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2022 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2022

作者： Xiang, Tian-Yu Zhou, Xiao-Hu Xie, Xiao-Liang Liu, Shi-Qi Wang, Chen Gui, Mei-Jiang Li, Hao Hou, Zeng-Guang University of Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Institute of Automation School of Artificial Intelligence Beijing100049 China

ISBN: (数字)9781665469838

ISBN: (纸本)9781665469838

Clinical experiments and scientific evidence show the relations between gait and cognition. Neurological diseases (like stroke and cerebral palsy) distract patients' attention and restrict their executive functions. Furthermore, these diseases impair patients' movement and limit their social participation. Quantitative motion assessment is significant to medical decision-making for neurological disorders but is possible only with expensive motion capture systems and professional medical workers. Here, an end-To-end video-based quantitative gait analysis algorithm is proposed. Unlike previous works, which only focus on image coordinate key-points, our algorithm also utilizes scaled key-points that can depict the size of patients. These two groups of features are sent to other neural networks to extract temporal and spatial characteristics, and then the deep features are fused by the attention mechanism. Finally, a statistic model is used to process the proposed outputs to get a final result. Experiments on the public patients' dataset show the effectiveness and accuracy of our model. Furthermore, the ablation study proves that every step of the proposed method contributes to the whole system's performance. © 2022 IEEE.

关键词： Gait analysis

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STGSA:A Novel Spatial-Temporal Graph Synchronous Aggregation Model for Traffic Prediction

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IEEE/CAA Journal of Automatica Sinica 2023年第1期10卷 226-238页

作者： Zebing Wei Hongxia Zhao Zhishuai Li Xiaojie Bu Yuanyuan Chen Xiqiao Zhang Yisheng Lv Fei-Yue Wang the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China the Shandong Key Laboratory of Smart Transportation(Preparation) Shandong Jiao Tong UniversityShanghai 200240China the College of Science China University of PetroleumQingdao 266580China the Department of Transportation Engineering School of Transportation Science and TechnologyHarbin Institute of TechnologyHarbin 150090China IEEE

The success of intelligent transportation systems relies heavily on accurate traffic prediction,in which how to model the underlying spatial-temporal information from traffic data has come under the *** existing frameworks typically utilize separate modules for spatial and temporal correlations ***,this stepwise pattern may limit the effectiveness and efficiency in spatial-temporal feature extraction and cause the overlook of important information in some ***,it is lacking sufficient guidance from prior information while modeling based on a given spatial adjacency graph(e.g.,deriving from the geodesic distance or approximate connectivity),and may not reflect the actual interaction between *** overcome those limitations,our paper proposes a spatial-temporal graph synchronous aggregation(STGSA)model to extract the localized and long-term spatial-temporal dependencies ***,a tailored graph aggregation method in the vertex domain is designed to extract spatial and temporal features in one graph convolution *** each STGSA block,we devise a directed temporal correlation graph to represent the localized and long-term dependencies between nodes,and the potential temporal dependence is further fine-tuned by an adaptive weighting ***,we construct an elaborated spatial adjacency matrix to represent the road sensor graph by considering both physical distance and node similarity in a datadriven ***,inspired by the multi-head attention mechanism which can jointly emphasize information from different r epresentation subspaces,we construct a multi-stream module based on the STGSA blocks to capture global *** projects the embedding input repeatedly with multiple different ***,the predicted values are generated by stacking several multi-stream *** experiments are constructed on six real-world datasets,and numerical results show that the proposed STGSA

关键词： Deep learning graph neural network(GNN) multistream spatial-temporal feature extraction temporal graph traffic prediction

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The DAO to Social Transportation: Towards Smart Mobility in Cyber-Physical-Social Space

The DAO to Social Transportation: Towards Smart Mobility in ...

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2023 IEEE International Conference on systems, Man, and Cybernetics, SMC 2023

作者： Chen, Yuanyuan Lv, Yisheng Wang, Fei-Yue Institute of Automation State Key Lab. of Multimodal Artificial Intelligence Systems Chinese Academy of Sciences Beijing100190 China Chinese Academy of Sciences State Key Lab. for Management and Control of Complex Systems Beijing100190 China

ISBN: (纸本)9798350337020

The rapid advancement of intelligence and connectivity technology (ICT) has enhanced the efficiency and safety of intelligent transportation systems (ITS). However, this also increases the complexity of the transportation systems, especially the social complexity, and poses new challenges for their management. This paper discusses the emergence of social transportation, as part of natural evolutionary adaptation to new traffic conditions and technology, which is a paradigm shift from engineering-centered technical systems to society-centered ecosystems. In social transportation systems, all participants including administrators, individual travelers and other stakeholders play a more proactive role in the operation and management of transportation systems. Therefore, we propose to employ a decen-tralized autonomous organization (DAO) as a model for the social transportation system that allows participants to collaborate and coordinate without relying on a central authority. To meet the challenges brought by this transition, we propose to apply ACP-based parallel system theory to restructure the methodology of transportation management. Finally, we present a case study of personal carbon trading system where economic incentives are used to manage traffic demand, affecting others' travel behaviors, and reduce carbon emissions. In the conclusive remarks, we provide our visions and future directions of social transportation systems. © 2023 IEEE.

关键词： block chain cyber-physical-social system decentralized and autonomous organization social transportation

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Automated Silicon-Substrate Ultra-Microtome for Automating the Collection of Brain Sections in Array Tomography

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IEEE/CAA Journal of Automatica Sinica 2021年第2期8卷 389-401页

作者： Long Cheng Weizhou Liu Chao Zhou Yongxiang Zou Zeng-Guang Hou the State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China

Understanding the structure and working principle of brain neural networks requires three-dimensional reconstruction of brain tissue samples using array tomography *** order to improve the reconstruction performance,the sequence of brain sections should be collected with silicon wafers for subsequent electron microscopic ***,the current collection of brain sections based on silicon substrate involve mainly manual collection,which requires the involvement of automation techniques to increase collection *** paper presents the design of an automatic collection device for brain ***,a novel mechanism based on circular silicon substrates is proposed for collection of brain sections;second,an automatic collection system based on microscopic object detection and feedback control strategy is *** results verify the function of the proposed collection *** objects(brain section,left baffle,right baffle)can be detected from microscopic images by the proposed detection *** efficiency can be further improved with position feedback of brain sections *** has been experimentally verified that the proposed device can well fulfill the task of automatic collection of brain *** the help of the proposed automatic collection device,human operators can be partially liberated from the tedious manual collection process and collection efficiency can be improved.

关键词： Array tomography automatic collection system brain sections microscopic object detection serial section

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Personalized gait trajectory generation based on anthropometric features using Random Forest

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Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 15597-15608页

作者： Ren, Shixin Wang, Weiqun Hou, Zeng-Guang Chen, Badong Liang, Xu Wang, Jiaxing Peng, Liang The 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 The CAS Center for Excellence in Brain Science and Intelligence Technology Beijing100190 China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an710049 China

Using lower limb rehabilitation robots (LLRRs) to help stroke patients recover their walking ability is attracting more and more attention presently. Previous studies have shown that gait rehabilitation training with natural gait pattern can improve the therapeutic outputs. However, how to generate the personalized gait trajectory has not been well researched. In this paper, a personalized gait generation method based anthropometric features is proposed. Firstly, gait trajectories are fitted and simplified into Fourier coefficient vectors, which are used to represent gait trajectories. Secondly, fourteen body features are used to generate the personalized gait trajectories and the feature set is further optimized based on the minimal redundancy maximal relevance criterion for easy application on the LLRR. Then, the relationship between the optimized feature set and gait trajectories is modeled by using the RF algorithm. Finally, the performance of the proposed method is demonstrated by several comparison experiments. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Trajectories

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Parallel Driving with Big Models and Foundation Intelligence in Cyber-hysical-ocial Spaces

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Research 2024年第2期2024卷 1-17页

作者： Xiao Wang Jun Huang Yonglin Tian Chen Sun Lie Yang Shanhe Lou Chen Lv Changyin Sun Fei-Yue Wang School of Artificial Intelligence Anhui UniversityHefeiChina Macao University of Science and Technology MacaoChina State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijingChina MVSLab Department of Mechanical and Mechatronics EngineeringUniversity of Waterloo200 University Ave WestWaterlooON N2L3G1Canada School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingaporeSingapore

Recent years have witnessed numerous technical breakthroughs in connected and autonomous vehicles (CAVs). On the one hand, these breakthroughs have significantly advanced the development of intelligent transportation systems (ITSs);on the other hand, these new traffic participants introduce more complex and uncertain elements to ITSs from the social space. Digital twins (DTs) provide real-time, data-driven, precise modeling for constructing the digital mapping of physical-world ITSs. Meanwhile, the metaverse integrates emerging technologies such as virtual reality/mixed reality, artificial intelligence, and DTs to model and explore how to realize improved sustainability, increased efficiency, and enhanced safety. More recently, as a leading effort toward general artificial intelligence, the concept of foundation model was proposed and has achieved significant success, showing great potential to lay the cornerstone for diverse artificial intelligence applications across different domains. In this article, we explore the big models embodied foundation intelligence for parallel driving in cyber-physical-social spaces, which integrate metaverse and DTs to construct a parallel training space for CAVs, and present a comprehensive elucidation of the crucial characteristics and operational mechanisms. Beyond providing the infrastructure and foundation intelligence of big models for parallel driving, this article also discusses future trends and potential research directions, and the ?S?goals of parallel driving.

关键词： breakthrough integrate corners

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Can language models be used for real-world urban-delivery route optimization?

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The Innovation 2023年第6期4卷 92-100页

作者： Yang Liu Fanyou Wu Zhiyuan Liu Kai Wang Feiyue Wang Xiaobo Qu State Key Laboratory of Itelligent Green Vehicle and Mobility Tsinghua UniversityBeiing 100084China Jiangsu Key Laboratory of Urban ITS Jjiangsu Province Collaborative Innovation Center of Modem Urban Traffic TechnologiesSchool of TransportationSoutheast UniversityNanjing 211189China School of Vehicle and Mobilit Tsinghua UniversityBejjing 100084China Institute of Automation State Key Laboratory for Management and Control of Complex SystemsChinese Academy of SciencesBeiing 100190China

Language models have contributed to breakthroughs in interdisciplinary research,such as protein design and molecular dynamics *** this study,we reveal that beyond language,representations of other entities,such as human behaviors,that are mappable to learmable se quences can be learned by language *** compelling example is the realworld delivery route optimization *** here propose a novel approach based on the language model to optimize delivery routes on the basis of drivers'historical *** a broad range of optimizationbased approaches have been designed to optimize delivery routes,they do not capture the implicit knowledge of complex delivery operating *** model we propose integrates this knowledge in the route optimization process by learning from driving behaviors in experienced drivers.A real-world delivery route that preserves drivers'implicit behavioral patterns is first analogized to a sentence in natural *** unsupervised learning,we then learn the vector representations of words and infer the drivers'delivery chains on the basis of the tailored chain-reaction-based *** also provide insights into the fusion of language models and operations research *** our approach,language models are applied to learn drivers'delivery behaviors and infer new deliveries at the delivery zone level,while the classic traveling salesman problem(TSP)model is embedded into the hybrid framework for intra-zone *** experiments performed on real-world data from Amazon's delivery service demonstrate that the proposed approach outperforms pure optimization,supporting the effectiveness,efficiency,and extensibility of our *** a versatile approach,the proposed framework can easily be extended to various disciplines in which the data follow cert ain grammar *** anticipate that our work will serve as a stepping stone toward the understanding and application of language models in

关键词： driving breakthrough versatile

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From RAG/RAT to SAGE for Social Transportation: A CoT and New Perspective on Smart Logistics and Mobility

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第6期9卷 5005-5008页

作者： Fei-Yue Wang Wuhan Institute of Industrial Innovation and Development Hubei China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Dear All

关键词： Intelligent vehicles Transportation Logistics

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Building Supporting SLAM Community for IEEE TIV: From DHWs to Smart Academic Organizations

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第7期9卷 5119-5123页

作者： Fei-Yue Wang Wuhan Institute of Industrial Innovation and Development Hubei China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

I would like to share with you the following information

关键词： Simultaneous localization and mapping Intelligent vehicles Buildings Organizations

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From RAG/RAT to SAGE: Parallel Driving for Smart Mobility

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第5期9卷 4821-4825页

作者： Fei-Yue Wang Wuhan Institute of Industrial Innovation and Development Wuhan China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

The current issue includes 2 perspectives, 2 letters, and 12 regular papers. These perspectives explore critical issues within the field of IVs and pontential research directions based on the evolution of foundation m... 详细信息

关键词： Intelligent vehicles

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