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arXiv 2025年

作者： Huang, Zhiwei Li, Jiaqi Zhong, Ping Fan, Rui The Department of Control Science & Engineering The College of Electronics & Information Engineering Tongji University Shanghai201804 China The School of Computer Science and Engineering Central South University Hunan Changsha410083 China The National Key Laboratory of Science and Technology on Automatic Target Recognition National University of Defense Technology Hunan Changsha410073 China The Department of Control Science & Engineering The College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China The National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Shaanxi Xi’an710049 China

LiDAR-camera extrinsic calibration (LCEC) is the core for data fusion in computer vision. Existing methods typically rely on customized calibration targets or fixed scene types, lacking the flexibility to handle variations in sensor data and environmental contexts. This paper introduces EdO-LCEC, the first environment-driven, online calibration approach that achieves human-like adaptability. Inspired by the human perceptual system, EdO-LCEC incorporates a generalizable scene discriminator to actively interpret environmental conditions, creating multiple virtual cameras that capture detailed spatial and textural information. To overcome cross-modal feature matching challenges between LiDAR and camera, we propose dual-path correspondence matching (DPCM), which leverages both structural and textural consistency to achieve reliable 3D-2D correspondences. Our approach formulates the calibration process as a spatial-temporal joint optimization problem, utilizing global constraints from multiple views and scenes to improve accuracy, particularly in sparse or partially overlapping sensor views. Extensive experiments on real-world datasets demonstrate that EdO-LCEC achieves state-of-the-art performance, providing reliable and precise calibration across diverse, challenging environments. Copyright © 2025, The Authors. All rights reserved.

关键词： Spatio-temporal data

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LDGCN: An Edge-End Lightweight Dual GCN Based on Single-Channel EEG for Driver Drowsiness Monitoring

arXiv

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arXiv 2024年

作者： Huang, Jingwei Wang, Chuansheng Huang, Jiayan Fan, Haoyi Grau, Antoni Zhang, Fuquan The College of Computer and Big Data Fuzhou University Fuzhou China The Department of Automatic Control Polytechnic University of Catalonia Barcelona Spain The New Engineering Industry College Putian University Putian China School of Computer and Artificial Intelligence Zhengzhou University China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China College of Computer and Control Engineering Minjiang University Fuzhou China Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu China Fuzhou Technology Innovation Center of Intelligent Manufacturing information System Minjiang University Fuzhou China Fujian Polytechnic Normal University Fujian Province University Fuzhou China

Driver drowsiness electroencephalography (EEG) signal monitoring can timely alert drivers of their drowsiness status, thereby reducing the probability of traffic accidents. Graph convolutional networks (GCNs) have shown significant advancements in processing the non-stationary, time-varying, and non-Euclidean nature of EEG signals. However, the existing single-channel EEG adjacency graph construction process lacks interpretability, which hinders the ability of GCNs to effectively extract adjacency graph features, thus affecting the performance of drowsiness monitoring. To address this issue, we propose an edge-end lightweight dual graph convolutional network (LDGCN). Specifically, we are the first to incorporate neurophysiological knowledge to design a Baseline Drowsiness Status Adjacency Graph (BDSAG), which characterizes driver drowsiness status. Additionally, to express more features within limited EEG data, we introduce the Augmented Graph-level Module (AGM). This module captures global and local information at the graph level, ensuring that BDSAG features remain intact while enhancing effective feature expression capability. Furthermore, to deploy our method on the fourth-generation Raspberry Pi, we utilize Adaptive Pruning Optimization (APO) on both channels and neurons, reducing inference latency by almost half. Experiments on benchmark datasets demonstrate that LDGCN offers the best trade-off between monitoring performance and hardware resource utilization compared to existing state-of-the-art algorithms. All our source code can be found at https://***/BryantDom/Driver-Drowsiness-Monitoring. Copyright © 2024, The Authors. All rights reserved.

关键词： Electroencephalography

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automatic removal algorithm of electrooculographic artifacts in non-invasive braincomputer interface based on independent component analysis

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Shengwu Yixue Gongchengxue Zazhi/Journal of Biomedical engineering 2022年第6期39卷 1074-1081页

作者： Song, Hao Xu, Song Liu, Guoming Liu, Jing Xiong, Peng Key Laboratory of Digital Medical Engineering of Hebei Province Hebei Baoding071002 China School of Quality and Technical Supervision Hebei University Hebei Baoding071002 China Beijing Aerospace Automatic Control Institute Beijing100854 China National Key Laboratory of Aerospace Intelligent Control Technology Beijing100854 China College of Computer and Cyber Security Hebei Normal University Shijiazhuang050024 China College of Electronic and Information Engineering Hebei University Hebei Baoding071002 China

The non-invasive brain-computer interface (BCI) has gradually become a hot spot of current research, and it has been applied in many fields such as mental disorder detection and physiological monitoring. However, the electroencephalography (EEG) signals required by the non-invasive BCI can be easily contaminated by electrooculographic (EOG) artifacts, which seriously affects the analysis of EEG signals. Therefore, this paper proposed an improved independent component analysis method combined with a frequency filter, which automatically recognizes artifact components based on the correlation coefficient and kurtosis dual threshold. In this method, the frequency difference between EOG and EEG was used to remove the EOG information in the artifact component through frequency filter, so as to retain more EEG information. The experimental results on the public datasets and our laboratory data showed that the method in this paper could effectively improve the effect of EOG artifact removal and improve the loss of EEG information, which is helpful for the promotion of non-invasive BCI. © 2022 West China Hospital, Sichuan Institute of Biomedical engineering. All rights reserved.

关键词： Electroencephalography

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A Learning-based Adaptive Compliance Method for Symmetric Bi-manual Manipulation

arXiv

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arXiv 2023年

作者： Cao, Yuxue Zhao, Wenbo Wang, Shengjie Zheng, Xiang Ma, Wenke Wang, Zhaolei Zhang, Tao Beijing Institute of Control Engineering China Department of Automation Tsinghua University China Institute for Interdisciplinary Information Sciences Tsinghua University China Department of Computer Science City University of Hong Kong Hong Kong Qian Xuesen Laboratory of Space Technology China Beijing Aerospace Automatic Control Institute China

Symmetric bi-manual manipulation is an essential skill in on-orbit operationsdue to its potent load capacity. Previous works have applied compliant controlto maintain the stability of manipulations. However, traditional methods haveviewed motion planning and compliant control as two separate modules, which canlead to conflicts with the simultaneous change of the desired trajectory andimpedance parameters in the presence of external forces and ***, the joint usage of these two modules requires experts to manuallyadjust parameters. To achieve high efficiency while enhancing adaptability, wepropose a novel Learning-based Adaptive Compliance algorithm (LAC) thatimproves the efficiency and robustness of symmetric bi-manual ***, the algorithm framework integrates desired trajectory generationand impedance-parameter adjustment under a unified framework to mitigatecontradictions and improve efficiency. Second, we introduce a centralizedActor-Critic framework with LSTM networks preprocessing the force states,enhancing the synchronization of bi-manual manipulation. When evaluated indual-arm peg-in-hole assembly experiments, our method outperforms baselinealgorithms in terms of optimality and robustness. Copyright © 2023, The Authors. All rights reserved.

关键词： Motion planning

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Game-Theoretic Protection Against Networked SIS Epidemics by Human Decision-Makers

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IFAC-PapersOnLine 2019年第34期51卷 145-150页

作者： Hota, Ashish R. Sundaram, Shreyas Automatic Control Laboratory ETH Zurich Switzerland School of Electrical and Computer Engineering Purdue University United States

We study networks of human decision-makers who independently decide how to protect themselves against Susceptible-Infected-Susceptible (SIS) epidemics. Motivated by studies in behavioral economics showing that humans perceive probabilities in a nonlinear fashion, we examine the impacts of such misperceptions on the equilibrium protection strategies. In our setting, nodes choose their curing rates to minimize the infection probability under the degree-based mean-field approximation of the SIS epidemic plus the cost of their selected curing rate. We establish the existence of a degree based equilibrium under both true and nonlinear perceptions of infection probabilities (under suitable assumptions). When the per-unit cost of curing rate is sufficiently high, we show that true expectation minimizers choose the curing rate to be zero at the equilibrium, while curing rate is nonzero under nonlinear probability weighting. © 2019

关键词： Epidemiology

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Performance guarantees of forward and reverse greedy algorithms for minimizing nonsupermodular nonsubmodular functions on a matroid

arXiv

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arXiv 2021年

作者： Karaca, Orcun Tihanyi, Daniel Kamgarpour, Maryam Automatic Control Laboratory D-ITET ETH Zürich Switzerland Electrical and Computer Engineering Department University of British Columbia Vancouver Canada

This letter studies the problem of minimizing increasing set functions, or equivalently, maximizing decreasing set functions, over the base of a matroid. This setting has received great interest, since it generalizes several applied problems including actuator and sensor placement problems in control theory, multi-robot task allocation problems, video summarization, and many others. We study two greedy heuristics, namely, the forward and the reverse greedy algorithms. We provide two novel performance guarantees for the approximate solutions obtained by these heuristics depending on both the submodularity ratio and the curvature. Copyright © 2021, The Authors. All rights reserved.

关键词： Combinatorial optimization

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Research on Black-Box Adversarial Attack Based on Transformer

Research on Black-Box Adversarial Attack Based on Transforme...

引用

Frontiers Technology of Information and computer (ICFTIC), IEEE International Conference on

作者： Wenjuan Zheng Hanxu Luo YuXin Ji Haipeng Li Longjian Cong Master of Computer Vision National Key Laboratory of Aerospace Intelligent Control Technology Beijing China Beijing Aerospace Automatic Control Institute Beijing China Department of Information Engineering Wuhan University of Technology Wuhan Hubei Province China

The classic adversarial samples of black-box attacks are all aimed at the models of Convolutional neural networks (CNNs), but they do not perform well on the new recognition networks based on Transformer. In this paper, we propose an adversarial sample generation algorithm based on Vision transformers (VITs)‘ self-attention mechanism and patch partition. We noticed that different blocks in the VIT have uneven attention distribution, so we first generated a patch-based attention map and performed threshold segmentation, which is used as a mask to perform data enhancement operations based on patches with high weight and patches with low weight, and then exchanged information between patches to generate adversarial samples. The experiment of simulating black-box attack shows that the adversarial samples generated by the algorithm in this paper have a high success rate in all kinds of models based on Transformer of attacks, and also perform well on CNNs.

关键词： Closed box Benchmark testing Transformers Partitioning algorithms Convolutional neural networks Convergence

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Microengineered platforms for characterizing the contractile function of in vitro cardiac models

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Microsystems & Nanoengineering 2022年第1期8卷 29-50页

作者： Wenkun Dou Manpreet Malhi Qili Zhao Li Wang Zongjie Huang Junhui Law Na Liu Craig A.Simmons Jason T.Maynes Yu Sun Department of Mechanical and Industrial Engineering University of TorontoTorontoON M5S 3G8Canada Program in Molecular Medicine The Hospital for Sick ChildrenTorontoON M5G 1X8Canada Department of Biochemistry University of TorontoTorontoON M5S 1A8Canada Institute of Robotics and Automatic Information System and the Tianjin Key Laboratory of Intelligent Robotics Nankai UniversityTianjin 300350China School of Mechanical&Automotive Engineering Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353China School of Mechatronics Engineering and Automation Shanghai UniversityShanghai 200444China Institute of Biomedical Engineering University of TorontoTorontoON M5S 3G9Canada Translational Biology&Engineering Program Ted Rogers Centre for Heart ResearchTorontoON M5G 1M1Canada Department of Anesthesiology and Pain Medicine University of TorontoTorontoON M5S 1A8Canada Department of Anesthesia and Pain Medicine The Hospital for Sick ChildrenTorontoON M5G 1X8Canada Department of Electrical and Computer Engineering University of TorontoTorontoON M5S 3G4Canada Department of Computer Science University of TorontoTorontoON M5T 3A1Canada

Emerging heart-on-a-chip platforms are promising approaches to establish cardiac cell/tissue models in vitro for research on cardiac physiology,disease modeling and drug cardiotoxicity as well as for therapeutic *** still exist in obtaining the complete capability of in situ sensing to fully evaluate the complex functional properties of cardiac cell/tissue *** to contractile strength(contractility)and beating regularity(rhythm)are particularly important to generate accurate,predictive *** new platforms and technologies to assess the contractile functions of in vitro cardiac models is essential to provide information on cell/tissue physiologies,drug-induced inotropic responses,and the mechanisms of cardiac *** this review,we discuss recent advances in biosensing platforms for the measurement of contractile functions of in vitro cardiac models,including single cardiomyocytes,2D monolayers of cardiomyocytes,and 3D cardiac *** characteristics and performance of current platforms are reviewed in terms of sensing principles,measured parameters,performance,cell sources,cell/tissue model configurations,advantages,and *** addition,we highlight applications of these platforms and relevant discoveries in fundamental investigations,drug testing,and disease ***,challenges and future outlooks of heart-on-a-chip platforms for in vitro measurement of cardiac functional properties are discussed.

关键词： cardiac contractile regularity

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A Review of RPL Based Routing Protocols in Vehicular Ad Hoc Networks (VANET)

A Review of RPL Based Routing Protocols in Vehicular Ad Hoc ...

引用

Mechatronic Systems Supervision (IW_MSS), IEEE International Workshop on

作者： Wafa Akermi Nouha Alyaoui Awatef Benfradj Guiloufi Karim Chabir Automatic Industrial Systems Department Higher Institute of Applied Sciences and Technology Gafsa University Gafsa Tunisia Laboratory of Modeling Analysis and Control of Systems (MACS) National Engineering School of Gabes (ENIG) Gabes University Gabes Tunisia Computer Science and Telecommunications Department Higher Institute of Applied Sciences and Technology Gafsa University Gafsa Tunisia Preparatory Institute for Engineering Studies of Gabes Laboratory of Modeling Analysis and Control of Systems (MACS) National Engineering School of Gabes (ENIG) Gabes University Gabes Tunisia

In the present day, the Internet of Things is becoming more and more popular. Enabling continuous connectivity and packet routing in this kind of network is difficult because of the resource limitations that are a feature of IoT. Reduce end-to-end latency, conserve energy, and balance network load are the three basic objectives of lightweight routing protocol. In this paper, we give a quick review of the IPv6 Routing Protocol for Low-Power and Lossy Networks-based routing protocols of VANET already in use and discuss their advantages and disadvantages. More specifically, we examine a few chosen VANET routing protocols and contrast their performance based on metrics like packet delivery ratio, Throughput, End to end delay, Routing- Cost, etc.

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Scalable Multi-Agent Reinforcement Learning for Residential Load Scheduling Under Data Governance

SSRN

引用

SSRN 2023年

作者： Qin, Zhaoming Dong, Nanqing Liu, Di Cao, Junwei Automatic Control Laboratory EPFL Lausanne1015 Switzerland Department of Computer Science University of Oxford OxfordOX1 3QD United Kingdom Department of Electrical Engineering Tsinghua University Beijing100084 China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

As a data-driven approach, multi-agent reinforcement learning (MARL) has made remarkable advances in solving cooperative residential load scheduling problems. In a simplified scenario, centralized training, the most common paradigm for MARL, precludes large-scale deployment of MARL in communication-constrained cloud-edge environments. As a remedy, distributed training shows unparalleled advantages in real-life application scenarios compared with its centralized counterpart. However, distributed training still faces the challenge of system scalability, e.g., the high communication burden during coordinating individual agents, and needs to comply with data governance in terms of privacy. In this work, we propose a novel MARL solution to address these two practical issues. The proposed solution is based on actor-critic methods, where the global critic is a learned function of individual critics computed solely based on local observations of residences. In this way, the private data of residences is preserved completely and the communication cost is reduced significantly. The simulation experiments demonstrate that the proposed framework can achieve comparable performance to the state-of-the-art actor-critic framework without data governance and communication constraints. © 2023, The Authors. All rights reserved.

关键词： Edge computing

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