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Detection and Positioning of Workpiece Grinding Area in Dark Scenes with Large Exposure 1

16th International Conference on intelligent Robotics and Applications, ICIRA 2023

作者： Guo, Zhentao Zhao, Guiyu Bian, Jinyue Ma, Hongbin School of Automation Beijing Institute of Technology Beijing100081 China State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing100081 China

ISBN: (数字)9789819964987

ISBN: (纸本)9789819964970

Workpiece grinding is a crucial process in the smart manufacturing chain. In order to meet the requirements of industrial precision and relieve heavy work, researchers have developed a vision-based grinding robot. However, the problem of workpiece grinding area detection and positioning is difficult to be solved in dark scenes with large exposure. This paper proposes a method that fuses technologies such as processing of the image, coordinate and point cloud, which can accurately detect and locate the workpiece grinding area. Firstly, A method based on YOLOv7 and improved image preprocessing is used to detect labels of the grinding area. Secondly, A model for the prediction of the coordinates based on multiple linear regression was used to predict the coordinates of missing labels for the same grinding area. Finally, Data processing of the point cloud and transformation of the system of coordinates are used to achieve the acquisition of the coordinates of positioning vertices in the grinding area and conversion from the camera coordinate system to the world coordinate system. We used several sets of data for evaluation in our experiments, and the experimental results show that our proposed method can effectively detect the workpiece grinding area. At the same time, our method can also predict the coordinates of missing labels, which provides a more stable and reliable guarantee for industrial production. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Multiple linear regression

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Computational Experiments for Complex Social Systems:Experiment Design and Generative Explanation

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 1022-1038页

作者： Xiao Xue Deyu Zhou Xiangning Yu Gang Wang Juanjuan Li Xia Xie Lizhen Cui Fei-Yue Wang the School of Computer Software College of Intelligence and ComputingTianjin UniversityTianjin 300350China the School of Software and the Joint SDUNTU Centre for Artificial Intelligence Research(C-FAIR) Shandong UniversityJinan 250101China the Information School Xi’an University of Financial and EconomicsXi’an 710100China the State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Hainan University Haikou 570228China the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 1001901China

Powered by advanced information technology,more and more complex systems are exhibiting characteristics of the cyber-physical-social systems(CPSS).In this context,computational experiments method has emerged as a novel approach for the design,analysis,management,control,and integration of CPSS,which can realize the causal analysis of complex systems by means of“algorithmization”of“counterfactuals”.However,because CPSS involve human and social factors(e.g.,autonomy,initiative,and sociality),it is difficult for traditional design of experiment(DOE)methods to achieve the generative explanation of system *** address this challenge,this paper proposes an integrated approach to the design of computational experiments,incorporating three key modules:1)Descriptive module:Determining the influencing factors and response variables of the system by means of the modeling of an artificial society;2)Interpretative module:Selecting factorial experimental design solution to identify the relationship between influencing factors and macro phenomena;3)Predictive module:Building a meta-model that is equivalent to artificial society to explore its operating ***,a case study of crowd-sourcing platforms is presented to illustrate the application process and effectiveness of the proposed approach,which can reveal the social impact of algorithmic behavior on“rider race”.

关键词： Agent-based modeling computational experiments cyber-physical-social systems(CPSS) generative deduction generative experiments meta model

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CFSeRec: A Contrastive Framework for Sequential Recommendation 42

CFSeRec: A Contrastive Framework for Sequential Recommendati...

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42nd Chinese control Conference, CCC 2023

作者： Wang, Tong Dai, Yaping Shao, Shuai School of Automation Beijing Institute of Technology Beijing100081 China State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing100081 China

ISBN: (纸本)9789887581543

In sequential recommender systems, the main problems are the long-tailed distribution of data and noise interference. A Contrastive Framework for Sequential Recommendation (CFSeRec) is proposed to solve these two problems respectively. Token shuffling and adversarial attack data augmentation methods are used in the framework to improve the quality and quantity of training data, so that the long-tailed problem is mitigated. Through the application of projection head method, the sequence representation becomes more general and robust, rather than just adapted to the task of contrastive learning. Therefore, the impact of noise on sequence recommender systems is effectively alleviated. Experiments on four public datasets show that CFSeRec achieves state-of-the-art performance in the metrics of hit ratio and normalized discounted cumulative gain, when comparing to the seven previous frameworks. © 2023 Technical Committee on control Theory, Chinese Association of automation.

关键词： Recommender systems

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A policy gradient algorithm integrating long and short-term rewards for soft continuum arm control

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Science China(Technological Sciences) 2022年第10期65卷 2409-2419页

作者： DONG Xiang ZHANG Jing CHENG Long XU WenJun SU Hang MEI Tao School of Electrical Engineering and Automation Anhui UniversityHefei 230601China State Key Laboratory for Control and Management of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Robotics Research Center Peng Cheng LaboratoryShenzhen 518055China

The soft continuum arm has extensive application in industrial production and human life due to its superior safety and flexibility. Reinforcement learning is a powerful technique for solving soft arm continuous control problems, which can learn an effective control policy with an unknown system model. However, it is often affected by the high sample complexity and requires huge amounts of data to train, which limits its effectiveness in soft arm control. An improved policy gradient method, policy gradient integrating long and short-term rewards denoted as PGLS, is proposed in this paper to overcome this issue. The shortterm rewards provide more dynamic-aware exploration directions for policy learning and improve the exploration efficiency of the algorithm. PGLS can be integrated into current policy gradient algorithms, such as deep deterministic policy gradient(DDPG). The overall control framework is realized and demonstrated in a dynamics simulation environment. Simulation results show that this approach can effectively control the soft arm to reach and track the targets. Compared with DDPG and other model-free reinforcement learning algorithms, the proposed PGLS algorithm has a great improvement in convergence speed and performance. In addition, a fluid-driven soft manipulator is designed and fabricated in this paper, which can verify the proposed PGLS algorithm in real experiments in the future.

关键词： soft arm control Cosserat rod deep reinforcement learning policy gradient algorithm high sample complexity

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Semantic Coherence Guided Multiview Similarity for Image Classification with Varied Supervision

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 3072-3082页

作者： Zhang, Chunjie Cao, Ping Bai, Huihui Zheng, Xiaolong Beijing Jiaotong University Institute of Information Science Beijing100044 China Beijing Jiaotong University Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China University of Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems The State of Key Laboratory of Management and Control for Complex System Institute of Automation Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100190 China

Pairwise similarity has been widely used for image classification by propagating the class information from labeled images to unlabeled images and predicting the classes of unlabeled images accordingly. Although widely used, pairwise similarity based classification methods have two drawbacks. On one hand, visual-semantic similarity consistency does not always hold due to semantic gap. On the other hand, the reliability of pairwise similarity is also influenced by the number and underlying distribution of labeled images. A well-designed classification model cannot be easily generalized to few-shot classification tasks. To solve these two problems, in this article, we propose a semantic coherence guided multiview similarity (SCG-MV) for image classification method with varied supervision levels. We measure the semantic coherence between image pairs for classifier learning. This is achieved by first conducting line search along the line segment between each image pair, and then measure the semantics of each interval. The similarity of one image pair can then be obtained by aggregating the semantic coherence of all the intervals. In this way, we can model image similarities more finely and discriminatively. To make use of multiview information, we also use the semantic coherence guided image similarities to learn multiview classifiers. The proposed semantic coherence guided method can be combined with other similarity based methods for image classification with various levels of supervision. We conduct both few-shot and fully supervised classification experiments on four datasets with the results well prove the discriminative power and generalization ability of the proposed method. © 2020 IEEE.

关键词： Semantics

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Online Multi-Object Tracking Under Moving Unmanned Aerial Vehicle Platform Based on Object Detection and Feature Extraction Network

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Journal of Shanghai Jiaotong university(Science) 2024年第3期29卷 388-399页

作者：刘增敏王申涛姚莉秀蔡云泽 Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai 200240China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240China Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong UniversityShanghai 200240China JD Business Growth Department Beijing 100176China Institute of Marine Equipment Shanghai Jiao Tong UniversityShanghai 200240China

In order to solve the problem of small object size and low detection accuracy under the unmanned aerial vehicle(UAV)platform,the object detection algorithm based on deep aggregation network and high-resolution fusion module is ***,a joint network of object detection and feature extraction is studied to construct a real-time multi-object tracking *** the problem of object association failure caused by UAV movement,image registration is applied to multi-object tracking and a camera motion discrimination model is proposed to improve the speed of the multi-object tracking *** simulation results show that the algorithm proposed in this study can improve the accuracy of multi-object tracking under the UAV platform,and effectively solve the problem of association failure caused by UAV movement.

关键词： moving unmanned aerial vehicle(UAV)platform small object feature extraction image registration multi-object tracking

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Working condition recognition of sucker rod pumping system based on 4-segment time-frequency signature matrix and deep learning

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Petroleum Science 2024年第1期21卷 641-653页

作者： Yun-Peng He Hai-Bo Cheng Peng Zeng Chuan-Zhi Zang Qing-Wei Dong Guang-Xi Wan Xiao-Ting Dong State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016LiaoningChina Key Laboratory of Networked Control Systems Chinese Academy of SciencesShenyang 110016LiaoningChina Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169LiaoningChina University of Chinese Academy of Sciences Beijing100049China School of Artifcial Intelligence Shenyang University of TechnologyShenyang 110870LiaoningChina

High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well *** learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis ***,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational ***,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different ***,there is heterogeneity in field data,which can dramatically impair the diagnostic *** solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this ***,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC ***,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data ***,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the *** on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational *** the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.

关键词： Sucker-rod pumping system Dynamometer card Working condition recognition Deep learning Time-frequency signature Time-frequency signature matrix

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A DAOs-Based Publishing System for Advancing Open Access 4

A DAOs-Based Publishing System for Advancing Open Access

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4th IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Ma, Siji Li, Juanjuan Guan, Sangtian Ni, Qinghua Lin, Fei Zhang, Tengchao Huang, Jun Wang, Fei-Yue Macau University of Science and Technology Faculty of Innovation Engineering 999078 China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing100190 China Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems Institute of Automation Beijing100190 China

ISBN: (纸本)9798350349252

The development of the scientific publishing system has remarkably enhanced global accessibility to research findings and substantially increased the visibility and dissemination of academic publications. However, significant challenges still exist in effectively safeguarding the intellectual property rights of contributors, such as the unauthorized usage of materials, the complexity of enforcing intellectual property rights across various legal jurisdictions, and high instances of plagiarism and content misuse. Additionally, financial barriers related to open access may restrict the participation of economically disadvantaged researchers, potentially biasing scientific records towards more affluent research initiatives. To address these issues, a novel decentralized framework is formulated to ensure truly open access. This framework leverages blockchain for immutable record-keeping and clear attribution of authorship, to prevent unauthorized usage and plagiarism. Besides, it also utilizes a copyright-sharing model based on decentralized autonomous organizations and operations (DAOs), where smart contracts automatically enforce copyright and access policies to ensure fair compensation for authors and researchers. Furthermore, the copyright sharing model based on non-fungible tokens (NFT) and gradual ownership optimization (GOO) mechanism is proposed to ensure fair and accurate recognition and compensation for scholarly contributions. © 2024 IEEE.

关键词： Decentralized finance

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Interval Type-2 Fuzzy Hierarchical Adaptive Cruise Following-control for intelligent Vehicles

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IEEE/CAA Journal of Automatica Sinica 2022年第9期9卷 1658-1672页

作者： Hong Mo Yinghui Meng Fei-Yue Wang Dongrui Wu the College of Electric and Information Engineering Changsha University of Science and TechnologyChangsha 410114China the State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 Qingdao Academy of Intelligent Industries Qingda 266109 the Center of China Economic and Social Security University of Chinese Academy of SciencesBeijing 100190China the Ministry of Education Key Laboratory on Image Information Processing and Intelligent Control the School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China

intelligent vehicles can effectively improve traffic congestion and road traffic *** cruise followingcontrol(ACFC)is a vital part of intelligent *** this paper,a new hierarchical vehicle-following control strategy is presented by synthesizing the variable time headway model,type-2 fuzzy control,feedforward+fuzzy proportion integration(PI)feedback(F+FPIF)control,and inverse longitudinal dynamics model of ***,a traditional variable time headway model is improved considering the acceleration of the lead ***,an interval type-2 fuzzy logic controller(IT2 FLC)is designed for the upper structure of the ACFC system to simulate the driver's operating *** reduce the nonlinear influence and improve the tracking accuracy for the desired acceleration,the control strategy of F+FPIF is given for the lower control ***,the lower control method proposed in this paper is compared with the fuzzy PI control and the traditional method(no lower controller for tracking desired acceleration)***,the proportion integration differentiation(PID),linear quadratic regulator(LQR),subsection function control(SFC)and type-1 fuzzy logic control(T1 FLC)are respectively compared with the IT2 FLC in control performance under different ***,the simulation results show the effectiveness of IT2 FLC for the upper structure and F+FPIF control for the lower structure.

关键词： Adaptive cruise following-control(ACFC) feedforward+fuzzy proportion integration(PI)feedback(F+FPIF) type-2 fuzzy control variable time headway

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Transformer-Based Macroscopic Regulation for High-Speed Railway Timetable Rescheduling

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IEEE/CAA Journal of Automatica Sinica 2023年第9期10卷 1822-1833页

作者： Wei Xu Chen Zhao Jie Cheng Yin Wang Yiqing Tang Tao Zhang Zhiming Yuan Yisheng Lv Fei-Yue Wang Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China Signal&Communication Research Institute China Academy Railway SciencesBeijing 100081China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049 State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China IEEE State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 Macao Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China

Unexpected delays in train operations can cause a cascade of negative consequences in a high-speed railway *** such cases,train timetables need to be ***,timely and efficient train timetable rescheduling is still a challenging problem due to its modeling difficulties and low optimization *** paper presents a Transformer-based macroscopic regulation approach which consists of two stages including Transformer-based modeling and policy-based ***,the relationship between various train schedules and operations is described by creating a macroscopic model with the Transformer,providing the better understanding of overall operation in the high-speed railway ***,a policy-based approach is used to solve a continuous decision problem after macro-modeling for fast *** experiments on various delay scenarios are *** results demonstrate the effectiveness of the proposed method in comparison to other popular methods.

关键词： High-speed railway reinforcement learning train timetable rescheduling Transformer

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