with astrocytes with the rules in any neuron used in the exhaustive manner, that is, the enabled rule in any neuron should be used as many times as possible at any moment. Specifically, it is obtained that such SN P systems can compute/generate any set of Turing computable natural numbers. © Springer International Publishing Switzerland 2014.

关键词： Neurons

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