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Parallel Tourism System With Foundation Models and Decentralized Autonomous Organizations

INTERNATIONAL JOURNAL OF TOURISM RESEARCH 2025年第2期27卷

作者： Huang, Jun Tian, Yonglin Zhang, Tengchao Ni, Qinghua Lin, Fei Lee, Timothy J. Wu, Naiqi Wang, Fei-Yue Macau Univ Sci & Technol Fac Innovat Engn Dept Engn Sci Macau Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing Peoples R China Macau Univ Sci & Technol Fac Hospitality & Tourism Management Macau Peoples R China

Tourism is a critical driver of economic growth and cultural exchange. However, traditional tourism models often struggle to address challenges such as insufficient understanding of diverse travel preferences, limited capacity for personalized services, and a lack of intelligent, convenient service solutions. In this paper, we propose a virtual-real interactive Parallel Tourism System (PTS) and a Smart Tourism and Lifestyle Service Large Model (STLS-LM) to handle these challenges. By integrating Decentralized Autonomous Organizations (DAOs) and Foundation Models (FMs) supported by Retrieval-Augmented Generation, PTS elevates tourism services by incorporating agentic intelligence and autonomous intelligence, advancing beyond traditional AI-powered applications. The proposed framework enables a deep understanding of task requirements, precise multi-source information sensing, personalized functionality customization, and timely public feedback adoption, forming a comprehensive intelligent travel service system that caters to the evolving needs of modern tourism.

关键词： agentic intelligence autonomous intelligence creative tourism decentralized autonomous organizations large language models parallel intelligence retrieval-augmented generation

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In Memory of Wolter J. Fabrycky: A Pioneer of systems Engineering and US-Sino Academic Exchange

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IEEE/CAA Journal of Automatica Sinica 2025年第5期12卷 839-840页

作者： Fei-Yue Wang State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing China Faculty of Innovation Engineering Macau University of Science and Technology Macao China

The passing of Professor Wolter “Wolt” Fabrycky, an outstanding member and great leader, is a big loss to our international systems engineering professional community. “Wolt was a legend in the systems engineering community with his teaching, advising, and dissemination of knowledge through the books he authored.”, as stated by Professor Eileen Aken, a former student of Wolt and the head of the Virginia Tech's Grado Department of Industrial and systems Engineeirng where Wolt had served and led for 30 years and retired as John L. Lawrence Professor emeritus.

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LC-LLM: Explainable lane-change intention and trajectory predictions with Large Language Models

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COMMUNICATIONS IN TRANSPORTATION RESEARCH 2025年 5卷

作者： Peng, Mingxing Guo, Xusen Chen, Xianda Chen, Kehua Zhu, Meixin Chen, Long Wang, Fei-Yue Hong Kong Univ Sci & Technol Guangzhou Intelligent Transportat INTR Syst Hub Guangzhou 511400 Peoples R China Hong Kong Univ Sci & Technol Acad Interdisciplinary Studies Div Emerging Interdisciplinary Areas EMIA Hong Kong 999077 Peoples R China Guangdong Prov Key Lab Integrated Commun Sensing & Guangzhou 511400 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

To ensure safe driving in dynamic environments, autonomous vehicles should possess the capability to accurately predict lane change intentions of surrounding vehicles in advance and forecast their future trajectories. Existing motion prediction approaches have ample room for improvement, particularly in terms of long-term prediction accuracy and interpretability. In this study, we address these challenges by proposing a Lane Change-Large Language Model (LC-LLM), an explainable lane change prediction model that leverages the strong reasoning capabilities and self explanation abilities of Large Language Models (LLMs). Essentially, we reformulate the lane change prediction task as a language modeling problem, processing heterogeneous driving scenario information as natural language prompts for LLMs and employing supervised fine-tuning to tailor LLMs specifically for lane change prediction task. Additionally, we finetune the Chain-of-Thought (CoT) reasoning to improve prediction transparency and reliability, and include explanatory requirements in the prompts during the inference stage. Therefore, our LC-LLM not only predicts lane change intentions and trajectories but also provides CoT reasoning and explanations for its predictions, enhancing its interpretability. Extensive experiments based on the large-scale highD dataset demonstrate the superior performance and interpretability of our LC-LLM in lane change prediction task. To the best of our knowledge, this is the first attempt to utilize LLMs for predicting lane change behavior. Our study shows that LLMs can effectively encode comprehensive interaction information for understanding driving behavior.

关键词： Lane change (LC) Large language models (LLMs) Intention prediction Trajectory prediction Fine-tuning Interpretability Autonomous driving

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Secure Containment control for Multi-UAV systems by Fixed-Time Convergent Reinforcement Learning

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IEEE TRANSACTIONS ON CYBERNETICS 2025年第4期55卷 1981-1994页

作者： Yang, Feisheng Gong, Zhenyu Wei, Qinglai Lei, Yifei Northwestern Polytech Univ Sch Automat Xian 710129 Shaanxi Peoples R China Northwestern Polytech Univ Chongqing Sci & Technol Innovat Ctr Chongqing 401135 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

This article concerns the secure containment control problem for multiple autonomous aerial vehicles. The cyber attacker can manipulate control commands, resulting in containment failure in the position loop. Within a zero-sum graphical game framework, secure containment controllers and malicious attackers are regarded as game players, and the attack-defense process is recast as a min-max optimization problem. Acquiring optimal distributed secure control policies requires solving the game-related Hamilton-Jacobi-Isaacs (HJI) equations. Based on the critic-only neural network (NN) structure, the reinforcement learning (RL) method is employed in solving coupled HJI equations. The fixed-time convergence technique is introduced to improve the convergence rate of RL, and the experience replay mechanism is utilized to relax the persistence of excitation condition. The associated NN convergence and closed-loop stability are analyzed. In the attitude loop, the optimal feedback control law is obtained by solving Hamilton-Jacobi-Bellman equations using the fixed-time convergent RL method. The simulation example and the quadrotor experiment are given to show the effectiveness of the proposed scheme.

关键词： Autonomous aerial vehicles Convergence Games Game theory Process control Heuristic algorithms Vectors Convex hulls Automation Multi-agent systems Cooperation fixed-time reinforcement learning (RL) secure control autonomous aerial vehicle zero-sum game

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Cardiovascular Outcomes in Patients with complex Type 2 Diabetes Mellitus Treated with the Dual SGLT Inhibitor Sotagliflozin: A Meta-analysis

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DIABETES THERAPY 2025年第3期16卷 485-498页

作者： Wang, Hong Zu, Quannan Lu, Ming Chen, Rongfa Tang, Zhangui Yang, Zhiren Peoples Hosp Guangxi Zhuang Autonomous Reg Dept Cardiol Nanning 530021 Guangxi Peoples R China Tianjin Univ Coll Management & Econ Tianjin 300072 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Huazhong Univ Sci & Technol Inst Cardiovasc Dis Tongji Med Coll Dept Endocrinol Wuhan 430032 Hubei Peoples R China

IntroductionScientific publications have shown sodium-glucose co-transporter-2 (SGLT2) inhibitors to have several beneficial effects in patients with complex type 2 diabetes mellitus (T2DM). However, sodium-glucose co-transporter-1 (SGLT-1) inhibitor is still under investigation in clinical trials. Recently, a dual inhibitor of sodium-glucose co-transporter (SGLT1/2), sotagliflozin, has been approved for use in patients with T2DM. In this analysis, we aimed to systematically compare the cardiovascular outcomes in patients with complex T2DM who were treated with the newly approved dual (SGLT 1 and 2) inhibitor *** databases, including Embase, MEDLINE, http://***, Web of Science, Google Scholar, the Cochrane database, and reference lists of relevant publications, were searched for publications comparing the novel SGLT1/2 inhibitor versus placebo for the treatment of patients with complex T2DM. The primary endpoint, including total number of deaths from cardiovascular causes, hospitalization for heart failure, and urgent visits for heart failure, death from cardiovascular causes, cardiac mortality, hospitalization for heart failure, non-fatal myocardial infarction, and total number of cardiac events, were considered as the endpoints in this analysis. The RevMan software version 5.4 was used to carry out the statistical analysis. Risk ratios (RR) with 95% confidence intervals (CI) were used to represent the data following *** total of 13,054 participants enrolled between 2017 and 2020 were included in this analysis, with 6734 participants assigned to sotagliflozin and 6320 assigned to placebo. The results of this analysis showed that the primary endpoint was significantly in favor of sotagliflozin with (RR: 0.73, 95% CI 0.67-0.80;P = 0.00001). Hospitalization for heart failure (RR: 0.67, 95% CI 0.60-0.75;P = 0.00001) and the total number of cardiac events (RR: 0.73, 95% CI 0.67-0.79;P = 0.00001) were also sign

关键词： Dual SGLT inhibitor Sotagliflozin Diabetes mellitus Heart failure Cardiovascular mortality Chronic kidney disease

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LLM-Driven Cognitive Modeling for Personalized Travel Generation

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2025年

作者： Ge, Shichao Ye, Peijun Zhang, Renrui Zhou, Min Dong, Hairong Wang, Fei-Yue Beijing Jiaotong Univ Sch Automation & Intelligence Beijing 100044 Peoples R China Chinese Acad Sci Inst Automation Beijing 100190 Peoples R China Chinese Acad Sci Inst Automation State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China Shandong Univ Sci & Technol Coll Elect Engn & Automation Qingdao 266590 Peoples R China Chinese Acad Sci Inst Automation State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Traditional cognitive travel modeling typically employs a unified cognitive model to simulate representative travel behaviors, which may usually result in a weak characterization of user heterogeneity in paths, modes, and other factors. Large language model (LLM), by contrast, has significantly enhanced the anthropomorphic and personalized features of intelligent systems. To integrate their advantages, this article proposes LLM-driven cognitive modeling to generate more diverse and personalized travel demands. The new method sufficiently exploits LLM such as the llama as a basis and provides personalized travel plans so that more heterogenous travel demands could be generated. Additionally, introducing LLM into cognitive modeling can significantly reduce the time of model development, thus accelerating the research or engineering deployment. By calibrating and testing with one month's data from public transportation (buses and subways) in Beijing, our method, compared to traditional cognitive models, not only achieves better accuracy in reproducing typical travel patterns, but also generates more diverse ones, providing a more comprehensive input for computational experiments on traffic management and control strategies.

关键词： Computational modeling Decision making Accuracy Planning Automation Computer architecture Traffic control Memory modules Cognition Adaptation models Agent-based model (ABM) cognitive model large language model (LLM) travel behavior

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Rethinking Appearance-Based Deep Gait Recognition: Reviews, Analysis, and Insights From Gait Recognition Evolution

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING systems 2025年 PP卷 PP页

作者： Li, Jingqi Zhang, Yuzhen Zeng, Yi Ye, Changxin Xu, Wenzheng Ben, Xianye Wang, Fei-Yue Zhang, Junping Fudan Univ Sch Comp Sci Shanghai 200433 Peoples R China Shandong Univ Shenzhen Res Inst Shenzhen 518063 Peoples R China Shandong Univ Sch Informat Sci & Engn Jinan 250100 Peoples R China Obuda Univ DeSci Ctr Parallel Intelligence H-1034 Budapest Hungary Macau Univ Sci & Technol Intelligent Syst Robot & Automat Lab Macau Peoples R China CASIA State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Gait recognition is a prominent biometric recognition technique extensively employed in public security. Appearance-based and model-based gait recognition are two categories of methods commonly used. Specifically, appearance-based methods, which use silhouettes to represent body information, typically outperform model-based methods that rely on skeleton data, making them more popular. Recently, the shift from single-frame templates to multiframe silhouettes has advanced appearance-based gait recognition with better spatiotemporal representation. However, there is a notable lack of comprehensive studies that deepen the understanding of multiframe appearance-based gait recognition methods. This article reviews various methods to trace the evolution of gait recognition. Furthermore, we unify various performant models in one framework, study the overlooked effects on data arrangement, and explore the scaling ability of existing methods. Besides the advancement in gait recognition, we also summarize the current challenges and future prospects to foster future research.

关键词： Gait recognition Feature extraction Data models Reviews Training Skeleton Shape Analytical models Deep learning Clothing Appearance-based biometric deep learning gait recognition GaitBase

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Completed Interaction Networks for Pedestrian Trajectory Prediction

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IEEE Transactions on Multimedia 2025年

作者： Zhang, Zhong Zhou, Jianglin Liu, Shuang Xiao, Baihua Tianjin Normal University Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission Tianjin300387 China Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems Institute of Automation Beijing100190 China

The social and environmental interactions, as well as the pedestrian goal are crucial for pedestrian trajectory prediction. This is because they could learn both complex interactions in the scenes and the intentions of the pedestrians. However, most existing methods either learn the one-moment social interactions, or supervise the pedestrian trajectories using long-term goal, resulting in suboptimal prediction performances. In this paper, we propose a novel network named Completed Interaction Network (CINet) to simultaneously consider the social interactions in all moments, the environmental interactions and the short-term goal of pedestrians in a unified framework for pedestrian trajectory prediction. Specifically, we propose the Spatio-Temporal Transformer Layer (STTL) to fully mine the spatio-temporal information among historical trajectories of all pedestrians in order to obtain the social interactions in all moments. Additionally, we present the Gradual Goal Module (GGM) to capture the environmental interactions under the supervision of the short-term goal, which is beneficial to understanding the intentions of the pedestrian. Afterwards, we employ the cross-attention to effectively integrate the all-moment social and environmental interactions. The experimental results on three standard pedestrian datasets, i.e., ETH/UCY, SDD and inD demonstrate that our method achieves a new state-of-the-art performance. Furthermore, the visualization results indicate that our method could predict trajectories more reasonably in complex scenarios such as sharp turns, infeasible areas and so on. © 1999-2012 IEEE.

关键词： Prediction models

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Coordinated Adaptive Impedance control of Redundantly Actuated Parallel Manipulators

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IEEE TRANSACTIONS ON HUMAN-MACHINE systems 2025年

作者： Ma, Nan Cheneler, David He, Guangping Yuan, Junjie Bian, Gui-Bin Northwestern Polytech Univ Sch Mech & Elect Engn Xian 710072 Peoples R China Univ Lancaster Sch Engn Lancaster LA1 4YW England North China Univ Technol Dept Mech & Elect Engn Beijing 100144 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Redundantly actuated parallel mechanisms (RAPMs) have been widely adopted in advanced robotic systems and precision machine tools. It has been demonstrated that redundant actuation can improve the performance of mechatronic systems but introduce challenges with respect to control. One main difficulty is in establishing an accurate dynamic model of the RAPM system. With an inaccurate dynamic model, the torque applied by the actuators will be incorrect, leading to increased antagonistic forces in the system. To solve this problem, a novel coordinated adaptive impedance control approach based on a new adaptive impedance control law is presented here, along with proof of the stability of the closed-loop system. The control algorithm has been validated experimentally by a prototype cable-driven parallel manipulator. It can be seen from the experimental results that the proposed control method is an effective way to correct the antagonistic forces of the system, thus facilitating the improvement of its dynamic performance and its efficacy in different applications.

关键词： Impedance Robots Adaptation models Robot kinematics Actuators Stability analysis Kinematics Wires Steel Manipulator dynamics Actuation redundancy adaptive impedance control coordination control parallel manipulators uncertain model

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Probabilistic Data-Driven Modeling of a Melt Pool in Laser Powder Bed Fusion Additive Manufacturing

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 4908-4925页

作者： Fang, Qihang Xiong, Gang Zhao, Meihua Tamir, Tariku Sinshaw Shen, Zhen Yan, Chao-Bo Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat Beijing Engn Res Ctr Intelligent Syst & Technol State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China Chinese Acad Sci Guangdong Engn Res Ctr Printing & Intelligent Mfg Cloud Comp Ctr Dongguan 523808 Peoples R China Guangdong Univ Technol State Key Lab Precis Elect Mfg Technol & Equipment Guangzhou Peoples R China Debre Markos Univ Inst Technol Debre Markos 269 Ethiopia Xi An Jiao Tong Univ Fac Elect & Informat Engn State Key Lab Mfg Syst Engn Xian 710049 Shaanxi Peoples R China Xi An Jiao Tong Univ Fac Elect & Informat Engn Sch Automat Sci & Engn Xian 710049 Shaanxi Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

The widespread adoption of laser powder bed fusion (LPBF) additive manufacturing is hampered by process unreliability problems. Modeling the melt pool behavior in LPBF is crucial to develop process control methods. While data-driven models linking melt pool dynamics to specific process parameters have shown appreciable advancements, existing models often oversimplify these relationships as deterministic, failing to account for the inherent instability of LPBF processes. Such simplifications can lead to overconfident and unreliable predictions, potentially resulting in erroneous process decisions. To address this critical issue, we propose a probabilistic data-driven approach to melt pool modeling that incorporates process noise and uncertainty. Our framework formulates a problem that includes distribution approximation and uncertainty quantification. Specifically, the Gaussian distribution with higher order priors, aided with variational inference and importance sampling, is used to approximate the probability distribution of melt pool characteristics. The uncertainty inherent in both LPBF process data and the modeling approach itself are then decomposed and approximated by using Monte Carlo sampling. The melt pool model is improved further by using a novel grid-based representation for the neighborhood of a fusion point, and a neural network architecture designed for effective feature fusion. This approach not only refines the accuracy of the model but also quantifies the uncertainty of the predictions, thereby enabling more informed decision-making with reduced risk. Two potential applications, including LPBF process planning and anomaly detection, are discussed. The implementation of our model is available at https://***/qihangGH/probabilistic_melt_pool_model. Note to Practitioners-Modeling the melt pool behavior in laser powder bed fusion (LPBF) processes is pivotal for enhancing its quality control. However, a problem is that most existing data-driven me

关键词： Laser powder bed fusion (LPBF) additive manufacturing melt pool uncertainty quantification quality control process planning anomaly detection Laser powder bed fusion (LPBF) additive manufacturing melt pool uncertainty quantification quality control process planning anomaly detection

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