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American control Conference (ACC)

作者： Shivam Bajaj Shaunak D. Bopardikar Alexander Von Moll Eric Torng David W. Casbeer Department of Electrical and Computer Engineering Michigan State University Control Science Center Air Force Research Laboratory Department of Computer Science and Engineering Michigan State University

We consider a perimeter defense problem in a planar conical environment comprising a turret that has a finite range and non-zero startup time. The turret seeks to defend a concentric perimeter against N ≥ 2 intruders. Upon release, each intruder moves radially towards the perimeter with a fixed speed. To capture an intruder, the turret's angle must be aligned with that of the intruder's angle and must spend a specified startup time at that orientation. We address offline and online versions of this optimization problem. Specifically, in the offline version, we establish that in general parameter regimes, this problem is equivalent to solving a Travelling Repairperson Problem with Time Windows (TRP-TW). We then identify specific parameter regimes in which there is a polynomial time algorithm that maximizes the number of intruders captured. In the online version, we present a competitive analysis technique in which we establish a fundamental guarantee on the existence of at best (N – 1)-competitive algorithms. We also design two online algorithms that are provably 1 and 2-competitive in specific parameter regimes.

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Carousel Storage and Picking Scheduling Issues: A Review

Carousel Storage and Picking Scheduling Issues: A Review

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Qin, Shujin Yang, Xinyi Wang, Jiacun Liu, Shixin Guo, Xiwang Qi, Liang Research Center of the Economic and Social Development of Henan East Provincial Joint Shangqiu Normal University Shangqiu476000 China Monmouth University Department of Computer Science and Software Engineering West Long BranchNJ07764 United States College of Information Science and Engineering Northeast University Shenyang110819 China College of Information and Control Engineering Liaoning Petrochemical University Fushun113001 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

ISBN: (纸本)9798350337020

This paper classifies and summarises the historical literature on carousel systems in automated storage and retrieval systems in recent years. As an automated storage and retrieval system for distribution centers and production facilities, carousels facilitate the storage and dispatching of goods, significantly improving warehouse turnover efficiency. Their performance have been investigated by many scholars and experts. As carousels evolve and upgrade, more and more innovative algorithms have been used to improve the efficiency of outbound carousel storage. In this paper, we collate articles investigating how the carousel system is stored inbound versus retrieved outbound. We then discuss articles on the dual-command model of automatic storage retrieval systems as a whole. By reviewing over 50 papers, we summarise research on how to store and unload goods, focusing on the performance of automatic storage retrieval systems under dual-command conditions. On this basis, we review the current research's limitations and suggest future research directions. © 2023 IEEE.

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Progress of Brain Network Studies on Anesthesia and Consciousness: Framework and Clinical Applications

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engineering 2023年第1期20卷 77-95页

作者： Jun Liu Kangli Dong Yi Sun Ioannis Kakkos Fan Huang Guozheng Wang Peng Qi Xing Chen Delin Zhang Anastasios Bezerianos Yu Sun Key Laboratory for Biomedical Engineering of Ministry of Education of China Zhejiang UniversityHangzhou 310007China Department of Neurology Sir Run Run Shaw HospitalSchool of MedicineZhejiang UniversityHangzhou 310020China School of Electrical and Computer Engineering National Technical University of AthensAthens 15780Greece Department of Control Science and Engineering College of Electronics and Information EngineeringTongji UniversityShanghai 200092China Department of Anesthesiology The First Affiliated HospitalSchool of MedicineZhejiang UniversityHangzhou 310003China The N1 Institute for Health Center for Life SciencesNational University of SingaporeSingapore 117456Singapore

Although the relationship between anesthesia and consciousness has been investigated for decades, our understanding of the underlying neural mechanisms of anesthesia and consciousness remains rudimentary, which limits the development of systems for anesthesia monitoring and consciousness evaluation. Moreover, the current practices for anesthesia monitoring are mainly based on methods that do not provide adequate information and may present obstacles to the precise application of anesthesia. Most recently, there has been a growing trend to utilize brain network analysis to reveal the mechanisms of anesthesia, with the aim of providing novel insights to promote practical application. This review summarizes recent research on brain network studies of anesthesia, and compares the underlying neural mechanisms of consciousness and anesthesia along with the neural signs and measures of the distinct aspects of neural activity. Using the theory of cortical fragmentation as a starting point, we introduce important methods and research involving connectivity and network analysis. We demonstrate that whole-brain multimodal network data can provide important supplementary clinical information. More importantly, this review posits that brain network methods, if simplified, will likely play an important role in improving the current clinical anesthesia monitoring systems.

关键词： Anesthesia Brain network Connectivity Graph theoretical analysis Clinical monitoring system

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Multi-Vehicle Trajectory Planning at V2I-enabled Intersections based on Correlated Equilibrium

arXiv

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arXiv 2024年

作者： Wang, Wenyuan Yi, Peng Hong, Yiguang Department of Control Science and Engineering Tongji University China Shanghai Research Institute for Intelligent Autonomous Systems Tongji University China National Key Laboratory of Autonomous Intelligent Unmanned Systems China Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education China

Generating trajectories that ensure both vehicle safety and improve traffic efficiency remains a challenging task at intersections. Many existing works utilize Nash equilibrium (NE) for the trajectory planning at intersections. However, NE-based planning can hardly guarantee that all vehicles are in the same equilibrium, leading to a risk of collision. In this work, we propose a framework for trajectory planning based on Correlated Equilibrium (CE) when V2I communication is also enabled. The recommendation with CE allows all vehicles to reach a safe and consensual equilibrium and meanwhile keeps the rationality as NE-based methods that no vehicle has the incentive to deviate. The Intersection Manager (IM) first collects the trajectory library and the personal preference probabilities over the library from each vehicle in a low-resolution spatial-temporal grid map. Then, the IM optimizes the recommendation probability distribution for each vehicle's trajectory by minimizing overall collision probability under the CE constraint. Finally, each vehicle samples a trajectory of the low-resolution map to construct a safety corridor and derive a smooth trajectory with a local refinement optimization. We conduct comparative experiments at a crossroad intersection involving two and four vehicles, validating the effectiveness of our method in balancing vehicle safety and traffic efficiency. Copyright © 2024, The Authors. All rights reserved.

关键词： Trajectories

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A Stackelberg Game Framework for Energy Internet System By Operator Approach

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IEEE Transactions on Network science and engineering 2025年

作者： Chen, Yue Yi, Peng Lei, Jinlong Li, Xiangjun Tongji University Department of Control Science and Engineering Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education China China Electric Power Research Institute National Key Laboratory of Renewable Energy Grid-Integration Energy Storage and Electrical Engineering Department Beijing100192 China

This paper proposes a Stackelberg game framework for managing an Energy Internet System that integrates renewable energy generation, energy conversion, and energy trading. The system comprises hubs controlled by competing energy companies, incorporating both external energy trading and peer-to-peer energy exchanges. It involves multiple stakeholders whose decisions are interdependent, creating challenges in decision-making. To tackle these challenges, we propose a networked Stackelberg game framework with two main objectives. First, from the energy companies' perspective, the companies make decisions by considering strategic interactions with other companies while accounting for the best responses of passive consumers within peer-to-peer constraints. Second, from the consumer side, consumers adjust their decisions based on their observations of the companies' strategies. The framework adopts a hierarchical decision-making structure in which energy companies act as leaders, formulating strategies that anticipate consumer demand responses, while competing within a Nash game. Meanwhile, consumer clusters, managed by demand management centers, modify their energy usage based on an incentive price mechanism. To search for the equilibrium, we develop an operator-theoretic approach that combines implicit gradient methods with proximal operator splitting techniques, and prove its convergence. Numerical studies and simulations on a combined IEEE 37-bus and gas source model with multiple energy hubs validate the model's effectiveness and the efficiency of the proposed algorithms. The results demonstrate the operational efficiency and strategic stability of the system, emphasizing the advantages of active market participation for both energy companies and consumers. © 2013 IEEE.

关键词： Demand response

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Forecasting the Temperature of BEV Battery Pack Based on Field Testing Data 1

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3rd EAI International Conference on Edge Computing and IoT, EAI ICECI 2022

作者： Chou, Ka Seng Wong, Kei Long Aguiari, Davide Tse, Rita Tang, Su-Kit Pau, Giovanni Faculty of Applied Sciences Macao Polytechnic University China Department of Computer Science and Engineering Alma Mater Studiorum University of Bologna Bologna Italy Autonomous Robotics Research Center Technology Innovation Institute Abu Dhabi United Arab Emirates UCLA Samueli Computer Science University of California Los Angeles Los Angeles United States

ISBN: (数字)9783031289903

ISBN: (纸本)9783031289897

Monitoring electric vehicles’ battery situation and indicating the state of health is still challenging. Temperature is one of the critical factors determining battery degradation over time. We have collected more than 2.3 million discharging samples via a custom Internet of Thing device for more than one year to build a machine-learning model that can forecast the battery pack’s average temperature in real-world driving. Our best Bi-LSTM model achieved the mean absolute error of 2.92 ∘ C on test data and 1.7 ∘ C on cross-validation for prediction of 10 min on the battery pack’s temperature. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Lithium-ion batteries

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Stealthy Attacks on Sampled-Data control and Encryption-Based Countermeasures

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IEEE Transactions on Automatic control 2025年

作者： Shang, Jun Li, Yuzhe Chen, Tongwen Tongji University Department of Control Science and Engineering Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai200092 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110004 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada

This paper investigates stealthy attacks on sampled-data control systems, where a continuous process is sampled periodically, and the resultant discrete output and control signals are transmitted through dual channels in wireless communication networks. Acknowledging the vulnerability of these transmitted data to cyber threats, we analyze two types of stealthy attacks that compromise either one or both channels. We explicitly present the necessary and sufficient conditions for the existence of stealthy single-channel attacks. To mitigate these security risks, we implement encryption-based countermeasures, including linear time-invariant and periodic encryption methods. We detail sufficient conditions under which these encryption strategies effectively thwart single- and dual-channel stealthy attacks. The effectiveness of these approaches and the theoretical findings are demonstrated through numerical simulations. © 1963-2012 IEEE.

关键词： Cyber attacks

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From Unmanned Systems to autonomous Intelligent Systems

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engineering 2022年第5期8卷 16-19页

作者： Jie Chen Jian Sun Gang Wang Department of Control Science and Engineering Tongji UniversityShanghai 201804China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai 200092China Key Lab of Intelligent Control and Decision of Complex Systems Beijing Institute of TechnologyBeijing 100081China Beijing Institute of Technology Chongqing Innovation Center Chongqing 401120China

*** Artificial intelligence(AI)is a rapidly growing field of technol-ogy,which“will enliven inert objects,much as electricity did more than a century *** that we formerly electrified will now cognitize”[1].AI advances are constantly pushing the frontier of what machines can *** attention is being placed on AI research,as well as its development and deployment by commer-cial investors,defense strategists,and policy makers[2].

关键词： frontier inert policy

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Trust Recognition in Human-Robot Cooperation Using EEG

arXiv

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arXiv 2024年

作者： Xu, Caiyue Zhang, Changming Zhou, Yanmin Wang, Zhipeng Lu, Ping He, Bin The Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai201804 China National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai201210 China Frontiers Science Center for Intelligent Autonomous Systems Shanghai201210 China

Collaboration between humans and robots is becoming increasingly crucial in our daily life. In order to accomplish efficient cooperation, trust recognition is vital, empowering robots to predict human behaviors and make trust-aware decisions. Consequently, there is an urgent need for a generalized approach to recognize human-robot trust. This study addresses this need by introducing an EEG-based method for trust recognition during human-robot cooperation. A human-robot cooperation game scenario is used to stimulate various human trust levels when working with robots. To enhance recognition performance, the study proposes an EEG Vision Transformer model coupled with a 3-D spatial representation to capture the spatial information of EEG, taking into account the topological relationship among electrodes. To validate this approach, a public EEG-based human trust dataset called EEGTrust is constructed. Experimental results indicate the effectiveness of the proposed approach, achieving an accuracy of 74.99% in slice-wise cross-validation and 62.00% in trial-wise cross-validation. This outperforms baseline models in both recognition accuracy and generalization. Furthermore, an ablation study demonstrates a significant improvement in trust recognition performance of the spatial representation. The source code and EEGTrust dataset are available at https://***/CaiyueXu/EEGTrust. © 2024, CC BY-NC-SA.

关键词： Topology

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Multi-Target Tracker for Low Light Vision

Multi-Target Tracker for Low Light Vision

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International Conference on Advanced Robotics (ICAR)

作者： Nadya Abdel Madjid Arjun Sharma Bilal Hassan Naoufel Werghi Jorge Dias Majid Khonji Department of Electrical Engineering and Computer Science Center for Autonomous Robotic Systems (KUCARS) Khalifa University of Science and Technology Abu Dhabi UAE

Recently, remarkable progress has been achieved in addressing the problem of multi-object tracking (MOT), especially in the context of autonomous vehicles (AV). One of the prospective domains of MOT tracking is thermal infrared (TIR) tracking, which can equip an AV with the ability to track pedestrians and vehicles in low light conditions. In this paper, we propose a multi-object tracker for TIR images with a focus on simple and light-weight algorithmic solution. We base our solution on DeepSORT algorithm and extend it to TIR tracking of both pedestrians and vehicles. To adopt DeepSORT algorithm, we design an appearance descriptor suitable for the association problem for TIR images. Furthermore, to address the problem of missing association and detection, we propose a fusion block to merge short tracklets belonging to the same object in one track. We evaluate the tracker on CAMEL dataset and experimentally on the sequences we collected using an IR-camera. The tracker's code is available at ***/AV-Lab/IR_tracking.

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