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3rd International Conference on New Energy and Future Energy System, NEFES 2018

作者： Ren, Q.Y. Qian, M.H. Zhu, C.L. Zhao, D.W. Liu, H.M. Zhu, L.Z. Li, G.J. College of Energy and Electrical Engineering Hohai University Nanjing211100 China State Key Laboratory of Operation and Control of Renewable Energy and Storage Systems China Electric Power Research Institute Beijing China Jiangsu Engineering Technology Research Center for Energy Storage Conversion and Application China Electric Power Research Institute Nanjing China

Doubly-fed induction generator (DFIG) with dual Pulse-Width Modulation (PWM) converter cannot maintain the active power balance or voltage stability of the power grid self-synchronously because of lacking mechanical inertia and mechanical damping. The virtual synchronous generator (VSG) technology can help solve this problem through simulating the mechanical equation and electromagnetic equation of synchronous generator to control the grid-connected converter. However, under the common first-order virtual inertia link used in the VSG control scheme, while DFIG providing active power support, there exists second frequency dip and the rotor speed recovers slowly, which is not conducive for DFIG to provide continuous and effective virtual inertia support. In order to overcome the drawbacks as well as avoid second frequency dip, this paper puts forward a virtual inertia control strategy of DFIG with additional inertia and damping torque. The additional torque is achieved by proportional-derivative (PD) controller with rotor slip as an input signal. Simulation results on DIgSILENT/powerfactory platform show that the proposed control strategy is effective to reduce frequency deviation and recover the rotor speed faster without second frequency dip phenomenon. © Published under licence by IOP Publishing Ltd.

关键词： Synchronous generators

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Decentralized Guaranteed Cost control of Modular and Reconfigurable Robots Based on Adaptive Dynamic Programming

Decentralized Guaranteed Cost Control of Modular and Reconfi...

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第30届中国控制与决策会议

作者： Yi An Bo Dong Fan Zhou Fu Liu Yuanchun Li College of Communications Engineering Jilin University Department of Control Science and Engineering Changchun University of Technology State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

This paper investigates a decentralized guaranteed cost control method of modular and reconfigurable robots(MRRs) based on adaptive dynamic programming(ADP) ***,we formulate the dynamic model of systems and the interconnected couplings are ***,we design a decentralized guaranteed cost *** establishing a critic neural network,a learning-based adaptive dynamic programming algorithm is proposed to solve the Hamilton-Jacobi-Bellman(HJB) equation,and then the approximate optimal control policy can be *** asymptotic stability of the closed-loop system is proved based on the Lyapunov ***,simulations are performed for the 2-DOF MRRs to verify the effectiveness of the proposed method.

关键词： Modular and reconfigurable robots(MRRs) decentralized guaranteed cost control adaptive dynamic programming(ADP) critic neural network

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An Overview of Smart Contract: Architecture, Applications, and Future Trends

An Overview of Smart Contract: Architecture, Applications, a...

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2018 IEEE Intelligent Vehicles Symposium, IV 2018

作者： Wang, Shuai Yuan, Yong Wang, Xiao Li, Juanjuan Qin, Rui Wang, Fei-Yue State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Qingdao Academy of Intelligent Industries Qingdao266109 China Research Center of Military Computational Experiments and Parallel Systems National University of Defense Technology Changsha410073 China

ISBN: (纸本)9781538644522

With the rapid development of cryptocurrency and its underlying blockchain technologies, platforms such as Ethereum and Hyperledger began to support various types of smart contracts. Smart contracts are computer protocols intended to digitally facilitate, verify, or enforce the negotiation or performance of a contract. Smart contracts have broad range of applications, such as financial services, prediction markets and Internet of Things (IoT), etc. However, there are still many challenges such as security issues and privacy disclosure that await future research. In this paper, we present a comprehensive overview on blockchain powered smart contracts. First, we give a systematic introduction for smart contracts, including the basic framework, operating mechanisms, platforms and programming languages. Second, application scenarios and existing challenges are discussed. Finally, we describe the recent advances of smart contract and present its future development trends, e.g., parallel blockchain. This paper is aimed at providing helpful guidance and reference for future research efforts. © 2018 IEEE.

关键词： Smart contract

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Optimal Constrained Self-learning Battery Sequential Management in Microgrid Via Adaptive Dynamic Programming

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IEEE/CAA Journal of Automatica Sinica 2017年第2期4卷 168-176页

作者： Qinglai Wei Derong Liu Yu Liu Ruizhuo Song IEEE State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences University of Chinese Academy of Sciences School of Automation and Electrical Engineering University of Science and Technology Beijing Institute of Automation Chinese Academy of Sciences

This paper concerns a novel optimal self-learning battery sequential control scheme for smart home energy systems. The main idea is to use the adaptive dynamic programming U+0028 ADP U+0029 technique to obtain the optimal battery sequential control iteratively. First, the battery energy management system model is established, where the power efficiency of the battery is considered. Next, considering the power constraints of the battery, a new non-quadratic form performance index function is established, which guarantees that the value of the iterative control law cannot exceed the maximum charging/discharging power of the battery to extend the service life of the battery. Then, the convergence properties of the iterative ADP algorithm are analyzed, which guarantees that the iterative value function and the iterative control law both reach the optimums. Finally, simulation and comparison results are given to illustrate the performance of the presented method. © 2017 Chinese Association of Automation.

关键词： Adaptive control systems Automation Battery management systems control theory Electric batteries Energy management Energy management systems Intelligent buildings Iterative methods Number theory Secondary batteries

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A Vision and Neural Network Based Air-Ground Coordinated control System

A Vision and Neural Network Based Air-Ground Coordinated Con...

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2017 IEEE International Conference on Unmanned systems (ICUS)

作者： Muyun Jiang Jiahui Shi Hongbin Ma You Li School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology

ISBN: (纸本)9781538631089;9781538631072

This paper presents an air-ground coordinated system made up of an Unmanned Aerial Vehicle(UAV) and an Unmanned Ground Vehicle(UGV).The movement of UGV is totally controlled by UAV and multiple sensors are adopted in the system to ensure the stability.A novel neutral network algorithm is introduced to identify the location and orientation of *** component is introduced and tested in detail and the coordinated system performs a satisfying result.

关键词： Air-ground system Neutral Network

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Understanding of users' response to the intervention of FDA's new deeming rules in twitter 18

Understanding of users' response to the intervention of FDA'...

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2nd International Conference on Medical and Health Informatics, ICMHI 2018

作者： Luo, Yin Li, Qiudan Sun, Song Zeng, Daniel Leischow, Scott J. School of Management and Economics Beijing Institute of Technology Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing China Department of Management Information Systems University of Arizona TucsonAZ85721 United States Mayo Clinic ScottsdaleAZ85259 United States

ISBN: (纸本)9781450363891

Background: On May 5, 2016, the U.S. Food and Drug Administration (FDA) finalized a rule extending its authority to all tobacco products, including e-cigarettes, cigars, hookah tobacco and pipe tobacco, among others, which aims to help protect Americans from the dangers of tobacco and nicotine, especially the youth. The policy can be seen as a new intervention and become a hot discussion topic among the users in Twitter. Objective: This paper aims to examine the tweets related to the FDA's new policy in the social media, and gain a systematic understanding of users' responses to the intervention and the regulation. Methods: A total of 13864 tweets related to the policy from May 1st, 2016 to May 31st, 2016 were collected and analyzed, the support and opposition viewpoints about the six aspects including Regulation, Health, Industry, Democracy, Science, and Children were summarized, the time-sequence co-occurrence pattern of high-frequency words and the analysis of retweet and user dissemination network were analyzed. Results: Tweets are classified into six broad themes: Regulation, Health, Children, Industry, Science, and Democracy, the number of tweets about the six themes is 7547, 2764, 1678, 795, 670, 45. There are more supports on Regulation, Health, Science, and Children, the ratio of support(opposition) on the four themes are 80.6%(19.4%), 74.9%(25.1%), 85.9%(14.1%), 75.8%(24.2%), respectively, as for the themes of industry and democracy, most users hold the opposing view. The co-occurrence frequencies of gvaping. and "vape", "stocks" and "bigTobacco", "vapingsaveslives" and "vapeivote" are 328, 61, 104. A network with 222 retweets and 361 replies is constructed. Among the 361 replies, there are 62 negative replies accounting for 17.2%, 120 positive replies accounting for 33.2%, and 179 neutral replies accounting for 49.6%. It shows clearly that social media such as Twitter can be employed to gather valuable user feedback effectively and response on the governm

关键词： Tobacco

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Linear convergence of first- And zeroth-order primal-dual algorithms for distributed nonconvex optimization

arXiv

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arXiv 2019年

作者： Yi, Xinlei Zhang, Shengjun Yang, Tao Chai, Tianyou Johansson, Karl H. Division of Decision and Control Systems School of Electrical Engineering and Computer Science Kth Royal Institute of Technology Digital Futures Stockholm100 44 Sweden Department of Electrical Engineering University of North Texas DentonTX76203 United States State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China

This paper considers the distributed nonconvex optimization problem of minimizing a global cost function formed by a sum of local cost functions by using local information *** first propose a distributed first-order primal-dual algorithm. We show that it converges sublinearly to a stationary point if each local cost function is smooth and linearly to a global optimum under an additional condition that the global cost function satisfies the Polyak-Lojasiewicz condition. This condition is weaker than strong convexity, which is a standard condition for proving linear convergence of distributed optimization algorithms, and the global minimizer is not necessarily unique. Motivated by the situations where the gradients are unavailable, we then propose a distributed zeroth-order algorithm, derived from the proposed distributed first-order algorithm by using a deterministic gradient estimator, and show that it has the same convergence properties as the proposed first-order algorithm under the same conditions. The theoretical results are illustrated by numerical simulations. Copyright © 2019, The Authors. All rights reserved.

关键词： Cost functions

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Convolutional Neural Networks Based Multi-task Deep Learning for Movie Review Classification

Convolutional Neural Networks Based Multi-task Deep Learning...

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International Conference on Data Science and Advanced Analytics (DSAA)

作者： Xuanyi Li Weimin Wu Hongye Su State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

Deep learning has achieved impressive success in natural language processing. However, most previous models are learned on the specific single tasks, suffering from insufficient training set. Multi-task deep learning can solve this dilemma by sharing the part of parameters, improving generalization. The common multi-task deep learning model consists of the shared layer and the task specific layer. In this paper, we attempt to enhance the performance of shared layer and proposed two variants based on convolutional neural networks. The first model is Agent Model-Direct Concatenate, where each task is assigned with a separate convolutional neural network for extracting the common and task specific features simultaneously. The second model is Agent Model-Gating Concatenation, where the task specific layer could automatically decide the information flow of each element of the output of shared layer. The two networks are trained jointly over three pair-wise groups of movie review data sets. Experiments show the effectiveness of our two networks, inspiring a potential direction for the related research of multi-task deep learning.

关键词： Feature extraction Machine learning Data models Semantics Motion pictures

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Rethinking temporal object detection from robotic perspectives

arXiv

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arXiv 2019年

作者： Chen, Xingyu Wu, Zhengxing Yu, Junzhi Wen, Li State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Beijing Innovation Center Engineering Science and Advanced Technology Peking University Beijing100871 China School of Mechanical Engineering and Automation Beihang University Beijing100191 China

Video object detection (VID) has been vigorously studied for years but almost all literature adopts a static accuracy-based evaluation, i.e., average precision (AP). From a robotic perspective, the importance of recall continuity and localization stability is equal to that of accuracy, but the AP is insufficient to reflect detectors' performance across time. In this paper, non-reference assessments are proposed for continuity and stability based on object tracklets. These temporal evaluations can serve as supplements to static AP. Further, we develop an online tracklet refinement for improving detectors' temporal performance through short tracklet suppression, fragment filling, and temporal location fusion. In addition, we propose a small-overlap suppression to extend VID methods to single object tracking (SOT) task so that a flexible SOT-by-detection framework is then formed. Extensive experiments are conducted on ImageNet VID dataset and real-world robotic tasks, where the superiority of our proposed approaches are validated and verified. Codes will be publicly available. Copyright © 2019, The Authors. All rights reserved.

关键词： Object recognition

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Distributed bandit online convex optimization with time-varying coupled inequality constraints

arXiv

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arXiv 2019年

作者： Yi, Xinlei Li, Xiuxian Yang, Tao Xie, Lihua Johansson, Karl H. Chai, Tianyou Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China

This paper considers the problem of distributed bandit online convex optimization with time-varying coupled inequality constraints. This problem can be defined as a repeated game between a group of learners and an adversary. The learners attempt to minimize a sequence of global loss functions and at the same time satisfy a sequence of coupled constraint functions. The global loss and the coupled constraint functions are the sum of local convex loss and constraint functions, respectively, which are adaptively generated by the adversary. The local loss and constraint functions are revealed in a bandit manner, i.e., only the values of loss and constraint functions at sampled points are revealed to the learners, and the revealed function values are held privately by each learner. We consider two scenarios, one- and two-point bandit feedback, and propose two corresponding distributed bandit online algorithms used by the learners. We show that sublinear expected regret and constraint violation are achieved by these two algorithms, if the accumulated variation of the comparator sequence also grows sublinearly. In particular, we show that O(T θ1 ) expected static regret and O(T 7/4−θ1 ) constraint violation are achieved in the one-point bandit feedback setting, and O(T max{κ,1−κ}) expected static regret and O(T 1−κ/2) constraint violation in the two-point bandit feedback setting, where θ1 ∈ (3/4, 5/6] and κ ∈ (0, 1) are user-defined trade-off parameters. Finally, these theoretical results are illustrated by numerical simulations of a simple power grid example. Copyright © 2019, The Authors. All rights reserved.

关键词： Convex optimization

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