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19th IEEE International Conference on Automation Science and Engineering, CASE 2023

作者： Song, Bing Xiong, Gang Shen, Zhen Zhu, Fenghua Lv, Yisheng Ye, Peijun Institute of Automation Chinese Academy of Sciences The State Key Laboratory of Management and Control for Complex Systems Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Institute of Automation Chinese Academy of Sciences The Beijing Engineering Research Center of Intelligent Systems and Technology China Chinese Academy of Sciences The Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing The Cloud Computing Center Dongguan523808 China Qingdao Academy of Intelligent Industries Intelligent Manufacturing Center Qingdao266000 China

ISBN: (纸本)9798350320695

As a representative topic in natural language processing and automated theorem proving, geometry problem solving requires an abstract problem understanding and symbolic reasoning. A major challenge here is to find a feasible reasoning sequence that is consistent with given axioms and the theorems already proved. Most recent methods have exploited neural network-based techniques to automatically discover eligible solving steps. Such a kind of methods, however, is greatly impacted by the expert solutions for training. To improve the accuracy, this paper proposes a new method called counterfactual evolutionary reasoning, which uses a generative adversarial network to generate initial reasoning sequences and then introduces counterfactual reasoning to explore potential solutions. By directly exploring theorem candidates rather than the neural network selection, the new method can sufficiently extend the searching space to get a more appropriate reasoning step. Through comparative experiments on the recent proposed Geometry3k, the largest geometry problem solving dataset, our method generally achieves a higher accuracy than most previous methods, bringing an overall improvement about 4.4% compared with the transformer models. © 2023 IEEE.

关键词： Geometry

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A Virtual Ultrasonography Simulator for Skill Training Using Magnetic-Inertial Probe Tracking

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2024年第1期29卷 445-454页

作者： Wang, Heng Dong, Shuda Yang, Qi Han, Jiancheng He, Ze'an He, Yihua Wang, Shuangyi South China Univ Technol Shien Ming Wu Sch Intelligent Engn Guangzhou 511442 Peoples R China Capital Med Univ Maternal Fetal Med Ctr Fetal Hear Beijing Anzhen Hosp Echocardiog Med Ctr Beijing 100029 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Hong Kong Inst Sci & Innovat Ctr Artificial Intelligence & Robot Hong Kong Peoples R China

Simulation-based ultrasonography training has been proven to be effective for novice practitioners to acquire operation skills. However, existing simulators are cumbersome, expensive, and sometimes fail to provide intuitive and interactive training experience. In this article, a compact, inexpensive, and interactive ultrasonography simulator is developed using magnetic-inertial 6-degree of freedom (DoF) probe pose tracking and virtual ultrasound image generation. Embedded with magnetic and inertial sensors, the sham ultrasound probe is tracked by fusing magnetic measurement of a single electromagnet embedded in the phantom and inertial measurement of the probe. Then the virtual scan plane is determined according to the estimated probe pose to slice the digital anatomical model for virtual image generation, providing visual feedback for skill training. The probe tracking accuracy is experimentally validated to be sufficiently accurate for correct image generation with the RMS position error of 3.67 mm and average orientation error of 0.74 degrees. The interactive performance of the simulator is evaluated by volunteer tests, showing shortened task completion time (40.68 s versus 50.46 s) and slightly improved accuracy of reaching target views compared to the slider control method. It is also shown that an inexperienced operator can learn to successfully acquire standard and diagnostic views of fetal hearts with congenital diseases. Due to intuitive freehand interaction, accurate probe tracking, and real-time visual guidance of ultrasound images and three-dimensional (3-D) virtual scene of the probe and anatomy, the proposed simulator can provide immersive training experience for quick ultrasound skill acquirement.

关键词： Probes Training Magnetic resonance imaging Phantoms Sensors Solid modeling Magnetic sensors

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Parallel-Data-Based Social Evolution Modeling

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Tsinghua Science and Technology 2021年第6期26卷 878-885页

作者： Weishan Zhang Zhaoxiang Hou Xiao Wang Zhidong Xu Xin Liu Fei-Yue Wang China University of Petroleum Qingdao 266580China Qingdao Academy of Intelligent Industry Qingdao 266111China State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100049China Institute of National Security National Defense UniversityBeijing 100081China

Abnormal or drastic changes in the natural environment may lead to unexpected events,such as tsunamis and earthquakes,which are becoming a major threat to national ***,no effective assessment approach can deduce a situation and determine the optimal response strategy when a natural disaster *** this study,we propose a social evolution modeling approach and construct a deduction model for self-playing,self-learning,and self-upgrading on the basis of the idea of parallel data and reinforcement *** proposed approach can evaluate the impact of an event,deduce the situation,and provide optimal strategies for *** the breakage of a submarine cable caused by earthquake as an example,we find that the proposed modeling approach can obtain a higher reward compared with other existing methods.

关键词： paral el data reinforcement learning decision-making

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3D Vehicle Detection With RSU LiDAR for Autonomous Mine

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2021年第1期70卷 344-355页

作者： Wang, Guojun Wu, Jian Xu, Tong Tian, Bin Jilin Univ State Key Lab Automot Simulat & Control Changchun 130022 Peoples R China Waytous Inc Beijing 100080 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Shandong Peoples R China

With the development of intelligent and connected vehicles, RSU (roadside unit) sensors are playing an increasingly important role for environment perception. For vehicle detection in autonomous mine, lack of diversity data on RSU LiDAR limits the application of deep learning based methods. To solve this issue, a voxel-based background filtering module is introduced into 3D object detectors for vehicle detection with RSU LiDAR in mine environments. The proposed background filtering method models average height and the number of points for each voxel as Gaussian distribution to generate a background table. To address the impact of the false negative points of the background filtering module, we also propose a multivariate Gaussian loss to model bounding box uncertainty. The predicted covariances between variates help to learn the relationship between the missed parts and the visible ones. Besides, a background filtering based data augmentation method for vehicle detection is also proposed in this paper. Three RSU LiDAR datasets with different terrains in the BaoLi mine area are used for comprehensive experiment evaluations. Experiments show that the proposed background filtering module and multivariate Gaussian loss can significantly improve the generalization ability and performance of several state-of-the-art 3D detectors on different terrain data. Moreover, most background voxels are filtered out, the inference time of the 3D detectors is about 2x faster. Besides, the effectiveness of the proposed data augmentation method is also demonstrated.

关键词： Laser radar Three-dimensional displays Filtering Vehicle detection Detectors Roads Feature extraction Background filtering 3D object detection deep learning roadside LiDAR point cloud

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Two-Level Energy control Strategy Based on ADP and A-ECMS for Series Hybrid Electric Vehicles

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IEEE TRANSACTIONS ON intelligent TRANSPORTATION systems 2022年第8期23卷 13178-13189页

作者： Shen, Zhen Luo, Can Dong, Xisong Lu, Wanze Lv, Yisheng Xiong, Gang Wang, Fei-Yue Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Intelligent Mfg Ctr Qingdao 266109 Peoples R China Beijing Univ Technol Sch Artificial Intelligence & Automat Beijing 100124 Peoples R China Chinese Acad Sci Guangdong Engn Res Ctr 3D Printing & Intelligent Cloud Comp Ctr Donggguan 523808 Peoples R China

The number of vehicles is rapidly increasing. An effective control strategy for Hybrid Electric Vehicles (HEVs) is important. In this paper, we present a two-level control strategy that combines the Adaptive-Equivalent Consumption Minimization Strategy (A-ECMS) and the Adaptive Dynamic Programming (ADP). At the lower level, the A-ECMS is used to convert the consumed charge into Equivalent Fuel Consumption (EFC) for every sample moment, and a PI controller is used to adjust the values of the equivalent factor of the A-ECMS. At the upper level, the ADP is used to find the minimum of EFC corresponding to equivalent factor for every sample moment, and it maintains the state of Charge (SOC) of battery to charge and discharge smoothly in a high-efficiency field for the HEV. As by the ADP, we look into the future and then we can have a better estimate for the equivalent factor than the ordinary A-ECMS. In this way, we can save energy as well as calculate instantaneous parameters for the control strategy. Compared with a typical rule-based control strategy, the proposed control method saves EFC up to 10.3% and the stability of the SOC is increased by more than 60%, tested on benchmarks.

关键词： Hybrid electric vehicles Fuels Electronic countermeasures Minimization Mechanical power transmission Optimization Energy management Adaptive dynamic programming adaptive-equivalent consumption minimization strategy hybrid electric vehicle energy control

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Mechanical design paradigm based on ACP method in parallel manufacturing 1

Mechanical design paradigm based on ACP method in parallel m...

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

作者： Li, Shimeng Wang, Yutong Wang, Xiao Wang, Feiyue Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex Systems Beijing China Qingdao Academy of Intelligent Industries Qingdao China

ISBN: (纸本)9781665433372

Parallel Manufacturing is a new manufacturing paradigm in industry, deeply integrating informalization, automation, and artificial intelligence. In this paper we propose a new mechanical design paradigm in Parallel Manufacturing based on ACP method. The key is to regard the design procedure based on artificial design and emulation method as two independent procedures, which can be modeled as a parallel system. The design procedure based on ACP method does not include a real system, which is an inventive extension of the traditional parallel system. This method can be implemented with social information by introducing the definition of SDV, SDM, and intelligent Design Manager, making it highly adaptive for social manufacturing and Parallel Manufacturing. © 2021 IEEE.

关键词： Manufacture

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Current Problems and Solutions of Industrial control Network Intrusion Detection: A Brief Survey

Current Problems and Solutions of Industrial Control Network...

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2022 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2022

作者： Jiang, Tianyu Zhang, Chen Wang, Rui Chai, Senchun Institute of Automation Chinese Academy of Sciences and University of Science and Technology Beijing China Beijing Institute of Technology China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems China Institute of Intelligent Information Processing and Control Beijing Institute of Technology China

ISBN: (数字)9781665469838

ISBN: (纸本)9781665469838

With the advancement of industrial intelligence, the Industrial Internet has been widely used in energy, manufacturing and other important industries. In recent years, security incidents have occurred frequently in industrial control network, which means that maintaining industrial control network security has become more and more important. Intrusion detection technology can actively detect abnormal behavior in the network, and is an important means to ensure the security of industrial control networks. Presently, the intrusion detection technology of industrial control network has problems such as extreme imbalance of classes and redundant interference of traffic characteristics. This paper analyzes these problems and clarifies the existing solutions for different problems in principle. © 2022 IEEE.

关键词： Network security

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Feature-Contrastive Graph Federated Learning: Responsible AI in Graph Information Analysis

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2023年第6期10卷 2938-2948页

作者： Zeng, Xingjie Zhou, Tao Bao, Zhicheng Zhao, Hongwei Chen, Leiming Wang, Xiao Wang, Feiyue China Univ Petr East China Dept Coll Comp Sci & Technol Qingdao 266580 Peoples R China Anhui Univ Sch Artificial Intelligence e Hefei 266114 Peoples R China Qingdao Acad Intelligent Ind Qingdao 230031 Peoples R China Chinese Acad Sci Inst Automation State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China

Federated learning enables multiple clients to learn a general model without sharing local data, and the federated learning system also improves information security and advances responsible artificial intelligence (AI). However, the data of different clients in the system are non-independently and identically distributed (IID), which results in weight divergence, especially for complex graph data extraction. This article proposes a novel feature-contrastive graph federated (FcgFed) learning approach to improve the robustness of the federated learning system in graph data. First, we design an architecture for FcgFed learning systems to analyze graph information. Furthermore, we present a graph federated learning method based on contrastive learning to alleviate the weight divergence in federated learning. The experiments in node classification and graph classification demonstrate that our method achieves better performance than model-contrastive federated learning (MOON) and federated average (FedAvg). We also test the adaptability of our method in image classification, and the results demonstrate that weight similarity evaluation works for other frameworks and tasks.

关键词： Federated learning Data models Artificial intelligence Graph neural networks Training Learning systems Servers graph neural networks responsible artificial intelligence (AI) weight divergence weight similarity evaluation

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The DAO to Social Transportation: Towards Smart Mobility in Cyber-Physical-Social Space

The DAO to Social Transportation: Towards Smart Mobility in ...

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IEEE International Conference on systems, Man and Cybernetics

作者： Yuanyuan Chen Yisheng Lv Fei-Yue Wang State Key Lab. of Multimodal Artificial Intelligence Systems Chinese Academy of Sciences Institute of Automation Beijing China State Key Lab. for Management and Control of Complex Systems Chinese Academy of Sciences Beijing China

The rapid advancement of intelligence and connectivity technology (ICT) has enhanced the efficiency and safety of intelligent transportation systems (ITS). However, this also increases the complexity of the transportation systems, especially the social complexity, and poses new challenges for their management. This paper discusses the emergence of social transportation, as part of natural evolutionary adaptation to new traffic conditions and technology, which is a paradigm shift from engineering-centered technical systems to society-centered ecosystems. In social transportation systems, all participants including administrators, individual travelers and other stakeholders play a more proactive role in the operation and management of transportation systems. Therefore, we propose to employ a decen-tralized autonomous organization (DAO) as a model for the social transportation system that allows participants to collaborate and coordinate without relying on a central authority. To meet the challenges brought by this transition, we propose to apply ACP-based parallel system theory to restructure the methodology of transportation management. Finally, we present a case study of personal carbon trading system where economic incentives are used to manage traffic demand, affecting others' travel behaviors, and reduce carbon emissions. In the conclusive remarks, we provide our visions and future directions of social transportation systems.

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Optimization of Sucker Rod Pump Operations Using Parallel systems

IEEE JOURNAL OF RADIO FREQUENCY IDENTIFICATION

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IEEE JOURNAL OF RADIO FREQUENCY IDENTIFICATION 2022年 6卷 977-981页

作者： Cheng, Xiang Wang, Xingxia Liu, Shuliang Lu, Jingwei Zhang, Zhixue Liu, Zhaohai Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Macau Univ Sci & Technol Macau Inst Syst Engn Macau Peoples R China Beijing Mot G Intelligent Technol Co Ltd Prod Management Dept Beijing Peoples R China Tangshan Jidong Petr Machinery Co Ltd Tangshan 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 101408 Peoples R China

With the exploitation of oil, oil pumping units are facing with the challenges such as low efficiency, increased mining difficulties and old infrastructures. The requirement of digital construction on oilfields is emerging, especially to these sucker sod pump (SRP) units built in last century. In this article, to facilitate the intelligent development of SRP systems, the concept of the parallel digital sucker rod pump systems (PDSRPS) was presented first, and their optimization framework was designed to efficiently improve the design and balance by data interactions. The real oil pump systems were integrated with their customized digital twins to establish the cyber-physical systems. The adaptive optimization on pumping processes was continuously developing with the interaction between physical pump units and their digital versions. The feasibility of the proposed framework has been demonstrated through the computational experiments on conventional beam-pumping units.

关键词： Parallel systems digital twins sucker rod pump operation optimization intelligent oilfields

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