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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 available. 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 scalability 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 controllable 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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Learning Greenhouse Climate control Policy from Monitored Data

Learning Greenhouse Climate Control Policy from Monitored Da...

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2022 Chinese Automation Congress, CAC 2022

作者： Zhao, Xiaoxuan Wang, Haoyu Wang, Xiujuan Lewlomphaisarl, Udom Li, Dong Hua, Jing Kang, Mengzhen The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Institute of Automation Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Beijing Engineering Research Center of Intelligent Systems and Technologies Beijing China National Electronics and Computer Technology Center Pathum Thani Thailand Institute of Digital Agriculture Zhejiang Academy of Agricultural Sciences Hangzhou China Qingdao AgriTech Co. Ltd. Qingdao China

ISBN: (纸本)9781665465335

The knowledge of solar greenhouse growers on environment control plays an important role in greenhouse production and management. We proposed a way to extract the control strategies from the monitored data of greenhouses by building a long short-term memory (LSTM) model. The result is verified according to the real monitored data of a solar greenhouse, which shows that the model can learn the control strategy of a ventilator in the solar greenhouse. Through monitored data and models, the knowledge of greenhouse ventilation control can be learned, and automatic control can be achieved in a greenhouse with a similar configuration. © 2022 IEEE.

关键词： Greenhouses

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MetaMining: Mining in the Metaverse

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2023年第6期53卷 3858-3867页

作者： Liu, Kunhua Chen, Long Li, Lingxi Ren, Huaiwei Wang, Fei-Yue Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510275 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100864 Peoples R China Indiana Univ Purdue Univ Indianapolis Purdue Sch Engn & Technol Dept Elect & Comp Engn Indianapolis IN 46202 USA China Coal Sci & Engn Grp Sci & Technol Ctr Beijing 100013 Peoples R China Qingdao Acad Intelligent Ind Parallel BlockchainTechnol Innovat Ctr Qingdao 266109 Peoples R China Natl Univ Def Technol Res Ctr Mil Computat Expt & Parallel Syst Changsha 410073 Peoples R China

Mines are one of the important energy sources in the world. Due to mining areas are often affected by adverse weather and environmental conditions (sand, dust, extreme cold, heavy snow, etc.), the efficiency and security of mining are low. As a new, efficient, prospective mode, Metaverse has been studied and successfully applied in various industries. However, there is still no research about its usage in mines. In this article, we apply Metaverse to mining and propose MetaMining. We present its definition and analyze the functions it could perform. Furthermore, we propose its development phases and architecture. Specifically, MetaMining is a mode, which aims to achieve high-efficiency, high-security mining in the physical world through the interaction between the physical world and the virtual world. Its development phases involve three steps: 1) digital twins;2) digital natives;and 3) surreality. Its architecture consists of three components: 1) human world;2) virtual mining system;and 3) physical mining system. In addition, we analyze key technologies and possible challenges in the construction of MetaMining.

关键词： Metaverse Data mining Task analysis Autonomous vehicles Safety Mining equipment Snow Artificial intelligence (AI) mining industry vehicles

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Fighting fire with fire: A spatial-frequency ensemble relation network with generative adversarial learning for adversarial image classification

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INTERNATIONAL JOURNAL OF intelligent systems 2021年第5期36卷 2081-2121页

作者： Zheng, Wenbo Yan, Lan Gou, Chao Wang, Fei-Yue Xi An Jiao Tong Univ Sch Software Engn Xian 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 Peoples R China Sun Yat Sen Univ Sch Intelligent Syst Engn Guangzhou Peoples R China

Adversarial images generated by generative adversarial networks are not close to any existing benign images, and contain nonrobust features that have been identified as critical to the robustness of a machine learning model. Since adversarial images have an underlying distribution that differs from normal images, these kinds of images can offer valuable features for training a robust model. To deal with these special features, we focus on a novel machine learning task of adversarial images classification, where adversarial images can be used to investigate the problem of classifying adversarial images themselves. In the setting of this novel task, adversarial images are the ONLY kind of data used in training and testing, rather than not just a set of testing images as usual. To this end, we propose a novel spatial-frequency ensemble relation network with generative adversarial learning. First, we present a spatial-frequency ensemble representation learning to extract the feature of training images. Second, we design a meta-learning-based relation model to gain the relationship between images. Third, to achieve a robust model, we utilize generative adversarial learning and transform the relationship into a Jacobian matrix. Finally, we design a discriminator model that determines whether an adversarial image is from the matching category or not. Experimental results demonstrate that our approach achieves significantly higher performance compared with other state-of-the-arts.

关键词： adversarial examples ensemble learning generative adversarial learning image classification meta-learning

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