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arXiv 2018年

作者： Yao, Nan Huang, Zi-Gang Ren, Hai-Peng Grebogi, Celso Lai, Ying-Cheng Department of Applied Physics Xi’an University of Technology Xi’an710048 China School of Life Science and Technology Xi’an Jiao Tong University Xi’an710049 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an710048 China Institute for Complex Systems and Mathematical Biology King’s College University of Aberdeen AberdeenAB24 3UE United Kingdom School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States Department of Physics Arizona State University TempeAZ85287 United States

Chimera states in spatiotemporal dynamical systems have been investigated in physical, chemical, and biological systems, and have been shown to be robust against random perturbations. How do chimera states achieve their robustness? We uncover a self-adaptation behavior by which, upon a spatially localized perturbation, the coherent component of the chimera state spontaneously drifts to an optimal location as far away from the perturbation as possible, exposing only its incoherent component to the perturbation to minimize the disturbance. A systematic numerical analysis of the evolution of the spatiotemporal pattern of the chimera state towards the optimal stable state reveals an exponential relaxation process independent of the spatial location of the perturbation, implying that its effects can be modeled as restoring and damping forces in a mechanical system and enabling the articulation of a phenomenological model. Not only is the model able to reproduce the numerical results, it can also predict the trajectory of drifting. Our finding is striking as it reveals that, inherently, chimera states possess a kind of "intelligence" in achieving robustness through self adaptation. The behavior can be exploited for controlled generation of chimera states with their coherent component placed in any desired spatial region of the system. Copyright © 2018, The Authors. All rights reserved.

关键词： Dynamical systems

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Bifurcation behaviors of an Euler discretized inertial delayed neuron model

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Science China(Technological Sciences) 2016年第3期59卷 418-427页

作者： HE Xing LI ChuanDong HUANG TingWen YU JunZhi School of Electronics and Information Engineering Southwest University Chongqing 400715 China Texas A & M University at Qatar Doha 5825 Qatar State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China

This paper presents an Euler discretized inertial delayed neuron model, and its bifurcation dynamical behaviors are discussed. By using the associated characteristic model, center manifold theorem and the normal form method, it is shown that the model not only undergoes codimension one(flip, Neimark-Sacker) bifurcation, but also undergoes cusp and resonance bifurcation(1:1 and 1:2) of codimension two. Further, it is found that the parity of delay has some effect on bifurcation behaviors. Finally, some numerical simulations are given to support the analytic results and explore complex dynamics, such as periodic orbits near homoclinic orbits, quasiperiodic orbits, and chaotic orbits.

关键词： resonance bifurcation Euler discretized inertial delayed neural network

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control 5.0: From Newton to Merton in Popper's Cyber-Social-Physical Spaces

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IEEE/CAA Journal of Automatica Sinica 2016年第3期3卷 233-234页

作者： Fei-Yue Wang IEEE State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences (SKL-MCCS CASIA) Research Center of Computational Experiments and Parallel Systems The National University of Defense Technology

The future of control in cyberspace of parallel worlds is discussed. It argues for the coming age of control 5.0,the control technology for the new IT capable of dealing with artificial worlds with VR, AR, AI and robotics. The discipline of automation needs a new interpretation of its core knowledge and skill set of modeling, analysis, and control for cyber-socialphysical systems, and a paradigm shift from Newtonian systems with Newton's Laws or Big Laws with Small Data to Mertonian systems with Merton's Laws or Small Laws with Big Data.

关键词： control 5.0 parallel control knowledge automation CPS CSP CPSS Merton Wiener Popper

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Who will win practical artificial intelligence? AI engineerings in China

arXiv

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arXiv 2017年

作者： Wu, Huai-Yu Wang, Feiyue Pan, Chunhong National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

Currently, Artificial Intelligence (AI) has won unprecedented attention and is becoming the increasingly popular focus in China. This change can be judged by the impressive record of academic publications, the amount of state-level investment and the presence of nationwide participation and devotion. In this paper, we place emphasis on discussing the progress of artificial intelligence engineerings in China. We first introduce the focus on AI in Chinese academia, including the supercomputing brain system, Cambrian Period supercomputer of neural networks, and biometric systems. Then, the development of AI in industrial circles and the latest layout of AI products in companies, such as Baidu, Tencent, and Alibaba, are introduced. Last, we bring in the opinions and arguments of the main intelligentsia of China about the future development of AI, including how to examine the relationship between humanity on one side and science and technology on the other. Copyright © 2017, The Authors. All rights reserved.

关键词： Artificial intelligence

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Data-based robust near-optimal decentralized stabilization of unknown large-scale systems

Data-based robust near-optimal decentralized stabilization o...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Bo Zhao Derong Liu Yuanchun Li State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China School of Automation Guangdong University of Technology Guangzhou China Department of Control Science and Engineering Changchun University of Technology Changchun China

This paper investigates a data-based robust decentralized stabilizing control scheme for unknown large-scale systems via adaptive critic designs. The control consists of near-optimal stabilizing control and adaptive robustifying compensation. In order to avoid the common assumptions of boundedness and matched condition on interconnections, the actual states of interconnected subsystems are replaced with their desired ones. By using local input-output data, the subsystem dynamics can be obtained by neural network (NN) identifier. Then, with the help of local critic NN, the near-optimal decentralized control is derived via identifier-critic architecture for corresponding subsystems. Considering the replacement error, identification error and approximation error as overall error, an adaptive robustifying term is added to overcome it in real-time. Simulation example is given to verify the effectiveness of the present control scheme.

关键词： Integrated circuits Robustness Artificial neural networks Cost function Decentralized control Approximation error Adaptive systems

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Neural Adaptive control of Microgrid Frequency Regulation with Wind Power

Neural Adaptive Control of Microgrid Frequency Regulation wi...

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Annual Conference of the IEEE Industrial Electronics Society

作者： Weiqiang Liu Chaoxu Mu Ding Wang Chao Luo Chao Ren Tianjin Key Laboratory of Process Measurement and Control Tianjin University Tianjin China School of Automation Southeast University Nanjing China The State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China School of Electrical Engineering Wuhan University Wuhan China

Due to the uncertainty of power load demand and the stochastic power generation from renewable energy, frequency fluctuation becomes a major concern of power system, especially for a microgrid. In this paper, an improved proportional-integral (PI) controller based on the neural adaptive control method is proposed to deal with the load frequency control (LFC) problem in a microgrid with wind power. The designed neural adaptive control auxiliary controller is used to provide the adaptive supplement control signal to PI controller in a real-time manner. Simulation studies on a benchmark microgrid system are carried out between the proposed compound controller and traditional PI controller. The simulation results demonstrate the proposed method has a superior performance for stabilizing the frequency over the traditional PI control under disturbances from the load change and wind power.

关键词： Adaptive control Microgrids Frequency control Wind turbines Benchmark testing Power system stability

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Traffic Signal Timing via Deep Reinforcement Learning

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IEEE/CAA Journal of Automatica Sinica 2016年第3期3卷 247-254,254+248-253页

作者： Li Li Yisheng Lv Fei-Yue Wang IEEE Department of Automation Tsinghua University Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences

In this paper, we propose a set of algorithms to design signal timing plans via deep reinforcement learning. The core idea of this approach is to set up a deep neural network (DNN) to learn the Q-function of reinforcement learning from the sampled traffic state/control inputs and the corresponding traffic system performance output. Based on the obtained DNN, we can find the appropriate signal timing policies by implicitly modeling the control actions and the change of system states. We explain the possible benefits and implementation tricks of this new approach. The relationships between this new approach and some existing approaches are also carefully discussed. © 2014 Chinese Association of Automation.

关键词： Algorithms Timing circuits Traffic control Traffic signals

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3D Attention Network(3DAN)to Capture Candidate Biomarkers for Alzheimer's Disease

3D Attention Network(3DAN)to Capture Candidate Biomarkers fo...

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2019中国阿尔茨海默病论坛(中国脑病大会CCBD2019)

作者： Dan Jin Bo Zhou Ying Han Jiaji Ren Tong Han Bing Liu Jie Lu Chengyuan Song Pan Wang Dawei Wang Jian Xu Zhengyi Yang Hongxiang Yao Chunshui Yu Kun Zhao Xinqing Zhang Yuying Zhou Xi Zhang Tianzi Jiang Qing Wang Yong Liu Brainnetome Center&National Laboratory of Pattern Recognition Institute of AutomationChinese Academ Department of Neurology the Second Medical CentreNational Clinical Research Centre for Geriatric Di Department of Neurology Xuanwu Hospital of Capital Medical UniversityBeijingChina Center of Alzhei Department of Radiology Tianjin Huanhu HospitalTianjinChina Brainnetome Center&National Laboratory of Pattern Recognition Institute of AutomationChinese Academ Department of Radiology Xuanwu Hospital of Capital Medical UniversityBeijingChina Department of Neurology Qilu Hospital of Shandong UniversityJi'nanChina Department of Neurology Tianjin Huanhu HospitalTianjinChina Department of Radiology Qilu Hospital of Shandong UniversityJi'nanChina State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese A Department of Radiology Chinese PLA General HospitalBeijingChina Department of Radiology Tianjin Medical University General HospitalTianjinChina Brainnetome Center&National Laboratory of Pattern Recognition Institute of AutomationChinese Academ Department of Neurology Xuanwu Hospital of Capital Medical UniversityBeijingChina Alzheimer's Disease Neuroimaging Initiative Multi-Centre Alzheimer Disease Neuroimaging Working Group

Brain structural alterations are promising biomarkers for tracking disease progression and diagnosing Alzheimer's disease(AD).Deep learning methods have been increasingly used for computer-aided diagnosis of AD due to its learning ability to optimize feature representation and ***,a major limitation of the existing methods is the lack of availability of valuable spatial information which is isolated from subsequent feature optimization and classification processes.

关键词： Attention mechanism Alzheimer's disease Computer-aided diagnosis Structural magnetic resonance imaging Neurobiological basis

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12.2: A 3D Display Parallel System: Light Field Re-rendering and Depth Sense Optimization

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SID Symposium Digest of Technical Papers 2018年第S1期49卷

作者： Renjing Pei Kui Ma Feiyue Wang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences East Zhongguanchun Road No. 95 Haidian District Beijing School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing Parallel Optics Technology Innovation Center Qingdao Academy of Intelligent Industries Qingdao

The main benefit of 3D display over 2D display is the obvious ability to create a more lifelike character with high depth sense. However, the limitation of human eye's visual mechanism, unartful 3D scene structure design, or bad viewing condition always emerges poor depth perception experience or even physiological discomfort during the watching time, which is often sub-optimal for mass high-quality 3D display productions. To solve this problem, we propose a novel 3D display parallel system for depth sense optimization and it empirically guides how the light field should be re-rendered. Structurally, the parallel system consists of an artificial perception measurement system, a display evaluation model and a light field display rendering system, which includes the display calibration, scene capture, light field data processing and display. Particularly, the system can systematically analyze and model various factors affecting the depth sense which learned through the measurement system, like scene structure, objects’ speeds in 3D video and so on. And those sense factors can be personally modified or increased according to the viewer's demands or technical improvement. Moreover, the light field could be real-time re-rendered, based on some image processing technology, optical flow analysis and object segmentation (or tracking) (especially the one-shot video segmentation). Theory and algorithms are developed and experimental validation results show a superior performance.

关键词： 3D Display Parallel System Depth Sense Light Field Re-rendering 3D Video Processing One-shot Segmentation

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Reaching a stochastic consensus in the noisy networks of linear MIMO agents:Dynamic output-feedback and convergence rate

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Science China(Technological Sciences) 2016年第1期59卷 45-54页

作者： WANG YunPeng CHENG Long YANG ChenGuang HOU ZengGuang TAN Min State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Plymouth University Plymouth PL48ABUnited Kingdom College of Automation Science and Engineering South China University of TechnologyGuangzhou 510640China

This paper addresses the leader-following consensus problem of linear multi-agent systems(MASs) with communication noise. Each agent's dynamical behavior is described by a linear multi-input and multi-output(MIMO) system, and the agent's full state is assumed to be unavailable. To deal with this challenge, a state observer is constructed to estimate the agent's full state. A dynamic output-feedback based protocol that is based on the estimated state is proposed. To mitigate the effect of communication noise, noise-attenuation gains are also introduced into the proposed protocol. In this study, each agent is allowed to have its own noise-attenuation gain. It is shown that the proposed protocol can solve the mean square leader-following consensus problem of a linear MIMO MAS. Moreover, if all noise-attenuation gains are of Q(t-β), where b∈(0,1), the convergence rate of the MAS can be quantitatively analyzed. It turns out that all followers' states converge to the leader's state in the mean square sense at a rate of O(t-β).

关键词： multi-agent system mean square consensus communication noise noise-attenuation gain convergence rate

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