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2024 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2024

作者： Kim, Yechan Park, JongHyun Kim, Sihyun Kim, Sungheon Kim, SooYeon Ko, YeongMin Oh, Junggyun Jeon, Moongu School of Electrical Engineering and Computer Science GIST Korea Republic of Kyungpook National University Department of Robot and Smart System Engineering Korea Republic of HDC Labs Korea Republic of

ISBN: (纸本)9798331530839

Precise object detection allows military personnel to clearly understand their surroundings, leading to planning effective military strategies. Particularly, satellites and drones allow real-time surveillance over large areas, which is crucial for military operations. Collaboration among military, academic, and industrial institutions is required to promote innovation in military target detection. However, potential security concerns usually restrict access to real data from the military. To mitigate this issue, this paper proposes a novel approach to generate synthetic data for remote sensing object detection on the battlefield with ARMA3, one of the renowned military tactic games. With our method, the data for model training can be easily generated without any manpower. To demonstrate the efficacy of our approach, we provide a detailed analysis of the examples from our method. As ARMA3 is well-known for its realistic military combat simulation, we believe our method can effectively contribute to military object detection in remote sensing. © 2024 IEEE.

关键词： Military satellites

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Composing Synergistic Macro Actions for Reinforcement Learning Agents

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IEEE Transactions on Neural Networks and Learning systems 2024年第5期35卷 7251-7258页

作者： Chen, Yu-Ming Chang, Kaun-Yu Liu, Chien Hsiao, Tsu-Ching Hong, Zhang-Wei Lee, Chun-Yi Hsinchu300 Taiwan Avery Design Systems Inc. Taipei100 Taiwan University of Rostock Faculty of Computer Science and Electrical Engineering Rostock18051 Germany National Tsing Hua University Department of Computer Science Hsinchu300 Taiwan Massachusetts Institute of Technology CambridgeMA02139 United States

Macro actions have been demonstrated to be beneficial for the learning processes of an agent and have encouraged a variety of techniques to be developed for constructing more effective ones. However, previous techniques usually do not further consider combining macro actions to form a synergistic macro action ensemble, in which synergism exhibits when the constituent macro actions are favorable to be jointly used by an agent during evaluation. Such a synergistic macro action ensemble may potentially allow an agent to perform even better than the individual macro actions within it. Motivated by the recent advances of neural architecture search (NAS), in this brief, we formulate the construction of a synergistic macro action ensemble as a Markov decision process (MDP) and evaluate the constructed macro action ensemble as a whole. Such a problem formulation enables synergism to be taken into account by the proposed evaluation procedure. Our experimental results demonstrate that the proposed framework is able to discover the synergistic macro action ensembles. Furthermore, we also highlight the benefits of these macro action ensembles through a set of analytical cases. © 2012 IEEE.

关键词： Reinforcement learning

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STaaS: Cost Effective and Scalable Union Mounting in ICP for Aerospace Applications

STaaS: Cost Effective and Scalable Union Mounting in ICP for...

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2024 International Conference on Green Energy, Computing and Sustainable Technology, GECOST 2024

作者： Sakthivel, V. Li, Jueying Rama Abirami, K. Min, Dugki Lee, Jae Woo Choi, Eunmi School of Computer Science and Engineering Vellore Institute of Technology Chennai India Konkuk University Department of Computer Science and Engineering Seoul Korea Republic of Curtin University Department of Electrical and Computer Engineering Malaysia Konkuk Aerospace Design - Airworthiness Institute Konkuk University Seoul Korea Republic of Kookmin University Department of Computer Science and Engineering Seoul Korea Republic of

ISBN: (纸本)9798350357905

Cloud computing widely used in various specific areas leads to the emergence of 'Short-Time as a Service' (STaaS) as a cost-effective and scalable way for enterprises and organizations to access and utilize computing resources. Union mounting, a method that enables multiple file systems to be combined into one virtual file system, has been proposed as a strategy to optimize STaaS by allowing resources to be allocated dynamically based on user requirements. This research paper explores the feasibility and benefits of STaaS with the union mounting method, using a project providing a simulation environment on the Industry Cloud Platform (ICP) for the aerospace organization that requires an environment that can automatically and quickly create cluster groups of Cloud In the Loop Simulation (CILS) for Unmanned Aerial Vehicle (UAV) operations. The results demonstrate that STaaS with a union mounting method can provide significant benefits in terms of CILS cluster group creation, performance optimization, and flexibility. Specifically, our proposed research could reduce instance creation time costs by up to 90% compared to the traditional creation method. Overall, STaaS with union mounting method improves the creation time of the CILS cluster group for the aerospace organization requiring flexibility and cost-effectiveness. © 2024 IEEE.

关键词： Antennas

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Renewable Integration for Green Urban Mobility: A Case Study of Public Transport Depot Electrification 14

Renewable Integration for Green Urban Mobility: A Case Study...

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14th International Conference on Power, Energy and electrical engineering, CPEEE 2024

作者： Colombo, Cristian Giovanni Saldarini, Alessandro Longo, Michela Ota, Ryosuke Politecnico di Milano Department of Energy Milan Italy Tokyo Metropolitan University Department of Electrical Engineering and Computer Science Faculty of Systems Design Japan

ISBN: (纸本)9781643685199

This paper analyses an electrification strategy for a depot for a public transport company, through an optimized model. The case study analyses both the transition of a depot able to host 10 buses towards e-buses and proposes the integration of a micro-grid, powered by PV panels to power the e-buses in a sustainable way. The study addresses key challenges such as spatial constraints, passenger demand, and financial considerations, through a Genetic Algorithm (GA). The analysis encompasses two scenarios: a mixed fleet of electric and diesel buses and an exclusive focus on e-buses. The study highlights the importance of considering both electric and diesel buses in fleet optimization, and combined with renewables emphasize the advantages of operational flexibility and sustainability. Thanks to the implementation of a micro-grid capable of providing 1123.87 kWh/day to power the e-buses as much as possible. © 2024 The Authors.

关键词： Electric buses

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Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 18-36页

作者： Ding Wang Ning Gao Derong Liu Jinna Li Frank L.Lewis IEEE the Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent SystemBeijing Laboratory of Smart Environmental Protectionand Beijing Institute of Artificial IntelligenceBeijing University of TechnologyBeijing 100124China the School of System Design and Intelligent Manufacturing Southern University of Science and TechnologyShenzhen 518055China the Department of Electrical and Computer Engineering University of Illinois at ChicagoChicago IL 60607 USA the School of Information and Control Engineering Liaoning Petrochemical UniversityFushun 113001China the UTA Research Institute the University of Texas at ArlingtonArlington TX 76118 USA

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, ***, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation ***, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

关键词： Adaptive dynamic programming(ADP) advanced control complex environment data-driven control event-triggered design intelligent control neural networks nonlinear systems optimal control reinforcement learning(RL)

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Position Measurement Based Slave Torque Feedback Control for Teleoperation systems With Time-Varying Communication Delays

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 388-402页

作者： Xian Yang Jing Yan Changchun Hua Xinping Guan the Institute of Information Science and Engineering Yanshan University the Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao 066004China IEEE the Institute of Electrical Engineering Yanshan UniversityQinhuangdao 066004China the Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China

Bilateral teleoperation system is referred to as a promising technology to extend human actions and intelligence to manipulating objects *** the tracking control of teleoperation systems,velocity measurements are necessary to provide feedback ***,due to hardware technology and cost constraints,the velocity measurements are not always *** addition,the time-varying communication delay makes it challenging to achieve tracking *** paper provides a solution to the issue of real-time tracking for teleoperation systems,subjected to unavailable velocity signals and time-varying communication *** order to estimate the velocity information,immersion and invariance(I&I)technique is employed to develop an exponential stability velocity *** the proposed velocity observer,a linear relationship between position and observation state is constructed,through which the need of solving partial differential and certain integral equations can be ***,the mean value theorem is exploited to separate the observation error terms,and hence,all functions in our observer can be analytically *** the estimated velocity information,a slave-torque feedback control law is presented.A novel Lyapunov-Krasovskii functional is constructed to establish asymptotic tracking *** particular,the relationship between the controller design parameters and the allowable maximum delay values is ***,simulation and experimental results reveal that the proposed velocity observer and controller can guarantee that the observation errors and tracking error converge to zero.

关键词： Asymptotic stability bilateral teleoperation time-varying delay velocity observer

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Cloud-edge-based We-Market:Autonomous Bidding and Peer-to-peer Energy Sharing Among Prosumers

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Journal of Modern Power systems and Clean Energy 2023年第4期11卷 1282-1293页

作者： Fangyuan Si Yinghua Han Qinqin Xu Jinkuan Wang Qiang Zhao College of Information Science and Engineering Northeastern UniversityShenyangChinaand F.Si is also with the State Key Laboratory of Power System and Generation EquipmentDepartment of Electrical EngineeringTsinghua UniversityBeijingChina School of Computer and Communication Engineering Northeastern University at QinhuangdaoQinhuangdaoChina School of Control Engineering Northeastern University at QinhuangdaoQinhuangdaoChina

With the extensive penetration of distributed renewable energy and self-interested prosumers,the emerging power market tends to enable user autonomy by bottom-up control and distributed *** paper is devoted to solving the specific problems of distributed energy management and autonomous bidding and peer-to-peer(P2P)energy sharing among prosumers.A novel cloud-edge-based We-Market is presented,where the prosumers,as edge nodes with independent control,balance the electricity cost and thermal comfort by formulating a dynamic household energy management system(HEMS).Meanwhile,the autonomous bidding is initiated by prosumers via the modified Stone-Geary utility *** the cloud center,a distributed convergence bidding(CB)algorithm based on consistency criterion is developed,which promotes faster and fairer bidding through the interactive iteration with the edge ***,the proposed scheme is built on top of the commercial cloud platform with sufficiently secure and scalable computing *** results show the effectiveness and practicability of the proposed We-Market,which achieves 15%cost reduction with shorter running *** analysis indicates better scalability,which is more suitable for largerscale We-Market implementation.

关键词： We-Market bidding energy sharing prosumer peer-to-peer cloud-edge

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CFD Simulation of Flight Vehicle Optical Dome and Flow Field Based on Solid-Fluid Coupling Approach 25

CFD Simulation of Flight Vehicle Optical Dome and Flow Field...

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25th IEEE China Conference on system Simulation Technology and its Application, CCSSTA 2024

作者： Li, Jing Wang, Qilong Xu, Liang Wan, Ziming Zhao, Yan Zhao, Xuewen Tianjin University of Technology School of Computer Science and Engineering Tianjin China Tianjin University of Technology School of Electrical Engineering and Automation Tianjin China Beijing Institude of Electronic System Engineering Beijing China

ISBN: (纸本)9798350366600

At high velocity flight, the infrared imaging system encounters significant aero-optical disturbances due to the uneven gas flow around the aircraft's forward section and the elevated temperatures of the optical dome. In the simulation study of aero-optical effects, hydrodynamic calculations of the gas flow field and optical dome are required. This paper examines the utilization of the solid-fluid coupling approach in simulating aero-optical phenomena from the perspective of fluid dynamics computations. This paper undertakes the geometric modelling of the airflow environment and optical dome of a blunt-nose front-window flight vehicle. Utilizing ICEM, the mesh generation for both the gaseous flow domain and the solid optical dome is executed. Subsequently, Fluent software facilitates the performance of hydrodynamic simulations, concurrently extracting vital parameters from both the flow field and the optical dome. The method in this paper can be used in the calculation and analysis of aero-optical effects of high-speed flight vehicles. © 2024 IEEE.

关键词： Flow of gases

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Autonomous Drone Control for Wireless Charging Using Power Lines 18

Autonomous Drone Control for Wireless Charging Using Power L...

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18th International Conference on Ubiquitous Information Management and Communication, IMCOM 2024

作者： Wang, Pengkai Kim, Jonghoek Mutahira, Husna Muhammad, Mannan Saeed Sungkyunkwan University Dept. of Electrical and Computer Engineering Suwon Korea Republic of Sejong University Dept. of Defense System Engineering Seoul Korea Republic of Sogang University Dept. of Computer Science and Engineering Seoul Korea Republic of

ISBN: (纸本)9798350331011

The limited autonomy of flight has long been considered a significant constraint in drone systems. In the context of drone inspections of power lines, this study focuses on a drone equipped with a coil designed for autonomous battery charging. Positioned atop the drone is a charging coil, and on the upper surface, there is an upward-facing camera with a restricted field-of-view (FOV), serving the crucial role of aligning the drone with power lines. This research introduces a wireless power transfer strategy to facilitate the self-charging process of the drone while maintaining its position beneath power lines. To enable the drone's autonomous approach to power lines, a method is devised to guide the drone from below. As the drone approaches the power lines, it monitors the current induced by these lines and gradually adjusts its altitude until a sufficiently high induced current is achieved. The proposed approach ensures safety, as the drone avoids direct contact with high-voltage power lines, setting it apart from other existing methods that require such contact. To the best of our knowledge, this study offers a novel approach to precisely controlling drone movements, and establishing reliable wireless power transfer from power lines. The effectiveness of the developed wireless transfer strategy has been verified through MATLAB simulations. © 2024 IEEE.

关键词： Drones

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AI-Assisted Markerless Activity Tracking system for Supporting Aging and Wellness

AI-Assisted Markerless Activity Tracking System for Supporti...

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2023 IEEE Microwaves, Antennas, and Propagation Conference, MAPCON 2023

作者： Abedi, Hajar Ansariyan, Ahmad Morita, Plinio P Wong, Alexander Boger, Jennifer Shaker, George University of Waterloo Electrical and Computer Engineering Waterloo Canada University of Waterloo School of Public Health Sciences Waterloo Canada University of Waterloo System Design Engineering Waterloo Canada

ISBN: (纸本)9798350328264

This paper presents a mm-Wave radar-based system to create a novel method of tracking seniors and monitoring them using ambient wireless signals. Radar sensors, coupled with deep learning models, facilitate the identification of various physical activities without requiring physical contact or the use of wearable devices. The system employs range-Doppler maps derived from a real-life in-home activities dataset for training deep learning networks. The gated recurrent units (GRU) model is chosen for real-time implementation due to its optimal trade-off between speed and accuracy. The system achieves an overall accuracy of 93% for trained subjects, with 86% accuracy for classifying the in-home physical activities of a new subject. Additionally, the system captures the frequency of washroom use, sedentary, duration of sleep, active and out-of-home periods, the level of activity performed by the subject over a period of time, current activity state, and gait parameters. © 2023 IEEE.

关键词： activity recognition aging mm-wave radar personalized care sequential deep learning

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