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Adaptive Sparse ISAR Imaging for Objects With Off-grid Scatterers 7

Adaptive Sparse ISAR Imaging for Objects With Off-grid Scatt...

引用

7th international conference on Intelligent computing and Signal Processing, ICSP 2022

作者： Yu, Xiang Wang, Zhuang Du, Xiaoyong Jiang, Libing National University of Defense Technology National Key Laboratory of Science and Technology on Automatic Target Recognition Changsha China

ISBN: (纸本)9781665478571

Due to the sparsity of inverse synthetic aperture radar (ISAR) images, compressive sensing (CS) has been introduced to ISAR imaging for resolution improvement. However, conventional CS ISAR algorithms assume that target scatterers are on the predefined range-Doppler grid, which will cause performance degradation in practical applications as scatterers are generally off-grid. In this paper, we investigate CS ISAR imaging technique for targets with off-grid scatterers. Based on adaptive grids, a two dimensional sparse ISAR imaging algorithm is presented. Simulation results validate the effectiveness of the proposed algorithm. © 2022 ieee.

关键词： Compressed sensing

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Optimizing Lithium-Ion Battery Performance and Safety for e-Bikes: A Review of Machine Learning-Driven Battery Management Systems 4

Optimizing Lithium-Ion Battery Performance and Safety for E-...

引用

4th international conference on Ubiquitous computing and Intelligent Information Systems, ICUIS 2024

作者： Tau, Robin K. e. Ditshego, Nonofo M. J. Yahya, Abid Mangwala, Mmoloki Botswana International University of Science and Technology Department of Electrical and Communications Systems Palapye Botswana

ISBN: (纸本)9798331529635

The growing demand for sustainable transportation has positioned electric bikes (e-bikes) as a key solution, with lithium-ion batteries (LIBs) critical for their performance and reliability. This review provides a comprehensive examination of recent advancements in optimizing LIBs for e-bikes, focusing on integrating machine learning (ML) into Battery Management Systems (BMS) and developing fast-charging solutions. The review explores state-of-the-art machine learning models used for State of Charge (SOC) and State of Health (SOH) estimation, significantly improving prediction accuracy, adaptability, and battery safety under real-world conditions. Fast-charging technologies, essential for enhancing the user experience of e-bikes, are also evaluated, focusing on balancing rapid charging and minimizing degradation. Despite these advancements, challenges remain in real-time system integration, computational efficiency, and thermal management. The review highlights future research opportunities, including developing lightweight, adaptive AI models and novel materials for improving energy density and safety. This work aims to advance the design and application of LIBs in e-bikes, contributing to the broader adoption of sustainable, electric transportation. © 2024 ieee.

关键词： State of charge

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A Short-Term electric Load Forecasting Method Based on the Fusion of Global and Local Features 9

A Short-Term Electric Load Forecasting Method Based on the F...

引用

9th ieee international conference on Data science in Cyberspace, DSC 2024

作者： Gu, Haozhan Tian, Hangwei Wang, Jingchun Li, Yumei Shandong University Of Arts Academy of Fine Arts China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350391367

Accurate short-term electric load forecasting is crucial for ensuring the stability and efficiency of the power grid. This study proposes an innovative dual-branch electric load forecasting model that enhances the accuracy of predictions by processing global and local features in parallel. Global features are extracted through the Savitzky-Golay filter and a linear model to capture the overall trends of the load data. Local features are identified using the Convolutional Self-Attention Module, which integrates the local perception capabilities of Convolutional neural network with the self-attention mechanism of Transformers. By employing a Weighted Feature Fusion Network, we effectively integrate global and local information to enhance the performance of load forecasting. experimental results demonstrate that the proposed model significantly outperforms existing models in predictive performance. The model presented in this study offers a new perspective on electric load forecasting and has the potential to become an effective tool for power system planning and scheduling. © 2024 ieee.

关键词： Deep learning Feature fusion Load prediction Time series data analysis

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Secure and efficient V2G Scheme through edge computing and Federated Learning 4

Secure and Efficient V2G Scheme through Edge Computing and F...

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4th international conference on Smart Power and Internet energy Systems (SPIeS)

作者： Shang, Yitong Li, Zekai Shao, Ziyun Jian, Linni Southern Univ Sci & Tech Jiaxing Res Inst Jiaxing Peoples R China Guangzhou Univ Sch Elect & Commun Engn Guangzhou Peoples R China Southern Univ Sci & Tech Dept Elect & Elect Engn Shenzhen Peoples R China

ISBN: (纸本)9781665489577

Since the road transport accounts for 15% of total carbon emissions, electric vehicles (eVs) have made great strides and large-scale uncoordinated eV charging will greatly increase the load pressure of power system. The vehicle-to-grid (V2G) technology can optimize the idle eVs to charge during the grid load valley period and feeding the grid as a power source during the load peak period. This bidirectional energy flow technology builds a bridge between the power grid and eVs. However, the user privacy-preserving and data asset protection have always been ignored in previous works. In this paper, the secure and efficient V2G scheme through edge computing and federated learning from the double layer has been proposed. Firstly, the edge computing unit is set in the data source, viz., the charging point, to avoid sensitive data leakage. And then, the desensitized charging data will be stored in charging station. Next, the federated learning is utilized among charging stations to jointly train a global model without breaching data asset. Finally, the real dataset is applied to the experiment, and the effectiveness of the proposed architecture is verified.

关键词： vehicle-to-grid user privacy-preserving data asset protection edge computing federated learning

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energy-efficient Resource Allocation for Relay-Assisted Mobile edge computing Systems

Energy-Efficient Resource Allocation for Relay-Assisted Mobi...

引用

ieee international conference on Satellite computing (Satellite)

作者： Shi, Jialun Chen, Shuang Chen, Han Wang, Fengdi Hua, Meihui Nie, Gaofeng Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beiing Smartchip Microelectron Technol Co Ltd State Grid Fujian Informat Telecommunicat Co Beijing Peoples R China

ISBN: (纸本)9781665457293

To meet the requirements of computation intensive and latency sensitive applications, mobile edge computing (MeC) is utilized to improve quality of service, which allows user equipments (Ues) to offload urgent tasks to adjacent edge base stations (BSs) for efficient data processing. Considering the deterioration of wireless channels caused by user mobility and obstacles, this paper investigates a relay-assisted MeC system, where a relay is deployed between Ue and BS with better channel condition. Ue has three modes for data computing, i.e., local computing, relay-assisted offloading, and direct transmission offloading. With the aim of energy consumption minimization, an iterative and low-complexity algorithm is proposed by jointly optimizing task partition, local CPU frequency, bandwidth allocation, and power control at both Ue and relay, and the closedform optimal solutions are derived. Numerical results validate that the designed algorithm outperforms static and fractional power control schemes, and the relay-assisted MeC system can significantly decrease energy consumption in comparison with that without the assistance of relay.

关键词： energy-efficiency mobile-edge-computing relay-assisted resource-allocation

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e-Talk: Accelerating Active Speaker Detection with Audio-Visual Fusion and edge-Cloud computing 20

E-Talk: Accelerating Active Speaker Detection with Audio-Vis...

引用

20th Annual ieee international conference on Sensing, Communication, and Networking (SeCON)

作者： Yu, Xiaojing Zhang, Lan Li, Xiang-yang Univ Sci & Technol China Dept Comp Sci Hefei Anhui Peoples R China Deqing Alpha Innovat Inst Huzhou Zhejiang Peoples R China

ISBN: (纸本)9798350300529

Active Speaker Detection (ASD) aims to enhance communication and interaction in various scenarios, including meetings, group discussions, and security surveillance systems. The primary objective of ASD is to identify and label the position of the main active speaker. In large-scale surveillance systems, real-time ASD can pose network congestion issues due to the extensive video data uploaded from numerous cameras. To address this challenge, we propose a collaborative edge-cloud solution called e-TALK for ASD. e-TALK leverages the simplicity of voiceprint comparison and processing, as opposed to analyzing video sequences. It utilizes voiceprint consistency as the criterion for determining if there has been a change in the active speaker. Our research focuses on evaluating the performance and computational costs of different voiceprint features and recognition models in speaker identification tasks. Additionally, e-TALK introduces a potential speaker tracking scheme for fixed-angle cameras, in conjunction with foreground extraction algorithms. Moreover, e-TALK incorporates a cloud-based high-precision facial ASD model, which utilizes historical information to determine the active speaker in real-time. We conducted experiments to evaluate the performance of our proposed solution in various scenarios and settings. The results demonstrate the effectiveness of the e-TALK approach in improving active speaker detection, highlighting its potential for practical application in surveillance systems.

关键词： active speaker detection filtering temporality-level stream

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grid File index in Big Data Crowdsourcing 2

Grid File index in Big Data Crowdsourcing

引用

2nd international conference on computing and Information Technology, ICCIT 2022

作者： Qari, Shahad Mosa Alharthi, Manal Abdulrahman Houmaidan, Leena Solayman Alqurashi, Raghad Mohammed Almalki, Ashwaq Ahmed Alarabi, Louai Umm Al-Qura University Dept. of Computer Science Mecca Saudi Arabia

ISBN: (纸本)9781665436052

The area of our research in computer science, particularly spatial computing. Spatial computing provides a very important computing technology for any geographically related applications and services. It helps in the construction of geographical websites' profiles and services. Spatial computing can be used in location identification such as google maps and have other uses and services in spatial planning, traffic congestion control, evacuation systems, crowd management, and spatial crowdsourcing. In this research, we have investigated the main component of spatial crowdsourcing. In particular, we introduced a spatial index (grid File) to a crowdsourcing platform. Next, we constructed the basic spatial operation of crowdsourcing applications. We conducted an experimental study to recommend a suitable big data framework and suitable spatial indexing for crowdsourcing applications. © 2022 ieee.

关键词： Crowdsourcing

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Software and Hardware Platform Technology of embedded Device Based on Heterogeneous SoC Chip in substation 3

Software and Hardware Platform Technology of Embedded Device...

引用

3rd ieee international conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2023

作者： Zhiqiang, Yao Hui, Ren Xin, Xu Xin, Chen Yanping, Wu Jiajia, Zhu Chen, Fan Electric Power Research Institute Limited Company Nanjing Department of China Nanjing China State Grid Jiangsu Electric Power Co.LTD Information & Telecommunication Branch Nanjing China

ISBN: (纸本)9781665490788

In order to meet the demand of high-precision harmonic sampling in electronics power grid, and further improve the performance of secondary equipment such as substation measurement and control, PMU and broadband measurement, the application scenarios and performance requirements of high-performance hardware platform are analyzed, and the technical status of domestic chips in mass production is investigated. The hardware platform scheme of heterogeneous SoC chips is adopted, and the dual-system software architecture based on Linux and RTOS system is adopted, We have designed a large-capacity real-time data parallel processing and layered drive technology scheme, fully developed the computing potential of heterogeneous multi-core chips, improved the performance of hardware platform based on domestic chips, and completed the development and testing of the hardware platform of broadband measurement device prototype. Finally, the feasibility of the hardware platform technology scheme based on heterogeneous SoC chips is verified through the comparison of various performance tests. © 2023 ieee.

关键词： System-on-chip

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Data analysis application platform at the end of the transformer region based on the edge computing technology of the power Internet of things

Data analysis application platform at the end of the transfo...

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2022 ieee international conference on Advances in electrical engineering and Computer Applications, AeeCA 2022

作者： Xiangyuan, Lv Haigang, Jin Zhengang, Xie Anqi, Chen Jiayi, Liu Yushu, Cheng State Grid Shanxi Marketing Service Center Shanxi Taiyuan030002 China

ISBN: (数字)9781665480901

ISBN: (纸本)9781665480901

This paper analyzes the functions and applications of the data analysis platform at the end of the transformer area. The role of edge computing and edge computing technology in Transformer region analysis and application management is analyzed. The structure of the Transformer region is briefly introduced, the end data analysis platform of the Transformer region is established, the platform structure and platform deployment method are analyzed, and the main functions of the Transformer region data analysis are explained. Taking the analysis of data line loss at the end of the Transformer region as an example, it is introduced to illustrate the effectiveness of this platform in data management. © 2022 ieee.

关键词： Internet of things

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Multi-Access edge computing Using Intelligent Mobile User Resource Allocation In 6G 4

Multi-Access Edge Computing Using Intelligent Mobile User Re...

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4th ieee international Maghreb Meeting of the conference on sciences and Techniques of Automatic Control and Computer engineering, MI-STA 2024

作者： Saeed, Mamoon M. Saeed, Rashid A. Mokhtar, Rania A. Gaid, AbdulGuddoos S. A. Ahmed, Zeinab e. elbasheir, Mohammed S. Faculty of Engineering Department of Communications and Electronics Engineering Yemen Taif University Faculty of Engineering Department of Electrical and Electronic Engineering Altaif Saudi Arabia College of Computers and Information Technology Taif University Department of Computer Engineering Taif21944 Saudi Arabia Taiz University Faculty of Engineering & Information Technology Dept. of Communication & Computer Engineering Taiz Yemen International Islamic University Malaysia Department of Electrical and Computer Malaysia School of Electronic Engineering Sudan University of Science and Technology Khartoum Sudan

ISBN: (纸本)9798350372632

edge computing plays a crucial role in the advancement of Beyond 5G (B5G) and 6G networks. The substantial growth in network traffic and computational requirements necessitates the implementation of multi-access edge computing (MeC) as a viable solution. MeC aims to bring the capabilities of cloud computing closer to end-users by integrating them into the radio access network (RAN). This research introduces an optimal approach to connect mobile users with MeC hosts and access points, effectively distributing radio and computing resources to individual mobile users. The objective is achieved by lowering the total user transmit power while abiding by latency constraints in both the communication and computation phases, using the Grey Wolf Optimization technique (GWO). A comparison to alternative smart techniques demonstrates the effectiveness of the suggested approach © 2024 ieee.

关键词： 5G mobile communication systems

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