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IEEE International Conference on Robotics and Biomimetics

作者： Wenjie Geng Zhiqiang Cao Yingbo Tang Shuo Wang Fengshui Jing The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665481106

Grasping the target object is an essential requirement for the robot to provide better services. It becomes complicated especially in cluttered environments, which still remains challenging. This paper proposes a grasping chain generation solution that enables the robot to grasp the target after other obstruction objects are moved in a good order. SSD is firstly adopted to acquire the information of detectable objects and then Euclidean clustering is employed to obtain the untrained objects. After that, the minimum bounding box of each object is obtained, which is then projected on the plane and represented by a smooth differentiable minimum ellipse. On this basis, an information density kernel function is designed to express the interaction between objects. By abstracting each ellipse as a node of the graph whose edge weight is calculated by this kernel function, the whole scene is described in a form of graph. To simplify the complexity of the scene graph, we use spectral clustering algorithm to classify the objects, and the task-oriented objects graph is constructed according to the objects closely related to the target one. As a result, the searching space is reduced. With space division of task-oriented objects graph, each candidate grasping chain is iteratively extended by using the heuristic searching and the best chain with the shortest length is determined. The proposed method can deal with secondary obstruction and its effectiveness is testified by experiments.

关键词： Visualization Clustering algorithms Grasping Search problems complexity theory Classification algorithms Iterative methods

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Cascade Network with Boundary-Adaptive Filter for Surgical Gesture Recognition

Cascade Network with Boundary-Adaptive Filter for Surgical G...

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2021 International Joint Conference on Neural Networks, IJCNN 2021

作者： Chen H.-B. Ni Z.-L. Li Z. Bian G.-B. Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China

ISBN: (纸本)9780738133669

Automatically recognizing surgical gestures plays a key role in computer-assisted surgery and automatic skill assessment. Solutions to this task that only rely on video without additional sensor hardware have attracted great attention since they are economical and feasible. However, it is still a challenging problem due to the variable gesture durations and the oversegmentation problem that occurred in a long gesture duration. To address the above problems, we propose a cascade network with boundary-adaptive filter to dynamically capture the long-range temporal dependencies and reduce the over-segmentation errors obviously. A cascade module is designed to enhance the temporal modeling abilities in different timescales, in which multiple similar temporal convolution networks are divided into cascade stages and a fusion stage. Every cascade stage with different temporal receptive fields has a frame-specific weight to adjust the distribution of each stage and they will be aggregated into the fusion stage to refine the predictions. The frame-specific model architecture may incur a more serious over-segmentation problem. A boundary-adaptive filter is designed to leverage gesture boundary information to smooth the prediction results adaptively, which maintains consistency within a gesture duration while keeping the gesture-crossing discrepancy. We evaluate our model on the JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS). The proposed method reaches the state-of-the-art frame-wise accuracy (83.79%) and comparable segment-level performance (83.35% F1@50). © 2021 IEEE.

关键词： action segmentation surgical gesture recognition temporal convolutional network video understanding

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Research Progress of Parallel control and management

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IEEE/CAA Journal of Automatica Sinica 2020年第2期7卷 355-367页

作者： Gang Xiong Xisong Dong Hao Lu Dayong Shen State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Computer Science and Technology Beijing Institute of TechnologyBeijing 100081China College of Systems Engineering National University of Defense TechnologyChangsha 410073China

Based on ACP(artificial systems,computational experiments,and parallel execution)methodology,parallel control and management has become a popularly systematic and complete solution for the control and management of complex *** paper focuses on summarizing comprehensive review of the research literature of parallel control and management achieved in the recent years including the theoretical framework,core technologies,and the application *** future research,application directions,and suggestions are also discussed.

关键词： ACP methodology artificial systems computational experiments parallel control parallel management parallel systems

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Research on Attention-Based Predictive Model in Multi-Agent Reinforcement Learning

Research on Attention-Based Predictive Model in Multi-Agent ...

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2022 Chinese automation Congress, CAC 2022

作者： Hong, Weixin Wu, Huaiyu Shen, Zhen Xiong, Gang Hu, Bin Dong, Xisong Wang, Fei-Yue The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing100190 China The Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan523808 China

ISBN: (纸本)9781665465335

Agent actions planning is a challenging problem in multi-agent reinforcement learning. Recent methods typically build their predictive models by full connection layers, but the shortage of utilizing action and observation information in complex multi-agent environment limits the performance of those methods. To address this issue, we propose a novel predictive model based on self-attention mechanism which can effectively use information in the environment. Specifically, we use the MADDPG algorithm to make a dataset where the input is the observations and actions and the output is the predicted observations and rewards. We also introduce the multi-head attention mechanism into the predictive model so that the predictive model can better combine different information for prediction. We perform extensive experiments to assess the proposed method and compare it with the representative baseline approach. Results demonstrate that our method outperforms competitors with higher accuracy in different tasks. Our implementation will be publicly available. © 2022 IEEE.

关键词： Reinforcement learning

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Multi-Sensor Fusion Perception System in Train 10

Multi-Sensor Fusion Perception System in Train

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

作者： Gao, Hongfei Huang, Yongzhen Li, Haoran Zhang, Qichao 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 Watrix Technology Beijing Co. Ltd. China

ISBN: (纸本)9781665424233

Environment perception is one of the most crucial modules in a self-driving system. In an open subway environment, pedestrians, equipment boxes and other unknown obstacles often appear. The perception module is required to quickly and accurately recognize the obstacle and measures the corresponding distance data. This paper proposes a detection and ranging fusion method based on one Lidar and two different focal length cameras, which be applied in the subway system, to alert drivers to possible obstacles and assist brake. First, the relative transformation between Lidar and camera is calibrated to find the intrinsic and extrinsic matrices. Modified SSD network is trained on the self-built dataset to detect the potential obstacle and produces the final detection bounding box. Then, rail track segmentation network RailNet is applied to obtain rail shape features, which offers a region of interest (ROI) for sensing systems in point cloud clustering algorithms. Further, the fusion information is utilized to estimate the distance of objects detected by the SSD detector. The proposed method can realize real-time pedestrian detection and range on the onboard embedded platform Jetson Xavier, which satisfies the subway environment's perception requirements. © 2021 IEEE.

关键词： Optical radar

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Generalized Zero-Shot Learning via Implicit Attribute Composition

Generalized Zero-Shot Learning via Implicit Attribute Compos...

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IEEE International Conference on systems, Man and Cybernetics

作者： Lei Zhou Yang Liu Qiang Li School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China College of Computer Science and Technology Zhejiang University Hangzhou China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Zero-shot learning (ZSL) is an important but challenging task in computer vision that aims to identify unseen classes without matching training samples. Current cutting-edge ZSL methods based on locality focus on acquiring the explicit locality of distinguishing characteristics, which could face a lack of adequate supervision at the class attribute level. This paper introduces a novel approach called IAC, which aims to learn Implicit Attribute Composition for ZSL. This method is more comprehensive compared to attribute localization that solely focuses on class-level attribute supervision. IAC utilizes subspace representations that efficiently capture the inherent structure of high-dimensional image features. Then, we learn implicit attribute composition through subspace representation learning. The superiority of the proposed IAC compared to the state-of-the-art is demonstrated through sufficient experiments conducted on three commonly used ZSL datasets, CUB, SUN, and AwA2.

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Deep learning radiomics of multimodal ultrasound for classifying metastatic cervical lymphadenopathy into primary cancer sites: a feasibility study

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Ultraschall in der Medizin - European Journal of Ultrasound 2023年第3期45卷 305-315页

作者： Yangyang Zhu Zheling Meng Hao Wu Xiao Fan Wenhao lv Jie Tian Kun Wang Fang Nie Medical Center of Ultrasound Lanzhou University Second Hospital Lanzhou China CAS Key Laboratory of Molecular Imaging The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of the Chinese Academy of Sciences School Beijing China Gansu Province Medical Engineering Research Center for Intelligence Ultrasound Lanzhou China

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Parallel Theaters in CPSS: From Shadows of ISDOS to Intelligence of Decision Theaters

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IEEE/CAA Journal of Automatica Sinica 2025年第6期12卷 1059-1062页

作者： Qinghua Ni Buday Viktória Fei Lin Jun Huang Levente Kovács Nan Zheng Fei-Yue Wang Department of Engineering Science Faculty of Innovation Engineering Macau University of Science and Technology Macau China Innovation Management Doctoral School Obuda University Budapest Hungary John von Neumann Faculty of Informatics Obuda University Budapest Hungary State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China DeSci Center of Parallel Intelligence and the University Research and Innovation Center at Obuda University State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing China

As Artificial Intelligence (AI) is moving fast from Large Language Models (LLMs) to AI Agents and Agentic Intelligence, the need to incorporate new AI into Decision Intelligence (DI) is becoming more and more urgent for both practical and theoretic reasons: both decision and process complexities would be significantly increased due to the use of advanced AI tools and agents, and both traditional and recent thinking must be rethought and reconstructed accordingly. Our perspective would like to address this important issue based on some historical milestone developments in Computer-Aided Software Engineering (CASE) and recent efforts in digital theatrical technology [1].

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Data Calibration Algorithm for Artificial Lateral Line Sensor of Robotic Fish on Improved LSTM

Data Calibration Algorithm for Artificial Lateral Line Senso...

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第40届中国控制会议

作者： Yaming Ou Zhuoliang Zhang Chao Zhou Bo Zhou School Of Automation Southeast University State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

All the time,the measurement of swimming speed has long been a major research challenge in the field of robotic *** artificial lateral line system can accurately measure the speed without modifying the structure of the robot fish too *** this paper,we developed a cantilever artificial lateral line（ALL） sensor based on piezoresistive effect by imitating the "SNs" cell structure of real fish,and established an accurate physical model for it considering the complex underwater boundary layer effect,which is conducive to the selection of hardware parameters such as sensor size and *** addition,a flowmeter-based speed calibration experimental platform was developed,based on which we proposed an "end to end" speed calibration algorithm based LSTM without *** a potential mapping relationship between the electrical signal of the sensor and the speed value of the flowmeter was obtained,which laid a strong foundation for the subsequent measurement of free-swimming speed of robotic fish by artificial lateral line system composed of this sensor.

关键词： velocity measurement robotic fish artificial lateral line sensor data processing

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Continuous-Time Linear Parallel Output Regulation

Continuous-Time Linear Parallel Output Regulation

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Chinese automation Congress (CAC)

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

ISBN: (数字)9781665426473

ISBN: (纸本)9781665426480

A novel parallel output regulation method is presented in this paper. The framework of the parallel output regulation problem is first introduced. Then, the existence of parallel controller is analyzed detailly in the situation that only the measurable information is obtained. Finally, the simulation example is given to show the performance of the presented method.

关键词： automation control systems Regulation

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