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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第1期9卷 1189-1203页

作者： Yuan, Jinbiao Liu, Zhenbao Xiong, Xiaoyu Ai, Yunfeng Chen, Long Tian, Bin Polytech Univ Sch Northwestern Xian 710072 Peoples R China Northwestern Polytech Univ Res & Dev Inst Shenzhen Sch Civil Aviat Xian 710072 Peoples R China Univ Chinese Acad Sci Dept Artificial Intelligence Beijing 100049 Peoples R China Waytous Inc Qingdao 266109 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100190 Peoples R China

A global path planning method constrained by dynamics, kinematics, and terrain is proposed for Vertical Takeoff and Landing (VTOL) Unmanned Aerial Vehicles (UAVs). Firstly, new constant-altitude waypoints are interpolated above the terrain based on altitude information and terrain resolution. Account for the coverage of airborne sensors over the surface area, a horizontal waypoint interpolation optimization (HOPT) is performed at positions with excessive bending in the horizontal direction. Secondly, for vertical plane optimization (VOPT), i.e., height optimization, a Soft Actor-Critic-based Particle Swarm Optimization (SAC-PSO) is employed to optimize the convergence speed of the method and achieve terrain following (TF). Thirdly, to evaluate the generated paths, a deep residual network (DRSN) is designed to mitigate the impact of optimization failures during the iteration process and improve the stability of the algorithm. Simulation experiments demonstrate the efficiency and path quality of the proposed method, while real-world tasks validate its practicality.

关键词： Autonomous aerial vehicles Optimization Task analysis Path planning Turning Kinematics Trajectory UAV DRSN SAC-PSO path planning terrain following

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FISS GAN:A Generative Adversarial Network for Foggy Image Semantic Segmentation

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IEEE/CAA Journal of Automatica Sinica 2021年第8期8卷 1428-1439页

作者： Kunhua Liu Zihao Ye Hongyan Guo Dongpu Cao Long Chen Fei-Yue Wang School of Computer Science and Engineering Sun Yat-sen UniversityGuangzhou 510006China State Key Laboratory of Automotive Simulation and Control and the Department of Control Science and Engineering Jilin University(Campus Nanling)Changchun 130025China Mechanical and Mechatronics Engineering Department at the University of Waterloo 200 University Avenue West WaterlooON N2L 3G1Canada State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China

Because pixel values of foggy images are irregularly higher than those of images captured in normal weather(clear images),it is difficult to extract and express their *** method has previously been developed to directly explore the relationship between foggy images and semantic segmentation *** investigated this relationship and propose a generative adversarial network(GAN)for foggy image semantic segmentation(FISS GAN),which contains two parts:an edge GAN and a semantic segmentation *** edge GAN is designed to generate edge information from foggy images to provide auxiliary information to the semantic segmentation *** semantic segmentation GAN is designed to extract and express the texture of foggy images and generate semantic segmentation *** on foggy cityscapes datasets and foggy driving datasets indicated that FISS GAN achieved state-of-the-art performance.

关键词： Edge GAN foggy images foggy image semantic segmentation GAN semantic segmentation

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Evaluating the Generalization of 3D Detectors with Hybrid Point Clouds 2

Evaluating the Generalization of 3D Detectors with Hybrid Po...

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2nd International Conference on Computer Science and management Technology, ICCSMT 2021

作者： Tian, Yonglin Wang, Xiao University of Science and Technology of China Department of Automation Hefei China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing China

ISBN: (纸本)9781665420631

With the development of LiDAR technologies and deep learning algorithms, tremendous methods have been proposed for 3D detection in intelligent transportation. However, the generalization of 3D detectors under different environments is not fully evaluated due to the lack of diverse testing datasets. Two of the main obstacles in building testing benchmark of point clouds are the huge cost of the annotation of point clouds and the low flexibility in data collection. In this paper, we propose to use hybrid point clouds to evaluate the generalization of multiple 3D detectors when they are transferred from real data to hybrid data. Compared with evaluating 3D detectors with real point clouds dataset, our approach is much more flexible and demands less human labor. © 2021 IEEE.

关键词： Deep learning

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Stress Detection Using Wearable Devices based on Transfer Learning

Stress Detection Using Wearable Devices based on Transfer Le...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Wu, Jinting Zhang, Yujia Zhao, Xiaoguang The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665401265

Excessive stress will have a negative impact on people's physical and mental health, especially for some special occupations. Because stressful stimuli can trigger a variety of physiological responses, analyzing physiological signals collected by wearable devices has become an important way to evaluate the stress state in recent years. However, the number of available subjects of a target group may be small, and collecting a large amount of data when the target group changes is costly and time-consuming. To solve this problem, we propose a stress detection framework for a small target group which uses adversarial transfer learning method to learn shared knowledge about stress between different groups. In order to verify the performance of the framework, we establish a dataset consisting of 264 ordinary college students and 32 police school students, aiming to evaluate the acute stress state of police school students under video stimuli for psychological training in the future. Comprehensive experiments show that our algorithm has achieved a significant improvement in the target group compared with the baseline methods. © 2021 IEEE.

关键词： Physiology

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Cross stage partial connections based weighted Bi-directional feature pyramid and enhanced spatial transformation network for robust object detection

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NEUROCOMPUTING 2022年 513卷 70-82页

作者： Lu, Yan-Feng Yu, Qian Gao, Jing-Wen Li, Yi Zou, Jun-Cheng Qiao, Hong Chinese Acad Sci Inst Automation State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Nanchang Univ Sch Informat Engn Nanchang 330031 Peoples R China Huizhou Univ Sch Elect Informat & Elect Engn Huizhou 516007 Peoples R China Chinese Acad Sci Inst Automation State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Structural information is an essential component for efficient object detection. In many visual detection tasks, the objects with large structural deformation usually make up a large proportion. The shape, con-tour, and internal structure of the objects tend toward dramatic change, which easily causes troubles for efficient object detection. Therefore, how to detect these objects robustly and accurately is one of the sig-nificant challenges. To address this issue, we introduce a Cross Stage Partial connections-based weighted Bi-directional Feature Pyramid Network (CSP-BiFPN), which allows easy and efficient multi-scale feature fusion by cross-stage partial connections. Second, to enhance the model's spatial transformation capacity, the multi-scale feature maps extracted from the YOLO backbone network are processed by an enhanced spatial transformation network (ESTN) for spatial deformations. Based on these architectural modifica-tions and optimizations, we further develop a novel real-time robust object detection model called Bi-STN-YOLO. We evaluate the performance of the proposed method on four image datasets. The experi-mental results demonstrate that the proposed approach achieves significant improvements compared with the typical YOLO families and competitive performance compared to the state-of-the-arts in detec-tion tasks. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Robust object detection Structural deformation Image detection Spatial transformation

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First-in-human orbital tumor surgery guided by near-infrared II window fluorescence imaging: A feasibility study

INTERDISCIPLINARY MEDICINE

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INTERDISCIPLINARY MEDICINE 2025年第1期3卷

作者： Zhang, Zeyu Guo, Lishuang Yao, Lan Li, Yueyue Hei, Yan Wang, Qi Wang, Xiaoyi Ma, Rui Yang, Xinji Hu, Zhenhua Wu, Wei Beihang Univ Beijing Adv Innovat Ctr Big Data Based Precis Med Sch Engn Med Beijing Peoples R China Beihang Univ Key Lab Big Data Based Precis Med Minist Ind & Informat Technol Beijing Peoples R China Chinese Peoples Liberat Army Gen Hosp Med Ctr 3 Sr Dept Ophthalmol Beijing 100143 Peoples R China Chinese Acad Sci CAS Key Lab Mol Imaging Inst Automat Beijing Key Lab Mol Imaging Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China

Precise resection of orbital tumors is a critically important but elusive issue. Fluorescence imaging in the near-infrared II window (NIR-II) holds the potential to provide the surgeons with real-time identification for orbital tumors. Here, for the first time, we evaluated the feasibility and clinical value of NIR-II fluorescence imaging in orbital tumor surgery. To establish the method of NIR-II fluorescence imaging for orbital tumors, we developed a NIR-II fluorescence imaging system and indocyanine green (ICG) served as the fluorescent contrast agent. Twenty-two patients diagnosed with orbital tumors and scheduled for standard-of-care surgery were enrolled in this study. Time-course NIR-II fluorescence imaging of two patients with superficial orbital tumors showed the optimum imaging time was 2 h post injection of ICG. Fifteen patients were allocated for diagnostic test, which showed that both the in situ and ex vivo NIR-II fluorescence imaging showed better sensitivity and specificity than the surgeon judgment. In the feasibility trial of the remaining five patients, the surgeon encountered 34 suspicious regions and surgical decisions were changed nine times due to NIR-II fluorescence imaging. The resultant seven additional resections were justified by histopathology and the two conservative treatments did not result in recurrence. Based on these findings, we suggested that ICG-based NIR-II fluorescence imaging was feasible to guide precise resection of orbital tumors. A future randomized controlled trial with a larger cohort is encouraged to further verify the clinical value.

关键词： fluorescence imaging ICG NIR-II imaging orbital tumor

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Omnidirectional Drift control of an Underwater Biomimetic Vehicle-Manipulator System via Reinforcement Learning 10

Omnidirectional Drift Control of an Underwater Biomimetic Ve...

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10th IEEE Data Driven control and Learning systems Conference, DDCLS 2021

作者： Ma, Ruichen Wang, Yu Wang, Rui Wang, Shuo 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

ISBN: (纸本)9781665424233

This paper proposes an omnidirectional drift control on an underwater biomimetic vehicle-manipulator system (UB-VMS). The UBVMS has two biomimetic propellers, they obtain propulsive force by actuating their undulating fins. A configuration of the system, the dynamics of the UBVMS and the kinematics of the fins are given respectively. Then the control problem is designed as a Markov decision process (MDP). A reinforcement learning method based on the twin delayed deep deterministic policy gradient (TD3) is proposed for this MDP. A control policy is well trained by the reinforcement learning method and tested in eight different simulations. An analysis of the simulation results is also given. © 2021 IEEE.

关键词： Reinforcement learning

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Multimodal Unknown Surface Material Classification and Its Application to Physical Reasoning

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2022年第7期18卷 4406-4416页

作者： Wei, Junhang Cui, Shaowei Hu, Jingyi Hao, Peng Wang, Shuo Lou, Zheng Univ Chinese Acad Sci Sch Future Technol Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Technol Shanghai 200031 Peoples R China Chinese Acad Sci State Key Lab Superlattices & Microstruct Inst Semicond Beijing 100083 Peoples R China

Unknown surface material classification (SMC) can inform a robot about material properties, enabling it to interact with environments appropriately. Recent research has leveraged multimodal data using deep learning to improve the performance of SMC. In this article, we present a deep learning model, multimodal temporal convolutional neural network (MTCNN), which integrates energy spectrum, dilated convolutions, and sequence poolings into a unified network architecture. The proposed model can learn material representations from auditory and multitactile (i.e., acceleration, normal force, and friction force) data generated by dragging a tool along surfaces, and distinguish unknown object surface materials into categories. For surface material data collection, a tool is also designed to detect different object surfaces. The performance of MTCNN is evaluated on a public dataset and the highest classification accuracy is 87.55%. A robotic curling example is provided to illustrate how the presented model helps the robot in manipulation.

关键词： Robots Convolutional neural networks Visualization Informatics Feature extraction Task analysis Haptic interfaces Auditory and haptic information deep learning multimodal fusion physical reasoning unknown surface material classification (USMC)

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Practical Predefined-Time Output-Feedback Consensus Tracking control for Multiagent systems

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IEEE TRANSACTIONS ON CYBERNETICS 2023年第8期53卷 5311-5322页

作者： Zou, An-Min Liu, Yangyang Hou, Zeng-Guang Hu, Zhipei Shantou Univ Coll Engn Dept Elect & Informat Engn Guangdong Prov Key Lab Digital Signal & Image Pro Shantou 515063 Peoples R China Shantou Univ Minist Educ Key Lab Intelligent Mfg Technol Shantou 515063 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Shantou Univ Coll Engn Guangdong Prov Key Lab Digital Signal & Image Pro Shantou 515063 Peoples R China Shantou Univ Coll Engn Dept Elect & Informat Engn Shantou 515063 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Macau Univ Sci & Technol Inst Syst Engn CASIA MUST Joint Lab Intelligence Sci & Technol Macau Peoples R China

This article addresses the issue of output-feedback consensus control of multiagent systems under the directed topology and subject to bounded external disturbances. By employing a smooth time-varying function, a distributed practical predefined-time (PPT) observer is developed to estimate the reference trajectory for the entire team (i.e., the leader's state) and a practical preset-time extended-state observer is also proposed to estimate bounded disturbances and unmeasurable system states. Next, a novel continuous and nonsingular PPT consensus control law is designed on the basis of the observers. Furthermore, the designed control protocol can achieve PPT stability, that is, consensus tracking errors are enforced to a neighborhood around zero within a predetermined time, which can be specified a priori, independent of initial states of agents and/or any other design parameters. Finally, illustrative numerical examples, including a comparative one, are provided to demonstrate the performance of the present predefined-time control approach.

关键词： Consensus control Observers Sliding mode control Numerical stability Multi-agent systems Topology Stability criteria Consensus control multiagent systems (MASs) practical predefined-time (PPT) stability sliding-mode control

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Advanced Air Mobility: An Innovation for Future Diversified Transportation and Society

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第2期9卷 3106-3110页

作者： Guo, Jingqiu Chen, Long Li, Lingxi Na, Xiaoxiang Vlacic, Ljubo Wang, Fei-Yue Tongji Univ Minist Educ Key Lab Rd & Traff Engn Shanghai 201804 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Indiana Univ Purdue Univ Purdue Sch Engn & Technol Dept Elect & Comp Engn Indianapolis IN 46202 USA Univ Cambridge Dept Engn Cambridge CB2 1TN England Griffith Univ Inst Intelligent & Integrated Syst Nathan Qld 4111 Australia Griffith Univ Sch Engn & Built Environm Nathan Qld 4111 Australia Macau Univ Sci & Technol Inst Engn Macau 999078 Peoples R China

Advanced Air Mobility (AAM) is an innovative transportation system that will provide safe and efficient mobility of cargo, passenger and emergency services in low-altitude airspace. This paper highlights AAM's vision and addresses fundamental research challenges and opportunities, e.g., vertical lift aircraft technologies, infrastructure and operation, unmanned traffic management and system security. The overall structure of the AAM ecosystem is discussed from a technology and service perspective. The SEEDS evaluation criteria for the AAM system are explored in terms of promoting safety, sustainability and service quality during the development, verification and validation, and operation phases. As a key sector of the Mobility 5.0 paradigm, new air mobility needs to be integrated into multimodal transport and logistics systems to deliver diverse, sustainable and trustworthy solutions in the near future.

关键词： Active appearance model Aircraft Transportation Air traffic control Batteries Industries Regulation Advanced air mobility low-altitude VTOL unmanned aircraft transportation

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