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The CASTlab.in CASIA [ITS Research lab.

IEEE intelligent TRANSPORTATION systems MAGAZINE 2020年第2期12卷 85-94页

作者： Olaverri-Monreal, Cristina Yisheng Lv is currently an associate professor with the State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences. His research interests include artificial intelligence intelligent control intelligent transportation systems and parallel traffic management and control systems. He is currently an ITS Society Board of Governors member. He is an associate editor of IEEE Transactions on Intelligent Transportation Systems and is on the editorial board of Acta Automatica Sinica. He received the 2015 IEEE ITS Outstanding Application Award. Contact him at yisheng.lv@***.

The complex Adaptive systems for Transportation lab.ratory (CASTlab. was established by Prof. Fei-Yue Wang in July 1999 for the task of designing and implementing the proposed intelligent traffic system for the city of Xinxiang, Henan, one of the first initiatives in intelligent transportation systems (ITS) in China. At the end of 1999, the CASTlab.became a part of the newly created Center for intelligent control and systems in the Institute of Automation, Chinese Academy of Sciences (CASIA), one of the premier and oldest national research organizations in information, automation, and artificial intelligence in China and worldwide.

关键词： Research lab.ratories

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Data Augmented Deep Behavioral Cloning for Urban Traffic control Operations Under a Parallel Learning Framework

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IEEE TRANSACTIONS ON intelligent TRANSPORTATION systems 2022年第6期23卷 5128-5137页

作者： Li, Xiaoshuang Ye, Peijun Jin, Junchen Zhu, Fenghua 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 Qingdao Acad Intelligent Ind Parallel Intelligence Res Ctr Qingdao 266109 Peoples R China Univ Chinese Acad Sci Ctr China Econ & Social Secur Beijing 100149 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Macau 999078 Peoples R China

It is indispensable for professional traffic signal engineers to perform manual operations of traffic signal control (TSC) to mitigate traffic congestion, especially with complicated scenarios. However, such a task is time-consuming, and the level of congestion mitigation heavily relies on individual expertise in engineering practice. Therefore, it is cost-effective to learn traffic engineers' knowledge to enhance the problem-solving skills for a large-scale urban traffic network. In this paper, a data augmented deep behavioral cloning (DADBC) method is proposed to imitate the problem-solving skills of traffic engineers. The method is under a conceptual framework, parallel learning (PL) framework, that incorporates machine learning techniques for solving decision-making problems in complex systems. The DADBC method enhances a hybrid demonstration by exploiting a generative adversarial network (GAN) and then uses the deep behavioral cloning (DBC) model to learn traffic engineers' control schemes. According to the validation results using the real manipulation data from Hangzhou, China, our method can imitate complex human behaviors in intervening traffic signal control operations to improve traffic efficiency in urban areas.

关键词： Generative adversarial networks Gallium nitride Cloning Data models Task analysis complex systems Knowledge engineering intelligent traffic signal operations parallel learning deep behavioral cloning generative adversarial networks

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Student Modeling and Analysis in Adaptive Instructional systems

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IEEE ACCESS 2022年 10卷 59359-59372页

作者： Liang, Jing Hare, Ryan Chang, Tianyu Xu, Fangli Tang, Ying Wang, Fei-Yue Peng, Shimeng Lei, Mingyu Squirrel AI Learning Yixue Educ Inc Highland Pk NJ 08904 USA Rowan Univ Dept Elect & Comp Engn Glassboro NJ 08028 USA Columbia Univ Teachers Coll New York NY 10027 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 266000 Peoples R China Nagoya Univ Grad Sch Informat Dept Intelligent Syst Nagoya Aichi 4648601 Japan China Acad Informat & Commun Technol Beijing 100191 Peoples R China

There is a growing interest in developing and implementing adaptive instructional systems to improve, automate, and personalize student education. A necessary part of any such adaptive instructional system is a student model used to predict or analyze learner behavior and inform adaptation. To help inform researchers in this area, this paper presents a state-of-the-art review of 11 years of research (2010-2021) in student modeling, focusing on learner characteristics, learning indicators, and foundational aspects of dissimilar models. We mainly emphasize increased prediction accuracy when using multidimensional learner data to create multimodal models in real-world adaptive instructional systems. In addition, we discuss challenges inherent in real-world multimodal modeling, such as uncontrolled data collection environments leading to noisy data and data sync issues. Finally, we reinforce our findings and conclusions through an industry case study of an adaptive instructional system. In our study, we verify that adding multiple data modalities increases our model prediction accuracy from 53.3% to 69%. At the same time, the challenges encountered with our real-world case study, including uncontrolled data collection environment with inevitably noisy data, calls for synchronization and noise control strategies for data quality and usability.

关键词： Adaptation models Artificial intelligence Predictive models Analytical models Adaptive systems Ontologies Mathematical models Adaptive instructional systems student modeling multimodal learning analytics

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Data-driven Self-triggered control via Trajectory Prediction

arXiv

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arXiv 2022年

作者： Liu, Wenjie Sun, Jian Wang, Gang Bullo, Francesco Chen, Jie The State Key Lab of Intelligent Control and Decision of Complex Systems The School of Automation Beijing Institute of Technology Beijing100081 China The Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China The Mechanical Engineering Department The Center of Control Dynamical Systems and Computation UC Santa BarbaraCA93106-5070 United States The Department of Control Science and Engineering Tongji University Shanghai201804 China The State Key Lab of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China

Self-triggered control, a well-documented technique for reducing the communication overhead while ensuring desired system performance, is gaining increasing popularity. However, existing methods for self-triggered control require explicit system models that are assumed perfectly known a priori. An end-to-end control paradigm known as data-driven control learns control laws directly from data, and offers a competing alternative to the routine system identification-then-control method. In this context, the present paper puts forth data-driven self-triggered control schemes for unknown linear systems using data collected offline. Specifically, for output feedback control systems, a data-driven model predictive control (MPC) scheme is proposed, which computes a sequence of control inputs while generating a predicted system trajectory. A data-driven self-triggering law is designed using the predicted trajectory, to determine the next triggering time once a new measurement becomes availab.e. For state feedback control systems, instead of capitalizing on MPC to predict the trajectory, a data-fitting problem using the pre-collected input-state data is solved, whose solution is employed to construct the self-triggering mechanism. Both feasibility and stability are established for the proposed self-triggered controllers, which are validated using numerical examples. © 2022, CC BY.

关键词： Model predictive control

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An Agent-Based Traffic Recommendation System: Revisiting and Revising Urban Traffic management Strategies

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2022年第11期52卷 7289-7301页

作者： Jin, Junchen Rong, Dingding Pang, Yuqi Ye, Peijun Ji, Qingyuan Wang, Xiao Wang, Ge Wang, Fei-Yue Zhejiang Supcon Informat Co Ltd Traff Res & Dev Dept Hangzhou 310052 Peoples R China Zhejiang Univ Coll Elect Engn Hangzhou 310027 Peoples R China Zhejiang Lab Intelligent Transport Res Ctr Hangzhou 311121 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Enjoyor Co Ltd Smart Transport Res Inst Hangzhou 310030 Peoples R China Zhejiang Univ Coll Comp Sci & Technol Hangzhou 310027 Peoples R China Univ Chinese Acad Sci Coll Econ & Management Beijing 100190 Peoples R China

Strategic traffic management is crucial for combating traffic congestion at the macroscopic level. However, such a field is still relatively unexplored, particularly for microscopic control objects, such as intersections and coordinated intersection groups. This article proposes a human-in-the-loop recommendation system for strategic urban traffic management, which follows an agent-based structure. A regional agent dispatcher is defined to assign agents for operation whenever ``operation on-demand'' is required. Such a requirement is identified by a daily-dependent operational mode on strategic traffic operations at a control object level. The strategic management scheme for each control object is guided by a strategic agent (customized), which is essentially a deep recommender model with a specific architecture. By featuring the multiagent design, a customized operational scheme can be generated at the intersection level, which instructs the corresponding controller to take specific operations. The utility of the recommendation system is demonstrated via a case study using real-world traffic data. In both offline and online evaluations, the system performs consistently at traffic operational recommendations in different scenarios and has the potential to provide more reasonable traffic operational strategies than a human-operated system.

关键词： Switches control systems Timing Task analysis Behavioral sciences Analytical models Research and development Agent-based recommendation system deep recommendation human-in-the-loop operation on-demand traffic management strategies

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A Collision-Free Person-Following Approach Based on Path Planning 17

A Collision-Free Person-Following Approach Based on Path Pla...

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17th IEEE International Conference on Mechatronics and Automation, ICMA 2020

作者： Pang, Lei Cao, Zhiqiang Yu, Junzhi Zhang, Weimin Chen, Xuechao Institute of Automation State Key Laboratory of Management and Control for Complex Systems China Beijing Institute of Technology Beijing Advanced Innovation Center for Intelligent Robots and Systems China

ISBN: (纸本)9781728164151

Person following is a crucial capability for mobile robots in tasks of human assistance and cooperation. In this paper, a person-following approach based on path planning is proposed for mobile robots, which takes person following and obstacle avoidance into account in a unified framework. The proposed approach is comprised of a sensing module and a planning module. The sensing module with a camera and a 3D LiDAR is employed to locate the target person and perceive obstacles. For the planning module, the combination of global planner and local planner effectively outputs collision-free paths and control commands for a mobile robot to keep following the target person. Experiments were conducted to evaluate the performance of the proposed approach. © 2020 IEEE.

关键词： Mobile robots

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RAG-Based Crowdsourcing Task Decomposition via Masked Contrastive Learning With Prompts

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2024年

作者： Yang, Jing Wang, Xiao Zhao, Yu Liu, Yuhang 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 Qingdao Acad Intelligent Ind Qingdao 230031 Peoples R China Natl Univ Def Technol Natl Key Lab Informat Syst Engn Changsha 410000 Peoples R China Macau Univ Sci & Technol Macau 999078 Peoples R China Chinese Acad Sci Beijing Engn Res Ctr Intelligent Syst & Technol Beijing 100098 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Crowdsourcing is a critical technology in social manufacturing, which leverages an extensive and boundless reservoir of human resources to handle a wide array of complex tasks. The successful execution of these complex tasks relies on task decomposition (TD) and allocation, with the former being a prerequisite for the latter. Recently, pretrained language models-based methods have garnered significant attention. However, they are constrained to handling straightforward common-sense tasks due to their inherent restrictions involving limited and difficult-to-update knowledge as well as the presence of "hallucinations." To address these issues, we propose a retrieval-augmented generation-based crowdsourcing framework that reformulates TD as event detection from the perspective of natural language understanding. However, the existing detection methods fail to distinguish differences between event types and always depend on heuristic rules and external semantic analyzing tools. Therefore, we present a prompt-based contrastive learning framework for TD (PBCT), which incorporates a prompt-based trigger detector to overcome dependence. Additionally, trigger-attentive sentinel and masked contrastive learning are designed to provide varying attention to trigger and contextual features according to different event types. Extensive experiment results demonstrate our method is highly competitive in both supervised and zero-shot detection. A case study on printed circuit board design and manufacturing is used to validate its adaptability and scalab.lity in unfamiliar professional domains.

关键词： Crowdsourcing event detection event detection pretrained lan- guage models (PLMs) retrieval-augmented generation (RAG) retrieval-augmented generation (RAG) task decomposition task decomposition

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Boosting Black-Box Adversarial Attacks with Meta Learning

arXiv

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arXiv 2022年

作者： Fu, Junjie Sun, Jian Wang, Gang The State Key Lab of Intelligent Control and Decision of Complex Systems The School of Automation Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China

Deep neural networks (DNNs) have achieved remarkable success in diverse fields. However, it has been demonstrated that DNNs are very vulnerable to adversarial examples even in black-box settings. A large number of black-box attack methods have been proposed to in the literature. However, those methods usually suffer from low success rates and large query counts, which cannot fully satisfy practical purposes. In this paper, we propose a hybrid attack method which trains meta adversarial perturbations (MAPs) on surrogate models and performs black-box attacks by estimating gradients of the models. Our method uses the meta adversarial perturbation as an initialization and subsequently trains any black-box attack method for several epochs. Furthermore, the MAPs enjoy favorable transferability and universality, in the sense that they can be employed to boost performance of other black-box adversarial attack methods. Extensive experiments demonstrate that our method can not only improve the attack success rates, but also reduces the number of queries compared to other methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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Federated control: Toward Information Security and Rights Protection

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2021年第4期8卷 793-798页

作者： Wang, Fei-Yue Zhu, Jing Qin, Rui Wang, Xiao Hu, Bin Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China Nanjing Univ Aeronut & Astronaut Coll Automat Engn Nanjing Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Peoples R China Lanzhou Univ Gansu Prov Key Lab Wearable Comp Sch Informat Sci & Engn Lanzhou 730000 Gansu Peoples R China

Welcome to the fourth issue of IEEE Transactions on Computational Social systems (TCSS) this year. I am excited to share some great news.

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Federated Ecology: Steps Toward Confederated Intelligence

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2021年第2期8卷 271-278页

作者： Wang, Fei-Yue Qin, Rui Chen, Yizhu Tian, Yonglin Wang, Xiao Hu, Bin Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Sci & Technol China Dept Automat Hefei 230026 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Peoples R China Lanzhou Univ Sch Informat Sci & Engn Gansu Prov Key Lab Wearable Comp Lanzhou 730000 Peoples R China

Welcome to the second issue of IEEE Transactions on Computational Social systems (TCSS) this year. First, I am grateful to report that, as of February 7, 2021, the Citescore of TCSS has leapfrogged back to 5.8, a new high, which indicates the high quality and relevance of IEEE TCSS in the field of social computing and computational social systems research. Many thanks to all of you for your great effort and support. After the usual introduction of our 21 regular articles, I would like to discuss the topic of federated ecology, a new way to address the problem of isolated data island in the era of big data. The concept of federated ecology was proposed based on blockchain technology and the premise of controllab.e data privacy. It can realize data federation, federated service, and then confederated intelligence for releasing the great potentials of big data and artificial intelligence.

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