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arXiv 2022年

作者： Wei, Qinglai Chen, Diancheng Yuan, Beiming State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

Great endeavors have been made to study AI's ability in abstract reasoning, along with which different versions of RAVEN's progressive matrices (RPM) are proposed as benchmarks. Previous works give inkling that without sophisticated design or extra meta-data containing semantic information, neural networks may still be indecisive in making decisions regarding the RPM problems, after relentless training. Evidenced by thorough experiments, we show that, neural networks embodied with felicitous inductive bias, intentionally design or serendipitously match, can solve the RPM problems efficiently, without the augment of any extra meta-data. Our work also reveals that multiviewpoint with multi-evaluation is a key learning strategy for successful reasoning. Nevertheless, we also point out the unique role of meta-data by showing that a pre-training model supervised by the meta-data leads to a RPM solver with better performance. Source code can be found in https://***/QinglaiWeiCASIA/RavenSolver. Copyright © 2022, The Authors. All rights reserved.

关键词： Matrix algebra

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Parallel Reinforcement Learning-Based Energy Efficiency Improvement for a Cyber-Physical System

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IEEE/CAA Journal of Automatica Sinica 2020年第2期7卷 617-626页

作者： Teng Liu Bin Tian Yunfeng Ai Fei-Yue Wang Department of Automotive Engineering Chongqing UniversityChongqing 400044 the Vehicle Intelligence Pioneers Inc. Qingdao 266109China the State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 101408China

As a complex and critical cyber-physical system(CPS),the hybrid electric powertrain is significant to mitigate air pollution and improve fuel *** management strategy(EMS)is playing a key role to improve the energy efficiency of this *** paper presents a novel bidirectional long shortterm memory(LSTM)network based parallel reinforcement learning(PRL)approach to construct EMS for a hybrid tracked vehicle(HTV).This method contains two *** high-level establishes a parallel system first,which includes a real powertrain system and an artificial ***,the synthesized data from this parallel system is trained by a bidirectional LSTM *** lower-level determines the optimal EMS using the trained action state function in the model-free reinforcement learning(RL)*** is a fully data-driven and learning-enabled approach that does not depend on any prediction and predefined ***,real vehicle testing is implemented and relevant experiment data is collected and *** results validate that the proposed EMS can achieve considerable energy efficiency improvement by comparing with the conventional RL approach and deep RL.

关键词： Bidirectional long short-term memory(LSTM)network cyber-physical system(CPS) energy management parallel system reinforcement learning(RL)

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Traffic Signal control Using Offline Reinforcement Learning

Traffic Signal Control Using Offline Reinforcement Learning

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Chinese Automation Congress (CAC)

作者： Xingyuan Dai Chen Zhao Xiaoshuang Li Xiao Wang Fei-Yue Wang School of Artificial Intelligence University of Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences

ISBN: (纸本)9781665426480

The problem of traffic signal control is essential but remains unsolved. Some researchers use online reinforcement learning, including the off-policy one, to derive an optimal control policy through interaction between agents and environments in simulation. However, it is difficult to deploy the policy in real transportation systems due to the gap between simulated and real traffic data. In this paper, we consider an offline reinforcement learning method to tackle the problem. First, we construct a realistic traffic environment and obtain offline data based on a classic actuated traffic signal controller. Then, we use an offline reinforcement learning algorithm, namely conservative Q-learning, to learn an efficient control policy via offline datasets. We conduct experiments on a typical road intersection and compare the conservative Q-learning policy with the actuated policy and two data-driven policies based on off-policy reinforcement learning and imitation learning. Empirical results indicate that in the offline-learning setting the conservative Q-learning policy performs significantly better than other baselines, including the actuated policy, but the other two data-driven policies perform poorly in test scenarios.

关键词： Q-learning Automation Roads Transportation Optimal control Cloning Data models

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Design of a curled hyper-redundant manipulator and the motion control with tip-following algorithm

Design of a curled hyper-redundant manipulator and the motio...

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Chinese control and Decision Conference, CCDC

作者： Aoshun Zhang En Li Feng Zhang Rui Guo Mingrui Luo Yuwei Zhang School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Engineering Laboratory for Intelligent Industrial Vision CAS Beijing China State Grid Intelligent Technology Co. Ltd. Shandong China State Grid Shandong Electric Power Company Jinan Power Supply Company Shandong China

In order to meet the operational needs in some narrow environments, the hyper-redundant snake-shaped manipulator has received extensive attention and has been widely studied due to its compact shape and multiple degrees of freedom. At present, the research of hyper-redundant manipulator mainly focuses on the straight push platform. Considering the limitation of working space, this paper designed a hyper-redundant manipulator delivered by a curled turntable. According to the characteristics of turntable delivery, a tip-following algorithm based on curve fitting and a tip-following strategy based on terminal posture record are proposed. Finally, with the algorithm applied to the physical robot we made, we successfully realized the tip-following motion control of the hyper-redundant manipulator.

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Greenhouse Environment control based on Computational Experiments 20

Greenhouse Environment Control based on Computational Experi...

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4th High Performance Computing and Cluster Technologies Conference, HPCCT 2020 and the 3rd International Conference on Big Data and Artificial Intelligence, BDAI 2020

作者： Weng, Yuchen Wang, Xiujuan Hua, Jing Wang, Haoyu Kang, Mengzhen State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Haidian Beijing China

ISBN: (纸本)9781450375603

Greenhouse environment control is challenging since it is a partially open system being complex, nonlinear and highly uncertain. For such a complex system, empirical knowledge is often not sufficient to control the environment well. This paper proposes an approach for environment control based on computational experiments. The data source is from the second Autonomous Greenhouses International Challenge. Descriptive model is built with lightGBM algorithm for air temperature and CO2 concentration inside greenhouse. Computational experiments were carried out to optimize the ventilation opening, which has double effect of cooling and CO2 concentration decreasing. The aim of optimization is to keep the temperature as close as possible to the set line, while keeping a necessary level of CO2 concentration. The results indicate that the temperature can be better controlled than the original empirical methods for different conditions, while the CO2 concentration is maintained. The proposed approach overcomes the shortage of controlling ventilation with static knowledge, and can be applied to the real greenhouse. © 2020 ACM.

关键词： Greenhouses

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Tensorial Hankel Reconstruction for Coherent DOA Estimation with Sensor Failure

Tensorial Hankel Reconstruction for Coherent DOA Estimation ...

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International Conference on Communication Technology (ICCT)

作者： Fei Cheng Hang Zheng Zhoubin Teng Zhiguo Shi Chengwei Zhou State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China Department of Information and Communications Engineering Aalto University Espoo Finland Zhejiang Emergency Management Digital and Technology Center Hangzhou China Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province Hangzhou China

The research on coherent direction of arrival (DOA) estimation is of great significance for high-precision DOA estimation in complex electromagnetic environments. However, existing coherent DOA estimation methods do not consider the issue of sensor failure that is prone to occur under harsh environments. In this paper, we propose a tensorial Hankel reconstruction method for coherent DOA estimation with sensor failure. Specifically, by analyzing the tensorial Hankel property of complete incoherent covariance tensor, we design the tensorial Hankel mapping rule to reconstruct a Hankel tensor from a slice of incomplete coherent covariance tensor. Meanwhile, the fiber-missing elements caused by sensor failure are dispersed to higher dimensions in the reconstruction process, ensuring the effectiveness of conventional low-rank tensor completion algorithm. Then, the completed Hankel tensor is proven to admit the canonical polyadic model, which can be decomposed to obtain DOA estimations. Simulation results confirm the advantage of the proposed tensorial Hankel reconstruction method.

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Mobility in China, 2020: a tale of four phases

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National Science Review 2021年第11期8卷 110-120页

作者： Suoyi Tan Shengjie Lai Fan Fang Ziqiang Cao Bin Sai Bing Song Bitao Dai Shuhui Guo Chuchu Liu Mengsi Cai Tong Wang Mengning Wang Jiaxu Li Saran Chen Shuo Qin Jessica R.Floyd Zhidong Cao Jing Tan Xin Sun Tao Zhou Wei Zhang Andrew J.Tatem Petter Holme Xiaohong Chen Xin Lu College of Systems Engineering National University of Defense Technology WorldPop School of Geography and Environmental ScienceUniversity of Southampton School of Mathematics and Big Data Foshan University State Key Laboratory on Blind Signal Processing State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences Chinese Evidence-Based Medicine Center National Clinical Research Center for Geriatrics West China Hospital Sichuan University Big Data Research Center University of Electronic Science and Technology of China West China Biomedical Big Data Center West China Hospital Sichuan University Tokyo Tech World Hub Research Initiative Institute of Innovative Research Tokyo Institute of Technology School of Business Central South University Institute of Big Data and Internet Innovations Hunan University of Technology and Business

2020 was an unprecedented year,with rapid and drastic changes in human mobility due to the COVID-19 *** understand the variation in commuting patterns among the Chinese population across stable and unstable periods,we used nationwide mobility data from 318 million mobile phone users in China to examine the extreme fluctuations of population movements in 2020,ranging from the Lunar New Year travel season(chunyun), to the exceptional calm of COVID-19 lockdown,and then to the recovery *** observed that cross-city movements,which increased substantially in chunyun and then dropped sharply during the lockdown,are primarily dependent on travel distance and the socio-economic development of *** the Lunar New Year holiday,national mobility remained low until mid-February,and COVID-19 interventions delayed more than 72.89 million people returning to large *** network analysis revealed clusters of highly connected cities,conforming to the social-economic division of urban agglomerations in *** the mass migration back to large cities was delayed,smaller cities connected more densely to form new *** the recovery period after travel restrictions were lifted,the netflows of over 55% city pairs reversed in direction compared to before the *** findings offer the most comprehensive picture of Chinese mobility at fine resolution across various scenarios in China and are of critical importance for decision making regarding future public-health-emergency response,transportation planning and regional economic development,among others.

关键词： human mobility travel restrictions COVID-19 mobile phone data behavioral response

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control strategies and their effects on the COVID-19 pandemic in 2020 in representative countries

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Journal of Biosafety and Biosecurity 2021年第2期3卷 76-81页

作者： Rongzhang Hao Yewu Zhang Zhidong Cao Jing Li Qing Xu Lingling Ye Xudong Guo Tao Zheng Hongbin Song Chinese PLA Center for Disease Control and Prevention BeijingChina Department of Toxicology and Sanitary Chemistry School of Public HealthCapital Medical UniversityBeijingChina National Center for Public Health Surveillance and Information Service Chinese Center for Disease Control and PreventionBeijingChina State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijingChina State Key Laboratory of Pathogen and Biosecurity Beijing Institute of Microbiology and EpidemiologyBeijingChina Division of Biological Safety Service Academy of Military Medical SciencesAcademy of Military Science of Chinese PLABeijingChina

COVID-19 is the most severe pandemic globally since the 1918 influenza *** responding to this once-in-a-century global pandemic is a worldwide challenge that the international community needs to jointly face and *** study reviews and discusses the key measures taken by major countries in 2020 to fight against COVID-19,such as lockdowns,social distancing,wearing masks,hand hygiene,using Fangcang shelter hospitals,large-scale nucleic acid testing,close-contacts tracking,and pandemic information monitoring,as well as their prevention and control *** hope it can help improve the efficiency and effectiveness of pandemic prevention and control in future.

关键词： COVID-19 control strategies Effect comparison

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Performance Analysis of Distributed Filtering under Mismatched Noise Covariances

arXiv

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arXiv 2024年

作者： Lyu, Xiaoxu Wen, Guanghui Shi, Ling Duan, Peihu Duan, Zhisheng The Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong The Laboratory of Security Operation and Control for Intelligent Autonomous Systems Department of Systems Science School of Mathematics Southeast University Nanjing211189 China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering Science College of Engineering Peking University Beijing100871 China

This paper systematically investigates the performance of consensus-based distributed filtering under mismatched noise covariances. First, we introduce three performance evaluation indices for such filtering problems, namely the standard performance evaluation index, the nominal performance evaluation index, and the estimation error covariance. We derive difference expressions among these indices and establish one-step relations among them under various mismatched noise covariance scenarios. We particularly reveal the effect of the consensus fusion on these relations. Furthermore, the recursive relations are introduced by extending the results of the one-step relations. Subsequently, we demonstrate the convergence of these indices under the collective observability condition, and show this convergence condition of the nominal performance evaluation index can guarantee the convergence of the estimation error covariance. Additionally, we prove that the estimation error covariance of the consensus-based distributed filter under mismatched noise covariances can be bounded by the Frobenius norms of the noise covariance deviations and the trace of the nominal performance evaluation index. Finally, the effectiveness of the theoretical results is verified by numerical simulations. Copyright © 2024, The Authors. All rights reserved.

关键词： Wiener filtering

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Conditional Goal-oriented Trajectory Prediction for Interacting Vehicles with Vectorized Representation

arXiv

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arXiv 2022年

作者： Li, Ding Zhang, Qichao Lu, Shuai Pan, Yifeng Zhao, Dongbin School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Baidu Inc. Beijing100085 China

Predicting joint future trajectories of two interacting vehicles faces great challenges, due to the high degree of multimodality and uncertainty in the future interaction process. For the traditional joint prediction models, each vehicle is treated as an individual agent, and its multimodal future modes can be learned from an efficient Goal-oriented Trajectory Prediction (GTP) method. However, such marginal methods produce multimodal self-consistent trajectories over individual agents without considering their future interactions. Motivated by this, this paper aims to tackle the interactive behavior prediction task, and proposes a novel Conditional Goal-oriented Trajectory Prediction (CGTP) framework to jointly generate scene-compliant trajectories of two interacting agents. Our CGTP framework is an end-to-end and interpretable model, including three main stages: context encoding, goal interactive prediction and trajectory interactive prediction. First, a Goals-of-Interest Network (GoINet) is designed to extract the interactive features between agent-to-agent and agent-to-goals using a graph-based vectorized representation. Further, the Conditional Goal Prediction Network (CGPNet) focuses on goal interactive prediction via a combined form of marginal and conditional goal predictors. Finally, the Goal-oriented Trajectory Forecasting Network (GTFNet) is proposed to implement trajectory interactive prediction via the conditional goal-oriented predictors, with the predicted future states of the other interacting agent taken as inputs. In addition, a new goal interactive loss is developed to better learn the joint probability distribution over goal candidates between two interacting agents. In the end, the proposed method is conducted on Argoverse motion forecasting dataset, In-house cut-in dataset, and Waymo open motion dataset. The comparative results demonstrate the superior performance of our proposed CGTP model than the mainstream prediction methods. Copyright © 202

关键词： Forecasting

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