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第36届中国控制会议

作者： Yunpeng Wang Yunchun Xie Long Cheng Beijing Institute of Control Engineering Science and Technology on Space Intelligent Control Laboratory State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

ISBN: (纸本)9781538629185

This paper studies a leader-following consensus problem of multi-agent systems with a dynamic *** is assumed that the leader moves along a polynomial trajectory with respect to time,while followers have first-order integral *** follower is equipped with the positioning system and ranger finder sensor to measure its own position and the relative positions with its *** measured information is corrupted by measurement *** first study a specials case of leader-following consensus problem（tracking problem）,then we extend the results to the general *** is assumed that leader’s velocity is unknown to all *** deal with this challenge,the least square is used to estimate leader’s *** on the estimated velocity and measured information,a consensus protocol is *** is proved that the proposed protocol can solve the mean square leader-following consensus problem under some mild *** simulation examples are also presented to demonstrate the effectiveness of the proposed protocol.

关键词： Leader-Following Consensus Mutli-Agent systems Dynamic Leader Measurement Noise Least Square Method

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Relaying Strategy Based on Estimated Information for Multi-Antenna Cooperative Networks

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China Communications 2017年第8期14卷 157-165页

作者： Shuangshuang Han Peng Zhang Feijin Shi Fei-Yue Wang The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of SciencesBeijingChina and Qingdao Academy of Intelligent Industries School of Computer Engineering Weifang University Communications Headquarters Ministry of Foreign Affairs of the People's Republic of China State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijingChina and Research Center of Computational Experiments and Parallel SystemsThe National University of Defense Technology

A sphere-based list forwarding scheme for multiple-input multiple-output(MIMO) relay networks is proposed and analyzed. Firstly, an estimate forwarding(EF) method is proposed, which forwards the minimum mean squared error(MMSE) estimate of the source data to the destination. Since it performs like amplify-and-forward(AF) and decode-and-forward(DF) for the low and high signal-to-noise ratio(SNR) regions, respectively, the EF relay thus outperforms conventional AF and DF across all SNRs without the need for switching algorithms for different SNRs. Because computational complexity is however high for relays with a large number of antennas(large MIMO) and/or high order constellations, list EF for large MIMO relay networks is proposed. It computes a list sphere decoder based MMSE estimate and retains the advantages of the exact EF relay at a negligible performance loss. The proposed list EF could offer a flexible trade-off between the performance and computational complexity.

关键词： MIMO system cooperative network soft information

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PDP: Parallel Dynamic Programming

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IEEE/CAA Journal of Automatica Sinica 2017年第1期4卷 1-5页

作者： Fei-Yue Wang Jie Zhang Qinglai Wei Xinhu Zheng Li Li IEEE State Key Laboratory of Management and Control for Complex Systems(SKL-MCCS) Institute of AutomationChinese Academy of Sciences(CASIA) School of Computer and Control Engineering University of Chinese Academy of Sciences Research Center for Military Computational Experiments and Parallel Systems Technology National University of Defense Technology State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences(SKL-MCCSCASIA) Qingdao Academy of Intelligent Industries Department of Computer Science and Engineering University of Minnesota Department of Automation Tsinghua University

Deep reinforcement learning is a focus research area in artificial intelligence. The principle of optimality in dynamic programming is a key to the success of reinforcement learning methods. The principle of adaptive dynamic programming ADP is first presented instead of direct dynamic programming DP , and the inherent relationship between ADP and deep reinforcement learning is developed. Next, analytics intelligence, as the necessary requirement, for the real reinforcement learning, is discussed. Finally, the principle of the parallel dynamic programming, which integrates dynamic programming and analytics intelligence, is presented as the future computational intelligence. © 2014 Chinese Association of Automation.

关键词： Artificial intelligence Neural networks Reinforcement learning

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A New Approach for Face Anti-Spoofing Using Handcrafted and Deep Network Features

A New Approach for Face Anti-Spoofing Using Handcrafted and ...

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IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI

作者： Polash Kumar Das Bin Hu Chang Liu Kaixin Cui Prabhat Ranjan Gang Xiong School of Electronic and Information Engineering South China University of Technology Guangzhou China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Science Beijing China College of Computer Science Sichuan University Chengdu China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China The Cloud Computing Center Chinese Academy of Science Dongguan China

In biometrics, face recognition methods are achieving momentum with recent progress in the computer vision(CV). Face recognition is widely used in the identification of an individual's identity. Unfortunately, in recent research work has revealed this face biometrics system is unprotected to spoofing attacks using by very low price instrument such as printed 2D photos attack, 3D masking attack and taking videos using smart devices (reply attack). Therefore, a Liveness Attack Detection (LAD) approach is needed to improve the high-quality security of face recognition system. Most of the earlier worked LAD methods for face anti-spoofing methods have highlight on using the handcrafted features, which are developed by expert knowledge of researcher. As example Gabor filter, Histogram of Oriented Gradients, local ternary pattern, and the Local Binary Pattern. Because of that, the extracted features consider limited factors of the problem, yielding a capture accuracy that is very low and changes with the point of presentation in attack face images. The deep learning method has developed in the computer vision research community, which is proven to be suitable for automatically training. In this article, we approach to mix or combine the handcrafted features and deep neural network features to design the discriminant face spoofing detection. The handcrafted features were based on LBP analysis. We examine the features information from the brightness and the chrominance channels using LBP descriptor. In deep features, we present an approach based on pre-trained convolutional neural network VGG-16 model using static features to recognize video and printed(2D) photo attacks. By attaching this two types of image features on our dataset and public databases, we get good results to identify real and attack images feature, called hybrid features, which has better discrimination ability to understand spoofing image feature.

关键词： Face Videos Feature extraction Databases Face recognition Password

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An Analysis of Blockchain-based Bitcoin Mining Difficulty: Techniques and Principles

An Analysis of Blockchain-based Bitcoin Mining Difficulty: T...

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Chinese Automation Congress

作者： Xiaojing Zhang Rui Qin Yong Yuan Fei-Yue Wang The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing China Beijing Engineering Research Center of Intelligent Systems and Technology Chinese Academy of Sciences Beijing China

Bitcoin is a decentralized peer-to-peer electronic cash system, and it realizes the circulation and distribution of cryptocurrency via miners contributing their computational power to solve a cryptographic puzzle. With more and more people participating in Bitcoin mining, the continuous growing of the computational power might greatly reduce the time for finding a new valid block. To keep the stability of the Bitcoin system, the difficulty of the Bitcoin network will be adjusted every 2016 blocks. This paper aims to study the principles of the difficulty adjustment of the whole Bitcoin blockchain network in Bitcoin mining. We first describe the computing process of the target and thus the difficulty of a block, and then study its influence factors and their effect on the difficulty. After that, we study the adjustment of the difficulty, and propose a framework for the adjustment of the difficulty that can be easily used in practice. Our work can help researchers better understand the principles of Bitcoin mining.

关键词： Bitcoin mining Blockchain Network difficulty Target Adjustment of difficulty

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A knowledge-and-data-driven modeling approach for simulating plant growth and the dynamics of CO₂/O₂ concentrations in a closed system of plants and humans by integrating mechanistic and empirical models

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2018年 148卷 280-290页

作者： Fan, Xing-Rong Wang, Xiujuan Kang, Mengzhen Hua, Jing Guo, Shuangsheng de Reffye, Philippe Hu, Bao-Gang Chongqing Univ Posts & Telecommun Elect Informat & Networking Res Inst Chongqing 400065 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Peoples R China China Astronaut Res & Training Ctr Natl Key Lab Human Factors Engn Beijing 100094 Peoples R China Cirad Amis UMR AMAP TA 4001 Ave Agropolis F-34398 Montpellier 5 France Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100190 Peoples R China Beijing Engn Res Ctr Intelligent Syst & Technol Beijing 100190 Peoples R China

Modeling and the prediction of material flows (plant production, CO2/O-2 concentrations, H2O) is an important but challenging task in the design and control of closed ecological life support systems (CELSS). The aim of this study was to develop a novel knowledge-and-data-driven modeling (KDDM) approach for simultaneously simulating plant production and CO2/O-2 concentrations in a closed system of plants and humans by integrating mechanistic and empirical models. The KDDM approach consists of a 'knowledge-driven (KD)' sub-model and a 'data-driven (DD)' sub-model. The KD sub-model describes hourly and up to daily plant photosynthesis, respiration and assimilation partitioning using the components of Greenlab.and TomSim models. The DD sub-model describes the dynamics of CO2 production and O-2 consumption by the crew member using a piecewise linear model. The two sub-models were integrated with a mass balance model for CO2/O-2 concentrations in a closed system. The KDDM was applied with a two-person, 30-day integrated CELSS test. This model provides accurate computation of both the dry weights of different plant compartments and CO2/O-2 concentrations. The model also quantifies the underlying material flows among the crew members, plants and environment. This approach provides a computational basis for lifetime optimization of cabin design and experimental setup of CELSS (e.g., environmental control, planting schedule). With extension, this methodology can be applied to a half-closed system such as a glasshouse.

关键词： Closed ecological life support system Functional-structural plant model Knowledge-and-data-driven model Mass balance model Model evaluation And model verification

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intelligent Optimal control With Critic Learning for a Nonlinear Overhead Crane System

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2018年第7期14卷 2932-2940页

作者： Wang, Ding He, Haibo Liu, Derong Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat Beijing Engn Res Ctr Intelligent Syst & Technol Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Comp & Control Engn Beijing 100049 Peoples R China Univ Rhode Isl Dept Elect Comp & Biomed Engn Kingston RI 02881 USA Guangdong Univ Technol Sch Automat Guangzhou 510006 Guangdong Peoples R China

In this paper, for achieving the discounted optimal feedback stabilization of a nonlinear overhead crane system, we establish an intelligent control strategy to obtain the solution of the corresponding Hamilton-Jacobi- Bellman equation. Specifically, neural networks are employed to serve as a necessary component to the control system, which exhibits strong online learning ability. A novel updating rule compared to the traditional adaptive critic algorithms is developed, which eliminates the requirement of the initial stabilizing controller and brings in unique advantages to the adaptive critic control design. Stability analysis of the closed-loop system based on the well-known Lyapunov approach and experimental simulation considering the nonlinear overhead dynamics with different case studies are performed to verify the effectiveness of the present control method both in theory and applications.

关键词： Adaptive dynamic programming artificial neural networks discounted optimal control intelligent control overhead crane

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Neural Network Learning and Robust Stabilization of Nonlinear systems With Dynamic Uncertainties

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING systems 2018年第4期29卷 1342-1351页

作者： Wang, Ding Liu, Derong Mu, Chaoxu Zhang, Yun Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat Beijing Engn Res Ctr Intelligent Syst & Technol Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Comp & Control Engn Beijing 100049 Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510006 Guangdong Peoples R China Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China

Due to the existence of dynamical uncertainties, it is important to pay attention to the robustness of nonlinear control systems, especially when designing adaptive critic control strategies. In this paper, based on the neural network learning component, the robust stabilization scheme of nonlinear systems with general uncertainties is developed. Through system transformation and employing adaptive critic technique, the approximate optimal controller of the nominal plant can be applied to accomplish robust stabilization for the original uncertain dynamics. The neural network weight vector is very convenient to initialize by virtue of the improved critic learning formulation. Under the action of the approximate optimal control law, the stability issues for the closed-loop form of nominal and uncertain plants are analyzed, respectively. Simulation illustrations via a typical nonlinear system and a practical power system are included to verify the control performance.

关键词： Adaptive critic dynamical uncertainty learning systems neural networks optimal control robust stabilization

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A Novel Forwarding Method for Full-Duplex Two-Way Relay Networks

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China Communications 2017年第4期14卷 65-72页

作者： Shuangshuang Han Xiang Cheng Liuqing Yang The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of SciencesBeijing China and Qingdao Academy of Intelligent Industries The State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Peking University Department of Electrical and Computer Engineering Colorado State University

An estimate-and-forward(EF) scheme for single-input single-output(SISO) and multiple-input multiple-output(MIMO) full-duplex two-way relay networks is proposed and analyzed. The relay estimates the received signal from two terminal nodes by a minimum mean squared error(MMSE) estimation and forwards a scaled version of the MMSE estimate to the destination. The proposed EF outperforms conventional amplify-and-forward(AF) and decode-and-forward(DF) across all signal-to-noise ratio(SNR) region. Because its computational complexity is high for relays with a large number of antennas(large MIMO) and/or high order constellations, an approximate EF scheme, called list EF, are thus proposed to reduce the computational complexity. The proposed list EF computes a candidate list for the MMSE estimate by using a sphere decoder, and it approaches the performance of the exact EF relay at a negligible performance loss. The proposed forwarding approach also could be used to other relay networks, such as half-duplex, one-way or massive MIMO relay networks.

关键词： estimate-and-forward full-duplex two-way relay wireless communications

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Dynamic Optimization of Employees Work Strategies in a WeChat-Based Evaluation System

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2018年第3期5卷 687-697页

作者： Li, Juanjuan Wang, Shuai Yuan, Yong Ni, Xiaochun Wang, Fei-Yue Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Natl Univ Def Technol Res Ctr Mil Computat Expt & Parallel Syst Changsha 410073 Hunan Peoples R China

In recent years, social media platforms and, especially, instant messaging applications have been witnessed to play an increasingly important role as a communication channel among employees to build effective organizations, which results in a considerably large part of individual activities moving onto the cyber-based workspace. In this paper, we collect the unique real-world data set from a WeChat-based work performance evaluation system built and used by a medium-sized organization in China, in order to study the optimization of the employees' work strategies, especially the work time determination, with the purpose of maximizing the total utility comprised of the work and nonwork utilities. In the system, the performance evaluation is completely based on the employees' work reports and includes three major measures including score, level, and rank. First, we formulate a dynamic optimization model, incorporating the work utility into the reputational and the substantive utility, so as to examine the employees' work strategy. Then, we conduct both empirical studies and computational experiments to make an in-depth analysis to probe the employees' optimal work strategies. The main results include: 1) the optimal work time is greatly affected by the weight of the reputational utility;2) the optimal work time is always the threshold time to achieve a certain level or a certain rank;3) the total utility is with the trend of decreasing after increasing with the growing work time;and 4) the optimal work time does not always make the employee achieve a good work performance.

关键词： Computational experiments dynamic optimization employees' work strategies reputational utility substantive utility

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