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Changes in brain functional network connectivity after stroke

Neural Regeneration Research 2014年第1期9卷 51-60页

作者： Wei Li Yapeng Li Wenzhen Zhu Xi Chen Key Laboratory of Image Processing and Intelligent Control Ministry of Education Department of Control Science and Engineering Huazhong University of Science and Technology Department of Radiology Tongji HospitalTongji Medical CollegeHuazhong University of Science and Technology

Studies have shown that functional network connection models can be used to study brain net- work changes in patients with schizophrenia. In this study, we inferred that these models could also be used to explore functional network connectivity changes in stroke patients. We used independent component analysis to find the motor areas of stroke patients, which is a novel way to determine these areas. In this study, we collected functional magnetic resonance imaging datasets from healthy controls and right-handed stroke patients following their first ever stroke. Using independent component analysis, six spatially independent components highly correlat- ed to the experimental paradigm were extracted. Then, the functional network connectivity of both patients and controls was established to observe the differences between them. The results showed that there were 11 connections in the model in the stroke patients, while there were only four connections in the healthy controls. Further analysis found that some damaged connections may be compensated for by new indirect connections or circuits produced after stroke. These connections may have a direct correlation with the degree of stroke rehabilitation. Our findings suggest that functional network connectivity in stroke patients is more complex than that in hea- lthy controls, and that there is a compensation loop in the functional network following stroke. This implies that functional network reorganization plays a very important role in the process of rehabilitation after stroke.

关键词： nerve regeneration brain injury stroke motor areas functional magnetic resonanceimaging brain network independent component analysis functional network connectivity neuralplasticity NSFC grant neural regeneration

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Reinforcement learning based predictive maintenance for a machine with multiple deteriorating yield levels

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Journal of Computational Information Systems 2014年第1期10卷 9-19页

作者： Wang, Xiao Wang, Hongwei Qi, Chao Sivakumar, Appa Iyer State Key Laboratory of Education Ministry for Image Processing and Intelligent Control Institute of Systems Engineering Huazhong University of Science and Technology Wuhan 430074 China School of Safety Engineering Shenyang Aerospace University Shenyang 110136 China Division of Systems and Engineering Management School of Mechanical and Aerospace Engineering Nanyang Technological University Singapore Singapore

This paper proposes a predictive maintenance methodology for a machine in manufacturing with deteriorating quality states represented by multiple deteriorating yield levels. Imperfect minor maintenance and perfect major repair are considered. The underlying yield level cannot be directly obtained. Instead, product quality inspection information is used as the observed system state. The optimal maintenance policy associated with each possible observed system state is learnt by modeling the problem as hidden semi-Markov decision processes and solving it using policy iteration based Q-P learning. Then the future maintenance time can be estimated by re-simulating the system model using the learned maintenance policy. A set of experimental studies is conducted to testify the effectiveness of the proposed methodology and to investigate the impacts of involved system parameters. Copyright © 2014 Binary Information Press.

关键词： Markov processes

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Adaptive pinning cluster synchronization of fractional-order complex dynamical networks

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Applied Mathematics and Computation 2014年 231卷 347-356页

作者： Guan-Sheng Wang Jiang-Wen Xiao Yan-Wu Wang Jing-Wen Yi School of Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Huazhong University of Science and Technology Wuhan 430074 PR China

The problem about cluster synchronization of fractional-order CDNs is studied via a pinning adaptive approach in this paper. Based on the stability theory of fractional differential equations, some sufficient criteria for local and global cluster synchronization of fractional-order CDNs are derived. In this paper, the coupling configuration matrix can be asymmetric as well as reducible and the inner coupling matrix can also be asymmetric. Moreover, the number of pinning nodes in each cluster can be evaluated. Especially, when the coupling strength is large enough and the coupling configuration matrix is symmetric, cluster synchronization can be achieved via pinning a single node in each cluster. Finally, some typical examples are given to illustrate the correctness and effectiveness of our results, a surprising finding is that the synchronization performance will become better as the fractional order decreases in this simulation.

关键词： Complex dynamical networks Cluster synchronization Fractional-order Pinning adaptive control

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DISCOVERING DISTINCTIVE ACTION PARTS FOR ACTION RECOGNITION

DISCOVERING DISTINCTIVE ACTION PARTS FOR ACTION RECOGNITION

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IEEE International Conference on image processing

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

ISBN: (纸本)9781479957521

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.

关键词： Action recognition Action part Exemplar LDA

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Multiple neural networks switched prediction for landslide displacement

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Engineering Geology 2014年 186卷 91-99页

作者： Lian, Cheng Zeng, Zhigang Yao, Wei Tang, Huiming School of Automation Huazhong University of Science and Technology Wuhan 430074 China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan 430074 China School of Computer Science South-Central University for Nationalities Wuhan 430074 China Faculty of Engineering China University of Geosciences Wuhan 430074 China

An accurate prediction of landslide displacement is challenging and of great interest to governments and researchers. In order to reduce the risk of selecting the types of influencing factors and artificial neural networks (ANNs), a multiple ANNs switched prediction method is proposed for landslide displacement forecasting. In the first stage, a set of individual neural networks are developed based on different environmental factors and/or different training algorithms. In the second stage, a switched prediction method is used to select the appropriate individual neural network for prediction purpose. For verification and testing, three typical landslides in Three Gorges Reservoir, namely Baishuihe landslide, Bazimen landslide and Shiliushubao landslide, are presented to test the effectiveness of our method. Application results demonstrate that the proposed method can significantly improve model generalization and perform similarly to, or better than, the best individual ANN predictor. © 2014 Elsevier B.V.

关键词： Forecasting

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Connectivity Exploration after Stroke with Structural Equation Modeling

Connectivity Exploration after Stroke with Structural Equati...

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2012 Third International Conference on Theoretical and Mathematical Foundations of Computer Science(ICTMF 2012)

作者： Wei Li Hongtao Tan Xiaojun Wang Zhiqiao Yan Department of Control Science and Engineering Image Processing and Intelligent Control Key Laboratory of Education Ministry of ChinaHuazhong University of Science and Technology

Neuroplasticity has been demonstrated to play an important role in function recovery *** this paper,stroke patients and controls were subjected to functional magnetic resonance imaging(fMRI) study for *** group independent componentanalysis was used to get the time courses of the interested regions in *** investigate the reorganization of cerebral cortex after stroke,Structural Equation Modeling(SEM) was applied to construct a locomotor brain network *** results were analyzed to investigate thebrain activity changes involved in the cerebral motor cortex circuitry aroused by the hand *** contrast to healthy people,we found that the brain activity changes of the stroke patientscould not onlyshow the local changes around injured regions,but also the global transformation of brain related to *** the differences were examined in terms of changes in path coefficients between brain regions.

关键词： fMRI,ICA,SEM,Stroke,Neuroplasticity

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Implementation of Memristive Neural Networks with Spike-rate-dependent Plasticity Synapses

Implementation of Memristive Neural Networks with Spike-rate...

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International Joint Conference on Neural Networks

作者： Yide Zhang Zhigang Zeng Shiping Wen School of Automation Huazhong University of Science and Technology and Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China

ISBN: (纸本)9781479914821

The property of changing resistance according to applied currents of memristors makes them candidates for emulating synapses in artificial neural networks. In this paper, we introduce a memristive synapse design into neural network circuits. Combined with modified integrate-and-fire (I&F) complementary metal-oxide-semiconducter (CMOS) neurons, the memristive neural network shows similarities to its biological counterpart, in respect of biologically realistic, current-controlled spikes and adaptive synaptic plasticity. Then, the spike-rate-dependent plasticity (SRDP) of the synapse, an extended protocol of the Hebbian learning rule, is originally implemented by the circuit. And some advanced neural activities including learning, associative memory and forgetting are realized based on the SRDP rule. These activities are comprehensively validated on a neural network circuit inspired by famous Pavlov's dog-experiment with simulations and quantitative analyses.

关键词： Neurons Memristors Biological neural networks Biological system modeling Protocols Semiconductor device modeling Integrated circuit modeling

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On the periodic dynamics of memristor-based neural networks with time-varying delays

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Information Sciences 2014年 279卷 358-373页

作者： Jiejie Chen Zhigang Zeng Ping Jiang School of Automation Huazhong University of Science and Technology Wuhan 430074 China Key Laboratory of Image Information Processing and Intelligent Control Ministry of Education Wuhan 430074 China

In this paper, we study the existence, uniqueness and stability of periodic solution for a wide class of memristor-based neural networks with time-varying delays. By employing the topological degree theory in set-valued analysis, differential inclusions theory and a new Lyapunov function method, we prove that the neural network has a unique periodic solution, which is globally exponentially stable. Moreover, we prove the existence, uniqueness and global exponential stability of equilibrium point for time-varying delayed memristor-based neural networks with constant coefficients. The obtained results improve and extend previous works on memristor-based or usual neural network dynamical systems with continuous or discontinuous right-hand side. Finally, two numerical examples are provided to show the applicability and effectiveness of our main results.

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Consensus of Edge Dynamics on Directed Multi-agent Systems

Consensus of Edge Dynamics on Directed Multi-agent Systems

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The 33th Chinese control Conference第33届中国控制会议

作者： Xiaofan Wang Xiaoling Wang Housheng Su Department of Automation Shanghai Jiaotong Universityand Key Laboratory of System Control and Infor School of Automation Image Processing and Intelligent Control Key Laboratory of Education Ministry o

ISBN: (纸本)9781479947249

Consensus in multi-agent systems means that reach an agreement among states of the whole ***,almost all the existed works on consensus have been in the description of the states of nodes,and fewer efforts have been devoted to the consensus taking place on the edges of multi-agent *** this paper,we focus on the dynamics proceed on the edges and establish a discrete-time and a continuous-time edge consensus protocols respectively for directed multi-agent *** mapping the edge topology to its line graph of the original nodal topology,we analyze the consensus of the edge protocols rigorously,and get that both the discrete-time protocol and the continuous-time protocol of directed multi-agent systems can guarantee that an edge consensus is asymptotically reached for all initial states when the original directed system is strongly *** simulations are provided to show the effectiveness of both the discrete-time and the continuous-time models.

关键词： Consensus Edge Protocol Multi-agent Systems Line Graph

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Exponential synchronization of coupled memristive neural networks via pinning control

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Chinese Physics B 2013年第5期22卷 203-212页

作者：王冠沈轶尹泉 Department of Control Science and Engineering Huazhong University of Science and Technology Key Laboratory of Ministry of Education for Image Processing and Intelligent Control

This paper is concerned with the exponential synchronization problem of coupled memristive neural networks. In contrast to general neural networks, memristive neural networks exhibit state-dependent switching behaviors due to the physical properties of memristors. Under a mild topology condition, it is proved that a small fraction of controlled sub- systems can efficiently synchronize the coupled systems. The pinned subsystems are identified via a search algorithm. Moreover, the information exchange network needs not to be undirected or strongly connected. Finally, two numerical simulations are performed to verify the usefulness and effectiveness of our results.

关键词： synchronization memristor coupled neural networks pinning control

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