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Insulating materials for realising carbon neutrality:Opportunities,remaining issues and challenges

High Voltage 2022年第4期7卷 610-632页

作者： Chuanyang Li Yang Yang Guoqiang Xu Yao Zhou Mengshuo Jia Shaolong Zhong Yu Gao Chanyeop Park Qiang Liu Yalin Wang Shakeel Akram Xiaoliang Zeng Yi Li Fangwei Liang Bin Cui Junpeng Fang Lingling Tang Yulin Zeng Xingtao Hu Jiachen Gao Giovanni Mazzanti Jinliang He Jianxiao Wang Davide Fabiani Gilbert Teyssedre Yang Cao Feipeng Wang Yunlong Zi The State Key Laboratory of Power System Department of Electrical EngineeringTsinghua UniversityBeijingChina Department of Mechanical and Automation Engineering The Chinese University of Hong KongHong KongChina Simpson Querrey Institute Northwestern UniversityEvanstonIllinoisUSA Department of Materials Science and Engineering The Pennsylvania State UniversityUniversity ParkPennsylvaniaUSA Department of Information Technology and Electrical Engineering ETH ZürichZürichSwitzerland School of Electrical and Information Engineering Tianjin UniversityTianjinChina Department of Electrical and Computer Engineering Mississippi State UniversityMississippi StateMississippiUSA Electrical and Electronic Engineering The University of ManchesterManchesterUK Department of Electrical Engineering School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghaiChina Key Laboratory of Control of Power Transmission and Conversion(SJTU) Ministry of EducationShanghaiChina College of Electrical Engineering Sichuan UniversityChengduChina Shenzhen Institute of Advanced Technology Chinese Academy of Sciences ShenzhenShenzhenChina School of Electrical Engineering Wuhan UniversityWuhanChina School of Integrated Circuits Tsinghua UniversityBeijingChina Jiangsu Jinxin Electric Appliance Co.Ltd. YangzhouChina Tai'an Tiancheng Safety Evaluation Co. Ltd.Tai'anChina Department Electrical Electronic and Information EngineeringUniversity of BolognaBolognaItaly National Engineering Laboratory for Big Data Analysis and Applications Peking UniversityBeijingChina Laplace Paul Sabatier Universityand CNRSToulouseFrance Electrical and Computer Engineering University of ConnecticutStorrsConnecticutUSA State Key Laboratory of Power Transmission Equipment&System Security and New Technology School of Electrical EngineeringChongqing UniversityChongqingChina Thrust of Sustainable Energy and Environment The Hong Kong University of Science and Technology(Guangzhou)GuangzhouChina De

The 2050 carbon-neutral vision spawns a novel energy structure revolution,and the construction of the future energy structure is based on equipment *** material,as the core of electrical power equipment and electrified transportation asset,faces unprecedented challenges and *** goal of carbon neutral and the urgent need for innovation in electric power equipment and electrification assets are first *** engineering challenges constrained by the insulation system in future electric power equipment/devices and electrified transportation assets are *** materials,including intelligent insulating material,high thermal conductivity insulating material,high energy storage density insulating material,extreme environment resistant insulating material,and environmental-friendly insulating material,are cat-egorised with their scientific issues,opportunities and challenges under the goal of carbon neutrality being *** the context of carbon neutrality,not only improves the understanding of the insulation problems from a macro level,that is,electrical power equipment and electrified transportation asset,but also offers opportunities,remaining issues and challenges from the insulating material *** is hoped that this paper en-visions the challenges regarding design and reliability of insulations in electrical equipment and electric vehicles in the context of policies towards carbon neutrality *** authors also hope that this paper can be helpful in future development and research of novel insulating materials,which promote the realisation of the carbon-neutral vision.

关键词： neutral insulation extreme

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Enhancing Bearing Reliability in Inverter-Fed IPMSMs Drive: control Method for Low-Speed Operation

Enhancing Bearing Reliability in Inverter-Fed IPMSMs Drive: ...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Lee, Sung Jun Choi, Jonghun Lim, Gyu Cheol Min Kim, Sang Kim, Eun Kyung Ha, Jung-Ik Seoul National University Department of Electrical and Computer Engineering Seoul Korea Republic of Ford Motor Company Dearborn United States Hyundai Mobis Motor Control Development Team Gyeonggi-do Uiwang-si Korea Republic of

ISBN: (纸本)9798350376067

Voltage source inverters are widely used in industry due to their high control performance and efficiency in electric motor drive systems. The output voltage of inverter at the switching instance causes the generation of bearing current, leading to bearing damage. To investigate the impact of bearing currents on bearing damage, this paper first analyzes the mechanism of bearing currents. Then, a control method for bearing current reduction is proposed. The two-phase pulse-width modulation method that considers the saliency of IPMSM and a capacitive filter topology is presented and verified by experiments in commercial electric vehicle drive system. As a result, the bearing current is reduced by 91% in comparison with space vector pulse-width modulation. © 2024 IEEE.

关键词： Pulse width modulation

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Learning Probabilistic Coordinate Fields for Robust Correspondences

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2023年第10期45卷 12004-12021页

作者： Zhao, Weiyue Lu, Hao Ye, Xinyi Cao, Zhiguo Li, Xin Huazhong University of Science and Technology The Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Wuhan430074 China West Virginia University The Lane Department of Computer Science and Electrical Engineering MorgantownWV26506-6109 United States

We introduce Probabilistic Coordinate Fields (PCFs), a novel geometric-invariant coordinate representation for image correspondence problems. In contrast to standard Cartesian coordinates, PCFs encode coordinates in correspondence-specific barycentric coordinate systems (BCS) with affine invariance. To know when and where to trust the encoded coordinates, we implement PCFs in a probabilistic network termed PCF-Net, which parameterizes the distribution of coordinate fields as Gaussian mixture models. By jointly optimizing coordinate fields and their confidence conditioned on dense flows, PCF-Net can work with various feature descriptors when quantifying the reliability of PCFs by confidence maps. An interesting observation of this work is that the learned confidence map converges to geometrically coherent and semantically consistent regions, which facilitates robust coordinate representation. By delivering the confident coordinates to keypoint/feature descriptors, we show that PCF-Net can be used as a plug-in to existing correspondence-dependent approaches. Extensive experiments on both indoor and outdoor datasets suggest that accurate geometric invariant coordinates help to achieve the state of the art in several correspondence problems, such as sparse feature matching, dense image registration, camera pose estimation, and consistency filtering. Further, the interpretable confidence map predicted by PCF-Net can also be leveraged to other novel applications from texture transfer to multi-homography classification. © 1979-2012 IEEE.

关键词： Image registration

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Multi-channel Target Speech Enhancement using Labeled Random Finite Sets and Deep Learning under Reverberant Environments 5

Multi-channel Target Speech Enhancement using Labeled Random...

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5th IEEE Eurasia Conference on IOT, Communication and engineering, ECICE 2023

作者： Datta, Jayanta Firoozabadi, Ali Dehghan Zabala-Blanco, David Soria, Francisco Ruben Castillo Adams, Martin Perez, Claudio University of Chile Department of Electrical Engineering Santiago Chile Universidad Tecnologica Metropolitana Department of Electricity Santiago Chile Universidad Católica Del Maule Department of Computer Science and Industry Talca Chile Faculty of Sciences Autonomous University of San Luis Potosi Telecommunications Engineering San Luis Potosi Mexico

ISBN: (纸本)9798350314694

We proposed a multi-channel speech enhancement procedure under reverberant conditions with acoustic source tracking and beamforming. A deep learning algorithm was applied to improve the construction of a measurement set for labeled random finite set (RFS)-based target source tracking for source localization and tracking to predict time-frequency (T-F) mask and enhance the target speech. During the source localization, steered response power phase transform (SRP-PHAT) was used to construct the measurement set for the labeled random finite set-based source tracking framework. However, owing to noise and reverberation effects, the constructed measurement set suffered from impairments that degraded the performance of the source tracking algorithm. Accurate location estimates of the target source in motion are crucial to the subsequent speech enhancement framework. Owing to its de-noising capability, a deep learning algorithm was applied to compensate for the impairments arising due to noise and reverberation for the construction of an improved measurement set. This enabled the source tracking framework to estimate the location of the target source with improved accuracy. Furthermore, a deep learning framework was applied in the speech enhancement to predict the T-F mask corresponding to the target source. T-F masking, originally used in computational auditory scene analysis (CASA), was treated with a speech enhancement method in which weights were assigned to the bins of T-F representations of the received mixture signal to enhance the received signal containing the target speech. By using the information from the source tracking sab-system, a deep learning framework was used to predict the T-F mask corresponding to the target source in the spectral domain. The inclusion of such a neural T-F mask prediction sub-system within the speech enhancement stage improved the target source separation of the time-varying beamformer. computer simulation results showed the applicati

关键词： de-reverberation deep learning denoising microphone array source separation

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computer vision for road imaging and pothole detection:a state-of-the-art review of systems and algorithms

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Transportation Safety and Environment 2022年第4期4卷 3-18页

作者： Nachuan Ma Jiahe Fan Wenshuo Wang JinWu Yu Jiang Lihua Xie Rui Fan Department of Control Science and Engineering Frontiers Science Center for Intelligent Autonomous Systemsand State Key Laboratory of Intelligent Autonomous SystemsTongji UniversityShanghai 201804P.R.China Department of Civil Engineering McGill UniversityMontréalQC H3A 0C3Canada Department of Electronics and Computer Engineering the Hong Kong University of Science and TechnologyHong Kong SAR 999077P.R.China CTO Office ClearMotion Inc.BillericaMA 01821USA School of Electrical and Electronic Engineering Nanyang Technological University50 Nanyang Avenue639798Singapore

computer vision algorithms have been utilized for 3-D road imaging and pothole detection for over two ***,there is a lack of systematic survey articles on state-of-the-art(SoTA)computer vision techniques,especially deep learningmodels,developed to tackle these *** article first introduces the sensing systems employed for 2-D and 3-D road data acquisition,including camera(s),laser scanners and Microsoft *** then comprehensively reviews the SoTA computer vision algorithms,including(1)classical 2-D image processing,(2)3-D point cloud modelling and segmentation and(3)machine/deep learning,developed for road pothole *** article also discusses the existing challenges and future development trends of computer vision-based road pothole detection approaches:classical 2-D image processing-based and 3-D point cloud modelling and segmentation-based approaches have already become history;and convolutional neural networks(CNNs)have demonstrated compelling road pothole detection results and are promising to break the bottleneck with future advances in self/un-supervised learning for multi-modal semantic *** believe that this survey can serve as practical guidance for developing the next-generation road condition assessment systems.

关键词： computer vision road imaging pothole detection deep learning image processing point cloud modelling convolutional neural networks

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Effective-aggregation Graph Convolutional Network for Imbalanced Classification 19

Effective-aggregation Graph Convolutional Network for Imbala...

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19th IEEE International Conference on Networking, Sensing and control, ICNSC 2022

作者： Wang, Kefan An, Jing Kang, Qi Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China Shanghai Institute of Technology The Department of Electrical and Electronic Engineering Shanghai China Tongji University The Department of Control Science and Engineering Shanghai China

ISBN: (纸本)9781665472432

Classification is a common task and can be achieved by learning a predictive model from a labeled training dataset. However, the imbalanced data distribution makes the model tend to favor the majority class, which reduces the classification performance. Unlike traditional classification models, graph convolutional networks (GCNs) can extract useful feature information from unlabeled data. In this paper, a novel framework for imbalanced classification named effective-aggregation graph convolutional network (EGCN) is proposed. First, a graph generator constructs graph-structured data using both labeled and unlabeled data. Then, an aggregation control unit (ACU) is performed to improve the effectiveness of aggregation. ACU uses local estimation density to limit the aggregation of inter-class edges from a local perspective, and it enhances the aggregation of the minority class from a global perspective based on the imbalance ratio. Finally, the prediction results are obtained by a graph convolutional network. Experimental results on several real-world datasets show that EGCN has promising performance. © 2022 IEEE.

关键词： Convolution

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Lyapunov-Inspired Deep Reinforcement Learning for Robot Navigation in Obstacle Environments

Lyapunov-Inspired Deep Reinforcement Learning for Robot Navi...

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Computational Intelligence on engineering/Cyber Physical Systems (CIES), IEEE Symposium on

作者： Halil Ibrahim Ugurlu Adrian Redder Erdal Kayacan Department of Electrical and Computer Engineering Aarhus University Aarhus Denmark Department of Automatic Control Paderborn University Paderborn Germany

ISBN: (数字)9798331508272

ISBN: (纸本)9798331508289

The inherent black-box nature of deep reinforcement learning (DRL) poses challenges in ensuring safety constraints. This paper, therefore, introduces a DRL reward design inspired by Lyapunov stability theory for safe robot navigation in the presence of obstacles. The navigation problem is formulated as a state-space control problem with close obstacle locations integrated into the state representation. To ensure safe obstacle avoidance, we introduce a novel reward-shaping strategy utilizing a Lyapunov function that discourages fast movement toward obstacles. Our numerical experiments demonstrate the effectiveness of the reward design strategy compared to baselines in achieving consistent superior learning with higher mission completion rates while maintaining speeds closer to a desired target speed. In addition, we show that our reward design enables a generally smaller choice for the discount factor for value-function-based DRL algorithms, which can lead to faster convergence. This is possible since the reward design merely penalizes the one-step decay of the Lyapunov function. Furthermore, policy training simulations employ an early episode termination method to constrain exploration and add more valuable samples to the DRL training replay memory. Finally, real-world experiments with a quadrotor validate the ability of our method to safely navigate around varying densities of obstacles. The proposed method consistently takes cautious maneuvers near obstacles by slowing down, achieving greater obstacle clearance compared to baseline, although with an increase in mission completion time.

关键词： Training Navigation Deep reinforcement learning Safety Planning Collision avoidance Robots Lyapunov methods Quadrotors Convergence

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The effect of PV modules on temperature conditions of nearby power cables

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Frontiers in Energy Research 2025年 13卷

作者： Czapp, Stanislaw Szultka, Seweryn Tomaszewski, Adam Khan, Laiq Faculty of Electrical and Control Engineering Gdansk University of Technology Gdansk Poland Institute of Fluid-Flow Machinery Polish Academy of Sciences Gdansk Poland Department of Electrical and Computer Engineering COMSATS University Islamabad Pakistan

The highest permissible load of power cables (ampacity) depends strongly on the ambient temperature, which in the case of cables surrounded by free air is generally assumed to be 25°C. Considering the installation of photovoltaic (PV) modules, the thermal conditions for the cables connected to these modules may be very unfavorable. The cables are laid under the module and the space underneath it is heated by this module. The aim of the paper is to present a numerical model of an example PV module mounted on the roof of a building, as well as the results of numerical calculations of the temperature around the module. Based on the numerical model of the PV system and advanced calculations it is possible to introduce a correction factor to determine the ampacity of the power cables located in the vicinity of the PV module. The authors’ research allows for the correct determination of the cable ampacity, avoiding its overheating and preventing the risk of fire. Copyright © 2025 Czapp, Szultka, Tomaszewski and Khan.

关键词： ampacity energy photovoltaic source power cable power system thermal effect

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Innate Cyber-Immunity and Acquired Cyber-Immunity across Multi-Area Power Systems

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IEEE Transactions on Power Systems 2025年

作者： Hou, Jiazuo Song, Yue Hou, Yunhe Peng, Jimmy Chih-Hsien National University of Singapore Department of Electrical and Computer Engineering Singapore Tongji University Department of Control Science and Engineering National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai Institute of Intelligent Science and Technology Shanghai201804 China The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong University of Hong Kong Shenzhen Institute of Research and Innovation Shenzhen518057 China

Organisms have evolved innate and acquired immune systems to defend against pathogens like coronaviruses. Similarly, power networks, threatened by cyber-attacks, desire cyber-immunity. Inspired by the immunology research, this study develops a cyber-immune strategy-characterizing innate cyber-immunity and establishing acquired cyber-immunity-for any selected portion of a power network (termed as power subnetwork) against stealthy false data injection (FDI) cyberattacks. Therein, without extrinsic protection and information asymmetry, this paper for the first time confirms the existence and provides closed-form conditions for power subnetworks that are innately immune to FDI cyber-attacks despite all measurements being compromised. Single-leader-multi-follower bi-level optimization models are then developed to strategically allocate measurement protection, leading to the acquired cyber-immunity, whose conditions for existence and the least cost are explicitly formulated. More importantly, a bi-directional cyber-immunity propagation mechanism is presented to provide the optimal spatial allocation of defense resources across adjacent subnetworks. It also explicitly determines the temporal allocation sequence, revealing its sequential and cumulative impacts on establishing cyber-immunity. This is achieved by designing recursive traversal algorithms, one of which is modelled as a post-order depth-first-search problem of a multi-fork tree. The proposed subnetwork-specific cyber-immune strategy is verified in a modified Australian National Electricity Market. © 1969-2012 IEEE.

关键词： Phishing

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FusionAraSA: Fusion-based Model for Accurate Arabic Sentiment Analysis

FusionAraSA: Fusion-based Model for Accurate Arabic Sentimen...

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Abdulfattah E. Ba Alawi Ahmed N. Nusari Ferhat Bozkurt İbrahim Yücel Özbek Computer Engineering Department Faculty of Engineering Ataturk University Erzurum Turkey Software Engineering Department Faculty of Engineering & IT Taiz University Taiz Yemen Electrical and Electronics Engineering Department Faculty of Engineering Ataturk University Erzurum Turkey Computer and Control Engineering Department Faculty of Engineering Sana’a University Sana’a Yemen Electrical and Electronics Department Faculty of Engineering Ataturk University Erzurum Turkey

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

The Arabic language is one of the most common spoken languages. Due to the complex structure and extensive range of potential meanings of Arabic texts, it is a challenging task to perform sentiment analysis using Natural Language Processing (NLP) approaches. Traditional methods of sentiment analysis often fail to perform Arabic sentiment analysis tasks. In this study, a fusion majority voting technique was employed to enhance the performance of Arabic sentiment analysis. The proposed method integrates four models (i.e. AraBERv1, MARBERT, Modified MARBERT, and AraBERv2 ) to analyze Arabic sentiments. Then, the outputs of these four models were forwarded to a fusion stage the perform majority voting and show the corresponding identified class. This technique is called FusionAraSA which relies on integrating four models and fusion to expand the application of pre-trained models. The FusionAraSA approach shows promising performance reaching more than 95% in terms of both accuracy and F1 Score utilizing two publicly available Arabic datasets. The results obtained using FusionAraSA illustrate the exceptional performance and the feasibility of this model in Arabic sentiment analysis. Additionally, the findings highlight the remarkable potential of the fusion model and underscore the necessity for further research to unravel the intricacies of Arabic dialects. Moreover, the FusionAraSA model represents a major step towards building more complex, culturally sensitive, and technologically advanced Arabic language analysis architecture.

关键词： Sentiment analysis Analytical models Accuracy Architecture Buildings Task analysis

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