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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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Transformer Neural Networks for Intrusion Diagnostic Unit (IDU) and Anomaly Detection in Distributed Energy Resources (DERs)

Transformer Neural Networks for Intrusion Diagnostic Unit (I...

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electrical engineering and Green Energy (CEEGE), International Conference on

作者： Shree Ram Abayankar Balaji Tanzim Jim Hassan Akshay Ram Ramchandra Prakash Ranganathan Siby Jose Plathottam Kumarsinh Jhala School of Electrical Engineering and Computer Science (SEECS) University of North Dakota Grand Forks ND USA Argonne National Laboratory Lemont IL USA

ISBN: (数字)9798350350623

ISBN: (纸本)9798350350630

Modern critical infrastructure such as an electric grid, uses information and communication technologies (ICT) to increase system performance. However, the integration of Distributed Energy Resources (DERs) and their interconnectedness to ICTs exposes them to different threat landscapes. To defend and mitigate cyberattacks targeting critical infrastructure, there is a critical need for robust situational awareness tool(s) and strategies. This paper reviews the existing work on the deployment of Transformer Neural Networks (TNN) in Intrusion Detection Units (IDUs) and Anomaly Detection (AD) applications. Additionally, the paper discusses specific stealthy attack scenarios and provides recommendations by mapping Confidentiality, Integrity, Availability, and Accountability (CIAA) properties to different attacks. To realize a resilient electric grid, authors suggest DER stakeholders to carefully review IEEE 1547.3-2023 standard recommendations.

关键词： Reviews System performance Neural networks Transformers Distributed power generation Information and communication technology Critical infrastructure Security Standards Anomaly detection

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Mode-Symmetry-Assisted Optical Pulling by Bound States in the Continuum

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Physical Review Letters 2024年第25期132卷 253802-253802页

作者： Hang Li Yongyin Cao Rui Feng Bojian Shi Yuzhi Shi Yang Chen Dongliang Gao Tongtong Zhu Donghua Tang Fangkui Sun Cheng-Wei Qiu Weiqiang Ding Institute of Advanced Photonics School of Physics Institute of Precision Optical Engineering School of Physics Science and Engineering Chinese Academy of Sciences Key Laboratory of Mechanical Behavior and Design of Materials Department of Precision Machinery and Precision Instrumentation School of Physical Science and Technology Collaborative Innovation Center of Suzhou Nano Science and Technology School of Optoelectronic Engineering and Instrumentation Science Department of Physics School of Science Department of Electrical and Computer Engineering Collaborative Innovation Center of Extreme Optics

Aside from optical pushing and trapping that have been implemented successfully, the transportation of objects backward to the source by the optical pulling forces (OPFs) has attracted tremendous attention, which was usually achieved by increasing the forward momentum of light. However, the limited momentum transfer between light and object greatly constrains the amplitudes of OPFs. Here, we present a mechanism to generate strong interactions between object and background through the bound states in the continuums, which can generate large OPFs without increasing the forward momentum of light. The underlying physics is the extraction of momentum from the designed background lattice units assisted by mode symmetry. This work paves the way for extraordinary optical manipulations and shows great potential for exploring the momenta of light in media.

关键词： Mechanical effects of light on material media Nanophotonics Photonic crystals

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Towards New Frontiers in Mobile Manipulation: Team CTU-UPenn-NYU at MBZIRC 2020

Field Robotics

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Field Robotics 2025年 2卷 75-106页

作者： Petr Štibinger George Broughton Filip Majer Zdeněk Rozsypálek Anthony Wang Kshitij Jindal Alex Zhou Dinesh Thakur Giuseppe Loianno Tomáš Krajník Martin Saska Department of Cybernetics Faculty of Electrical Engineering Czech Technical University in Prague Department of Computer Science Faculty of Electrical Engineering Czech Technical University in Prague Department of MAE Tandon School of Engineering New York University GRASP Laboratory University of Pennsylvania Philadelphia PA USA Department of ECE and MAE Tandon School of Engineering New York University

In this paper we present an autonomous robotic system for picking, transporting, and precisely placing magnetically graspable objects. Such a system would be especially beneficial for construction tasks where human presence is not possible, e.g. due to chemical or radioactive pollution. The system comprises of two primary components — a wheeled, mobile platform and a manipulator arm. Both are interconnected through an onboard computer and utilize various onboard sensors for estimating the state of the robot and its surroundings. By using efficient processing algorithms, data from the onboard sensors can be used in a feedback loop during all critical operational sections, resulting in a robust system capable of operating on uneven terrain and in environments without access to satellite navigation. System functionality has been proven in Challenge II of the MBZIRC 2020 competition. The Challenge required a ground robot to build an L-shaped structure of colored bricks laid in a predefined pattern. Such a mission incorporates several demanding subchallenges, spanning multiple branches of computer science, cybernetics, and robotics. Moreover, all the subchallenges had to be performed flawlessly in rapid succession, in order to complete the Challenge successfully. The extreme difficulty of the task was highlighted in the MBZIRC 2020 finals, where our system was among the only two competitors (out of 32) that managed to complete the task in autonomous mode.

关键词： Robots Manipulators Robot sensing systems Reliability Autonomous aerial vehicles Service robots Multi-robot systems Assembly Valves Safety

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Feature Correlation Aggregation: on the Path to Better Graph Neural Networks

Feature Correlation Aggregation: on the Path to Better Graph...

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Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： Jieming Zhou Tong Zhang Pengfei Fang Lars Petersson Mehrtash Harandi College of Engineering and Computer Science Australian National University Canberra Australia Image and Visual Representation Laboratory EPFL Lausanne Switzerland Imaging and Computer Vision Group CSIRO Canberra Australia Department of Electrical and Computer Systems Engineering Monash University Melbourne Australia

Prior to the introduction of Graph Neural Networks (GNNs), modelling and analyzing irregular data, particularly graphs, was thought to be the Achilles’ heel of deep learning. The core concept of GNNs is to find a representation by recursively aggregating the representations of a central node and those of its neighbor, and many GNNs’ designs have demonstrated its success. However, most only focus on using the first-order information between a node and its neighbors. In this paper, we introduce a central node permutation variant function through a frustratingly simple and innocent-looking modification to the core operation of a GNN, namely the Feature cOrrelation aGgregation (FOG) module, which learns the second-order information from feature correlation between a node and its neighbors in the pipeline. By adding FOG into existing variants of GNNs, we empirically verify this second-order information complements the features generated by original GNNs across a broad set of benchmarks. A tangible boost in the model’s performance is observed where the model surpasses previous state-of-the-art results by a significant margin while employing fewer parameters. e.g., 26.202% improvement on a real-world molecular dataset using graph convolutional networks.

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An Automatic Reflective Clothing Detection Algorithm Based on YOLOv5 for Work Type Recognition 11

An Automatic Reflective Clothing Detection Algorithm Based o...

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11th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2022

作者： He, Xinyi Ma, Ping Chen, Yiyang Liu, Yuan School of Artificial Intelligence and Computer Science Jiangnan University Jiangsu Key Laboratory of Media Design and Software Technology Wuxi214122 China School of Mechanical and Electrical Engineering Soochow University Suzhou215137 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

At present, traditional manual methods are still utilized for the inspection of reflective clothing on a wide range of construction sites. Using this manual inspection methods wastes much human resource. In this paper, the YOLOv5 model is used as the network framework to automatically detects whether the workers wear reflective clothing, and determines types of work by examining reflective clothing of different colors. By identifying the color difference of the reflective clothing, the network is capable of distinguish whether the target work type can enter relevant construction areas with different danger levels. In this paper, by constructing the labeled data set and training on the basis of YOLOv5 model framework, a target model is obtained. Its detection speed can adapt to real-time monitoring. The accuracy and recall rates can meet the requirements on the training set and verification set. It has a desired effect on the testing set of real website captured images, and can realize the intelligent recognition of reflective clothing on construction sites. © 2022 IEEE.

关键词： Deep learning

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Evolutionary Classifier Chain for Multi-Dimensional Classification 39

Evolutionary Classifier Chain for Multi-Dimensional Classifi...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhang, Yu-Yang Jia, Bin-Bin Zhang, Min-Ling School of Computer Science and Engineering Southeast University Nanjing210096 China College of Electrical and Information Engineering Lanzhou University of Technology Lanzhou730050 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China

ISBN: (纸本)157735897X

In multi-dimensional classification (MDC), the classifier chain approach is based on a chain structure to model dependencies between class spaces. However, current research on constructing a chain order is usually based on a greedy criterion or random generation, which is highly likely to lead to an incorrect chain order and fit incorrect class dependencies. Moreover, existing classifier chain-based approaches do not consider the misleading effects of irrelevant input features on the classifiers. To fill the above gap, a classifier chain-based approach incorporating evolutionary chain order optimization and feature selection (ECCO) is proposed. Specifically, this approach designs a meta-heuristic algorithm to optimize the chain order of multiple classifiers. Simultaneously, the approach selects dimension-specific feature combinations that are more conducive to class prediction of each dimension. These strategies enhance the class prediction capability of the constructed MDC model. Comparative experiments on 14 real datasets validate that ECCO outperforms 7 state-of-the-art MDC approaches. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Model Predictive Thrust Control for Linear Induction Machine: A Fuzzy Optimization Approach

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IEEE Transactions on Industry Applications 2022年第2期59卷 1-14页

作者： Tang L. Xu W. Tang Y. Ge J. Yang K. Islam M.R. Li W. Su S. School of Management School of Electrical Engineering Xi&#x0027 State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan Hubei China School of Electrical Computer and Telecommunications Engineering University of Wollongong NSW Australia Xiangyang CRRC Motor Technology Co. Ltd. Xiangyang China CRRC Zhuzhou Motor Co. Ltd. Zhuzhou China

Model predictive thrust control (MPTC) is one of the most effective approaches for linear induction machine (LIM) drive system. It can achieve the optimization of multiple objectives with suitable weighting factors, such as low ripples and fast dynamic performance. Due to the longitudinal end effect, the selection of weighting factor becomes particularly important in LIM. However, the process of tuning the weighting factors in the objective function is very long and tedious. In this paper, a dynamic fuzzy MPTC approach is proposed to solve the multi-objective problem for minimizing the flux and thrust ripples. Based on the fuzzy optimization technique, the tuning process of weighting factors can be transformed into a discrete membership function. A “max-min” objective function of voltage vectors is employed to select a more balanced switch combination on the Pareto optimal-frontier for LIM. Furthermore, this approach can adapt to different operating conditions by adjusting objective priority of membership function. At last, comprehensive simulation and experimental results are conducted to demonstrate the effectiveness and feasibility of the proposed method. IEEE

关键词： fuzzy optimization Linear induction machine (LIM) model predictive thrust control (MPTC) optimization methods weighting factors

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Efficient Object Rearrangement via Multi-view Fusion

arXiv

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arXiv 2023年

作者： Huang, Dehao Tang, Chao Zhang, Hong Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen China Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China

The prospect of assistive robots aiding in object organization has always been compelling. In an image-goal setting, the robot rearranges the current scene to match the single image captured from the goal scene. The key to an image-goal rearrangement system is estimating the desired placement pose of each object based on the single goal image and observations from the current scene. In order to establish sufficient associations for accurate estimation, the system should observe an object from a viewpoint similar to that in the goal image. Existing image-goal rearrangement systems, due to their reliance on a fixed viewpoint for perception, often require redundant manipulations to randomly adjust an object’s pose for a better perspective. Addressing this inefficiency, we introduce a novel object rearrangement system that employs multi-view fusion. By observing the current scene from multiple viewpoints before manipulating objects, our approach can estimate a more accurate pose without redundant manipulation times. A standard visual localization pipeline at the object level is developed to capitalize on the advantages of multi-view observations. Simulation results demonstrate that the efficiency of our system outperforms existing single-view systems. The effectiveness of our system is further validated in a physical experiment. Copyright © 2023, The Authors. All rights reserved.

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ALFLAT: Chinese NER Using ALBERT, Flat-Lattice Transformer, Word Segmentation and Entity Dictionary 2nd

ALFLAT: Chinese NER Using ALBERT, Flat-Lattice Transformer, ...

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2nd EAI International Conference on Applied Cryptography in computer and Communications, AC3 2022

作者： Lv, Haifeng Ding, Yong School of Data Science and Software Engineering Wuzhou University Wuzhou China Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou China

ISBN: (纸本)9783031170805

Recently, the character-word lattice structure has been proved to be effective for Chinese named entity recognition (NER) by incorporating the word information. However, one hand, since the lattice structure is dynamic and complex, although some existing lattice-based models are effectively utilize the parallel computation of GPUs, they do not fully utilize word segmentation boundary tags that as features are helpful for NER task. On the other hand, the character-word vector needs to be trained, and the user-defined entity dictionary cannot be effectively used. In this paper, we propose ALFLAT: based on a flat-lattice Transformer to incorporate ALBERT pre-trained model, word segmentation information and user-defined entity dictionary for Chinese NER. ALFLAT converts the lattice structure into a flat structure consisting of spans, integrate word segmentation embedding with the output of flat-lattice Transformer model, then modifies the emission scores according to the user-defined entity dictionary, finally utilize Viterbi decoding of the CRF layer to obtain the correct entity results. Each span corresponds to a character or latent word and its position in the original lattice. With the power of ALBERT pre-trained model, Transformer and position encoding, ALFLAT can fully leverage the lattice, word segmentation and user-defined entity dictionary information. Experiments on MSRA dataset show ALFLAT outperforms other lexicon-based models in performance and efficiency. © 2022, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Computational linguistics

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