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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Li, Xuan Duan, Haibin Zhang, Hui Wang, Fei-Yue Peng Cheng Laboratory Shenzhen China Beihang University Beijing China Beijing Jiaotong University Beijing China Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex Systems Beijing China

ISBN: (纸本)9781665433372

Learning-based change detection (CD) in water scenarios is a key functionality for unmanned aerial vehicle (UAV). However, computer vision algorithms require large number of labeled datasets. Inspired by parallel intelligence, we propose a systematic framework for data generation. In this work, the framework consists of simulated scene and image generation network. In simulated scene, simulated images with pixel-level annotations are automatically generated. Then, image generation network uses paired images (real and simulated) to generate synthetic images. We use simulated and synthetic images in combination with publicly available real-world images to conduct experiments. The experimental results indicate that: 1) simulated images can be used in change detection research;2) synthetic images effectively improve the performance of supervised change detection model. © 2021 IEEE.

关键词： Change detection

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DeCASA in AgriVerse: Parallel Agriculture for Smart Villages in Metaverses

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IEEE/CAA Journal of Automatica Sinica 2022年第12期9卷 2055-2062页

作者： Xiujuan Wang Mengzhen Kang Hequan Sun Philippe de Reffye Fei-Yue Wang The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 Beijing Engineering Research Center of Intelligent Systems and Technology Beijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100190 Department of Chromosome Biology Max Planck Institute for Plant Breeding ResearchBeijing 100049China Faculty of Biology LMU MunichGroßhaderner Str.2Planegg-Martinsried D-82152Germany AMAP Univ MontpellierCIRADCNRSINRAEIRDMontpellier F-34398France IEEE Macao Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China

The demand for food is tremendously increasing with the growth of the world population,which necessitates the development of sustainable agriculture under the impact of various factors,such as climate *** fulfill this challenge,we are developing Metaverses for agriculture,referred to as Agri Verse,under our Decentralized complex Adaptive systems in Agriculture(De CASA)project,which is a digital world of smart villages created alongside the development of Decentralized Sciences(De Sci)and Decentralized Autonomous organizations(DAO)for Cyber-Physical-Social systems(CPSSs).Additionally,we provide the architectures,operating modes and major applications of De CASA in *** achieving sustainable agriculture,a foundation model based on ACP theory and federated intelligence is ***,we discuss the challenges and opportunities.

关键词： Parallel Agriculture management and control AgriVerse Agriculture CPSS ACP DAO-Based Platform Precision Agriculture

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A Collaborative Robot Torque Prediction Method Based on CNN-TCN Model

A Collaborative Robot Torque Prediction Method Based on CNN-...

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IEEE International Conference on Real-time Computing and Robotics (RCAR)

作者： Lina Tong Decheng Cui Chen Wang Liang Peng School of Mechanical Electronic and Information Engineering China University of Mining and Technology Beijing Beijing China State Key Laboratory of Management and Control for Complex Systems (SKLMCCS) Institute of Automation Chinese Academy of Science Beijing China

The traditional dynamical models show lower accuracy when predicting joint movement, and should be compensated. This paper proposed a model combined with the convolutional network(CNN) and temporal convolutional network(TCN) to compensate for the joint torque prediction values that are calculated from the sensing information. The experiments on the Cooperative Universal Robotic Assistant 6 DoF(CURA6) open dataset, including multi-load and multi-velocity, showed the prediction error can be reduced by 20% compared to other network models. Since there are many kinds of joint movement information, the input data form of the deep learning model should be improved. Thus, the kinetic linearization model is proposed to modify the input of sensing data. According to the different motion types of the CURA6 dataset, comparative experiments were taken, and the mean absolute error was less than 6.8%.

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Discrete-time Linear Tracking control Based on Parallel System Theory

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Applied Mathematics and Nonlinear Sciences 2024年第1期9卷

作者： Gao, Junjie Chen, Mingliang Wei, Qinglai The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment China Nuclear Power Engineering Co. Ltd Guangdong Shenzhen518172 China

Inspired by parallel system theory, a novel parallel tracking controller is presented for discrete-time linear systems in this paper. The core point is to model the time derivative of system control, which makes the control system and the control law completely equivalent in mathematical form. First, the architecture of discrete-time linear parallel tracking control is introduced. Then, in the sight of the reachability of control systems, the pole assignment theorems are given. The merits of this presented method comparing with traditional feedback control are analyzed. Eventually, the efficiency of this method is proved through numerical results. © 2024 Gao, J., Chen, M. & Wei, Q.

关键词： Discrete time control systems

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Learning Pre-Grasp Manipulation of Flat Objects in Cluttered Environments using Sliding Primitives

Learning Pre-Grasp Manipulation of Flat Objects in Cluttered...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Jiaxi Wu Haoran Wu Shanlin Zhong Quqin Sun Yinlin Li State Key Laboratory of Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing China Department of Automation University of Science and Technology of China Heifei China State Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Science Beijing China Science and Technology on Thermal Energy and Power Laboratory Wuhan China

Flat objects with negligible thicknesses like books and disks are challenging to be grasped by the robot because of the width limit of the robot's gripper, especially when they are in cluttered environments. Pre-grasp manipulation is conducive to rearranging objects on the table and moving the flat objects to the table edge, making them graspable. In this paper, we formulate this task as Parameterized Action Markov Decision Process, and a novel method based on deep reinforcement learning is proposed to address this problem by introducing sliding primitives as actions. A weight-sharing policy network is utilized to predict the sliding primitive's parameters for each object, and a Q-network is adopted to select the acted object among all the candidates on the table. Meanwhile, via integrating a curriculum learning scheme, our method can be scaled to cluttered environments with more objects. In both simulation and real-world experiments, our method surpasses the existing methods and achieves pre-grasp manipulation with higher task success rates and fewer action steps. Without fine-tuning, it can be generalized to novel shapes and household objects with more than 85% success rates in the real world. Videos and supplementary materials are available at https://***/view/pre-grasp-sliding.

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A Study on Decentralized Autonomous organizations Based Intelligent Transportation System enabled by Blockchain and Smart Contract

A Study on Decentralized Autonomous Organizations Based Inte...

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2021 China Automation Congress, CAC 2021

作者： Hou, JiaChen Ding, WenWen Liang, Xiaolong Zhu, FengHua Yuan, Yong Wang, FeiYue Institute of Systems Engineering Macau University of Science and Technology China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Mathematics Renmin University of China Beijing China

ISBN: (纸本)9781665426473

With the rapid development of the transportation industry, Intelligent Transportation System (ITS) tries to adapt to industry changes through constructing new organizational forms, management methods, and incentive mechanisms. However, many new governance issues have arisen due to the transportation system's complexity, diversity, and uncertainty. DAO(Decentralized autonomous organization) enabled by blockchain and smart contracts are regarded as effective methods to solve the organizational governance problems for complex systems. Therefore, this article analyzes the main issues currently faced by ITS. Then, this paper proposed a 6-layer DAO structure framework for ITS and discussed its key research issues. Finally, a case study for the Macau transportation system is carried out, and further research directions are proposed. The DAO-based transportation system will represent the future trend of ITS development. This paper aims to motivate more discussion for this new direction and provide helpful guidance and reference for future research work. © 2021 IEEE

关键词： Smart contract

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Notice of Retraction: Cascade Network with Boundary-Adaptive Filter for Surgical Gesture Recognition

Notice of Retraction: Cascade Network with Boundary-Adaptive...

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2021 International Joint Conference on Neural Networks, IJCNN 2021

作者： Chen, Hua-Bin Ni, Zhen-Liang Li, Zhen Bian, Gui-Bin 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： Gesture recognition

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Application of machine learning method in optical molecular imaging: a review

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Science China(Information Sciences) 2020年第1期63卷 5-20页

作者： Yu AN Hui MENG Yuan GAO Tong TONG Chong ZHANG Kun WANG Jie TIAN CAS Key Laboratory of Molecular Imaging Beijing Key Laboratory of Molecular ImagingThe State Key Laboratory of Management and Control for Complex SystemsInstitute of Automation Chinese Academy of Sciences Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of MedicineBeihang University University of Chinese Academy of Sciences

Optical molecular imaging(OMI) is an imaging technology that uses an optical signal, such as near-infrared light, to detect biological tissue in organisms. Because of its specific and sensitive imaging performance, it is applied in both preclinical research and clinical surgery. However, it requires heavy data analysis and a complex mathematical model of tomographic imaging. In recent years, machine learning(ML)-based artificial intelligence has been used in different fields because of its ability to perform powerful data processing. Its analytical capability for processing complex and large data provides a feasible scheme for the requirement of OMI. In this paper, we review ML-based methods applied in different OMI modalities.

关键词： optical molecular imaging machine learning artificial intelligence

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Autonomous Mobile Robot Navigation using Adaptive Neuro Fuzzy Inference System

Autonomous Mobile Robot Navigation using Adaptive Neuro Fuzz...

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2022 International Conference on Innovations and Development of Information Technologies and Robotics, IDITR 2022

作者： Haider, Muhammad Husnain Ali, Hub Khan, Abdullah Aman Zheng, Hao Bhutta, M. Usman Maqbool Usman, Shaban Zhi, Pengpeng Wang, Zhonglai University of Electronic Science and Technology of China Chengdu China Yangtze Delta Region Institute Huzhou China Casia State Key Laboratory for Management & Control of Complex Systems Beijing China Sichuan Artificial Intelligence Research Institute Yibin China The Chinese University of Hong Kong Robotics Lab Mae Hong Kong

ISBN: (纸本)9781665409322

Navigation of autonomous robots in unknown and cluttered environments lies among the marked trends in robotics. Unlike animals and humans, the collision-free movement of a robot is challenging and requires processing complex information. An autonomous robot needs to cope with a large amount of uncertainty while navigating. The previous methods have limitations, such as lacking obstacle avoidance behaviour, having a large number of governing rules, designing a separate controller for each navigation and obstacle avoidance, not considering the robot's dynamics, computationally expensive training, and poor performance in a cluttered environment. This paper proposes a method that comprises a single adaptive neuro fuzzy inference system (ANFIS) based controller with 16 rules compared to hundred of rules used by previous methods to address such problems. Our method takes heading angle along with distance sensors data as input. AU the inputs are fuzzified into linguistic variables such as near-far and left-right. Additionally, a fuzzy inference system (FIS) is designed and trained using the generated dataset for optimum performance of ANFIS. The proposed method efficiently provides collision-free navigation of the mobile robot in densely cluttered environments. Comprehensive experiments are performed to prove the robustness and potency of the proposed ANFIS controller. Moreover, the performance of the proposed method is compared with various previous methods. The results of these comparisons indicate our proposed method's superiority in finding a near-optimal path. © 2022 IEEE.

关键词： Navigation

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Parallel medical imaging: An ACP-based approach for intelligent medical image recognition with small samples 1

Parallel medical imaging: An ACP-based approach for intellig...

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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Shen, Tianyu Gou, Chao Wang, Jiangong Huang, Jun He, Yonglan Xue, Huadan Jin, Zhengyu Wang, Fei-Yue Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing China Shenzhen Campus of Sun Yat-sen University Sun Yat-sen University Guangzhou China Peking Union Medical College Hospital Chinese Academy of Medical Sciences Peking Union Medical College Department of Radiology Beijing China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing China

ISBN: (纸本)9781665433372

The deep learning methods trained with large-scale manually annotated datasets have led to significant breakthroughs in medical image community. However, obtaining such datasets remains a challenging work in medical domain. In this paper, we propose an ACP-based approach named parallel medical imaging (PMI) for analyzing and addressing the small sample problem of medical image recognition. Firstly, we present the basic framework and key techniques of PMI, in which the Artificial imaging systems are constructed to effectively augment the 'medical small sample', the Computational experiments are carried out for designing and evaluating the visual models trained with small samples, and the Parallel execution is conducted to achieve closed-loop interaction and optimization in the domain-knowledge guidance. Furthermore, we illustrate a preliminary implementation and application of PMI approach through the case study of mammogram analysis. © 2021 IEEE.

关键词： Medical imaging

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