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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Xiaojing Yang Tianlu Gao Shiqian Ma Xiaoyan Gong Zhigang Huang Dazhi Hao Power Dispatching and Control Center State Grid Tianjin Electric Power Company Tianjin China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Science Beijing China Electric Power Research Institute State Grid Tianjin Electric Power Company Tianjin China

ISBN: (数字)9781728176840

ISBN: (纸本)9781728176857

The emergence of deep learning is undoubtedly a landmark breakthrough in artificial intelligence, and the emergence of this technology makes it possible for us to realize the real smart grid. A major challenge currently facing deep learning technology is the requirement of enough training samples to ensure the accuracy of results of the learning. But for the current power grid, training data can be obtained because a lot of real time data is collected to maintain the stability of its operation. With the deep learning technology, there is no doubt that these data can be utilized to realize the intelligent operation of the power grid. First, this paper gives a brief introduction to the current artificial intelligence, followed by the introduction of deep learning technology. Some potential applications of the deep learning in the daily operation of the power grid are proposed. And in the end, there is a brief description of some of the features that future smart grids should have.

关键词： Deep learning Training Training data Learning (artificial intelligence) Power system stability Real-time systems Smart grids

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Programming by Visual Demonstration for Pick-and-Place Tasks using Robot Skills

Programming by Visual Demonstration for Pick-and-Place Tasks...

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IEEE International Conference on Robotics and Biomimetics

作者： Peng Hao Tao Lu Yinghao Cai Shuo Wang The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing China

In this paper, we present a vision-based robot programming system for pick-and-place tasks that can generate programs from human demonstrations. The system consists of a detection network and a program generation module. The detection network leverages convolutional pose machines to detect the key-points of the objects. The network is trained in a simulation environment in which the train set is collected and auto-labeled. To bridge the gap between reality and simulation, we propose a design method of transform function for mapping a real image to synthesized style. Compared with the unmapped results, the Mean Absolute Error (MAE) of the model completely trained with synthesized images is reduced by 23% and the False Negative Rate FNR (FNR) of the model fine-tuned by the real images is reduced by 42.5% after mapping. The program generation module provides a human-readable program based on the detection results to reproduce a real-world demonstration, in which a longshort memory (LSM) is designed to integrate current and historical information. The system is tested in the real world with a UR5 robot on the task of stacking colored cubes in different orders.

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Parallel Hospital: ACP-Based Hospital Smart Operating system

Parallel Hospital: ACP-Based Hospital Smart Operating System

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Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Yongjun Wang Fei-Yue Wang Xiao Wang Zhongjun Guan Liwei Ouyang Jiangong Wang Lan Yan Wenbo Zheng Wenwen Zhang Beijing Tiantan Hospital Capital Medical University Beijing Beijing China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Qingdao Academy of Intelligent Industries Qingdao China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China School of Software Engineering Xi'an Jiaotong University Xi'an China

This article aims to propose a parallel hospital based on parallel medicine. The parallel hospital takes the parallel idea of virtual and real interaction as its core concept and uses parallel idea's artificial scenarios, computational experiments, and parallel execution (ACP) theory as the guiding theory to construct a new generation of intelligent hospital management system with virtual and real interaction. We conducted a case study of the parallel hospital practice case of Tiantan Hospital-Tiantan Smart Brain, so as to confirm the feasibility and scientificity of the proposed parallel hospital.

关键词： Training Hospitals Operating systems Digital twin Conferences

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A Hybrid Deep Learning Approach with GCN and LSTM for Traffic Flow Prediction*

A Hybrid Deep Learning Approach with GCN and LSTM for Traffi...

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International Conference on Intelligent Transportation

作者： Zhishuai Li Gang Xiong Yuanyuan Chen Yisheng Lv Bin Hu Fenghua Zhu Fei-Yue Wang State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781538670255

Traffic flow prediction is an important functional component of Intelligent Transportation systems (ITS). In this paper, we propose a hybrid deep learning approach, called graph and attention-based long short-term memory network (GLA), to efficiently capture the spatial-temporal features in traffic flow. Firstly, we apply graph convolutional network (GCN) to mine the spatial relationships of traffic flow over multiple observation stations, in which the adjacent matrix is determined by a data-driven approach. Then, we feed the output of the GCN model to the long short-term memory (LSTM) model which extracts temporal features embedded in traffic flow. Further, we implement a soft attention mechanism on the extracted spatial-temporal traffic features to make final prediction. We test the proposed method over the PeMS data sets. Experimental results show that the proposed model performs better than the competing methods.

关键词： Feature extraction Machine learning control systems complex systems Automation Predictive models Data models

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Fast and Robust RGB-D Multiple Human Tracking Based on Part Model for Mobile Robots

Fast and Robust RGB-D Multiple Human Tracking Based on Part ...

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第三十八届中国控制会议

作者： Shiying Sun Xiaoguang Zhao Min Tan State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences

In this paper, we address the problem of multiple human tracking for mobile robots and proposed a fast and robust RGB-D multiple human tracking approach based on part model. Firstly, a simplified deformable part model method is used for fast human detection using RGB-D information. Then the partial occlusion is detected with depth information. Finally, part model and partial occlusion information are combined into data association process and an on-line appearance classification method with dynamic occlusion handling is proposed for robust tracking. The performance of the proposed method is evaluated on the public dataset, and experiment results have demonstrated the effectiveness of the proposed method.

关键词： Multiple Human Tracking Deformable Part Model Human Detection Occlusion Handling On-line Classification

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Learning Category-level Implicit 3D Rotation Representations for 6D Pose Estimation from RGB Images

Learning Category-level Implicit 3D Rotation Representations...

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IEEE International Conference on Robotics and Biomimetics

作者： Xiaocan Li Yinghao Cai Shuo Wang Tao Lu State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China

We exploit the embedding ability of a de-noising autoencoder for an implicit 3D rotation representation learning at the category level. Contrast to the exact 3D reconstruction model of each instance-level physical object, we leverage the inexact CAD/Reconstruction models of an object as the representative model for some category. Under our assumptions that objects within the same category share resemble geometry, we train a de-noising autoencoder on synthetic 3D views of category-level objects to extract the homogenous features at the bottleneck layer. The latent representation is agnostic not only to heterogeneous textures, colors, and illuminations, but also ambiguous pose caused by object symmetry. To extend the instance-level 3D translation estimation to the category level, we considered the 3D diagonal length ratio between the source and target object. We achieved a frame rate of 17Hz.

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A Novel Person Re-ID Method based on Multi-Scale Feature Fusion

A Novel Person Re-ID Method based on Multi-Scale Feature Fus...

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第三十九届中国控制会议

作者： Yuanyuan Li Xiaofei Wang Zhiqin Zhu Xinghua Huang Penghua Li Guanqiu Qi Yujun Rong Chongqing University of Posts and Telecommunications Key Laboratory of Complex System Safety and Control Ministry of EducationChongqing University Computer Information Systems Department Buffalo State College China Mobile (Hangzhou) Information Technology Co. Ltd.

Person re-identification（Re-ID） is a challenging but significant research topic. Previous methods based on global features are not pay much attention to the fine-grained feature information. While the methods based on local features strictly rely on the accurate pedestrian detection bounding-boxes to align the image pairs. In this paper, the Multi-Scale Feature Fusion Network is proposed which can extract global and local features simultaneously and to learn jointly. The network structure consists of two branches. One is a global branch for global feature learning, in this branch, we extract the whole body feature information of human body to express the pedestrian image as a whole. The other is a local branch for local feature learning. In the local branch, according to the characteristics of pedestrian’s human structure, we segment the image into 6 horizontal strips to bring the local feature representation of the pedestrian image. In addition, we use a novel integration of multiple loss functions to further improve the recognition accuracy of the network. Experiments on Market-1501 and DukeMTMC-reID datasets show that our proposed method can achieve state-of-the-art results.

关键词： Person re-identification Multi-scale feature fusion Feature learning Multiple loss functions

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Group Consensus for Euler-Lagrange Multi-Agent systems with Dynamic Event-Triggered control

Group Consensus for Euler-Lagrange Multi-Agent Systems with ...

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International Conference on control and Automation (ICCA)

作者： Ran Cao Long Cheng Zhe Dong State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China School of Electrical and Control Engineering North China University of Technology Beijing China

ISBN: (数字)9781728190938

ISBN: (纸本)9781728190945

This paper mainly investigates the group consensus of Euler-Lagrange multi-agent systems based on the dynamical event-triggered controller. All agents in the system are divided into several groups. The agents in the same group converge to one state and those in the different groups may not coincide. To use fewer communication resources, an event-triggered controller using the dynamical event-triggered law is designed. Several mild assumptions are made to achieve the group consensus. And Lyapunov analysis is given to prove that the group consensus can be achieved under the designed control algorithm. Compared to existing results, this paper mainly designs the dynamical event-triggered law to achieve the group consensus rather than consensus or tracking, and the concerned agent is the nonlinear Euler-Lagrange system.

关键词： Nonlinear dynamical systems Directed graphs Multi-agent systems Laplace equations Symmetric matrices Network topology Torque

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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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Decision-Making in Driver-Automation Shared control:A Review and Perspectives

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IEEE/CAA Journal of Automatica Sinica 2020年第5期7卷 1289-1307页

作者： Wenshuo Wang Xiaoxiang Na Dongpu Cao Jianwei Gong Junqiang Xi Yang Xing Fei-Yue Wang IEEE California PATH University of CaliforniaBerkeleyCA 94720 USA Department of Engineering University of CambridgeCambridge CB21PZU.K. Mechanical and Mechatronics Engineering University of WaterlooWaterloo N2L3G1Canada Department of Mechanical Engineering Beijing Institute of TechnologyBeijing 100081China School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 639798Singapore State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190and also with the Research Center for Military Computational Experiments and Parallel Systems TechnologyNational University of Defense TechnologyChangsha 410073China

Shared control schemes allow a human driver to work with an automated driving agent in driver-vehicle systems while retaining the driver’s abilities to *** human driver,as an essential agent in the driver-vehicle shared control systems,should be precisely modeled regarding their cognitive processes,control strategies,and decision-making *** interactive strategy design between drivers and automated driving agents brings an excellent challenge for human-centric driver assistance systems due to the inherent characteristics of *** open-ended questions arise,such as what proper role of human drivers should act in a shared control scheme?How to make an intelligent decision capable of balancing the benefits of agents in shared control systems?Due to the advent of these attentions and questions,it is desirable to present a survey on the decision making between human drivers and highly automated vehicles,to understand their architectures,human driver modeling,and interaction strategies under the driver-vehicle shared ***,we give a further discussion on the key future challenges and *** are likely to shape new potential research directions.

关键词： Automated vehicle decision-making human driver human-vehicle interaction shared control

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