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8th International Conference on Power and Renewable Energy, ICPRE 2023

作者： Li, Shenwang Jin, Zhaofei Deng, Wenyang Su, Qiuren Hu, Likun Wu, Thomas Guangxi University School of Electrical Engineering Nanning China South China University of Technology School of Electric Power Engineering Guangzhou China Guangxi Vocational University of Agriculture Department of Information and Electronic Engineering Nanning China Guangxi University College of Computer and Electronic Information Nanning China

ISBN: (纸本)9798350328813

Magneto-optical current transformer has the defect of poor anti-interference ability. In this paper, the interference characteristics of differential magneto-optical current transformer are studied based on the background of interphase magnetic field interference in three-phase power system. By establishing the magnetic field interference model, the anti-magnetic field interference characteristics of the differential magneto-optical current transformer are studied. The research shows that there is a zero magnetic field interference point in the difference magneto-optical current transformer and the zero-interference angle presents a specific distribution law. When the transmission light path of the difference magneto-optical current transformer degenerates to a square arrangement, the value of magnetic field interference has nothing to do with the distance of interference current. Moreover, the square structure makes it have zero-interference angle at a specific angle. The analysis shows that when the interference current deviates from zero-interference angle, the differential magneto-optical current transformer has fault tolerance within a certain range of distance and angle. And it can still meet the requirements of measurement accuracy under the influence of interference current. The simulation results are consistent with the theoretical analysis, which proves the correctness of the theoretical analysis. This research can provide a useful idea for the design of anti-phase magnetic field interference for the application of differential magneto-optical current transformer in power system. © 2023 IEEE.

关键词： difference magneto-optical current transformer fault tolerance perspective magnetic field interference degree square structure zero-interference angle

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Long-Term Water Quality Prediction with Patch Savitsky-Golay Filtering and Transformer

Long-Term Water Quality Prediction with Patch Savitsky-Golay...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Lin, Yongze Qiao, Junfei Bi, Jing Yuan, Haitao Zhai, Jiahui Zhou, MengChu Beijing University of Technology Beijing Laboratory of Smart Environmental Protection Beijing100124 China School of Information Science and Technology Beijing University of Technology Beijing100124 China College of Computer Beijing University of Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China New Jersey Institute of Technology Department of Electrical and Computer Engineering Newark07102 United States

ISBN: (纸本)9781665410205

In many fields, time series prediction is gaining more and more attention, e.g., air pollution, geological hazards, and network traffic prediction. Water quality prediction is based on historical data to predict future water quality. However, it is difficult to learn a representation map from a time series that captures the trends and fluctuations to effectively remove noise from time series data and capture complex nonlinear relationships. To solve these problems, this work proposes a time series prediction model, called PSGT for short, which integrates Patch Savitsky-Golay filtering and Transformer. First, this work adopts a Patching method to embed sub-time series data and obtains the trends and semantic information of the time series. Second, it uses the Savitsky-Golay filtering to effectively remove the noise data in the patch and improve the prediction accuracy. Third, it uses a Transformer mechanism to address the nonlinear problem of water quality time series and improve long-term prediction capability. Two real-world datasets are utilized to evaluate the proposed PSGT, and experiments prove that PSGT performs better than other benchmark models by at least 6%. © 2024 IEEE.

关键词： Self-supervised learning

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Torque Ripple Reduction in Brushless DC Motors with 180° Commutation

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IEEE Transactions on Industry Applications 2025年

作者： Feng, Ziliang Ramesh, Rahul Raman Tahim, Ekamjot Singh Zhang, Jiahao Ebrahimi, Seyyedmilad Jatskevich, Juri University of British Columbia Electrical and Computer Engineering Department VancouverV6T 1Z4 Canada Jiangsu University School of Electrical and Information Engineering Zhenjiang212013 China Washington State University School of Electrical Engineering and Computer Science PullmanWA99163 United States

Hall-sensor-based brushless DC (BLDC) motors are utilized extensively in low-cost applications due to their manufacturing simplicity and ease of control. Often, BLDC motors are controlled using the 120° commutation logic, which naturally approximates the maximum torque per ampere (MTPA) operation. However, in some applications, the 180° commutation is preferred since it allows higher DC voltage utilization and continuous phase currents, although it comes at the cost of higher torque ripple. This paper proposes a new torque control strategy to reduce the torque ripple in the 180°-commutated BLDC motors. The proposed method dynamically adjusts the duty cycle within each switching interval based on the reference, the estimated electromagnetic torque, and the derivative of the torque. The proposed method is analyzed under different speeds and back EMF shapes, and its effectiveness is validated through simulation and experimental results on a typical industrial BLDC motor. © 1972-2012 IEEE.

关键词： Brushless DC motors

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Addressing Trust Issues for Vehicle to Grid in Distributed Power Grids Using Blockchains

arXiv

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arXiv 2024年

作者： Chen, Yunwang Lei, Xiang Jian, Linni Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen China School of Mechanical and Electrical Engineering Shenzhen Polytechnic University Shenzhen China

While blockchain offers inherent security, trust issues among stakeholders in vehicle-to-grid (V2G) applications remain unresolved due to a lack of regulatory frameworks and standardization. Additionally, a tailored decentralized privacy-preserved coordination scheme for blockchain in V2G networks is needed to ensure user privacy and efficient energy transactions. This paper proposes a V2G trading and coordination scheme tailored to the decentralized nature of blockchain as well as the interests of stakeholders utilizing smart charging points (SCPs) and Stackelberg game model. Case studies using real-world data from Southern University of Science and Technology demonstrate the efficacy of the proposed scheme in reducing EV charging costs and the potential for supporting auxiliary grid services. Copyright © 2024, The Authors. All rights reserved.

关键词： Vehicle-to-grid

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Classification and Source Location Indication of Jamming Attacks Targeting UAVs via Multi-output Multiclass Machine Learning Modeling

Classification and Source Location Indication of Jamming Att...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： M. Alkhatib M. McCormick L. Williams A. Leon L. Camerano K. Al Shamaileh V. Devabhaktuni N. Kaabouch Electrical and Computer Engineering Department Purdue University Northwest Hammond IN USA Electrical and Computer Engineering Department Illinois State University Normal IL USA School of Electrical Engineering and Computer Science University of North Dakota Grand Forks ND USA

This paper introduces machine learning (ML) as a solution for the detection and range localization of jamming attacks targeting the global positioning system (GPS) technology, with applications to unmanned aerial vehicles (UAVs). Different multi-output multiclass ML models are trained with GPS-specific sample datasets obtained from exhaustive feature extraction and data collection routines that followed a set of realistic experimentations of attack scenarios. The resulting models enable the classification of four attack types (i.e., barrage, single-tone, successive-pulse, protocol-aware), the jamming direction, and the distance from the jamming source by yielding a detection rate (DR), misdetection rate (MDR), false alarm rate (FAR), and F-score (FS) of 98.9%, 1.39%, 0.28%, and 0.989, respectively.

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Power System Transient Security Assessment using Unsupervised Probabilistic Deep Bayesian Neural Network

Power System Transient Security Assessment using Unsupervise...

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Energy Technologies for Future Grids (ETFG), IEEE International Conference on

作者： Shahabodin Afrasiabi Sarah Allahmoradi Xiaodong Liang Mousa Afrasiabi Jamshid Aghaei C.Y Chung Department of Electrical and Computer Engineering University of Saskatchewan Saskatoon Canada Department of Electrical Engineering School of Energy Systems Lappeenranta University of Technology Lappeenranta Finland Department of Electrical Engineering School of Engineering and Technology Central Queensland University Rockhampton Australia Department of Electrical Engineering The Hing Kong Polytechnic University Hong Kong China

This paper introduces an unsupervised deep Bayesian network, built upon normalizing flow and deep Bayesian network principles, to precisely evaluate the transient security condition of a power system. The proposed approach can capture locational and temporal features using an imbalanced dataset, is noise-model-free, and can handle unlabeled data. It can learn interdependencies between different signals and understand high-dimensional signals in power systems. To validate its effectiveness, the proposed method is studied using the New England power system and shows accuracy and reliability in comparison with state-of-the-art deep networks (convolutional neural network (CNN) and long short-term memory (LSTM)) and shallow networks (support vector machine (SVM) and artificial neural network (ANN)).

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Insurance Plan for Service Assurance in Cloud Computing Market with Incomplete Information

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Journal of Communications and Information Networks 2022年第1期7卷 11-22页

作者： Yanru Zhang Chunxiao Jiang Nguyen H.Tran Shengrong Bu FeiRichard Yu Zhu Han College of Computer Science and Engineering the University of Electronic Science and Technology of ChinaChengdu 611731China Shenzhen Institute for Advanced Study UESTCLonghuaShenzhen 518110China Tsinghua Space Center Tsinghua UniversityBeijing 10084China School of Computer Science the University of SydneySydneyNew South WalesAustralia Department of Engineering Brock UniversityONCanada School of Information Technology Depart.of Systems and Computer EngineeringCarleton UniversityOttawa K1S 5B6ONCanada Department of Electrical and Computer Engineering the University of HoustonHouston 77004TXUSA

Nowadays,cloud computing has been identified as new opportunities for migrating to the expected agility,reuse,and adaptive capabilities that can support the ever changing IT trends and ***,the rapid evolution of those technologies also comes with open issues such as security,privacy,integrity,quality of services,and their possible detrimental *** this work,the concept of insurance is introduced to compensate the cloud computing customers when encountering those failures if service providers(SPs)have insurance ***,we consider the situation when the insurer is unable to see the system failure risk levels of the SPs,which is usually seen as an incomplete information market,in contrast with the optimal situation in a complete information ***,an insurance-based cloud computing architecture is proposed to build a monetary credit system in which the cloud computing SP pays a premium for a certain coverage to the ***,problem is formulated to solve the optimal insurance plan in complete and incomplete information markets,together with detail analysis of insurance policies in both cases are ***,simulation results show the properties of the two insurance plans and parameters that affect the design of the insurance plan.

关键词： service assurance cloud computing market incomplete information game theory insurance plan

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Cluster Head Selection using Dingo Optimization Algorithm in Mobile Ad Hoc Network

Cluster Head Selection using Dingo Optimization Algorithm in...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Vadakkethil Somanathan Pillai, Sanjaikanth E Kumar Pareek, Piyush Polimetla, Kiran Zanke, Pankaj Ali, Hassan Mohamed University of North Dakota School of Electrical Engineering and Computer Science Grand Forks United States Nitte Meenakshi Institute of Technology Department of Aiml Bengaluru India Adobe Inc Department. of Information Technology AustinTX United States Sapiens International Corporation The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq

ISBN: (纸本)9798350360660

Efficient Energy-Efficient Cluster Head Selection (EECHS) remains a critical issue in Mobile Ad Hoc Networks (MANETs), where the selection of an appropriate cluster head (CH) can significantly enhance power efficiency. Clustering in routing plays a vital role in optimizing MANET performance by coordinating pathways through various analytics and criteria. Nodes in MANETs form clusters to improve communication, employing strategies such as distributed clustering and cluster-based routing. Despite their application, these methods often suffer from complexity and high overhead, which compromise their *** study proposes the Dingo Optimization Algorithm (DOA) as a viable solution to the EECHS problem. DOA inherits its exploration and exploitation processes from the natural behavior of dingoes, involving techniques such as pursuing, approaching, encircling, and pestering to identify the optimal CHs. Extensive experimental research conducted on the NS2 simulator demonstrates that the proposed method achieves a packet delivery ratio (PDR) of 96%, a throughput of 240 Kbps, and an energy consumption of 21 mJ for a network with 500 nodes. The simulation results and replication findings indicate that this strategy is particularly suitable for sensitive MANET applications, outperforming existing routing tactics in terms of efficiency and effectiveness. © 2024 IEEE.

关键词： Mobile ad hoc networks

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Towards an Automatic Extracting UML Class Diagram from System's Textual Specification 24

Towards an Automatic Extracting UML Class Diagram from Syste...

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7th International Conference on Networking, Intelligent Systems and Security, NISS 2024

作者： Babaalla, Zakaria Jakimi, Abdeslam Oualla, Mohamed Saadane, Rachid Chehri, Abdellah GL-ISI Team Department of Informatics Faculty of Sciences and Techniques Errachidia Morocco Electrical Engineering Department Hassania School of Public Works Casablanca Morocco Department of Mathematics and Computer Science Royal Military College of Canada Kingston Canada

ISBN: (纸本)9798400709296

Developing a software system from natural language requirements is a complex and delicate task that requires a high level of design and programming expertise. Increasing the level of abstraction used to describe these requirements is the most natural solution. Model-Driven engineering (MDE) also takes this route, using abstract models as primary entities to generate source code automatically or semi-automatically. Among these models, the UML class diagram occupies a privileged place in object-oriented systems because it not only serves as a basis for communication between developers but also provides a closely aligned static representation of the system implementation. However, creating a UML class diagram from a textual system specification poses a significant challenge due to the inherent imprecision and ambiguity commonly found in natural language expressions. In this paper, we propose a model-centric approach based on deep learning for the automatic extraction of UML class diagrams from textual requirements. © 2024 ACM.

关键词： Object oriented programming

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Diagnosis of Acute Respiratory Syndromes from X-Rays using Customised CNN Architecture 4

Diagnosis of Acute Respiratory Syndromes from X-Rays using C...

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4th IEEE International Conference on Artificial Intelligence in engineering and Technology, IICAIET 2022

作者： Palaniappan, S. Sai Sripriya, S. Varshaa Lalitha Pranathi, Amalladinna Rama Muthulakshmi, M. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science Engineering Chennai India School of Engineering Amrita Vishwa Vidyapeetham Department of Electronics and Communication Engineering Amrita Chennai India Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Electrical and Electronics Engineering Chennai India

ISBN: (数字)9781665468374

ISBN: (纸本)9781665468374

This work presents the diagnosis of various acute respiratory syndromes using customized CNN architecture from X-ray images. Complications of viral pneumonia results in influenza and COVID-19. The respiratory syndromes occur due to bacterial and fungal infections as well. Hence, the objective was to use customized CNN architecture to perform a multi-class pneumonia classification. VGG16 architecture is carefully trained for pneumonia classification with ReLU activation and categorical cross-entropy loss function. The proposed model is efficient and robust and yielded 97.87% accuracy on the train set and 90% accuracy on the test set. The experimental results suggest that the model efficiently detects all sorts of lung diseases, including COVID 19. © 2022 IEEE.

关键词： COVID-19

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