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International Conference on Intelligent control and Information Processing (ICICIP)

作者： Yinfeng Gao Yuqi Liu Qichao Zhang Yu Wang Dongbin Zhao Dawei Ding Zhonghua Pang Yueming Zhang School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China The state Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China China Automotive Technology & Research Center Co. Ltd Tianjin China Key Laboratory of Fieldbus Technology and Automation of Beijing North China University of Technology China

ISBN: (数字)9781728100159

ISBN: (纸本)9781728100166

Recently, some learning-based methods such as reinforcement learning and imitation learning have been used to address the control problem for autonomous driving. Note that reinforcement learning has strong reliance on the simulation environment and requires a handcraft design of the reward function. Considering different factors in autonomous driving, a general evaluation method is still being explored. The purpose of imitation learning is to learn the control policy through human demonstrations. It is meaningful to compare the control performances of current main imitation learning methods based on the provided dataset. In this paper, we compare three typical imitation learning algorithms: Behavior cloning, Dataset Aggregation (DAgger) and Information maximizing Generative Adversarial Imitation Learning (InfoGAIL) in the The Open Racing Car Simulator (TORCS) and Car Learning to Act (CARLA) simulators, respectively. The performance of algorithms is evaluated on lane-keeping task in racing and urban environment. The experiment results show DAgger performs best in simple lane keeping problem, and InfoGAIL has the unique advantage of distinguishing different driving styles from expert demonstrations.

关键词： Gallium nitride Trajectory Data models Entropy Task analysis Linear programming Autonomous vehicles

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Coherent H ∞ control for Markovian jump linear quantum systems

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IFAC-PapersOnLine 2020年第2期53卷 269-274页

作者： Yanan Liu Daoyi Dong Ian R. Petersen Qing Gao Steven X. Ding Hidehiro Yonezawa School of Engineering and Information Technology University of New South Wales Canberra ACT 2600 Australia Center for Quantum Computation and Communication Technology Australian Research Council Canberra ACT 2600 Australia Institute for Automatic Control and Complex Systems (AKS) University of Duisburg-Essen. 47057 Duisburg Germany Research School of Electrical Energy and Materials Engineering The Australian National University Canberra ACT 2601 Australia School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment Beijing and also with Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing 100191 China

The purpose of this paper is to design a coherent feedback controller for a Markovian jump linear quantum system suffering from a fault signal. The control objective is to bound the effect of the disturbance input on the output for the time-varying quantum system. We prove the relation between the H ∞ control problem, the dissipation properties, and the solutions of Riccati differential equations, by which the H ∞ controller of the Markovian jump linear quantum system is given by the solutions of Linear Matrix Inequalities (LMIs).

关键词： Coherent quantum feedback H∞ control fault-tolerant control Markovian jump linear quantum systems

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Study on Hardware-in-the-loop Model and Low Voltage Ride-through Characteristics of Photovoltaic Power Station

Study on Hardware-in-the-loop Model and Low Voltage Ride-thr...

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IEEE Electronic Power Grid (eGrid)

作者： Liqiang Wang Bin Cao Qi Wang Yu Cong Haiyan Wu Zhang Xiaolin Institute of New Energy Technology Inner Mongolia Electric Power Research Institute Hohhot China Institute of Electric Power System Technology Inner Mongolia Electric Power Research Institute Hohhot China State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems China Electric Power Research Institute Beijing China

ISBN: (数字)9781728157047

ISBN: (纸本)9781728157054

In order to prevent large-scale disconnection accidents of photovoltaic power stations caused by voltage fluctuations and faults in power grids, low voltage ride-through characteristics and other detection tests are needed when photovoltaic power stations are connected to power grids. At present, field test and type test are the main methods of detection, but due to the limitation of detection devices and test conditions, the evaluation of low voltage ride-through characteristics of photovoltaic power stations still remains in the single-machine detection mode, which ignores the interaction between photovoltaic inverters and inverters, inverters and static var generator(SVG) devices. The test results cannot truly reflect the low voltage traversing characteristics of the photovoltaic power stations. Using RT-LAB hardware-in-the-loop simulation platform, real photovoltaic inverters and SVG controllers are introduced to build simulation models of photovoltaic power stations. The differences between single-machine detection of photovoltaic inverters and reactive power compensation devices and low-voltage ride-through characteristics detection of the whole station are compared and studied. The simulation results show that, compared with the stand-alone test, the whole station simulation modeling test method can reflect the voltage rise caused by the reactive power characteristics of photovoltaic inverters and SVG devices after the drop, and more truly reflect the low voltage traversing characteristics of photovoltaic power stations.

关键词： Inverters Photovoltaic systems Power system stability Low voltage Computational modeling

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Rodent's Reproductive Health Benefits from the OCP Ban in China Since 1983

Rodent's Reproductive Health Benefits from the OCP Ban in Ch...

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作者： Yuanchen Chen Zhe Sun Lei Xu Quan Zhang Chuan Yan Zhibin Zhang Meirong Zhao Key Laboratory of Microbial Technology for Industrial Pollution Control of Zhejiang Province College of Environment Research Centre of Environmental Science Zhejiang University of Technology Cardiovascular Epidemiology Unit Department of Public Health and Primary Care University of Cambridge State Key Laboratory of Integrated Management on Pest Insects and Rodents in Agriculture Institute of Zoology Chinese Academy of Sciences

Organochlorine pesticides (OCPs) have been globally consumed in agricultural activities in China. They have been demonstrated to be harmful to ecosystems and human beings. Since1950s, OCPs were started to be used in China, and in early 1980s, the government banned them. Though many studies have studied the OCP pollution in the environments, none of

关键词： Rodent's Reproductive Health Benefits from the OCP Ban in China Since 1983

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Autonomous RC-Car for Education Purpose in iSTEM Projects

Autonomous RC-Car for Education Purpose in iSTEM Projects

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2018 IEEE Intelligent Vehicles Symposium, IV 2018

作者： Feng, Weihang Pei, Yun Hai, Bin Huang, Wuling Gong, Xiaoyan Zhu, Fenghua Beijing NO.161 High School China Jilin University China Beijing Institute of Fashion Technology China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781538644522

Software simulation and real environment running parallel execution is a lately proposed method, which also provides full coverage and convenience to accomplish autonomous driving education purpose. This paper introduces a new high-school iSTEM program of autonomous vehicles education. This program uses a Scaled RC-Car platform with several sensors and Raspberry Pi embedded platform, to build an autonomous driving car in scaled indoor simulation environment. The RC-Car is capable of safely autonomous driving. Many existing algorithms are put together to provide the necessary functions of autonomous driving, such lane detection, obstacle detection, lane following, vehicle control etc. In this paper, we provide the details of this program, hardware and software components of the RC-Car, Deep learning end-to-end approaching of algorithm deployment, and future works. © 2018 IEEE.

关键词： Autonomous vehicles

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A Novel Scalable Semi-supervised GMM and Its Application for Multimode Process Quality Prediction with Big Data

A Novel Scalable Semi-supervised GMM and Its Application for...

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Data Driven control and Learning systems (DDCLS)

作者： Le Yao Zhiqiang Ge Weiming Shao Zhihuan Song State Key Laboratory of Industrial Control Technology Institute of Industrial Process Control College of Control Science and Engineering Zhejiang University Hangzhou P. R. China

ISBN: (纸本)9781538626191

In this paper, a novel variational inference semi-supervised GMM (VI-S 2 GMM) model is firstly proposed for multimode process predictive modeling with semi-supervised data. Since all the labeled and unlabeled data samples are involved in each iteration of parameter updating, an intractable computing problem occurs when facing a high-dimension and large-scale dataset. To tack this problem, a scalable Stochastic Variational Inference semi-supervised GMM (SVI-S 2 GMM) is further proposed for massive semi-supervised data. Through taking advantage of stochastic gradient optimization algorithm to maximize the Evidence of Lower Bound (ELBO), the VI-based algorithm becomes scalable. In SVI-S 2 GMM, only one or a mini-batch of samples is randomly selected to update parameters in each iteration, which is more efficient than VI-S 2 GMM. In this way, a large number of unlabeled process data can be useful in the modeling, which will benefit the parameter identification. The SVI-S 2 GMM is then exploited for the prediction of quality-related key performance index (KPI). Two modeling cases with large scale of semi-supervised datasets demonstrate the feasibility and effectiveness of the proposed algorithms.

关键词： Data models Inference algorithms Big Data Predictive models Adaptation models Computational modeling Stochastic processes

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Corrigendum to <'The evolution of carbon footprint in the yangtze river delta city cluster during economic transition 2012–2015′>

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Resources, Conservation and Recycling 2022年 184卷

作者： Chengqi Xia Heran Zheng Jing Meng Shuping Li Pengfei Du Yuli Shan State Key Laboratory of Environmental Simulation and Pollution Control School of Environment Tsinghua University Beijing 100084 China Industrial Ecology Programme Department of Energy and Process Engineering Norwegian University of Science and Technology Trondheim 7491 Norway The Bartlett School of Sustainable Construction University College London London WC1H0QB UK Institute of Blue and Green Development Shandong University Weihai 264209 China Integrated Research for Energy Environment and Society (IREES) Energy and Sustainability Research Institute Groningen University of Groningen Groningen the Netherlands

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Accelerating minibatch stochastic gradient descent using typicality sampling

arXiv

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arXiv 2019年

作者： Peng, Xinyu Li, Li Wang, Fei-Yue Department of Automation Tsinghua University Beijing100084 China Department of Automation Tsinghua National Laboratory for Information Science and Technology Tsinghua University Beijing100084 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100080

Machine learning, especially deep neural networks, has been rapidly developed in fields including computer vision, speech recognition and reinforcement learning. Although Minibatch SGD is one of the most popular stochastic optimization method in training deep networks, it shows a slow convergence rate due to the large noise in gradient approximation. In this paper, we attempt to remedy this problem by building more efficient batch selection method based on typicality sampling, which reduces error of gradient estimation in conventional Minibatch SGD. We analyze convergence rate of the resulting typical batch SGD algorithm, and compare convergence properties between Minibatch SGD and the algorithm. Experimental results demonstrate that our batch selection scheme works well and more complex Minibatch SGD variants can benefit from the proposed batch selection strategy. Copyright © 2019, The Authors. All rights reserved.

关键词： Reinforcement learning

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arXiv

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arXiv 2020年

作者： Li, Jiashuo Zhou, Sili Wei, Wendong Qi, Jianchuan Li, Yumeng Chen, Bin Zhang, Ning Guan, Dabo Qian, Haoqi Wu, Xiaohui Miao, Jiawen Chen, Long Liang, Sai Feng, Kuishuang Institute of Blue and Green Development Shandong University Weihai264209 State Key Laboratory of Coal Combustion School of Energy and Power Engineering Huazhong University of Science and Technology Wuhan430074 China School of International and Public Affairs Shanghai Jiao Tong University Shanghai200030 China State Key Joint Laboratory of Environment Simulation and Pollution Control School of Environment Beijing Normal University Beijing100875 China Laboratory of Anthropogenic Systems Ecology College of Engineering Peking University Beijing100871 China Department of Earth System Science Tsinghua University Beijing100871 China Institute for Global Public Policy Fudan University Shanghai200433 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Key Laboratory of Geographic Information Science Ministry of Education School of Geographic Sciences East China Normal University Shanghai200241 China

China has implemented retrofitting measures in coal-fired power plants (CFPPs) to reduce air pollution through small unit shutdown (SUS), the installation of air pollution control devices (APCDs) and power generation efficiency (PGE) improvement. The reductions in highly toxic Hg emissions and their related health impacts by these measures have not been well studied. To refine mitigation options, we evaluated the health benefits of reduced Hg emissions via retrofitting measures during China's 12th Five-Year Plan by combining plant-level Hg emission inventories with the China Hg Risk Source-Tracking Model. We found that the measures reduced Hg emissions by 23.5 tons (approximately 1/5 of that from CFPPs in 2010), preventing 0.0021 points of per-foetus intelligence quotient (IQ) decrements and 114 deaths from fatal heart attacks. These benefits were dominated by CFPP shutdowns and APCD installations. Provincial health benefits were largely attributable to Hg reductions in other regions. We also demonstrated the necessity of considering human health impacts, rather than just Hg emission reductions, in selecting Hg control devices. This study also suggests that Hg control strategies should consider various factors, such as CFPP locations, population densities and trade-offs between reductions of total Hg (THg) and Hg2+. Copyright © 2020, The Authors. All rights reserved.

关键词： Fossil fuel power plants

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Adaptive Neural Admittance control for Collision Avoidance in Human-Robot Collaborative Tasks

Adaptive Neural Admittance Control for Collision Avoidance i...

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2019 IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Xinbo Yu Wei He Chengqian Xue Bin Li Long Cheng Chenguang Yang School of Automation and Electrical Engineering Institute of Artificial Intelligence University of Science and Technology Beijing Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Bristol Robotics Laboratory University of the West of England Bristol U.K

This paper proposed an adaptive neural admittance control strategy for collision avoidance in human-robot collaborative tasks. In order to ensure that the robot end-effector can avoid collisions with surroundings, robot should be operated compliantly by human within a constrained task space. An impedance model and a soft saturation function are employed to generate a differentiable reference trajectory. Then, adaptive neural network control with position constraint, based on integral barrier Lyapunov function (IBLF), is designed to achieve precise tracking while guaranteeing constrained satisfaction. Utilizing Lyapunov stability principles, we prove that semi-globally uniformly bounded stability is guaranteed for all states of the closed-loop system. At last, the effectiveness of the proposed algorithm is verified on a Baxter robot experimental platform. Collisions with surroundings can be avoided in human-robot collaborative tasks.

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