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An ACP Approach to Public Health Emergency management: Using a Campus Outbreak of H1N1 Influenza as a Case Study

IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2013年第5期43卷 1028-1041页

作者： Duan, Wei Cao, Zhidong Wang, Youzhong Zhu, Bin Zeng, Daniel Wang, Fei-Yue Qiu, Xiaogang Song, Hongbing Wang, Yong Natl Univ Def Technol Coll Informat Syst & Management Res Ctr Mil Computat Experiments & Parallel Syst Changsha 410073 Hunan Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Oregon State Univ Coll Business Corvallis OR 97330 USA Acad Mil Med Sci Inst Dis Control & Prevent Beijing 100071 Peoples R China

In order to tackle the infeasibility of building mathematical models and conducting physical experiments for public health emergencies in the real world, we apply the Artificial societies, Computational experiments, and Parallel execution (ACP) approach to public health emergency management. We use the largest collective outbreak of H1N1 influenza at a Chinese university in 2009 as a case study. We build an artificial society to simulate the outbreak at the university. In computational experiments, aiming to obtain comparable results with the real data, we apply the same intervention strategy as that was used during the real outbreak. Then, we compare experiment results with real data to verify our models, including spatial models, population distribution, weighted social networks, contact patterns, students' behaviors, and models of H1N1 influenza disease, in the artificial society. In the phase of parallel execution, alternative intervention strategies are proposed to control the outbreak of H1N1 influenza more effectively. Our models and their application to intervention strategy improvement show that the ACP approach is useful for public health emergency management.

关键词： Agent-based simulation artificial societies computational experiments emergency management parallel execution (ACP) public health

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Modeling, control and Software Implementation of Astronomical Tracking of Focus Cabin Suspension of FAST

Modeling, Control and Software Implementation of Astronomica...

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IEEE International Conference on Robotics and Biomimetics (ROBIO)

作者： Hao, Jianlin Jing, Fengshui Yang, Guodong Liang, Zize Zhu, Lichun Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Measurement & Control Grp Natl Astron Observ Beijing 100012 Peoples R China

ISBN: (纸本)9781479927449

This article describes the control algorithm and software implementation of astronomical tracking based on the mechanical analysis for Five-hundred-meter Aperture Spherical Telescope(FAST) that is being built in Guizhou province of China, which aims to help astronomers resolve questions in cosmology. On the basis of position and attitude feedback algorithm of cable driven parallel robot, the control of X-Y positioner with feed-forward was introduced and the astronomical tracking experiments were conducted on scale model to verify the validity of control strategy. The future works for focus cabin suspension of FAST are also pointed out.

关键词： Focus cabin suspension of FAST mechanical analysis and planning software design position and attitude feedback control X-Y positioner with feed-forward control

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Large scale online kernel classification

Large scale online kernel classification

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23rd International Joint Conference on Artificial Intelligence, IJCAI 2013

作者： Wang, Jialei Hoi, Steven C.H. Zhao, Peilin Zhuang, Jinfeng Liu, Zhi-Yong School of Computer Engineering Nanyang Technological University Singapore Singapore Microsoft Corporation United States State Key Lab of Management and Control for Complex System Chinese Academy of Sciences China

ISBN: (纸本)9781577356332

In this work, we present a new framework for large scale online kernel classification, making kernel methods efficient and scalable for large-scale online learning tasks. Unlike the regular budget kernel online learning scheme that usually uses different strategies to bound the number of support vectors, our framework explores a functional approximation approach to approximating a kernel function/matrix in order to make the subsequent online learning task efficient and scalable. Specifically, we present two different online kernel machine learning algorithms: (i) the Fourier Online Gradient Descent (FOGD) algorithm that applies the random Fourier features for approximating kernel functions;and (ii) the Nyström Online Gradient Descent (NOGD) algorithm that applies the Nyström method to approximate large kernel matrices. We offer theoretical analysis of the proposed algorithms, and conduct experiments for large-scale online classification tasks with some data set of over 1 million instances. Our encouraging results validate the effectiveness and efficiency of the proposed algorithms, making them potentially more practical than the family of existing budget kernel online learning approaches.

关键词： Budget control

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Neuro-Optimal control of Unknown Nonaffine Nonlinear systems with Saturating Actuators

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IFAC Proceedings Volumes 2013年第20期46卷 574-579页

作者： Xiong Yang Derong Liu Qinglai Wei The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China

This paper develops an adaptive optimal control for the infinite-horizon cost of unknown nonaffine nonlinear continuous-time systems with control constraints. A recurrent neural network (NN) is constructed to identify the unknown system dynamics with stability proof. Then, two feedforward NNs are used as the actor and the critic to approximate the optimal control and the optimal value, respectively. By using this architecture, the action NN and the critic NN are tuned simultaneously, without the requirement of the knowledge of system dynamics. In addition, the weights of the action NN and the critic NN are guaranteed to be uniformly ultimately bounded based on Lyapunov's direct method. A simulation example is provided to verify the effectiveness of the developed theoretical results.

关键词： Actuator saturation Adaptive control Neural networks Nonaffine systems Optimal control Unknown nonlinear systems

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Direct adaptive output feedback control for MIMO uncertain nonlinear discrete-time systems based on neural networks

Direct adaptive output feedback control for MIMO uncertain n...

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Chinese control Conference (CCC)

作者： Xiong Yang Derong Liu Ding Wang The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China The State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China

This paper develops a novel neural-network-based direct adaptive control scheme for a class of multi-input-multi-output uncertain nonlinear discrete-time (DT) systems in the presence of unknown bounded disturbances. By employing feedback linearization methods, neural network (NN) approximation can cancel the nonlinearity of the DT systems. Meanwhile, the weights of NNs are directly updated online instead of preliminary offline training. In addition, unlike most literatures, the condition for persistent excitation is removed. Based on Lyapunov's direct method, both tracking errors and weight estimates are guaranteed to be uniformly ultimately bounded, while keeping the closed-loop system stable. Finally, an example is provided to demonstrate the effectiveness of the proposed approach.

关键词： Artificial neural networks Nonlinear systems Vectors Adaptive control Trajectory MIMO

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Design and Implementation of A Novel Biomimetic Robotic Jellyfish

Design and Implementation of A Novel Biomimetic Robotic Jell...

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IEEE International Conference on Robotics and Biomimetics (ROBIO)

作者： Xiao, Jundong Duan, Jinghui Yu, Junzhi Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China China Informat Secur Certificat Ctr Beijing 100020 Peoples R China

ISBN: (纸本)9781479927456

This paper deals with the design, construction, and motion control of a jellyfish-inspired swimming robot that uses jet propulsion for thrust generation. The robotic jellyfish consists of a streamlined head, a cavity shell, four separate drive units with bevel gears, as well as an elastic rubber skin around the drive units. In order to replicate the locomotion of jellyfish including a relaxation phase and a contraction phase, four six-bar linkage mechanisms that are centrally symmetric are adopted as the actuators. A triangular wave control algorithm is then proposed to produce desired control signals with an embedded controller. Through independent and coordinated control of the four drive units, the robotic jellyfish is capable of diverse propulsion and maneuvers like swimming forward, turning, and diving/surfacing. Aquatic experiments are further conducted to verify the proposed design and control methods. As a new type of bio-inspired robots, the robotic jellyfish will serve as an effective platform for underwater reconnaissance and environmental monitoring.

关键词： Robotics

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On controllability of Neuronal Networks With Constraints on the Average of control Gains

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IEEE TRANSACTIONS ON CYBERNETICS 2014年第12期44卷 2670-2681页

作者： Tang, Yang Wang, Zidong Gao, Huijun Qiao, Hong Kurths, Juergen Potsdam Inst Climate Impact Res D-14473 Potsdam Germany Humboldt Univ Inst Phys D-12489 Berlin Germany Brunel Univ Dept Comp Sci Uxbridge UB8 3PH Middx England King Abdulaziz Univ Fac Engn Jeddah 21589 Saudi Arabia Harbin Inst Technol Res Inst Intelligent Control & Syst Harbin 150080 Peoples R China King Abdulaziz Univ Jeddah 21589 Saudi Arabia Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Aberdeen Inst Complex Syst & Math Biol Aberdeen AB24 3UE Scotland

control gains play an important role in the control of a natural or a technical system since they reflect how much resource is required to optimize a certain control objective. This paper is concerned with the controllability of neuronal networks with constraints on the average value of the control gains injected in driver nodes, which are in accordance with engineering and biological backgrounds. In order to deal with the constraints on control gains, the controllability problem is transformed into a constrained optimization problem (COP). The introduction of the constraints on the control gains unavoidably leads to substantial difficulty in finding feasible as well as refining solutions. As such, a modified dynamic hybrid framework (MDyHF) is developed to solve this COP, based on an adaptive differential evolution and the concept of Pareto dominance. By comparing with statistical methods and several recently reported constrained optimization evolutionary algorithms (COEAs), we show that our proposed MDyHF is competitive and promising in studying the controllability of neuronal networks. Based on the MDyHF, we proceed to show the controlling regions under different levels of constraints. It is revealed that we should allocate the control gains economically when strong constraints are considered. In addition, it is found that as the constraints become more restrictive, the driver nodes are more likely to be selected from the nodes with a large degree. The results and methods presented in this paper will provide useful insights into developing new techniques to control a realistic complex network efficiently.

关键词： complex networks controllability evolutionary algorithms multiagent systems neural networks synchronization/consensus

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Neuro-control to energy minimization for a class of chaotic systems based on ADP algorithm

Neuro-control to energy minimization for a class of chaotic ...

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4th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2013

作者： Song, Ruizhuo Xiao, Wendong Wei, Qinglai School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing 100083 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9783642420566

This paper discusses the energy minimization problem of a class of chaotic systems, and constructs an optimal neuro-controller based on adaptive dynamic programming (ADP) algorithm. To learn the optimal performance index and control policy, an iterative algorithm is established. To prove the convergence of the presented iterative algorithm, theorems with rigorous and detailed proofs are given. It is proven that the iterative performance index functions are monotone decreasing and converge to the minimum energy. A simulation example is used to indicate that the presented energy minimization control method is effective. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Chaotic systems

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A Trajectory Prediction Algorithm Based on Fuzzy Rectification for Spinning Ball

A Trajectory Prediction Algorithm Based on Fuzzy Rectificati...

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9th Asian control Conference (ASCC)

作者： Ren, Yanqing Fang, Zaojun Xu, De Tan, Min Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat Res Ctr Precis Sensing & Control Beijing 100190 Peoples R China

ISBN: (纸本)9781467357692;9781467357678

Trajectory prediction plays a very important role in the process of playing table tennis for robot. Its accuracy determines whether the striking action will succeed or not. This paper first analyzes how the spinning influences the flight model and the rebound model for the table tennis robot. Two models are designed for spinning balls. Meantime, it indicates that the offset on y axis is related to the flight distance on x axis and rotation degree. Then a fuzzy controller is designed for online rectification of the trajectory. And a trajectory prediction algorithm is presented for the spinning ball combined with the algorithms based on the parameter model and experience learning. The experiment results validate the effectiveness of the proposed method.

关键词： Spinning ball Magnus force Trajectory prediction Fuzzy rectification

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Data-based control, optimization, modeling and applications

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NEURAL COMPUTING & APPLICATIONS 2013年第7-8期23卷 1839-1842页

作者： Zhao, Dongbin Shen, Yi Wang, Zhanshan Hu, Xiaolin Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Huazhong Univ Sci & Technol Dept Control Sci & Engn Key Lab Minist Educ Image Proc & Intelligent Cont Wuhan 430074 Peoples R China Northeastern Univ Sch Informat Sci & Engn Shenyang 110004 Peoples R China Tsinghua Univ Dept Comp Sci & Technol State Key Lab Intelligent Technol & Syst Tsinghua Natl Lab Informat Sci & Technol Beijing 100084 Peoples R China

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