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2023 IEEE International Conference on Industrial Engineering and Engineering management, IEEM 2023

作者： Wang, Haonan Lee, C.K.M. Ji, Ping Li, Gang The Hong Kong Polytechnic University Department of Industrial and Systems Engineering Hong Kong Laboratory for Artificial Intelligence in Design Hong Kong Science Park New Territories Hong Kong Xi'an Jiaotong - Liverpool University China School of Management Xi'an Jiaotong University The State Key Lab for Manufacturing Systems Engineering The Key Lab of the Ministry of Education for Process Control and Efficiency Engineering Xi'an China

ISBN: (纸本)9798350323153

Recently, the platform economy allows enterprises to collaborate with business partners and provide additional channels for various parties. E-commerce platforms have become integral channels for connecting suppliers with consumers. In the platform economy, agency selling and reselling are two predominant sales models that often coexist for the same product types, resulting in a competitive relationship. This study analyzes transaction data for 30,000 Stock Keeping Units (SKUs) provided by the Chinese e-commerce platforms over one month. We employ three models to compare the sales quantity and profits of manufacturers and online platforms and to identify the most efficient mode for stakeholders based on the model establishment and empirical evidence. Our findings suggest that apart from the existing channel to provide products to customers, manufacturers enter the platform which will increase the profit of platform operators. On the other hand, platform operators may also affect the profit of the manufacturers. © 2023 IEEE.

关键词： Channel structure Data-analysis Online platform

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Parallel systems Framework: A Safe and Efficient Testbed for Unmanned Ships 4

Parallel Systems Framework: A Safe and Efficient Testbed for...

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

作者： Fu, Long Shen, Zhen Dong, Xisong Han, Yunjun Yang, Shuguang Xiong, Gang Wang, Fei-Yue University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Beijing100190 China Chinese Academy of Sciences Guangdong Engineering Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Dongguan523808 China Shandong Jiaotong University Schoool of Rail Transportation Jinan250399 China Chinese Academy of Sciences State Key Laboratory for Management and Control for Complex Systems Institute of Automation Beijing100190 China

ISBN: (纸本)9798350349252

In this study, unmanned ships test platform based on the parallel systems framework is proposed to improve the test efficiency and accuracy of unmanned ships in complex ocean environment. The parallel intelligence theory and ACP approach, the efficient interaction between virtual and real environment is achieved, which provides more comprehensive and accurate data for unmanned ships testing. Future research will further expand the scope of virtual environment simulation and improve systems accuracy and response speed to a wider range of unmanned ships test requirements. © 2024 IEEE.

关键词： Virtual environments

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Visual Human-Computer Interactions for Intelligent Vehicles and Intelligent Transportation systems: The state of the Art and Future Directions

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2021年第1期51卷 253-265页

作者： Wang, Xumeng Zheng, Xinhu Chen, Wei Wang, Fei-Yue Zhejiang Univ State Key Lab CAD&CG Hangzhou 310058 Peoples R China Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55455 USA Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Research on intelligent vehicles has been popular in the past decade. To fill the gap between automatic approaches and man-machine control systems, it is indispensable to integrate visual human-computer interactions (VHCIs) into intelligent vehicles systems. In this article, we review existing studies on VHCI in intelligent vehicles from three aspects: 1) visual intelligence;2) decision making;and 3) macro deployment. We discuss how VHCI evolves in intelligent vehicles and how it enhances the capability of intelligent vehicles. We present several simulated scenarios and cases for future intelligent transportation system.

关键词： Augmented reality (AR) federated learning intelligent vehicles visual human-computer interactions visualization

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Multi-Home Energy Coordination Scheduling Based on Adaptive Dynamic Programming

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The International Journal of Intelligent control and systems 2024年第1期29卷 30-36页

作者： Kang Xiong Qinglai Wei Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China.

Implementing cooperative scheduling of multi-home microgrid energy and reducing the dependence on the main grid have become the focus of microgrid energy management *** paper proposes a new multi-agent adaptive dynamic programming(MAADP)method for the cooperative control of distributed home *** home is defined as a learning agent that needs to reasonably schedule the energy storage system to meet the respective load demand while accomplishing cooperative scheduling among the individual *** addition,an energy clearing center(ECC)is introduced to complete the energy exchange between each microgrid to protect the benefits of all *** proposed method adopts the learning strategy of“centralized learning and decentralized execution”to avoid the leakage of private *** experimental comparison with the benchmark method verifies that the method can realize the cooperative scheduling of each home and reduce the dependence on the main grid.

关键词： Multi-home scenarios adaptive dynamic programming(ADP) optimal home energy management smart grid

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A Deep Learning Method for Breast Cancer Classification in the Pathology Images

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2022年第10期26卷 5025-5032页

作者： Liu, Min Hu, Lanlan Tang, Ying Wang, Chu He, Yu Zeng, Chunyan Lin, Kun He, Zhizi Huo, Wujie Hubei Univ Technol Hubei Key Lab High Efficiency Utilizat Solar Ener Wuhan 430068 Peoples R China Rowan Univ Dept Elect & Comp Engn Glassboro NJ 08028 USA Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Inst Smart Educ Qingdao Peoples R China

Objective: Breast cancer is the most common female cancer in the world, and it poses a huge threat to women's health. There is currently promising research concerning its early diagnosis using deep learning methodologies. However, some commonly used Convolutional Neural Network (CNN) and their variations, such as AlexNet, VGGNet, GoogleNet and so on, are prone to overfitting in breast cancer classification, due to both small-scale breast pathology image datasets and overconfident softmax-cross-entropy loss. To alleviate the overfitting issue for better classification accuracy, we propose a novel framework for breast pathology classification, called the AlexNet-BC model. The model is pre-trained using the ImageNet dataset and fine-tuned using an augmented dataset. We also devise an improved cross-entropy loss function to penalize overconfident low-entropy output distributions and make the predictions suitable for uniform distributions. The proposed approach is then validated through a series of comparative experiments on BreaKHis, IDC and UCSB datasets. The experimental results show that the proposed method outperforms the state-of-the-art methods at different magnifications. Its strong robustness and generalization capabilities make it suitable for histopathology clinical computer-aided diagnosis systems.

关键词： Feature extraction Pathology Deep learning Breast cancer Training Transfer learning Smoothing methods Deep learning breast cancer transfer learning loss function AlexNet

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Adaptive Human-Robot Interaction Torque Estimation With High Accuracy and Strong Tracking Ability for a Lower Limb Rehabilitation Robot

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2024年第6期29卷 4814-4825页

作者： Liang, Xu Yan, Yuchen Wang, Weiqun Su, Tingting He, Guangping Li, Guotao Hou, Zeng-Guang Beijing Jiaotong Univ Sch Automat & Intelligence Beijing 100044 Peoples R China North China Univ Technol Dept Mech & Elect Engn Beijing 100144 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China Beijing Univ Technol Fac Informat Technol Beijing 100124 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 Macau Univ Sci & Technol Inst Syst Engn CASIA MUST Joint Lab Intelligence Sci & Technol Macau Peoples R China

Accurate acquisition of interactive information is crucial for the effective execution of rehabilitation training. However, due to model and sensor errors, it is difficult to obtain interactive information accurately and quickly. To overcome these challenges, a novel accurate and fast estimation method for the human-robot interaction torques (HRITs) is proposed in this article. First, the HRIT model with order adaptive adjustment ability (HMOAA) is constructed. The polynomial order of HMOAA can be adaptively adjusted based on the partial state estimation, which is more consistent with the dynamic time-varying characteristics of HRIT. Second, the Sage-Husa adaptive strong tracking Kalman filter (SHASTKF) is designed based on the modified Sage-Husa adaptive Kalman filter (SHAKF) and strong tracking Kalman filter (STKF). The SHASTKF can quickly track the abrupt HRIT changes when the subject suddenly exerts active torques in rehabilitation training. Moreover, it also has the ability to recursively estimate the noise characteristics, and can stably complete the HRIT estimation task when the noise characteristics are unknown. Finally, simulations and experiments are conducted to validate the proposed method, and the comparison results demonstrate that the proposed method has good torque estimation precision and fast tracking ability of abrupt changes in HRITs.

关键词： Adaptive learning human-robot interaction rehabilitation robot torque estimation

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Parallel Treasury for TRUE DAOs: Model, Indicators and Mechanism*

Parallel Treasury for TRUE DAOs: Model, Indicators and Mecha...

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control Conference (ANZCC), Australian and New Zealand

作者： Sangtian Guan Juanjuan Li Wenwen Ding Fei-Yue Wang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Faculty of Innovation Engineering Macau University of Science and Technology Macao China The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing China

In response to concerns over the centralization tendency in the decentralized autonomous organizations (DAOs), TRUE autonomous organizations and operations (TAOs or TRUE DAOs) have been proposed recently. TAOs aim at spreading equitable value distribution and democratized decision-making, distinguishing them from their DAOs counterparts. This study focuses on the treasury within TAOs, which acts as a central fund pool and a crucial element in the decentralized economy (DeEco) system. First, against a backdrop of potential black swan events and other long-tail unforeseen challenges, a reference model for the intelligent treasury management of TAOs is proposed. Then, an evaluation system, namely VALID, is presented with metrics including verifiability, anti-volatility, legitimacy, inclusiveness, and decentralization. Furthermore, a novel parallel treasury management mechanism is proposed to demonstrate a virtual-real interactive closed-loop management and control paradigm of the treasury, thereby fostering the formulation and development of DeEco. This research provides a comprehensive perspective on intelligent treasury management of TAOs and their role in sustainable advancement of DeEco.

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Adaptive Decentralized Asymptotic Tracking control for Large-Scale Nonlinear systems With Unknown Strong Interconnections

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 173-186页

作者： Ben Niu Jidong Liu Ding Wang Xudong Zhao Huanqing Wang College of Information Science and Engineering Shandong Normal UniversityJinan 250014China School of Artificial Intelligence Nankai UniversityTianjin 300071China Faculty of Information Technology Beijing University of TechnologyBeijing 100124 State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Control Science and Engineering Dalian University of TechnologyDalian 116023China Department of Mathematical Science and Automatization Institute Bohai UniversityJinzhou 121000China

An adaptive decentralized asymptotic tracking control scheme is developed in this paper for a class of large-scale nonlinear systems with unknown strong interconnections,unknown time-varying parameters,and ***,by employing the intrinsic properties of Gaussian functions for the interconnection terms for the first time,all extra signals in the framework of decentralized control are filtered out,thereby removing all additional assumptions imposed on the interconnec-tions,such as upper bounding functions and matching ***,by introducing two integral bounded functions,asymptotic tracking control is ***,the nonlinear filters with the compensation terms are introduced to circumvent the issue of“explosion of complexity”.It is shown that all the closed-loop signals are bounded and the tracking errors converge to zero *** the end,a simulation example is carried out to demonstrate the effectiveness of the proposed approach.

关键词： Adaptive decentralized control asymptotic tracking control large-scale nonlinear systems unknown strong interconnections

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A Railway Traffic management Advisory System for High-Speed Trains in Case of Emergencies

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IEEE INTELLIGENT TRANSPORTATION systems MAGAZINE 2025年

作者： Zhou, Min Yuan, Zhilu Wu, Xingtang Dong, Hairong Wang, Fei-Yue Beijing Jiaotong Univ Sch Automat & Intelligence Beijing 100044 Peoples R China Shenzhen Univ Sch Architecture & Urban Planning Shenzhen 518060 Peoples R China North China Elect Power Univ Sch Control & Comp Engn Beijing 100096 Peoples R China Shandong Univ Sci & Technol Coll Elect Engn & Automat Qingdao 266590 Peoples R China Chinese Acad Sci CASIA Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Macau Univ Sci & Technol Macau Inst Syst Engn Macau 999078 Peoples R China

The timetable is vulnerable to the impact of emergencies such as severe weather and equipment faults, potentially disrupting train services. Swift and efficient timetable adjustments are crucial for maintaining the operational efficiency of high-speed railways (HSRs). This study introduces a railway traffic management advisory system (RTMAS) tailored for high-speed trains, aiding dispatchers in the rapid rescheduling of timetables during emergencies. The RTMAS's structure and its emergency handling protocols are designed with an emphasis on human-in-the-loop and user-friendly principles. The core modules, including risk event scenarios, delay time prediction, expert systems, rescheduling scheme generation, and strategy evaluation systems, and their functions are given from the whole process of the dispatchers' emergency responses. The system employs metaheuristic algorithms and deep reinforcement learning (DRL) for predicting delays and generating rescheduling plans. A practical examination of the Beijing-Shanghai HSR Line validates the effectiveness of the RTMAS. The results indicate that the system not only furnishes dispatchers with near-optimal rescheduling solutions but also markedly diminishes total delays and the number of trains impacted by emergencies.

关键词： Railroads

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Fault Diagnosis for Sucker Rod Pumping systems: A Parallel System Approach

IEEE JOURNAL OF RADIO FREQUENCY IDENTIFICATION

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IEEE JOURNAL OF RADIO FREQUENCY IDENTIFICATION 2022年 6卷 743-747页

作者： Wang, Xingxia Cheng, Xiang Lu, Jingwei Liu, Shuliang Zhang, Zhixue Liu, Zhaohai Wang, Fei-Yue 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 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 100102 Peoples R China Tangshan Jidong Petr Machinery Co Ltd Tangshan 063299 Hebei Peoples R China

To solve the problem of insufficient physical faulty data in sucker rod pumping systems, the parallel system theory is utilized to investigate fault diagnosis of sucker rod pumping systems. Moreover, a novel framework of fault diagnosis, parallel fault diagnosis (PFD), is proposed. Firstly, to achieve PFD, the parallel sucker rod pumping system, named Bo-Yi, is proposed in this paper. Then, based on the proposed Bo-Yi, parallel fault diagnosis is investigated through the parallel learning theory, which can generate more useful systematic data and facilitate analyzing sucker rod pumping systems more thoroughly. Finally, the framework of parallel fault diagnosis for the parallel sucker rod pumping system is given to clearly demonstrate the related operational process.

关键词： Parallel systems sucker rod pumping systems (SRPs) parallel fault diagnosis (PFD) parallel learning parallel sucker rod pumping systems (Bo-Yi)

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