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arXiv 2024年

作者： Hu, Jia Lei, Mingyue Li, Duo Li, Zhenning So, Jaehyun Wang, Haoran Key Laboratory of Road and Traffic Engineering The Ministry of Education Institute for Advanced Study Tongji University 4800 Cao’an Road Shanghai China Key Laboratory of Road and Traffic Engineering The Ministry of Education Tongji University 4800 Cao’an Road Shanghai China Department of Civil and Geospatial Engineering Newcastle University Newcastle upon TyneNE1 7RU United Kingdom State Key Laboratory of Internet of Things for Smart City Departments of Civil and Environmental Engineering and Computer and Information Science University of Macau Avenida da Universidade Taipa China Department of Transportation Systems Engineering Ajou University 206 World cup-ro Yeongtong-gu Gyeonggi-do Suwon-si16499 Korea Republic of State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha410082 China

Personalization is crucial for the widespread adoption of advanced driver assistance system. To match up with each user's preference, the online evolution capability is a must. However, conventional evolution methods learn from naturalistic driving data, which requires a lot computing power and cannot be applied online. To address this challenge, this paper proposes a lesson learning approach: learning from driver's takeover interventions. By leveraging online takeover data, the driving zone is generated to ensure perceived safety using Gaussian discriminant analysis. Real-time corrections to trajectory planning rewards are enacted through apprenticeship learning. Guided by the objective of optimizing rewards within the constraints of the driving zone, this approach employs model predictive control for trajectory planning. This lesson learning framework is highlighted for its faster evolution capability, adeptness at experience accumulating, assurance of perceived safety, and computational efficiency. Simulation results demonstrate that the proposed system consistently achieves a successful customization without further takeover interventions. Accumulated experience yields a 24% enhancement in evolution efficiency. The average number of learning iterations is only 13.8. The average computation time is 0.08 seconds. © 2024, CC BY.

关键词： Discriminant analysis

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Optimizing the performance of the feedback controller for state-based switching bilinear systems

Optimizing the performance of the feedback controller for st...

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作者： Lin, Shu Li, Dewei Schutter, Bart School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China Department of Automation Shanghai Jiao Tong University Shanghai China Delft Center for Systems and Control Delft University of Technology Delft Netherlands

This article is concerned with the design and performance optimization of feedback controllers for state-based switching bilinear systems (SBLSs), where subsystems take the form of bilinear systems in different state space polyhedra. First, by further dividing the subregions into smaller regions and designing region-dependent feedback controllers in the resulting regions, the SBLSs can be transformed into corresponding switching linear systems (SLSs). Then, for these SLSs, by imposing contractility conditions on the Lyapunov functions, an upper bound on the infinite horizon quadratic cost can be obtained. Optimizing this upper bound yields the controller design. The optimization problem is formulated as a linear matrix inequalities optimization problem, which can be solved efficiently. Finally, the stability of the close-loop system under the proposed controller is established step by step through a decreasing overall Lyapunov function. © 2020 John Wiley & Sons, Ltd.

关键词： Lyapunov functions

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Parallel Planning:A New Motion Planning Framework for Autonomous Driving

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IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 236-246页

作者： Long Chen Xuemin Hu Wei Tian Hong Wang Dongpu Cao Fei-Yue Wang IEEE School of Data and Computer Science Sun Yat-sen University the School of Computer Science and Information Engineering Hubei University Institute of Measurement and Control Systems Karlsruhe Institute of Technology Department of Mechanical and Mechatronics Engineering University of Waterloo the State Key Laboratory of Management and Control for Complex Systems.Institute of Automation Chinese Academy of Sciences the Research Center for Military Computational Experiments and Parallel Systems Technology National University of Defense Technology

Motion planning is one of the most significant technologies for autonomous driving. To make motion planning models able to learn from the environment and to deal with emergency situations, a new motion planning framework called as"parallel planning" is proposed in this paper. In order to generate sufficient and various training samples, artificial traffic scenes are firstly constructed based on the knowledge from the reality.A deep planning model which combines a convolutional neural network(CNN) with the Long Short-Term Memory module(LSTM) is developed to make planning decisions in an end-toend mode. This model can learn from both real and artificial traffic scenes and imitate the driving style of human ***, a parallel deep reinforcement learning approach is also presented to improve the robustness of planning model and reduce the error rate. To handle emergency situations, a hybrid generative model including a variational auto-encoder(VAE) and a generative adversarial network(GAN) is utilized to learn from virtual emergencies generated in artificial traffic scenes. While an autonomous vehicle is moving, the hybrid generative model generates multiple video clips in parallel, which correspond to different potential emergency scenarios. Simultaneously, the deep planning model makes planning decisions for both virtual and current real scenes. The final planning decision is determined by analysis of real observations. Leveraging the parallel planning approach, the planner is able to make rational decisions without heavy calculation burden when an emergency occurs.

关键词： Autonomous driving artificial traffic scene deep learning emergencies motion planning parallel planning

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CEBRA Method: Decoding Brain Activity for Advanced Brain-computer Interface Technology

CEBRA Method: Decoding Brain Activity for Advanced Brain-Com...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Jingcheng Yang Frank Kulwa Xuanwei Liu Yiqing Lu Yufa Fu Guanglin Li Yaping Huai Xin Zhang Yongcheng Li CAS Key Laboratory of Human-Machine Intelligence Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences (CAS) Shenzhen China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China Shenzhen Dapeng New District Nanao People’s Hospital Shenzhen China Department of Rehabilitation Medicine Shenzhen Longhua District Central Hospital Shenzhen China Brain Cognition and Brain-Computer Intelligence Integration Group Kunming University of Science and Technology Kunming China Shandong Zhongke Advanced Technology Co. Ltd. Jinan China

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

The emerging neurorehabilitation technology, Brain-computer Interface (BCI), provides a novel prospect for stroke recovery. However, decoding brain activity during the movement present substantial challenges, and feature extraction is crutial to build a better decoder. In this study the CEBRA method were employed to extract features first based on electroencephalogram(EEG) data during Motor Execution(ME) and Motor lmagery (Ml) tasks for 20 participants (including 10 stroke patients). The results revealed that, in MI tasks, CEBRA-RF (Random Forests) achieved an average classification accuracy of 91.33%, with an average F1-score of 91.19%, and CEBRA-SVM (Support Vector Machine) achieved an average classification accuracy of 91.32%, with an average F1-score of 90.83%. Compared to other conventional feature extraction methods, CEBRA shows significantly higher accuracy (t-test, p<0.01). However, in ME tasks, CEBRA-RF achieved an average classification accuracy of 75.67%, with an average F1-score of 75.39%, and CEBRA-SVM achieved an average classification accuracy of 76.13%, with an average F1-score of 75.80%. Nevertheless, no significant differences were observed compared to other feature extraction methods. These findings demonstrate the potential of CEBRA in decoding patients’ brain activity. The results of this study hold promise in addressing the current challenges of low decoding accuracy in BCI systems, offering a new approach for designing BCI-assisted rehabilitation systems for stroke patients.

关键词： Support vector machines Brain Accuracy Stroke (medical condition) Feature extraction Motors Brain-computer interfaces Neurorehabilitation Decoding Paralysis

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A Learning Convolutional Neural Network Approach for Network Robustness Prediction

arXiv

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arXiv 2022年

作者： Lou, Yang Wu, Ruizi Li, Junli Wang, Lin Li, Xiang Chen, Guanrong The Department of Computing and Decision Sciences Lingnan University Hong Kong The Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China The College of Computer Science Sichuan Normal University Chengdu610066 China The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China The Department of Control Science and Engineering Tongji University Shanghai200240 China The Department of Electrical Engineering City University of Hong Kong Hong Kong

Network robustness is critical for various societal and industrial networks again malicious attacks. In particular, connectivity robustness and controllability robustness reflect how well a networked system can maintain its connectedness and controllability against destructive attacks, which can be quantified by a sequence of values that record the remaining connectivity and controllability of the network after a sequence of node- or edge-removal attacks. Traditionally, robustness is determined by attack simulations, which are computationally very time-consuming or even practically infeasible. In this paper, an improved method for network robustness prediction is developed based on learning feature representation using convolutional neural network (LFR-CNN). In this scheme, higher-dimensional network data are compressed to lower-dimensional representations, and then passed to a CNN to perform robustness prediction. Extensive experimental studies on both synthetic and real-world networks, both directed and undirected, demonstrate that 1) the proposed LFR-CNN performs better than other two state-of-the-art prediction methods, with significantly lower prediction errors;2) LFR-CNN is insensitive to the variation of the network size, which significantly extends its applicability;3) although LFR-CNN needs more time to perform feature learning, it can achieve accurate prediction faster than attack simulations;4) LFR-CNN not only can accurately predict network robustness, but also provides a good indicator for connectivity robustness, better than the classical spectral measures. © 2022, CC BY.

关键词： Complex networks

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Motion Planning Integrated with Vehicle-Terrain Interactions for Off-Road Autonomous Ground Vehicles

Motion Planning Integrated with Vehicle-Terrain Interactions...

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International Conference on Intelligent Transportation

作者： Jianhua Yin Xianyuan Zhan Tianying Ji Bingrong Xu Yi He Daqing Zhang Lingxi Li Intelligent Transportation Systems Research Center Wuhan University of Technology Wuhan China Engineering Research Center of Transportation Information and Safety Ministry of Education Wuhan China Chongqing Research Institute Wuhan University of Technology Chongqing China Institute for AI Industry Research Tsinghua University Beijing China Shanghai Artificial Intelligence Laboratory Shanghai China Department of Computer Science and Technology Tsinghua University Beijing China School of Automation Wuhan University of Technology Wuhan China College of Mechanical and Electrical Engineering Hunan Agricultural University Changsha China Sunward Intelligent Equipment Co. Ltd. Changsha China Elmore Family School of Electrical and Computer Engineering College of Engineering Purdue University IN USA

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Off-road autonomous ground vehicles (AGVs) work in unstructured environments with varied elevation and deformable ground, where vehicle-terrain interactions are more complex resulting in unpredictable dynamics behavior. Current sensor-based hazards/obstacle identification methods mainly focus on geometric hazards/obstacles with explicit geometry, which might be adequate for vehicles in on-road conditions. However, in the off-road environment, vehicle-terrain interactions could introduce non-geometric hazards that cannot be detected by current geometric hazards-oriented sensing techniques, e.g. vehicle stuck in mud. To enable AGVs to traverse the target terrain and meet the goal while avoiding non-geometric hazards, this work proposes a motion planning method integrated with coupled vehicle-terrain dynamics response. Specifically, terramechanics-based modeling and simulation (M&S) is utilized to characterize vehicle-terrain interactions. Based on the dynamics response, a reinforcement learning (RL) algorithm with a specialized reward function is used to navigate the AGV to find the goal with high mobility and safety. We demonstrate the proposed method with two case studies, 1) high mobility is the foremost concern with less safety requirement, and 2) both high mobility and safety are required. The results show that the proposed motion planning method is effective for navigating the AGV in off-road terrains.

关键词： Dynamic response Geometry Navigation Heuristic algorithms Reinforcement learning Land vehicles Hazards Planning Sensors Vehicle dynamics

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BESS aided reconfigurable energy supply using deep reinforcement learning for 5G and beyond

arXiv

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arXiv 2021年

作者： Yuan, Hao Tang, Guoming Guo, Deke Wu, Kui Shao, Xun Yu, Keping Wei, Wei Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology Hunan Changsha China Peng Cheng Laboratory Guangdong Shenzhen China Department of Computer Science University of Victoria VictoriaBC Canada School of Regional Innovation and Social Design Engineering Kitami Institute of Technology Kitami Japan Global Information and Telecommunication Institute Waseda University Shinjuku Tokyo Japan School of Computer Science and Engineering Xi'an University of Technology Xi'an China

The year of 2020 has witnessed the unprecedented development of 5G networks, along with the widespread deployment of 5G base stations (BSs). Nevertheless, the enormous energy consumption of BSs and the incurred huge energy cost have become significant concerns for the mobile operators. As the continuous decline of the renewable energy cost, equipping the power-hungry BSs with renewable energy generators could be a sustainable solution. In this work, we propose an energy storage aided reconfigurable renewable energy supply solution for the BS, which could supply clean energy to the BS and store surplus energy for backup usage. Specifically, to flexibly reconfigure the battery's discharging/charging operations, we propose a deep reinforcement learning based reconfiguring policy, which can adapt to the dynamical renewable energy generations as well as the varying power demands. Our experiments using the real-world data on renewable energy generations and power demands demonstrate that, our reconfigurable power supply solution can achieve an energy saving ratio of 74:8%, compared to the case with traditional power grid supply. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep learning

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Hybrid Reinforcement Learning-based Sustainable Multi-User Computation Offloading for Mobile Edge-Quantum Computing

arXiv

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arXiv 2025年

作者： Xu, Minrui Niyato, Dusit Kang, Jiawen Xiong, Zehui Chen, Mingzhe Kim, Dong In Shen, Xuemin College of Computing and Data Science Nanyang Technological University Singapore School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

Exploiting quantum computing at the mobile edge holds immense potential for facilitating large-scale network design, processing multimodal data, optimizing resource management, and enhancing network security. In this paper, we propose a pioneering paradigm of mobile edge quantum computing (MEQC) that integrates quantum computing capabilities into classical edge computing servers that are proximate to mobile devices. To conceptualize the MEQC, we first design an MEQC system, where mobile devices can offload classical and quantum computation tasks to edge servers equipped with classical and quantum computers. We then formulate the hybrid classical-quantum computation offloading problem whose goal is to minimize system cost in terms of latency and energy consumption. To solve the offloading problem efficiently, we propose a hybrid discrete-continuous multi-agent reinforcement learning algorithm to learn long-term sustainable offloading and partitioning strategies. Finally, numerical results demonstrate that the proposed algorithm can reduce the MEQC system cost by up to 30% compared to existing baselines. © 2025, CC BY.

关键词： Qubits

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衰亡链揭示互利共生生态系统中安全与崩溃的中间阶段（英文）

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Engineering 2024年第12期 89-98页

作者：王光炜刘雪明肖颖袁烨潘林强管晓宏高建喜张海涛 The MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems State Key Laboratory of Digital Manufacturing Equipment and Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Guangdong HUST Industrial Technology Research Institute Huazhong University of Science and Technology Guangdong Provincial Engineering Technology Center of Autonomous Unmanned Vessels MOE Key Laboratory of Intelligent Networks and Network Security&State Key Laboratory of Manufacturing Systems Xi'an Jiaotong University Tsinghua National Laboratory of Information Science and Technology Department of Automation Tsinghua University Department of Computer Science Rensselaer Polytechnic Institute

Ecosystems are undergoing unprecedented persistent deterioration due to unsustainable anthropogenic human activities, such as overfishing and deforestation, and the effects of such damage on ecological stability are uncertain. Despite recent advances in experimental and theoretical studies on regime shifts and tipping points, theoretical tools for understanding the extinction chain, which is the sequence of species extinctions resulting from overexploitation, are still lacking, especially for large-scale nonlinear networked systems. In this study, we developed a mathematical tool to predict regime shifts and extinction chains in ecosystems under multiple exploitation situations and verified it in 26 real-world mutualistic networks of various sizes and densities. We discovered five phases during the exploitation process: safe,partial extinction, bistable, tristable, and collapse, which enabled the optimal design of restoration strategies for degraded or collapsed systems. We validated our approach using a 20-year dataset from an eelgrass restoration project. Counterintuitively, we also found a specific region in the diagram spanning exploitation rates and competition intensities, where exploiting more species helps increase *** computational tool provides insights into harvesting, fishing, exploitation, or deforestation plans while conserving or restoring the biodiversity of mutualistic ecosystems.

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Reliable Distributed Computing for Metaverse: A Hierarchical Game-Theoretic Approach

arXiv

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arXiv 2021年

作者： Jiang, Yuna Kang, Jiawen Niyato, Dusit Ge, Xiaohu Xiong, Zehui Miao, Chunyan Shen, Xuemin School of Electronic Information and Communications Huazhong University of Science and Technology Hubei Wuhan430074 China Shenzhen Huazhong University Science and Technology Research Institute Guangdong Shenzhen518063 China The School of Automation Guangdong University of Technology Guangdong Guangzhou510006 China The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

The metaverse is regarded as a new wave of technological transformation that provides a virtual space for people to interact through digital avatars. To achieve immersive user experiences in the metaverse, real-time rendering is the key technology. However, computing intensive tasks of real-time rendering from metaverse service providers cannot be processed efficiently on a single resource-limited mobile device. Alternatively, such mobile devices can offload the metaverse rendering tasks to other mobile devices by adopting the collaborative computing paradigm based on Coded Distributed Computing (CDC). Therefore, this paper introduces a hierarchical game-theoretic CDC framework for the metaverse services, especially for vehicular metaverse. In the framework, idle resources from vehicles, acting as CDC workers, are aggregated to handle intensive computation tasks in the vehicular metaverse. Specifically, in the upper layer, a miner coalition formation game is formulated based on a reputation metric to select reliable workers. To guarantee the reliable management of reputation values, the reputation values calculated based on the subjective logical model are maintained in a blockchain database. In the lower layer, a Stackelberg game based incentive mechanism is considered to attract reliable workers selected in the upper layer to participate in rendering tasks. The simulation results illustrate that the proposed framework is resistant to malicious workers. Compared with the best-effort worker selection scheme, the proposed scheme can improve the utility of metaverse service provider and average profit of CDC workers. Copyright © 2021, The Authors. All rights reserved.

关键词： Blockchain

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