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Sociolinguistic Radar of Phonological Variation and Social Meaning: Variables, Quantitative Methods, and Prospects

IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2024年第6期11卷 7734-7741页

作者： Wang, Wei Fan, Lili Wang, Yutong Ni, Qinghua Wang, Fei-Yue Beijing Jiaotong Univ Sch Languages & Commun Studies Beijing 100089 Peoples R China Beijing Inst Technol Sch Informat & Elect Beijing 100081 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 999078 Peoples R China Chinese Acad Sci State Key SLab Management & Control Complex Syst Beijing 100190 Peoples R China Macau Univ Sci & Technol Fac Innovat Engn Macau 999078 Peoples R China

Inspired by the term "social radar," which collects and processes information about social behaviors, this article proposed "sociolinguistic radar," which represents an emerging branch in social investigation and evaluation system aiming to explore the dynamic correlation between sociophonetic variants and macrosociological categories, such as age, gender, ethnicity, and socioeconomic status. The classic quantitative methods in this field include sociolinguistic surveys and interview, quantitative sociophonetics, and social network analysis. These methods have been proved to be effective in tracking the cognitive correlates of phonological variables. Under the emerging framework of sociolinguistic radar, speakers are no longer passive carriers, but active agents in transforming linguistic styles in the process of forming social differentiations, thus contributing to the construction of new social meaning. With the advancement in neuroscience and artificial intelligence (AI), the neurosociolinguistic and AI-based sociolinguistic radar research will thrive and empower the scope and strength of detecting linguistic variation. The working mechanism of this emerging model leverages neural and AI tool packages to radar and analyze linguistic variation, communication patterns, and diverse sociolinguistic phenomena. This interdisciplinary approach combines the principles of sociolinguistics, which will thoroughly examine the relationship between language and society.

关键词： Linguistics Radar Social networking (online) Interviews Surveys Radar tracking Systematics Artificial intelligence (AI)-based sociolinguistics linguistic variation neurosociolinguistics phonological variables social stratifications sociolinguistic radar sociophonetic variants

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A Data-Driven Iterative Learning Approach for Optimizing the Train control Strategy

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2023年第7期19卷 7885-7893页

作者： Su, Shuai Zhu, Qingyang Liu, Junqing Tang, Tao Wei, Qinglai Cao, Yuan Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China Beijing Jiaotong Univ Frontiers Sci Ctr Smart High speed Railway Syst Beijing 100044 Peoples R China Univ Chinese Acad Sci Inst Automat Chinese Acad Sci Sch Artificial IntelligenceKey Lab Management & C Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100049 Peoples R China

The energy-efficient train control (EETC) problem is investigated in this article. And a soft actor-critic (SAC)-based method is proposed to optimize the train driving strategy. First, EETC problem is converted to the inverse problem, i.e., minimizing the trip time of the journey with constant energy consumption. Based on the conversion, the EETC problem is reformulated as a finite Markov decision process, which can be solved by deep reinforcement learning algorithms. Second, an optimization method based on the SAC method is designed to calculate the optimal driving strategy of the train with introducing the reservoir sampling method. Finally, some case studies are conducted to verify the effectiveness and performance of the proposed method. Simulation results demonstrate that a good energy-saving performance can be achieved. In single interval, the SAC-based method can reduce about 1.65% of the energy consumption compared with numerical method. And the energy consumption reduction can be extended to be 6.49% when the proposed approach is applied in multiple intervals.

关键词： Deep reinforcement learning (RL) driving strategy energy-efficient train control (EETC) soft actorcritic (SAC)

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Highway Lane Change Decision-Making via Attention-Based Deep Reinforcement Learning

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IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 567-569页

作者： Junjie Wang Qichao Zhang Dongbin Zhao State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China IEEE

Dear editor,Deep reinforcement learning(DRL),combining the perception capability of deep learning(DL)and the decision-making capability of reinforcement learning(RL)[1],has been widely investigated for autonomous driving decision-making *** this letter,Fund:supported in part by the National Natural Science Foundation of China(NSFC)(62173325);the Beijing Municipal Natural Science Foundation(L191002).

关键词： driving Deep has

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Event-Triggered Parallel control Using Deep Reinforcement Learning With Application to Comfortable Autonomous Driving

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第3期9卷 4470-4479页

作者： Lu, Jingwei Li, Lefei Wang, Fei-Yue Tsinghua Univ Dept Ind Engn Beijing 100084 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 999078 Peoples R China

A novel event-triggered control (ETC) method, called deep event-triggered parallel control (deep-ETPC), is presented to achieve path tracking for comfortable autonomous driving (CAD) using parallel control and deep deterministic policy gradient (DDPG). Based on parallel control, the developed deep-ETPC method constructs a dynamic control policy by introducing variation rates of controls. By employing variation rates of controls, the developed deep-ETPC method is capable of indicating communication loss and comfortable driving indices in the reward, and then enables reinforcement learning (RL) agents to learn comfortable ETC driving policies directly. Moreover, the communication loss, which reflects ETC, is integrated into the reward, so there is no need to additionally design/train triggering conditions, which can be considered a type of multi-tasking learning. Furthermore, an ETPC-oriented DDPG algorithm is developed to achieve the developed deep-ETPC method, making DDPG applicable to ETC. Empirical results, including tracking a simple straight line trajectory and a complicated sinusoidal trajectory, demonstrate the effectiveness of the developed deep-ETPC method.

关键词： Autonomous driving comfort driving deep deterministic policy gradient (DDPG) event-triggered control (ETC) parallel control

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Guest Editorial: Cyber-Physical-Social Intelligence: Toward Metaverse-Based Smart Societies of 6I and 6S

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2023年第4期53卷 2018-2024页

作者： Wang, Fei-Yue Tang, Ying Werbos, Paul J. Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Rowan Univ Dept Elect & Comp Engn Glassboro NJ 08028 USA Missouri Univ Sci & Technol Rolla MO 65409 USA

Transformational development in science and engineering rapidly integrates machine intelligence and human intelligence to form cyber-physical-social intelligence (CPSI), underpinning the growing interactions of cyberspace, physical space, and social space. A multidimensional reality is emerging and evolving with these interactions, leading to the creation of many new concepts and frameworks, such as parallel intelligence, digital twin, and metaverse. Meanwhile, new challenges continue to arise when we seek to reveal the fundamental principles for command and control of the new and extended reality by identifying and characterizing the relations of machines, humans, and nature. To meet these challenges, it is important for us to understand and explore algorithmic theories and processes that constitute CPSI, and to build cyber-physical-social systems (CPSS) [1] with the features of agility, focus, and convergence that are vital to ensure an inclusive, sustainable, equitable, and resilient future for people and our planet.

关键词： Special issues and sections Cyber-physical systems Metaverse Machine intelligence Human intelligence Extended reality Digital twins Cyberspace Command and control systems

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The Milestones of IEEE TIV: The Best Associate Editors and George N. Saridis Best Papers Awards

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2022年第4期7卷 801-803页

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

Presents the recipients of ITSS society awards.

关键词： Awards

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Logistics 5.0: From Intelligent Networks to Sustainable Ecosystems

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第7期8卷 3771-3774页

作者： Li, Juanjuan Qin, Rui Olaverri-Monreal, Cristina Prodan, Radu 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 Johannes Kepler Univ Linz Chair Sustainable Transport Logist 4 0 A-4040 Linz Austria Univ Klagenfurt Inst Informat Technol A-9020 Klagenfurt Austria Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Macau Univ Sci & Technol Macao Inst Syst Engn Macau 999078 Peoples R China

As part of TIV's DHW on Vehicle 5.0, this letter introduces a novel concept, Logistics 5.0, to address high complexities in logistics Cyber-Physical-Social systems (CPSS). Building upon the theory of parallel intelligence and leveraging advanced technologies and methods such as blockchain, scenarios engineering, Decentralized Autonomous Organizations and Operations (DAOs), Logistics 5.0 promises to accelerate the paradigm shift towards intelligent and sustainable logistics. First, the parallel logistics framework is proposed, and the logistics ecosystem is discussed. Then, the human-oriented operating systems (HOOS) are suggested to providing intelligent Logistics 5.0 solutions. Logistics 5.0 serves as a critical catalyst in realizing the "6S" objectives, i.e. Safety, Security, Sustainability, Sensitivity, Service, and Smartness, within the logistics industry.

关键词： Cyber-physical-social systems decentralized autonomous organizations and operations human-oriented operating systems Logistics 5.0 parallel logistics

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Society-Centered and DAO-Powered Sustainability in Transportation 5.0: An Intelligent Vehicles Perspective

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第4期8卷 2635-2638页

作者： Chen, Yuanyuan Zhang, Hui Wang, Fei-Yue Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China Beihang Univ Sch Transportat Sci & Engn Beijing 100091 Peoples R China Beihang Univ Ningbo Inst Technol NIT Ningbo 315323 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 999078 Peoples R China

As economic and social activities continue to increase, transportation is increasingly contributing to climate change, air pollution and other environmental damage. The growing concerns about the sustainability of transportation are forcing everyone in this field to think about solutions to keep our mobility environmentally, economically and socially sustainable. To provide a forum for the exchange of ideas and experiences from industry, academia and the public sector, we have recently held a series of seminars and the first Distributed/Decentralized Hybrid Workshop on Sustainability for Transportation and Logistics (DHW-STL), part of Distributed/Decentralized Hybrid Symposia on Sustainability for Transportation and Logistics (DHS-STL), and Distributed/Decentralized Hybrid Conferences on Sustainability for Transportation and Logistics (DHC-STL). This letter provides a brief report of the First DHW-STL and discusses the potentials, possibilities and perspectives driven by Intelligent Vehicles (IV) technologies to achieve sustainable intelligent systems for transportation and logistics.

关键词： Carbon neutral Intelligent vehicles Sustainable development Logistics Air pollution Behavioral sciences Climate change Sustainability intelligent vehicles parallel transportation system carbon emission air pollution carbon neutrality green transportation

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Parallel Learning:Overview and Perspective for Computational Learning Across Syn2Real and Sim2Real

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IEEE/CAA Journal of Automatica Sinica 2023年第3期10卷 603-631页

作者： Qinghai Miao Yisheng Lv Min Huang Xiao Wang Fei-Yue Wang School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China School of Artificial Intelligence Anhui UniversityHefei 266114China Qingdao Academy of Intelligent Industries Qingdao 230031China State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China IEEE

The virtual-to-real paradigm,i.e.,training models on virtual data and then applying them to solve real-world problems,has attracted more and more attention from various domains by successfully alleviating the data shortage problem in machine *** summarize the advances in recent years,this survey comprehensively reviews the literature,from the viewport of parallel ***,an extended parallel learning framework is proposed to cover main domains including computer vision,natural language processing,robotics,and autonomous ***,a multi-dimensional taxonomy is designed to organize the literature in a hierarchical ***,the related virtual-toreal works are analyzed and compared according to the three principles of parallel learning known as description,prediction,and prescription,which cover the methods for constructing virtual worlds,generating labeled data,domain transferring,model training and testing,as well as optimizing the strategies to guide the task-oriented data generator for better learning *** issues remained in virtual-to-real are ***,the future research directions from the viewpoint of parallel learning are suggested.

关键词： Machine learning parallel learning parallel systems sim-to-real syn-to-real virtual-to-real

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Decomposing Visual and Semantic Correlations for Both Fully Supervised and Few-Shot Image Classification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1658-1668页

作者： Zhang, Chunjie 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 Chinese Academy of Sciences School of Artificial Intelligence Beijing100190 China

Most image classification methods are designed to either boost the classification accuracies with abundant supervision, or cope with the shortage of supervision information. This is often achieved by using the visual and semantic information of other sources. However, these methods use the visual and semantic information within the same class independently, leaving their intrinsic correlations unconsidered. Objects and disturbing components as well as noise exist on the same image. Besides, semantic representations also contain noisy information. To solve the problems mentioned above, we propose a novel method for both fully supervised image classification and few-shot image classification by decomposing visual and semantic correlations. We jointly explore the intrinsic correlations of visual and semantic information of images within the same class. For each class, we decompose its visual and semantic correlations using low-rank and sparse constraint respectively. The decomposed low-rank parts character the intrinsic correlations of images can be used in a linear transformation way. Using the decomposed parts of each class, Classification can be achieved by reconstruction error minimization. We conduct experiments on several datasets for both fully supervised image classification and few-shot image classification. Experimental results well show the effectiveness of the proposed method. © 2020 IEEE.

关键词： Semantics

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