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2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023

作者： Carrasco-González, David Sarrias-Mena, Raúl Horrillo-Quintero, Pablo Llorens-Iborra, Francisco Fernández-Ramírez, Luis M. Suret Research Group Department of Electrical Engineering Algeciras Spain Uca Suret Reseach Group Dept. of Engineering in Automation Electronics and Computer Architecture and Networks Algeciras Spain

ISBN: (纸本)9781665471640

Currently, microgrid clusters (MGC) are being increasingly used owing to their benefits and human needs. MGs can be composed of power generation, consumption, and storage elements. They permit the combination of DC and AC technologies, even for a single microgrid (MG). In this paper, a new control system of a MGC consisting of two MGs connected to the main grid is developed. One of them is a DC MG that is composed of a wind turbine (WT), electrolyzer (EZ), ultracapacitor (UC), fuel cell (FC) and DC loads. The other MG is an AC MG, which is made up of a PV generator, a battery bank and AC loads. The MGC is tested under different working conditions in an experimental setup composed of a Raspberry Pi microcontroller, an OPAL-RT 4510 unit and a PC host. The experimental setup allows the system to run in real time and validate the results obtained. Finally, the control system implemented in the MGC exhibits a correct behaviour under different conditions, according to the results obtained. © 2023 IEEE.

关键词： energy management system Microgrid cluster Raspberry Pi real-time simulation

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AIoT for Aircraft Final Assembly:An Intelligent and Collaborative Framework

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Journal of Communications and Information Networks 2024年第3期9卷 262-276页

作者： Cheng Ren Xiaojing Wen Xinping Guan Cailian Chen Yehan Ma Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai 200240China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong UniversityShanghai 200240China

Aircraft final assembly line(AFAL)involves thousands of processes that must be completed before ***,the heavy reliance on manual labor in most assembly processes affects the quality and prolongs the delivery *** the advent of artificial intelligence of things(AIoT)technologies has introduced advancements in certain AFAL scenarios,systematically enhancing the intelligence level of the AFAL and promoting the widespread deployment of artificial intelligence(AI)technologies remain significant *** address these challenges,we propose the intelligent and collaborative aircraft assembly(ICAA)framework,which integrates AI technologies within a cloud-edge-terminal *** ICAA framework is designed to support AI-enabled applications in the AFAL,with the goal of improving assembly efficiency at both individual and multiple process *** analyze specific demands across various assembly scenarios and introduce corresponding AI technologies to meet these *** three-tier ICAA framework consists of the assembly field,edge data platform,and assembly cloud platform,facilitating the collection of heterogeneous terminal data and the deployment of AI *** framework enhances assembly efficiency by reducing reliance on manual labor for individual processes and fostering collaboration across multiple *** provide detailed descriptions of how AI functions at each level of the ***,we apply the ICAA framework to a real AFAL,focusing explicitly on the flight control system testing *** practical implementation demonstrates the effectiveness of the framework in improving assembly efficiency and promoting the adoption of AIoT technologies.

关键词： intelligent aircraft final assembly line AIoT cloud-edge-terminal framework design

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Accurate threat hunting in industrial internet of things edge devices

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Digital Communications and Networks 2023年第5期9卷 1123-1130页

作者： Abbas Yazdinejad Behrouz Zolfaghari Ali Dehghantanha Hadis Karimipour Gautam Srivastava Reza M.Parizi Cyber Science Lab School of Computer ScienceUniversity of GuelphOntarioCanada Department of Electrical and Software Engineering University of CalgaryAlbertaCanada Department of Mathematics and Computer Science Brandon UniversityBrandonCanada College of Computing and Software Engineering Kennesaw State UniversityGAUSA Research Center for Interneural Computing China Medical UniversityTaichungTaiwanChina Department of Computer Science and Mathematics Lebanese American UniversityBeirut1102Lebanon

Industrial Internet of Things(IIoT)systems depend on a growing number of edge devices such as sensors,controllers,and robots for data collection,transmission,storage,and *** kind of malicious or abnormal function by each of these devices can jeopardize the security of the entire ***,they can allow malicious software installed on end nodes to penetrate the *** paper presents a parallel ensemble model for threat hunting based on anomalies in the behavior of IIoT edge *** proposed model is flexible enough to use several state-of-the-art classifiers as the basic learner and efficiently classifies multi-class anomalies using the Multi-class AdaBoost and majority *** evaluations using a dataset consisting of multi-source normal records and multi-class anomalies demonstrate that our model outperforms existing approaches in terms of accuracy,F1 score,recall,and precision.

关键词： IIoT Threat hunting Edge devices Multi-class anomalies Ensemble methods

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Power Quality of DQ Synchronous Frame for Wind Connected to Grid 13

Power Quality of DQ Synchronous Frame for Wind Connected to ...

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13th International Conference on Power, Energy and electrical engineering, CPEEE 2023

作者： Alarbidi, Omar Mater F Awaji, Hassan Hadi H Althobiti, Ahmad Awadh Alhussainy, Abdullah Ali Alghamdi, Sultan Ajour, M.N. Rawa, Muhyaddin Abusorrah, A.M. King Abdulaziz University Department of Electrical and Computer Engineering Jeddah21589 Saudi Arabia K. A. Care Energy Research and Innovation Center King Abdulaziz University Department of Electrical and Computer Engineering Jeddah21589 Saudi Arabia King Abdulaziz University K. A. Care Energy Research and Innovation Center Smart Grids Research Group Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer Engineering Jeddah21589 Saudi Arabia King Abdulaziz University Smart Grids Research Group Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer Engineering 21589 Jeddah Saudi Arabia

ISBN: (纸本)9781665490146

A wind turbine in wind energy conversion systems (WECS) captures a portion of the wind energy. It converts it to mechanical energy through a dynamic interaction with the electrical generator, transforming this mechanical energy into electrical energy. Therefore, wind turbine dynamics are crucial when researching and developing the whole WECS. It is highly desirable to study the behavior of wind energy and monitor the impact on power quality and short circuit level. In this study, a wind turbine using a permanent magnet synchronous machine (PMSM) simulation system is designed, simulated, cases study is discussed. The PMSM is torque-controlled using the field orientation method. A three-phase IGBT inverter with closed loop current PI-controllers powers the PMSM. The study and control system are executed on MATLAB SIMULINK. © 2023 IEEE.

关键词： Wind turbines

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Generalization-Enhanced Few-Shot Object Detection in Remote Sensing

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第6期35卷 5445-5460页

作者： Lin, Hui Li, Nan Yao, Pengjuan Dong, Kexin Guo, Yuhan Hong, Danfeng Zhang, Ying Wen, Congcong China Academy of Electronics and Information Technology Beijing100846 China China Meteorological Administration National Satellite Meteorological Center Innovation Center for FengYun Meteorological Satellite Beijing100081 China Tsinghua University State Key Laboratory of Hydroscience and Engineering Department of Hydraulic Engineering Beijing100084 China Chinese Academy of Sciences Aerospace Information Research Institute Beijing100094 China University of Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering Beijing100049 China University of Science and Technology Beijing School of Automation and Electrical Engineering Beijing100083 China New York University Abu Dhabi Department of Electrical and Computer Engineering Abu Dhabi United Arab Emirates University of Science and Technology of China Anhui China

Object detection is a fundamental task in computer vision that involves accurately locating and classifying objects within images or video frames. In remote sensing, this task is particularly challenging due to the high resolution, multi-scale features, and diverse ground object characteristics inherent in satellite and UAV imagery. These challenges necessitate more advanced approaches for effective object detection in such environments. While deep learning methods have achieved remarkable success in remote sensing object detection, they typically rely on large amounts of labeled data. Acquiring sufficient labeled data, particularly for novel or rare objects, is both challenging and time-consuming in remote sensing scenarios, limiting the generalization capabilities of existing models. To address these challenges, few-shot learning (FSL) has emerged as a promising approach, aiming to enable models to learn new classes from limited labeled examples. Building on this concept, few-shot object detection (FSOD) specifically targets object detection challenges in data-limited conditions. However, the generalization capability of FSOD models, particularly in remote sensing, is often constrained by the complex and diverse characteristics of the objects present in such environments. In this paper, we propose the Generalization-Enhanced Few-Shot Object Detection (GE-FSOD) model to improve the generalization capability in remote sensing FSOD tasks. Our model introduces three key innovations: the Cross-Level Fusion Pyramid Attention Network (CFPAN) for enhanced multi-scale feature representation, the Multi-Stage Refinement Region Proposal Network (MRRPN) for more accurate region proposals, and the Generalized Classification Loss (GCL) for improved classification performance in few-shot scenarios. GE-FSOD demonstrates superior robustness and accuracy in remote sensing FSOD tasks through these enhancements. Extensive experiments on the DIOR and NWPU VHR-10 datasets show that our

关键词： Labeled data

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The Impact of Weather Conditions on the Harmonic Behaviour of Grid-Connected Photovoltaic Systems 13

The Impact of Weather Conditions on the Harmonic Behaviour o...

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13th International Conference on Power, Energy and electrical engineering, CPEEE 2023

作者： Althobiti, Ahmad Awadh Almalki, Faisal Alhussainy, Abdullah Ali Alghamdi, Sultan Rawa, Muhyaddin Al-Turki, Yusuf A. King Abdulaziz University K. A. Care Energy Research and Innovation Center Department of Electrical and Computer Engineering Jeddah21589 Saudi Arabia King Abdulaziz University Department of Electrical and Computer Engineering Jeddah Saudi Arabia King Abdulaziz University Smart Grids Research Group Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer Engineering Jeddah21589 Saudi Arabia King Abdulaziz University K. A. Care Energy Research and Innovation Center Smart Grids Research Group Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer Engineering Jeddah21589 Saudi Arabia

ISBN: (纸本)9781665490146

The utilization of solar photovoltaic systems has seen a marked uptick in recent years. research efforts have been geared towards improving efficiency and reducing the cost of solar PV plants. In light of concerns over the potential consequences of high penetration of PV systems, governments have been implementing renewable energy programs. This study presents a time-domain model for analyzing harmonic effects in utility-scale PV plants under varying levels of irradiance and temperature conditions. © 2023 IEEE.

关键词： Harmonic analysis

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5G Antenna Gain Enhancement Using a Novel Metasurface

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computers, Materials & Continua 2022年第8期72卷 3601-3611页

作者： Mubashir Ashfaq Shahid Bashir Syed Imran Hussain Shah Nisar Ahmad Abbasi Hatem Rmili Muhammad Abbas Khan Department of Electrical Engineering UETPeshawar25000Pakistan Department of Electrical Engineering Chungnam National UniversityDaejeonKorea Department of Electrical Engineering University of Hafr Al BatinKSA Electrical and Computer Engineering Department Faculty of EngineeringKing Abdulaziz UniversityJeddahKSA K.A.CARE Energy Research and Innovation Center King Abdulaziz UniversityJeddah21589KSA Department of Electrical Engineering Balochistan University of Information TechnologyEngineering and Management SciencesQuettaPakistan

This article presents a Sub-6 GHz microstrip patch antenna(MPA)with enhanced gain using metamaterial(MTM)*** source MPA operates at 4.8 GHz and has a peak gain of 5.3 dBi at the resonance frequency.A window-shaped unit cell is designed and investigated through the material wave propagation *** unit cell shows an EpsilonNear Zero(ENZ)-Mu Very Large(MVL)behavior around 4.8 *** unit cell has a fourfold geometry which makes it a polarization independent metamaterial.A double layer antenna is designed by placing a 4×4 MTM slab as a superstrate above the MPA at a height of 0.208λ0;whereλ0 is the free-space wavelength at the resonance frequency of the *** matching and gain of the source antenna is improved using the *** simulation as well as measurement results show that novel antenna system has a peak gain of 7.68 dBi at 4.8 GHz and an impedance bandwidth of 0.22 *** overall dimension of the antenna is 75×60×15 mm^(3).The proposed antenna operates in the Sub-6 GHz band and is suitable for 5G applications.

关键词： Metasurface 5G gain directivity polarization

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Efficient Object Detection Acceleration Methods for Autonomous-driving Embedded Platforms

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IEIE Transactions on Smart Processing and Computing 2022年第4期11卷 255-261页

作者： Choi, Jiwoong Chun, Dayoung Lee, Hyuk-Jae Kim, Hyun Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Research Center for Electrical and Information Technology Department of Electrical and Information Engineering Seoul National University of Science and Technology Seoul01811 Korea Republic of

Object detection in autonomous vehicles is typically operated in an embedded system to reduce power consumption. The use of an object detection algorithm with high accuracy and real-time detection speed in the embedded systems is essential for ensuring safe driving. This study proposes a parallel processing method for GPU and CPU operations to enhance the detection speed of the model. In addition, this study proposes data augmentation and image resize techniques that consider the camera input size of autonomous driving, which increases the accuracy significantly while improving the detection speed. The application of these proposed schemes to a baseline algorithm, tiny Gaussian YOLOv3, improves the mean average precision by 1.14 percent points (pp) for the Berkeley Deep Drive (BDD) dataset and 1.34 pp for the KITTI dataset compared to the baseline. Furthermore, in the NVIDIA Jetson AGX Xavier, which is an embedded platform for autonomous driving, the proposed algorithm improves the detection speed by 22.54 % for the BDD, and 24.67 % for the KITTI compared to the baseline, thereby enabling high-speed real-time detection on both datasets. © 2022 Institute of Electronics and Information Engineers. All rights reserved.

关键词： Object detection

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Temporally consistent video colorization with deep feature propagation and self-regularization learning

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Computational Visual Media 2024年第2期10卷 375-395页

作者： Yihao Liu Hengyuan Zhao Kelvin CKChan Xintao Wang Chen Change Loy Yu Qiao Chao Dong Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518055China University of Chinese Academy of Sciences Beijing 100049China Department of Electrical&Computer Engineering Nanyang Technological University50 Nanyang AvenueSingapore 639798 Applied Research Center Tencent PCGShenzhenChina

Video colorization is a challenging and highly ill-posed *** recent years have witnessed remarkable progress in single image colorization,there is relatively less research effort on video colorization,and existing methods always suffer from severe flickering artifacts(temporal inconsistency)or unsatisfactory *** address this problem from a new perspective,by jointly considering colorization and temporal consistency in a unified ***,we propose a novel temporally consistent video colorization(TCVC)*** effectively propagates frame-level deep features in a bidirectional way to enhance the temporal consistency of ***,TCVC introduces a self-regularization learning(SRL)scheme to minimize the differences in predictions obtained using different time *** does not require any ground-truth color videos for training and can further improve temporal *** demonstrate that our method can not only provide visually pleasing colorized video,but also with clearly better temporal consistency than state-of-the-art methods.A video demo is provided at https://***/watch?v=c7dczMs-olE,while code is available at https://***/lyh-18/TCVC-Tem porally-Consistent-Video-Colorization.

关键词： video colorization temporal consistency feature propagation self-regularization

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Analysis of the Status Quo and Coupling Mechanisms of the Ancillary Services Market with Carbon Market, Green Certificate Market and Green Electricity Market Under the Green Energy Transformation 1

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8th Purple Mountain Forum on Smart Grid Protection and Control, PMF 2023

作者： Wang, Jiahao Ma, Hengrui Wang, Bo Alharbi, Abdullah Wang, Hongxia Li, Xiaozhu Industry Research Center Qinghai University Xining810016 China School of Electrical and Automation Wuhan University Hubei Wuhan430072 China Hubei Key Laboratory of Power Equipment and System Security for Integrated Energy School of Electrical Engineering and Automation Wuhan University Wuhan China Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Department of Electrical and Computer Engineering University of Denver Denver United States Engineering Research Center of Ministry of Education for Renewable Energy Generation and Grid Connection Technology Xinjiang University Xinjiang Urumqi830047 China School of Electrical and Automation Xinjiang University Urumqi830047 China

ISBN: (数字)9789819992515

ISBN: (纸本)9789819992508

Against the backdrop of the "dual carbon" goal and the green of energy transformation, the volatility and randomness issues of the new power system dominated by wind and solar power are becoming increasingly prominent, affecting the safe and stable operation of the power system. Therefore, the role of auxiliary services in promoting the use of alternative energy sources and ensuring power quality is becoming increasingly. In this context, this article summarizes and organizes the current development and operations status of the auxiliary services market, carbon market, green certificate market, and green electricity market in mature foreign markets, as well as the experience that can be used for reference. It also sorts out the relationship between the interaction and influence of the auxiliary services market and various other markets. Finally, based on the practical needs for market development to facilitate China’s green energy transformation, development recommendations are provided for the design of the auxiliary services market and various market coupling mechanisms, to fully leverage the synergistic effect of the auxiliary services market with the carbon market, green certificate market, and green electricity market, support China's green energy transformation. © 2024, State Grid Electric Power.

关键词： Carbon

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