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The state of IEEE Transactions on Intelligent Vehicles: From Passive Publication to Active and Smart Exchange and Dissemination

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2022年第2期7卷 157-163页

作者： Wang, Fei-Yue Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China

Presents the introductory editorial for this issue of the publication.

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Parallel Dispatching: A New Paradigm for High-Speed Train Operation management and Optimization

IEEE JOURNAL OF RADIO FREQUENCY IDENTIFICATION

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IEEE JOURNAL OF RADIO FREQUENCY IDENTIFICATION 2024年 8卷 392-401页

作者： Zhou, Min Dong, Hairong Li, Juanjuan Li, Runmei Liu, Xuan Wang, Hongwei Beijing Jiaotong Univ Sch Automat & Intelligence Beijing 100044 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

In order to tackle the intricate challenges of train dispatching and command within China's extensive high-speed railways (HSRs) network, this paper presents a novel parallel dispatching approach founded on the ACP (Artificial systems, Computational experiments, and Parallel execution) methodology. We provide a comprehensive overview of the framework and technical architecture of the parallel dispatching system (PDS), offering detailed insights into each fundamental module. It initiates by establishing the PDS for HSRs, emphasizing the deep integration of the actual and artificial systems. The methods for train operation situation deduction, rapid dispatching strategy generation, and comprehensive evaluation of parallel dispatching are then expounded, each tailored to different scales. Subsequently, the paper proposes the creation of a parallel dispatching platform for HSRs, utilizing a real line's dispatching system as a prototype. Two typical scenarios are considered to validate the effectiveness of the PDS. The computational experiments are designed and executed in the artificial dispatching system to facilitate accurate deduction of operational scenarios and swift generation of dispatching solutions within defined computational constraints. The adjustment effects are assessed and iteratively optimized through parallel execution. The research outcomes of this paper can serve as a theoretical foundation and technical resource for the design, implementation, and validation of PDS in China's high-speed railway network.

关键词： Dispatching Rail transportation Computational modeling Radiofrequency identification Transportation Delays Computer architecture Parallel dispatching high-speed train ACP method framework implementation scheme

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Computational Experiments: A New Analysis Method for Cyber-Physical-Social systems

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IEEE TRANSACTIONS ON systemS MAN CYBERNETICS-systemS 2024年第2期54卷 813-826页

作者： Xue, Xiao Shen, Yifan Yu, Xiangning Zhou, De-Yu Wang, Xiao Wang, Gang Wang, Fei-Yue Tianjin Univ Coll Intelligence & Comp Tianjin 300072 Peoples R China Shandong Univ Sch Softwar Jinan 250100 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100080 Peoples R China Shanxi Univ Finance & Econ Sch Informat Taiyuan 710100 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Given the complex nature of cyber-physical-social systems (CPSSs), understanding their mechanism is essential for analyzing and controlling their actions while minimizing potential harm. However, studying CPSS in the real world is costly and constrained by legal and institutional factors. Computational experiments have emerged as a new method for quantitative analysis, and this article proposes a method of using computational experiments for analyzing CPSS, which consists of model docking, experiment design, and experiment analysis. The cloud manufacturing service ecosystem (CMSE) is used as a typical case study to verify the effectiveness of the proposed method by simulating different operation strategies. The results show that the computational experiments method is effective in providing new means and ideas for analyzing CPSS.

关键词： Cloud manufacturing service ecosystem (CMSE) computational experiments cyber-physical-social systems (CPSSs) experiment analysis experiment design

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Blockchain Intelligence: Intelligent Blockchains for Web 3.0 and Beyond

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IEEE TRANSACTIONS ON systemS MAN CYBERNETICS-systemS 2024年第11期54卷 6633-6642页

作者： Li, Juanjuan Qin, Rui Guan, Sangtian Hou, Jiachen Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence Beijing 100190 Peoples R China Macau Univ Sci & Technol Fac Innovat Engn Macau Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

As the next-generation Internet characterized by readability, writability, and ownability, Web 3.0 necessitates the fusion of blockchain and artificial intelligence (AI) technologies to realize its vision of decentralization, user autonomy, and intelligent openness. To this end, this article proposes the integration of "AI for blockchain" and "blockchain for AI" to form a bidirectional enhancement loop, for establishing genuinely intelligent blockchains and ushering in a new paradigm referred to as blockchain intelligence. On this basis, the technical architecture of intelligent blockchains is proposed, which infuses intelligence into every layer of traditional blockchain architectures while enables the parallel execution between virtual and artificial intelligent blockchain systems. This architecture facilitates blockchain systems to cultivate an ecosystem of intelligence, extending from foundation intelligence to application intelligence. Moreover, the core attributes of blockchain intelligence are examined, from the perspectives of smart contracts, data, identity, and governance. Furthermore, the main challenges and research issues faced by blockchain intelligence are outlined. This article is committed to the advancement of blockchain intelligence, laying the groundwork for Web 3.0 and the impending era of smart societies.

关键词： Blockchains Artificial intelligence Semantic Web Smart contracts Security Technological innovation Behavioral sciences Blockchain intelligence foundation intelligence intelligent blockchain intelligent contracts Web 3 0

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HetGRec: Heterogeneous Graph Attention Network for Group Recommendation

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IEEE INTELLIGENT systemS 2023年第1期38卷 9-18页

作者： Zhang, Song Zheng, Nan Wang, Danli Chinese Acad Sci Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Due to the popularity of group activities in social media, group recommendation becomes increasingly significant. It aims to pursue a list of preferred items for a target group. Recently, some methods perform graph neural network on the interaction graph. However, these works ignore that the interaction relationships among groups, users, and items are heterogeneous, i.e., two objects can be connected via different paths. In this article, we propose a heterogeneous graph attention network for group recommendation. It first employs meta-path-based random walk with restart to search for strongly correlated neighbors for each node. Then, it performs a dual-hierarchical attention network to extract semantics existing in each meta-path and fuse them to obtain hybrid representation of groups and items. Extensive experiments on three public datasets demonstrate its superiority over the state-of-the-art methods for group recommendation.

关键词： Fuses Semantics Aggregates Intelligent systems Task analysis Social networking (online) Representation learning

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Social Radars for Social Vision of Intelligent Vehicles From Skyways in Low Altitude Airspaces

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第1期9卷 5-38页

作者： Wang, Fei-Yue Wuhan Inst Ind Innovat & Dev Inst Intelligent Vehicles Wuhan 430000 Hubei Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Welcome to 2024 and Happy New Year!, The following content provides a brief summary of what we have accomplished in 2023:

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A Secure Medical Information Storage and Sharing Method Based on Multiblockchain Architecture

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systemS 2024年第5期11卷 6392-6406页

作者： Hu, Jian Zhu, Peng Li, Juanjuan Qi, Yong Xia, Youbing Wang, Fei-Yue Nanjing Univ Sci & Technol Nanjing 210094 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Nanjing Med Univ Affiliated Hosp 1 Nanjing 211166 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Macau Univ Sci & Technol Fac Innovat Engn Macau 999078 Peoples R China

With the increasing application of technology in the healthcare industry, it has become imperative to establish a robust medical information ecosystem for effective management of medical information secure storage and sharing. This article proposes a healthier ecosystem in collaboration with the main consortium chain and data side chain, using multiblockchain architecture. In the implementation methods for this ecosystem, we store medical information in JavaScript Object Notation (JSON) format within different side chain structures. Additionally, we introduce an improved Practical Byzantine Fault Tolerant (PBFT) consensus mechanism based on a point nomination system and a dynamic RBAC access mechanism. By simulating a blockchain environment with multiple nodes, we analyze the efficiency of this method in terms of record retrieval, consensus mechanism, and storage execution time. The results demonstrate that compared to a single chain structure, the proposed method in this article achieves a substantial 30% improvement in query time efficiency. Moreover, the improved PBFT outperforms the traditional PBFT algorithm without dishonest nodes. Medical records stored in JSON format require shorter storage and execution time compared to text format records. This research contributes toward enhancing the security and efficiency of medical information storage and sharing among different medical subject information systems, thereby fostering a healthier healthcare alliance ecosystem.

关键词： Blockchain information storage and sharing medical information multiblockchain architecture

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Personality Shaping: A Prescriptive Approach Based on Virtual-Real Human Interaction

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IEEE TRANSACTIONS ON systemS MAN CYBERNETICS-systemS 2024年第12期54卷 7548-7557页

作者： Ye, Peijun Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Macau Univ Sci & Technol Inst Engn Macau Peoples R China

The era has been witnessing our entering into a more professional and cooperative society and also a bloom of various digital humans that help us complete more challenging industrial work than ever. As a major human-machine communication channel, virtual-real interaction between digital and real humans is an efficient way to adjust the mismatch between specialized tasks and participants with unsuitable personalities, elevating the overall performance of such human participated systems. This article proposes a new paradigm for temporary personality shaping by prescriptively interacting the human operator/user with his "digital assistant" in cyber space. As a first proposed personality shaping with AI-aided approach, the new paradigm involves digital human modeling with uncertain personality, computational experiments on personality evolution, and prescriptive interaction with user counterpart. By iteratively and repeatedly executing the three steps, the personality of human participants is gradually tailored and prescribed so that he can well undertake specific tasks. Experiments based on online social media data and human-machine shared driving have indicated that our new personality shaping paradigm is feasible and effective in performing representative tasks.

关键词： Digital humans Task analysis Computational modeling Psychology Data preprocessing Cognition Data models Digital human human-machine cooperation personality

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MetaEconomics and Metamanagement for MetaCities and MetaSocieties in Metaverse

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IEEE TRANSACTIONS ON systemS MAN CYBERNETICS-systemS 2024年第11期54卷 6849-6858页

作者： Qin, Rui Li, Juanjuan Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Macau Univ Sci & Technol Fac Innovat Engn Macau Peoples R China

With the advent of Web 3.0, the evolution of cities and societies is increasingly oriented toward virtual spaces. This shift signifies an inevitable trend where the integration of virtual and real elements becomes vital to their development. Such a transition will bring huge changes to the organizational structure and methods, development modes, as well as operating mechanisms of cities and societies. In the virtual-real integrated cities and societies, traditional economic and management principles and models are no longer applicable. Consequently, it is crucial to explore new economic and management models tailored to Web 3.0. This article integrates virtual cities/societies with actual cities/societies, and proposes the innovative paradigm of MetaCities/MetaSocieties. Based on parallel intelligence theory and metaverse technologies, the research framework of MetaCities/MetaSocieties is established, and its main participants and operating mode are discussed. In addition, in view of the new economic and management issues faced in MetaCities/MetaSocieties, the innovative paradigms of MetaEconomics and Metamanagement are proposed, and the operational logic and models of MetaEconomics, as well as the Metamanagement big models and management-oriented operating systems, are proposed. This work aims to offer valuable insights for the evolution of cities and societies in the upcoming intelligent era, and inspire the development of new MetaEconomics and Metamanagement models in MetaCities/MetaSocieties.

关键词： Urban areas Decision making Biological system modeling Economics Real-time systems Training Metaverse Blockchain decentralized autonomous organization and operation (DAO) metaverse parallel intelligence Web 3 0

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New Paradigm for Economic and Financial Research With Generative AI: Impact and Perspective

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systemS 2024年第3期11卷 3457-3467页

作者： Zheng, Xiaolong Li, Jingyu Lu, Mengyao Wang, Fei-Yue Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 101408 Peoples R China Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Beijing Univ Technol Sch Econ & Management Beijing 100124 Peoples R China

In the past few years, we have witnessed the rapid development and exponential growth of generative artificial intelligence (GAI) technologies including large language models (LLMs)-enabled ChatGPT and peripheral innovations. These technologies are designed to be humanlike intelligence and intuitive by providing direct access to systems using application programming interfaces (APIs). The GAI applications can fundamentally change economic and financial activities, through revolutionizing the ways that humans interact with machines and giving rise to new modes of production and behavior patterns. It is imperative to develop a new research paradigm that is more suitable than the currently dominating conventional research paradigms. This article presents the new paradigm for economic and financial research with GAI, covering the research objectives, scientific data, and models, and explores the underlying impact and perspective that bring to this field. We elaborate on the potential five scenarios including portfolio management, economic and financial prediction, extreme scenario analysis, policy analysis, and financial fraud detection. The new research paradigm with GAI proposed in this article can provide significant insights for a comprehensive understanding of innovation and transformation in this domain.

关键词： Economics Biological system modeling Data models Task analysis Behavioral sciences Big Data Analytical models finance generative artificial intelligence (GAI) reinforcement learning from human feedback (RLHF) research paradigm

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