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2022 IEEE International Conference on Unmanned Systems, ICUS 2022

作者： Wu, Sifan Tian, Daxin Zhou, Jianshan Duan, Xuting Sheng, Zhengguo Zhao, Dezong Beijing Key Laboratory For Cooperative Vehicle Infrastructure Systems&Safety Cooperative Control Beijing China Beijing Advanced Innovation Center For Big Data and Brain Computing Beijing China Department of Engineering and Design Richmond United Kingdom University of Sussex Richmond United Kingdom James Watt School of Engineering Glasgow United Kingdom University of Glasgow Glasgow United Kingdom

ISBN: (数字)9781665484565

ISBN: (纸本)9781665484565

On-ramp merge is a complex traffic scenario in autonomous driving. Because of the uncertainty of the driving environment, most rule-based models cannot solve such a problem. In this study, we design a Deep Reinforcement Learning (DRL) method to solve the issue of ramp merges in uncertain scenarios and modify the structure of the Twin Delayed Deep Deterministic policy gradient algorithm (TD3), using Long Short-Term Memory (LSTM) to select an action based on temporal information. The proposed method is applied in the on-ramp merge and verified in the Simulation of Urban Mobility (SUMO). Results show that the proposed method performs significantly better generalization in uncertain traffic scenarios. © 2022 IEEE.

关键词： Long short-term memory

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Cooperative guidance strategy for multiple hypersonic gliding vehicles system

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Chinese Journal of Aeronautics 2020年第3期33卷 990-1005页

作者： Jianglong YU Xiwang DONG Qingdong LI Zhang REN Jinhu LV School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control LaboratoryBeihang UniversityBeijing 100083China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityBeijing 100083China Shananxi Key Laboratory of Integrated and Intelligent Navigation Xi’an 710068China

Cooperative guidance strategy for multiple hypersonic gliding vehicles system with flight constraints and cooperative constraints is *** paper mainly cares about the coordination of the entry glide flight phase and driving-down *** from the existing results,both the attack time and the attack angle constraints are considered ***, for the entry glide flight phase, a two-stage method is proposed to achieve the rapid cooperative trajectories planning, where the control signal corridors are designed based on the quasi-equilibrium gliding *** the first stage, the bank angle curve is optimized to achieve the attack angle *** the second stage, the angle of attack curve is optimized to achieve the attack time *** optimized parameters can be obtained by the secant ***, for the driving-down phase, the cooperative terminal guidance law is designed where the terminal attack time and attack angle are *** guidance law is then transformed into the bank angle and angle of attack *** cooperative guidance strategy is summarized as an ***, a numerical simulation example with three hypersonic gliding vehicles is provided for revealing the effectiveness of the acquired strategy and algorithm.

关键词： Attack angle constraints Attack time constraints Cooperative guidance Cooperative path planning Entry guidance Hypersonic vehicles

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Automatic Life Event Tree Generation for Older Adults 1

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24th International Conference on Human-Computer Interaction, HCII 2022

作者： Gui, Fang Wu, Xi Hu, Min Yang, Jiaoyun School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei China National Smart Eldercare International S&T Cooperation Base Hefei University of Technology Hefei China Laboratory of Affective Computing and Advanced Intelligent Machine Hefei University of Technology Hefei China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China

ISBN: (数字)9783031179020

ISBN: (纸本)9783031179013

Studies have shown that learning personal stories could help provide individualized eldercare services. However, personal stories are often disordered because of the scattered collection, including informal interviews or daily interactions, which brings difficulties in acquiring valuable information quickly. One solution to this problem is to extract events from personal stories and automatically organize them in chronological order. Events extracted by current methods from social media or news corpus are mainly organized in a linear structure. These works usually focus on the event time and ignore the consistency of event contents when organizing events. This paper aims to organize events into a tree structure based on an event network, with stem nodes representing key event topics and branch nodes representing detailed events. Social workers or caregivers can clarify the life experience of the older adults quickly through the event tree and have a preliminary understanding of them. The experiments show that the event tree generated by our method has a better performance in consistency than current event organization methods. A survey study shows that our method achieves the highest logical coherence for the event tree branches compared with other algorithms. © 2022, Springer Nature Switzerland AG.

关键词： Trees (mathematics)

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A Channel-Augmented Multi-Domain Graph Convolutional Network for Fatigue Driving Detection

A Channel-Augmented Multi-Domain Graph Convolutional Network...

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Industrial Automation, Robotics and Control Engineering (IARCE), International Conference on

作者： Mingyi Sun Weigang Cui Yang Li Department of Automation Science and Electrical Engineering Beihang University Beijing China School of Engineering Medicine Beihang University Beijing China Department of Automation Science and Electrical Engineering State Key Laboratory of Virtual Reality Technology and Systems The Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China

ISBN: (数字)9798350380323

ISBN: (纸本)9798350380330

Electroencephalography (EEG) has emerged as a crucial cornerstone within the realm of brain-computer interface (BCI) applications, with its significance notably pronounced in the field of fatigue detection. However, the inherent limitations of EEG acquisition equipment during real driving scenarios have contributed to the constrained robustness of existing models. Moreover, most of recent methods failed to extract robust multi-domain features, leading to a suboptimal performance. To address these challenges, we propose a novel channel-augmented multi-domain graph convolutional network (CA-MDGCNet). Specifically, the initial EEG signals are enriched by incorporating supplementary virtual EEG channels, distinguished as learnable parameters within the network architecture. Then, differential entropy features are extracted from the augmented EEG signals. Following this, a multi-domain graph convolutional network is designed to encode high-level EEG features by means of convolutions in diverse paths, which is beneficial to integrating the characteristics extracted from multiple domains. Finally, the classification block derives the detection outcome from the refined feature maps. To substantiate the potency of the proposed method, the validation was conducted on the publicly accessible SEED-VIG. The proposed CA-MDGCNet not only demonstrates more promising performance compared to state-of-the-art approaches but also underscores the potential viability of our method for the realm of fatigue driving detection.

关键词： Graph convolutional networks Convolution Service robots Neural activity Feature extraction Fatigue Brain modeling Electroencephalography Entropy Robustness

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BiKT: Unleashing the potential of GNNs via Bi-directional Knowledge Transfer

arXiv

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arXiv 2023年

作者： Zheng, Shuai Liu, Zhizhe Zhu, Zhenfeng Zhang, Xingxing Li, Jianxin Zhao, Yao The Institute of Information Science Beijing Jiaotong University Beijing100044 China The Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China Qiyuan Lab Beijing China The Beijing Advanced Innovation Center for Big Data and Brain Computing School of Computer Science and Engineering Beihang University Beijing100083 China

Based on the message-passing paradigm, there has been an amount of research proposing diverse and impressive feature propagation mechanisms to improve the performance of GNNs. However, less focus has been put on feature transformation, another major operation of the message-passing framework. In this paper, we first empirically investigate the performance of the feature transformation operation in several typical GNNs. Unexpectedly, we notice that GNNs do not completely free up the power of the inherent feature transformation operation. By this observation, we propose the Bi-directional Knowledge Transfer (BiKT), a plug-and-play approach to unleash the potential of the feature transformation operations without modifying the original architecture. Taking the feature transformation operation as a derived representation learning model that shares parameters with the original GNN, the direct prediction by this model provides a topological-agnostic knowledge feedback that can further instruct the learning of GNN and the feature transformations therein. On this basis, BiKT not only allows us to acquire knowledge from both the GNN and its derived model but promotes each other by injecting the knowledge into the other. In addition, a theoretical analysis is further provided to demonstrate that BiKT improves the generalization bound of the GNNs from the perspective of domain adaption. An extensive group of experiments on up to 7 datasets with 5 typical GNNs demonstrates that BiKT brings up to 0.5% - 4% performance gain over the original GNN, which means a boosted GNN is obtained. Meanwhile, the derived model also shows a powerful performance to compete with or even surpass the original GNN, enabling us to flexibly apply it independently to some other specific downstream tasks. Copyright © 2023, The Authors. All rights reserved.

关键词： Message passing

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Resource-Aware Multi-Criteria Vehicle Participation for Federated Learning in Internet of Vehicles

SSRN

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SSRN 2023年

作者： Wen, Jie Zhang, Jingbo Zhang, Zhixia Cui, Zhihua Cai, Xingjuan Chen, Jinjun The Shanxi Key Laboratory of Advanced Control and Equipment intelligence Taiyuan University of Science and Technology Shanxi Taiyuan China The Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and Technology Shanxi Taiyuan China The State Key Lab for Novel Software Technology Nanjing University China Department of Computing Technologies Swinburne University of Technology Melbourne Australia

Federated learning (FL), as a safe distributed training mode, provides strong support for the edge intelligence of the Internet of Vehicles (IoV) to realize efficient collaborative control and safe data sharing. However, due to the resource limitation and the instability of training environment in the complex IoV, ideal performance of FL cannot be achieved. Since considering the actual resource constraints and federated task requirements, the diversified device selection criteria make the resource-aware vehicle selection problem become a multi-criteria selection problem. To effectively support FL for IoV, the resource-aware multi-criteria vehicle selection problem was described as a many-objective optimization problem, and proposed a resource-aware many-objective vehicle selection model (RA-MaOVSM) to optimize resource efficiency. The RA-MaOVSM considering heterogeneous resources (like computation resources, communication resources, energy resources and data resources) of on-board devices in IoV, and realizes the joint optimization of learning efficiency, energy cost and global performance. Additionally, a novel probability distribution combination game strategy is applied to many-objective evolutionary algorithm (MaOEA) for improving the model solving performance. Simulation results demonstrate that RA-MaOVSM can effectively optimize the IoV resources and FL model performance, and the designed algorithm exhibits good convergence and distribution, achieving a good balance among multiple device selection criteria. © 2023, The Authors. All rights reserved.

关键词： Evolutionary algorithms

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Optimal Linear Crossover for Mitigating Negative Transfer in Evolutionary Multitasking

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IEEE Transactions on Evolutionary Computation 2024年

作者： Liu, Zhaobo Yuan, Jianhua Zhang, Haili Zeng, Tao Zhu, Zexuan Shenzhen University Institute for Advanced Study Shenzhen518060 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Shenzhen Polytechnic University School of Undergraduate Education Shenzhen518055 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China Shenzhen University Shenzhen City Key Laboratory of Embedded System Design Shenzhen518060 China Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education of China Chongqing400044 China

Evolutionary multitasking algorithms use information exchange among individuals in a population to solve multiple optimization problems simultaneously. Negative transfer is a critical factor that affects the performance of evolutionary multitasking algorithms. In this study, we propose an innovative approach to mitigate negative transfer in evolutionary multitasking algorithms. The proposed approach is grounded in rigorous theoretical analysis, which provides valuable theoretical insights into the design of an optimal linear crossover operator for mitigating negative transfer. By identifying interpretable conditions, we establish a solid theoretical foundation to prevent negative transfer in diverse scenarios. Building upon these findings, we theoretically derive a closed-form expression for the optimal crossover operator and propose practical design methods based on approximations. Furthermore, we integrate the proposed optimal crossover operator into a fundamental evolutionary multitasking algorithm framework. The resultant algorithm is comparable or superior to other state-of-the-art methods. Empirical validation through comprehensive experiments confirms the effectiveness of our theoretical findings. © 1997-2012 IEEE.

关键词： Optimization algorithms

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DNN Task Assignment in UAV Networks: A Generative AI Enhanced Multi-Agent Reinforcement Learning Approach

arXiv

引用

arXiv 2024年

作者： Tang, Xin Chen, Qian Weng, Wenjie Liao, Binhan Wang, Jiacheng Cao, Xianbin Li, Xiaohuan Guangxi University Key Laboratory of Intelligent Networking and Scenario System School of Information and Communication Guilin University of Electronic Technology Guilin541004 China The College of Computing and Data Science Nanyang Technological University 639798 Singapore School of Architecture and Transportation Engineering Guilin University of Electronic Technology Guilin541004 China National Engineering Laboratory for Comprehensive Transportation Big Data Application Technology Guangxi Nanning530001 China College of Computing and Data Science Nanyang Technological University 639798 Singapore School of Electronic and Information Engineering The Key Laboratory of Advanced Technology of Near Space Information System Ministry of Industry and Information Technology of China Beihang University Beijing100191 China

Unmanned Aerial Vehicles (UAVs) possess high mobility and flexible deployment capabilities, prompting the development of UAVs for various application scenarios within the Internet of Things (IoT). The unique capabilities of UAVs give rise to increasingly critical and complex tasks in uncertain and potentially harsh environments. The substantial amount of data generated from these applications necessitates processing and analysis through deep neural networks (DNNs). However, UAVs encounter challenges due to their limited computing resources when managing DNN models. This paper presents a joint approach that combines multiple-agent reinforcement learning (MARL) and generative diffusion models (GDM) for assigning DNN tasks to a UAV swarm, aimed at reducing latency from task capture to result output. To address these challenges, we first consider the task size of the target area to be inspected and the shortest flying path as optimization constraints, employing a greedy algorithm to resolve the subproblem with a focus on minimizing the UAV’s flying path and the overall system cost. In the second stage, we introduce a novel DNN task assignment algorithm, termed GDM-MADDPG, which utilizes the reverse denoising process of GDM to replace the actor network in multi-agent deep deterministic policy gradient (MADDPG). This approach generates specific DNN task assignment actions based on agents’ observations in a dynamic environment. Simulation results indicate that our algorithm performs favorably compared to benchmarks in terms of path planning, Age of Information (AoI), energy consumption, and task load balancing. Copyright © 2024, The Authors. All rights reserved.

关键词： Motion planning

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Low-Loss Zero-Index Waveguides and Devices

Low-Loss Zero-Index Waveguides and Devices

引用

Conference on Lasers and Electro-Optics (CLEO)

作者： Tian Dong Tianxiang Dai Ye Chen Yueyang Liu Hancheng Liu Yiting Wang Anqi Ma Haifeng Hu Lihua Xu Le Zhao Weiguo Chu Chao Peng Jianwei Wang Yang Li Department of Precision Instrument State Key Laboratory of Precision Measurement Technology and Instrument Tsinghua University Beijing China State Key Laboratory for Mesoscopic Physics School of Physics Peking University Beijing China Department of Electronics and Frontiers Science Center for Nano-optoelectronics State Key Laboratory of Advanced Optical Communication Systems and Networks Peking University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China Nanofabrication Laboratory National Center for Nanoscience and Technology Beijing China

We report flexible zero-index waveguides and devices whose loss is two orders of magnitude lower than the state of the art, enabling phase-error-free high-dense photonic integrated circuits for classical and quantum i... 详细信息

ISBN: (纸本)9798350369311

关键词： Optical losses Quantum computing Photonic integrated circuits Waveguide lasers Information processing Lasers and electrooptics Electro-optical waveguides

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Research on Hardware-in-the-Loop Test System for the Main Control Unit of Unmanned Vehicles

Research on Hardware-in-the-Loop Test System for the Main Co...

引用

Chinese Intelligent Systems Conference, CISC 2020

作者： Ni, Haoyuan Yu, Guizhen Zhou, Bin Liu, Guoqiang School of Transportation Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control Beihang University Beijing100191 China School of Electronic Information Engineering Beihang University Beijing100191 China

ISBN: (纸本)9789811584572

At present, there are more researches on the automatic driving technology of unmanned vehicles. It needs a main control unit, which enables the vehicle to automatically drive. The main control unit supports the access of various sensors and controllers. It can process data of each device and control the movement state of vehicles. Therefore, the main control unit needs to be tested repeatedly to ensure its functionality and stability. Based on Prescan simulation software and Matlab/Simulink software, this paper studies a hardware-in-the-loop (HIL) test system for testing the main control unit of unmanned vehicles according to the communication protocol and communication method of the real sensor and controller hardware devices. At the same time, this paper studies a simulation method of the main control unit, which can effectively verify the function of the test system and conduct the feasibility test of the communication protocol and communication method used. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Simulink

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