检索结果-内蒙古大学图书馆

The current pulses characteristics of the negative corona discharge in SF_(6)/CF_(4)mixtures

Plasma Science and Technology 2023年第2期25卷 119-130页

作者：高青青王小华 Kazimierz ADAMIAK 杨爱军刘定新纽春萍张嘉伟 School of Electrical Engineering Xi'an University of TechnologyXi'an 710048People's Republic of China State Key Laboratory of Electrical Insulation and Power Equipment Xi'an Jiaotong UniversityXi'an 710049People's Republic of China Department of Electrical and Computer Engineering Western UniversityOntario N6A5B9Canada

This paper presents the results of numerical investigation of the current pulses characteristics in SF_(6)/CF_(4)mixtures for the negative point-plane corona *** pressure and the temperature of gas mixtures are 0.4 MPa and 300 K,*** CF_(4)content varies from20%to 80%.The 2D axisymmetric geometry with point-plane electrodes is investigated,and the three drift-diffusion equations are solved to predict the characteristics of the negative corona *** addition,Poisson’s equation is coupled with the above three continuity equations to calculate the electric *** order to calculate the electron impact coefficients,including the Townsend ionization and attachment coefficients,as well as the mobilities and diffusion coefficients for electrons,the two-term Boltzmann equation is *** characteristics of three ionic species at five stages of the first current pulse in 60%SF_(6)-40%CF_(4)and20%SF_(6)-80%CF_(4)mixtures are selected to discuss the development mechanism of current ***,the reduced electric field strengths at the corresponding time instants are presented to help understand the discharge *** current waveform and the total number of three species are compared in all the cases to analyze the effects of the CF_(4)content on the *** reduced electric field strength is also helpful in understanding the effects of CF_(4)*** the CF_(4)content increases to 80%,the discharge is more intensive and the pulse frequency also increases.

关键词： SF_(6)/CF_(4)mixtures negative corona discharge current pulses

来源：评论

学校读者我要写书评

暂无评论

Enhanced State of Charge Estimation for Lithiumion Batteries using Polynomial Voltage Approximation 3

Enhanced State of Charge Estimation for Lithiumion Batteries...

引用

3rd Odisha International Conference on electrical Power engineering, Communication and Computing Technology, ODICON 2024

作者： Karna, Sparsh Satpathy, Priya Ranjan Bhowmik, Pritam Vit University Department of Electrical and Computer Science Engineering Chennai India Institute of Power Engineering Universiti Tenaga Nasional Malaysia School of Electrical Engineering Vellore Institue of Technology Vit University Chennai India

ISBN: (纸本)9798350354379

The escalating adoption of electric machinery as a replacement for the fossil fuel-powered counterparts has underscored the critical need for robust energy storage solutions, with lithium-ion (Li-ion) batteries emerging as a cornerstone technology, particularly in electric vehicles (EVs). However, the intrinsic vulnerability of Li-ion batteries to degradation, caused by cyclic charge-discharge operations, poses significant challenges to accurate state of charge (SOC) estimation and capacity assessment, thereby impeding optimal EV performance [12] [16]. This study presents a novel approach to address these challenges by elucidating a direct correlation between battery voltage and SOC. Through rigorous empirical experimentation and advanced mathematical modelling, a polynomial equation is derived to precisely quantify SOC dynamics in response to voltage fluctuations. This framework facilitates real-time capacity estimation, empowering proactive management of EV energy systems [18]. By integrating empirical data with sophisticated mathematical analysis, this research contributes to deeper understanding of Li-ion battery behavior, paving the way for enhanced energy storage management strategies. The findings hold promise for optimizing EV efficiency, reliability, and longevity in the evolving landscape of electric machinery. © 2024 IEEE.

关键词： State of charge

来源：评论

学校读者我要写书评

暂无评论

Improving Online Education in Educational Institutions through the Integration of Internet of Things (IoT) 8

Improving Online Education in Educational Institutions throu...

引用

8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Ramaraj, Kottaimalai Mathews, Ann Lynsha Helena Pratheeba, Hp Ramani, U. Sujitha, K.L. Thilagaraj, M. Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Tamilnadu Krishnankoil India Cmr Institute of Technology Department of Computer Science and Engineering Bengaluru India K Ramakrishnan College of Engineering Department of Electrical and Electronics Engineering Trichy India Mvj College of Engineering Department of Computer Science and Engineering Bengaluru India Mvj College of Engineering Department of Industrial Internet of Things Bengaluru India

ISBN: (纸本)9798350367904

A key facilitating infrastructure for building intelligent structures that enable efficient in-person and virtual learning environments is the Internet of Things (IoT). The shift to smart learning, that includes IoT and Artificial Intelligence (AI) into the educational system, is enticing since it directly affects the motivation of learners, enrolment, participation, and in-depth understanding. Teaching and learning, management, examination, and class monitoring are only a few of the numerous issues that afflict conventional education. Many ingenious approaches to many facets of our lives are being made possible by modern advancements in information and communication technology (ICT);nonetheless, these imaginative concepts aren't adequately incorporated into the framework of education. The COVID-19 pandemic scenario, in specific, has brought out the need for innovative, creative approaches in the field of teaching. The present research examines previous investigations in the field and discusses the following topics: (i) issues with the current educational system and potential fixes;(ii) the shift to smart learning;and (iii) scientific hurdles (such as technological and cultural barriers) in making the move to smart learning. In accordance with these outcomes, creative approaches to the issues with the conventional system-such as smart teaching, smart evaluation, smart school environment, and smart management-are presented. The business and academic community may now include IoT, AI, and ICT into smart learning owing to the aforementioned experimental research's novel developments. © 2024 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Integration Of Convolutional Neural Network and Support Vector Regression For Electricity Price Forecasting

Integration Of Convolutional Neural Network and Support Vect...

引用

2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Iqbal, Rashed Mokhlis, Hazlie Khairuddin, Anis Salwa Mohd Mansor, Nurulafiqah Nadzirah Zahari, Nur Elida Mohamad Sankaranarayanan, Suresh School of Electrical Computer and Telecommunications Engineering University of Wollongong NSW Australia Department of Electrical Engineering Faculty of Engineering Universiti Malaya Kuala Lumpur Malaysia Department of Computer Science King Faisal University Saudi Arabia

ISBN: (纸本)9798350325188

Accurate electricity price forecasting (EPF) plays a crucial role in bid strategies and risk reduction for market participants in the competitive electricity market. However, forecasting of electricity prices accurately is a highly demanding task due to the intricate nonlinearity present in the price dynamics. This paper introduces a combination of deep learning (DL) technique with machine learning (ML) model to forecast electricity prices in the Australian energy market operator (AEMO) for hourly price prediction utilizing load and renewable energy supply data from five major economical states New South Wales (NSW), Queensland (QLD), South Australia (SA), Tasmania (TAS) and Victoria (VIC). The proposed work integrates the convolutional neural network (CNN) and support vector regression (SVR) to identify short-term local dependency patterns among variables and uncover long-term correlations for time series patterns and forecast the hourly ahead price. The suggested model (CNN-SVR) is contrasted to various models such as CNN-LSTM, BiLSTM, SVR, and their performance is evaluated using various metrics, including mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). © 2023 IEEE.

关键词： convolutional neural network deep learning electricity price forecasting machine learning

来源：评论

学校读者我要写书评

暂无评论

Direct Drive Permanent Magnet Synchronous Generator: Design, Modeling, and Control for Wind Energy Applications

Periodica polytechnica Electrical engineering and computer s...

引用

Periodica polytechnica electrical engineering and computer science 2024年第2期68卷 168-177页

作者： Larabi, Zina Ghedamsi, Kaci Aouzellag, Djamal Department of Electrical Engineering Faculty of Electrical Engineering and Computer Sciences University of Tizi-Ouzou P. O. B. 17 RP Tizi-Ouzou15000 Algeria Department of Electrical Systems Engineering Faculty of Technology University of Boumerdes Independence Avenue Boumerdes35000 Algeria Laboratoire de Maitrise des Energies Renouvelables Faculty of Technology University of Bejaia Road of Targa Ouzemmour Bejaia06000 Algeria

The prominent trend in wind turbine technology centers on the adoption of direct-drive permanent magnet synchronous generators (DD-PMSG), a choice driven by their capacity to deliver superior efficiency through the elimination of *** paper presents a comprehensive exploration of the design, modeling, and control aspects of a DD-PMSG intended for harnessing wind energy conversion. Initially, the geometrical design of the generator is meticulously carried out, adhering to predefined technical specifications and constraints. Subsequently, an in-depth internal modeling, focusing on the electromagnetic behavior of the designed generator, is executed using finite element analysis (FEA) through the Ansys Maxwell RMXpert software. Finally, the external modeling and control system integration of the designed generator, connected to the grid through power electronic converters, are simulated using the Matlab/Simulink software suite. The resulting findings underscore the efficacy and viability of the proposed generator for wind turbine applications, affirming its potential to enhance wind energy conversion systems. © 2024 Budapest University of Technology and Economics. All rights reserved.

关键词： Wind turbines

来源：评论

学校读者我要写书评

暂无评论

Early Detection of Fire in EV Battery Using Machine Learning Approach 2

Early Detection of Fire in EV Battery Using Machine Learning...

引用

2nd Edition IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural engineering, MetroXRAINE 2023

作者： Kaur, Ishpreet Singh, Mukesh Kasana, Singara Singh Thapar Institute of Engineering and Technology Computer Science and Engineering Department Patiala India Thapar Institute of Engineering and Technology Electrical and Instrumentation Engineering Department Patiala India

ISBN: (纸本)9798350300802

In recent years, Electric Vehicles (EVs) have revolutionized the automobile industry. Nowadays, EVs are the preferred means of transportation for people because they are easy to drive, convenient, and make less noise. EVs use Lithium-Ion Batteries (LIB) to power the vehicle. However, in past years, there have been multiple instances of EVs catching fire and becoming one of the most urgent problems in EVs. The leading causes of such mishaps include faulty Battery Management System (BMS), no proper venting mechanism for overheated cells to release heat, poor quality cells, and short-circuiting. Many researchers proposed fire handling systems that consider sensor values, such as temperature, voltage, etc. while some focus on battery health management. However, there is a need for a detection mechanism to detect fire in advance to save people's life and property from damage. Some researchers have proposed fire detection strategies considering only a few sensor readings that cannot detect the extent of a fire early and inform users about the situation. Therefore, in the proposed work, an approach is designed considering both environmental and battery data using multi-sensors for developing an intelligent early fire detection system. The proposed approach first performs exploratory data analysis on real dataset to get an insight into data for fault diagnosis, leading to early thermal runaway. Further, Machine Learning (ML) and ensemble methods are used to check the potentiality of the fire or detect fire in advance. Finally, the system is evaluated concerning accuracy, recall, precision, and Fl-Score. © 2023 IEEE.

关键词： Battery Management System Electric Vehicles Fire Detection Lithium-Ion Battery Machine Learning

来源：评论

学校读者我要写书评

暂无评论

Enhancing electrical Transformer Fault Prediction with Deep Learning: A Focus on ANN-Based Classification

Enhancing Electrical Transformer Fault Prediction with Deep ...

引用

2024 International Conference on Circuit, Systems and Communication, ICCSC 2024

作者： Hadiki, Hanane Hasnaoui, Fouad Slaoui Georges, Semaan University of Quebec in Abitibi-Témiscamingue Electrical Engineering Department Rouyn-Noranda Canada University of Quebec in Abitibi-Témiscamingue Engineering Department Rouyn-Noranda Canada University Notre de Dame Electrical Computer and Communication Engineering Department Lebanon

ISBN: (纸本)9798350365306

electrical transformers play an essential role in the power distribution network, acting as key connectors that facilitate the flow of electricity over large distances to meet diverse power needs. Despite their reliability, the rare occurrence of faults emphasizes the necessity for diligent maintenance to maintain their peak performance. The significant expenses linked to both the maintenance and repair of these components highlight the imperative for innovative maintenance strategies that extend beyond traditional approaches. This situation underscores the urgent need for maintenance strategies that surpass conventional methods. Our study leverages two unique datasets: one featuring data on three-phase currents and voltages and the other including seven essential indicators of transformer health. To predict faults within these datasets, we employ advanced deep learning techniques, specifically using Feedforward Back-propagation neural networks, demonstrating the potential of modern methodologies in enhancing the reliability and efficiency of electrical transformers. © 2024 IEEE.

关键词： Power distribution faults

来源：评论

学校读者我要写书评

暂无评论

SMCS TEAM: Open Course on Cyber Physical Systems Foundation and Design for Unmanned Aerial Vehicles (UAVs)

SMCS TEAM: Open Course on Cyber Physical Systems Foundation ...

引用

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

作者： Wan, Yan Fu, Shengli Xie, Junfei Lu, Kejie The University of Texas at Arlington Electrical Engineering ArlingtonTX75025 United States University of North Texas Electrical Engineering DentonTX United States San Diego State University Department of Electrical and Computer Engineering San DiegoCA92182 United States The University of Puerto Rico at Mayaguez Department of Computer Science and Engineering Mayaguez Puerto Rico

ISBN: (纸本)9781665410205

This abstract describes the project funded by the IEEE SMCS on Transforming Educational Assets and Materials (TEAM) in Systems, Man, and Cybernetics. The project develops an open course on Cyber Physical Systems (CPS) Foundation and Design for Unmanned Aerial Vehicles (UAVs). The course will be available to the public and serve the need of researchers, students and professionals who are interested in conducting UAVs related research. The open course contains integrated modules on control, communication and networking, computing, and artificial intelligence (AI) to provide trainees a comprehensive knowledge needed for UAVs. The course is self-paced and contains quizzes in each module for help students assess the quality of learning and also allow course designers to evaluate the effectiveness of the course materials for continuous improvement. The open course promotes CPS which is a SMCS technical field. It will also attract students and professionals to the SMC community. © 2024 IEEE.

关键词： Antennas

来源：评论

学校读者我要写书评

暂无评论

Eggplant Leaf Semantic Segmentation in Aerial Imagery for Precision Agriculture 15

Eggplant Leaf Semantic Segmentation in Aerial Imagery for Pr...

引用

15th IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2023

作者： Usman, Nouran M. Aleluya, Earl Ryan M. Clar, Steve E. Alagon, Francis Jann A. Salaan, Carl John O. Bahinting, Maria Fe P. College of Computer Studies Department of Computer Applications Philippines College of Engineering Department of Computer Engineering and Mechatronics Philippines College of Engineering Department of Electrical Engineering Philippines Graduate School of Engineering Mindanao State University-Iligan Institute of Technology Iligan City9200 Philippines

ISBN: (纸本)9798350381177

Eggplant is one of the prominent vegetable crops in the Philippines. Traditional farming practices, like agrochemical spraying, often lack real-time, high-resolution data crucial for informed decisions. Monitoring vegetation indices is vital for crop growth assessment, health monitoring, and precision agriculture. However, optimizing agrochemical spraying for specific crop needs remains a challenge. Unmanned aerial vehicles (UAV s) offer a transformative solution. RGB cameras, designed for visual imagery, may not provide accurate vegetation index measurements. computer vision, through leaf detection, plays a crucial role in assessing crop health. This study aims to distinguish eggplant leaves from their surroundings. Subsequently, the authors compared two open-source segmentation models, namely U-Net and FPN. Two pre-trained encoders, VGG16 and VGG19, were used in both models to further evaluate their performance. Using a drone-mounted RGB camera, the authors gathered images of mature eggplant leaves. The experimental findings demonstrate the effectiveness of the VGGl6-based FPN architecture for real-time leaf segmentation tasks as it acquires 97.24 % and 96.73 % accuracy with a latency time of 31.5 ms and 32.6 ms, on 0.5 and 0.75 threshold values. The study recommends FPN with VGG16 for its accurate and low-latency performance in time-sensitive leaf segmentation applications. © 2023 IEEE.

关键词： Antennas

来源：评论

学校读者我要写书评

暂无评论

RDP: Ranked Differential Privacy for Facial Feature Protection in Multiscale Sparsified Subspaces

引用

IEEE Internet of Things Journal 2025年第11期12卷 17333-17348页

作者： Ou, Lu Liao, Shaolin Gao, Shihui Huang, Guandong Qin, Zheng Hunan University School of Journalism and Communication Hunan Changsha410082 China Sun Yat-sen University School of Electronics and Information Technology Guangzhou510275 China Illinois Institute of Technology Department of Electrical and Computer Engineering ChicagoIL60616 United States Purdue University Elmore School of Electrical and Computer Engineering West LafayetteIN47907 United States Hunan University College of Computer Science and Electronic Engineering Hunan Changsha410082 China State Grid Hunan Materials Company Department of Deployment Management Hunan Changsha410118 China

With the widespread sharing of personal face images on the Internet, systems based on face recognition encounter the real threat of being breached by potential adversaries who are able to access individuals’ face images and use them to intrude the systems. In this article, we propose a novel privacy protection method in the sparsified multiscale feature subspaces to protect sensitive facial features, taking care of the influence or weight-ranked subspace coefficients on the privacy budget, named "ranked differential privacy (RDP)." After the multiscale subspaces’ decomposition, the lightweight Laplacian noise is added to the dimension-reduced sparsified subspaces’ coefficients according to the geometric superposition method. Then, we rigorously prove that the RDP satisfies Ε0-differential privacy. After that, the nonlinear Lagrange multiplier method (LM) is formulated for the constraint optimization problem of maximizing the utility of protected face images of high-visualization quality with sanitizing noise, under a given privacy budget Ε0. Then, two methods are proposed to solve the nonlinear Lagrangian multiplier method (LM) problem and obtain the optimal noise scale parameters: 1) the analytical normalization approximation (NA) method with identical average noise scale parameter for real-time online applications and 2) the LM optimization method via gradient descent (LMGD) to obtain the nonlinear solution through iterative updating for more accurate offline applications. Experimental results on two real-world datasets show that our proposed RDP outperforms other state-of-the-art methods: at a privacy budget of Ε0 = 0.2 , the peak signal-to-noise ratio (PSNR) of the optimized RDP is about ~10 dB higher than (10 times as high as) the highest PSNR of all state-of-the-art methods compared. © 2014 IEEE.

关键词： Differential privacy

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：