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

作者： Xu, Ruijie Chen, Shichao Sun, Wenqiao Lv, Yisheng Luo, Jialiang Tang, Ying Institute of Automation Chinese Academy of Sciences College of Information Science & Technology Beijing University of Chemical Technology The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Institute of Automation Chinese Academy of Sciences The Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Transportation and Economics Research Institute The Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited Beijing China Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Institute of Automation Chinese Academy of Sciences China University of Geosciences Beijing School of Information Engineering The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Rowan University Department of Electrical and Computer Engineering Glassboro United States

ISBN: (纸本)9798350337020

Global Navigation Satellite systems (GNSS) can provide real-time positioning information for outdoor users, but cannot for indoor scenarios or heavily occluded outdoor scenarios. Strap-down Inertial Navigation System (SINS) are widely used to locate people in complex interior or heavily occluded outdoor scenarios due to its light weight and low power consumption. However, IMU of SINS are noisy, and the sampling data error is large, which is a divergence of the error with time. Therefore, it will generate a positioning accumulation error, which affects the final positioning accuracy. The problem of cumulative IMU errors is usually dealt with by Zero-Velocity Update (ZUPT). The zero-velocity detection part of basic ZUPT method usually uses a single threshold to determine the gait of pedestrian, which often has the problem of gait misjudgment and omission. To address these problems, this paper proposes a composite conditional detection method to solve the problem of misjudgment in the zero-velocity interval. In addition, we redesign the zero-velocity update algorithm and uses the Cubature Kalman filter (CKF) for pedestrian positioning error correction. The experimental results demonstrate that the proposed ZUPT method based on dual-threshold detection can better detect the interval between pedestrian motion and stationery than ones with single threshold. The zero-velocity update algorithm based on CKF has higher performance than conventional EKF and UKF methods, which constrains the cumulative error of SINS to about 0.2% of the whole walking distance. © 2023 IEEE.

关键词： Cubature Kalman Filter Inertial Pedestrian Navigation Zero-Velocity Update

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AirVista: Empowering UAVs with 3D Spatial Reasoning Abilities Through a Multimodal Large Language Model Agent 27

AirVista: Empowering UAVs with 3D Spatial Reasoning Abilitie...

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27th IEEE International Conference on Intelligent Transportation systems, ITSC 2024

作者： Lin, Fei Tian, Yonglin Wang, Yunzhe Zhang, Tengchao Zhang, Xinyuan Wang, Fei-Yue State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Capital University of Economics and Business Beijing100070 China University of Chinese Academy of Sciences Department of Artificial Intelligence Beijing100049 China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing100190 China Macau University of Science and Technology Faculty of Innovation Engineering Department of Engineering Science 999078 China

ISBN: (纸本)9798331505929

In urban environments, complex transportation demands, including land, air, and maritime transport, are increasingly growing. While significant advancements have been made in land transportation and autonomous driving, research on urban air mobility (UAM) systems is still in its early stages. This paper presents an innovative urban air unmanned aerial vehicle (UAV) framework: AirVista, which is designed and built based on the Artificial systems, Computational experiments, and Parallel execution (ACP) approach, integrated with a multimodal large language model (MLLM) agent. Considering that UAM tasks often require UAVs to possess fine-grained spatial perception and reasoning capabilities, and given that existing MLLMs are somewhat lacking in exploring 3D spaces, this paper further proposes an instruction fine-tuning strategy integrated with 3D spatial knowledge, which has been validated experimentally for its effectiveness. Additionally, to enhance the understanding of the efficiency of MLLM for UAV tasks, this paper delves into prompt fine-tuning templates tailored for UAV task decomposition. Through experimental demonstrations, we showcase that the prompt-tuned MLLM exhibits efficient task decomposition and execution capabilities in handling complex UAV tasks. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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A New Era of Intelligent Vehicles and Intelligent Transportation systems: Digital Twins and Parallel Intelligence

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第4期8卷 2619-2627页

作者： Wang, Ziran Lv, Chen Wang, Fei-Yue Purdue Univ Coll Engn W Lafayette IN 47907 USA Nanyang Technol Univ Sch Mech & Aerosp Engn Singapore 639798 Singapore Chinese Acad Sci Inst Automation State Key Lab Management & Control Complex Syst Beijing Peoples R China

By March 25, our transaction has received a total of 619 original submissions. Our current average number of submissions per day (SPD) rate is 7.37. Given this SPD, I would like to expand our editorial board to includ...

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Civilian Autonomous Carriers for Peaceful Services: Intelligent Vehicle Carries Enabled Future Transportation and Logistics

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2023年 1-5页

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

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Speed and Accuracy Tradeoff for LiDAR Data Based Road Boundary Detection

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IEEE/CAA Journal of Automatica Sinica 2021年第6期8卷 1210-1220页

作者： Guojun Wang Jian Wu Rui He Bin Tian State Key Laboratory of Automotive Simulation and Control Jilin UniversityChangchun 130022China State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 the Qingdao Academy of Intelligent Industries ShandongChina

Road boundary detection is essential for autonomous vehicle localization and decision-making,especially under GPS signal loss and lane *** road boundary detection in structural environments,obstacle occlusions and large road curvature are two significant ***,an effective and fast solution for these problems has remained *** solve these problems,a speed and accuracy tradeoff method for LiDAR-based road boundary detection in structured environments is *** proposed method consists of three main stages:1)a multi-feature based method is applied to extract feature points;2)a road-segmentation-line-based method is proposed for classifying left and right feature points;3)an iterative Gaussian Process Regression(GPR)is employed for filtering out false points and extracting boundary *** demonstrate the effectiveness of the proposed method,KITTI datasets is used for comprehensive experiments,and the performance of our approach is tested under different road *** experiments show the roadsegmentation-line-based method can classify left,and right feature points on structured curved roads,and the proposed iterative Gaussian Process Regression can extract road boundary points on varied road shapes and traffic ***,the proposed road boundary detection method can achieve real-time performance with an average of 70.5 ms per frame.

关键词： 3D-LiDAR autonomous vehicle object detection point cloud road boundary

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A Deep Learning Based Personal Authentication Method Using sEMG Signal 41

A Deep Learning Based Personal Authentication Method Using s...

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第41届中国控制会议

作者： Lina Tong Mingjia Zhang Daisong Liu Hanghang Ma Yuxiang Zhang Liang Peng School of Mechanical Electronic & Information Engineering Chinese University of Mining and Technology-Beijing State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChina Academy of Science

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Personal authentication means that the relevant system confirms whether the identity of the user is real,legal and unique.A safe and convenient personal authentication method is a core for guarantee the security of our information and *** Electromyography(sEMG),which exists on the surface of the skin,is a good signal to achieve personal authentication,for it is difficult to be extracted and *** this paper,a new personal authentication method using forearm sEMG is ***,8-channel sEMG signals are recorded by Myo armband that is placed on right forearm of 14 subjects who perform the same hand open ***,two different deep learning models are presented to classify the *** multi-layers Convolution Neural Network(ML-CNN) and two stages Long Short Term Memory(2 s-LSTM) Network can achieve accuracy of 97.50% and 93.60% respectively,shows it is feasible to achieve the goal of personal *** the time cost of ML-CNN is higher than the 2 s-LSTM,both are lower than human reaction *** the architecture of ML-CNN is more suitable to achieving the goal of personal authentication.

关键词： Personal Authentication Surface Electromyography(sEMG) Myo multi-layers Convolution Neural Network(CNN) LSTM Network

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Towards Intelligent and Trustworthy Crowdsourcing systems with Cyber-physical Social Intelligence via Retrieval-augmented Generation

Towards Intelligent and Trustworthy Crowdsourcing Systems wi...

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2024 International Conference on Cyber-Physical Social Intelligence, ICCSI 2024

作者： Yang, Jing Wang, Xingxia Ni, Qinghua Liu, Yuhang Wang, Jing Wang, Fei-Yue Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing100190 China Macau University of Science and Technology Faculty of Innovation Engineering 999078 China Macau University of Science and Technology 999078 China Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems Beijing100190 China

ISBN: (纸本)9798350376739

In recent years, crowdsourcing systems that tackle complex tasks through the collective efforts of many individuals have garnered substantial attention. However, the existing crowd-sourcing systems face some challenges such as overreliance on human experience and effort, information overload, and risks of privacy breaches. To overcome these challenges, this paper proposes an intelligent and trustworthy crowdsourcing frame-work based on blockchain and retrieval-augmented generation. The framework centers around foundational models to integrate the three distinct and sequentially interdependent phases: task decomposition, task allocation or recommendation, and task execution, enabling the automatic provision of a personalized, comfortable, and efficient user experience. Blockchain and smart contracts are utilized to protect user privacy and interests without leaning on any third trusted authority. Additionally, the operational process of the framework is explained and validated by taking printed circuit board design and manufacturing as a case study and ChatGPT as its generator. © 2024 IEEE.

关键词： Embedded systems

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Single Camera Based Gait Analysis Method with Scaled and Image Coordinate key-points

Single Camera Based Gait Analysis Method with Scaled and Ima...

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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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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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Pavement Cracks Coupled With Shadows:A New Shadow-Crack Dataset and A Shadow-Removal-Oriented Crack Detection Approach

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IEEE/CAA Journal of Automatica Sinica 2023年第7期10卷 1593-1607页

作者： Lili Fan Shen Li Ying Li Bai Li Dongpu Cao Fei-Yue Wang School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China School of Civil Engineering Tsinghua UniversityBeijing 100084China School of Mechanical Engineering Beijing Institute of TechnologyBeijing 100081China State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body and the College of Mechanical and Vehicle Engineering Hunan UniversityChangsha 410082China School of Vehicle and Mobility Tsinghua UniversityBeijing 100084China State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

Automatic pavement crack detection is a critical task for maintaining the pavement stability and driving *** task is challenging because the shadows on the pavement may have similar intensity with the crack,which interfere with the crack detection *** to the present,there still lacks efficient algorithm models and training datasets to deal with the interference brought by the *** fill in the gap,we made several contributions as ***,we proposed a new pavement shadow and crack dataset,which contains a variety of shadow and pavement pixel size *** also covers all common cracks(linear cracks and network cracks),placing higher demands on crack detection ***,we designed a two-step shadow-removal-oriented crack detection approach:SROCD,which improves the performance of the algorithm by first removing the shadow and then detecting *** addition to shadows,the method can cope with other noise ***,we explored the mechanism of how shadows affect crack *** on this mechanism,we propose a data augmentation method based on the difference in brightness values,which can adapt to brightness changes caused by seasonal and weather ***,we introduced a residual feature augmentation algorithm to detect small cracks that can predict sudden disasters,and the algorithm improves the performance of the model *** compare our method with the state-of-the-art methods on existing pavement crack datasets and the shadow-crack dataset,and the experimental results demonstrate the superiority of our method.

关键词： Automatic pavement crack detection data augmentation compensation deep learning residual feature augmentation shadow removal shadow-crack dataset

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