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A Semantic and Intelligent Focused Crawler based on BERT Semantic Vector Space Model and Hybrid Algorithm (October 2024)

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IEEE Access 2025年

作者： Huang, Wenhao Zhang, Jiahao Li, Xin Zhou, Xiao Qi, Deyu Xi, Jianqing Liu, Wenjun School of Software Engineering South China University of Technology Guangzhou510006 China School of Computer and Software Engineering XiHua University Chengdu610039 China Guangdong University of Foreign Studies South China Business College Guangzhou510545 China Sichuan Provincial Engineering Research Center of Hydroelectric Energy Power Equipment Technology Chengdu610039 China

The goal of a focused crawler is to selectively fetch pages that are relevant to a given topic. Previous crawlers use text content to determine text topic relevance and manually determined weighting factors to predict the priority of unvisited URLs. However, there are still some problems in the above focused crawler methods, the calculation formula of semantic similarity between words is flawed. The weighting factor for the priority of unvisited URLs is determined arbitrarily. In order to solve the above problems, this paper proposes a semantic and intelligent focused crawler based on BERT semantic vector space model and hybrid algorithm. This method used BERT semantic vector space model to calculate the topic relevance of documents, and used a hybrid algorithm to optimize the weighting factor of unvisited URL priority. The experimental results show that the proposed BSVSM-HA crawler can obtain better evaluation indicators compared with the other three crawlers including Word2vec crawler, ELMO crawler and BSVSM crawler. In conclusion, the semantic and intelligent crawler proposed in this paper makes the semantic similarity between terms more accurate, and improves the topic relevance of the text, and the optimized weighting factor makes the priority evaluation of unvisited URLs more accurate. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Vectors

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Single Cell Energy Router Justification for Three Phase Near Zero Energy Buildings

Single Cell Energy Router Justification for Three Phase Near...

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Annual IEEE Conference on Applied power Electronics Conference and Exposition (APEC)

作者： Hossein Nourollahi Hokmabad Tala Hemmati Shahsavar Oleksandr Matiushkin Tanel Jalakas Oleksandr Husev Juri Belikov Department of Electrical Power Engineering and Mechatronics Tallinn University of Technology Tallinn Estonia Faculty of Electrical and Automation Engineering Gdansk University of Technology Gdansk Poland Department of Software Science Tallinn University of Technology Tallinn Estonia

ISBN: (数字)9798331516116

ISBN: (纸本)9798331516123

The rise of building-integrated photovoltaics and distributed electric vehicle charging has led to significant phase imbalances in utility grids, challenging service providers due to limited behind-the-meter visibility. This paper introduces a novel Single-Cell Three-Phase (SC-TP) Energy Router (ER) that accesses all phases and balances them without the complexities and costs associated with conventional three-phase systems. Our comparative analysis shows that the SC-TP ER reduces phase unbalancing by 16% and achieves cost savings, offering a viable alternative to three-phase solutions with 25% reduction in cost.

关键词： Renewable energy sources Costs Buildings power system stability Stability analysis power electronics Topology Smart grids Reliability Resilience

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Research and development of two-factor authentication methods using neural networks 4

Research and development of two-factor authentication method...

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4th IEEE International Conference on Smart Information Systems and Technologies, SIST 2024

作者： Darbek, Temirkhan Mukasheva, Assel Bissembayev, Alibek Gnatyuk, Sergiy Mukammejanova, Dinargul The Department of Information Systems and Cybersecurity Almaty university of power engineering and telecommunications Almaty Kazakhstan The School of Information Technology and Engineering Kazakh-British Technical University Almaty Kazakhstan The School of Information Technology and Engineering Kazakh British Technical University Almaty Kazakhstan The Faculty of Cybersecurity Computer and Software Engineering at National Aviation University National Aviation University Kyiv Ukraine The Department of Artificial Intelligence and Big Data Kazakh National University named after Al-Farabi Almaty Kazakhstan

ISBN: (纸本)9798350374865

This article is devoted to the research and development of innovative two-factor authentication (2FA) methods using neural networks. 2FA plays a key role in ensuring the security of data and accounts in the modern digital world, and the use of neural networks opens up new prospects in strengthening protection and improving the user experience. The article presents an analysis of existing 2FA methods, identifies their limitations, and discusses the advantages of using neural networks in this area. The main focus is on the development of new authentication algorithms based on deep learning, providing effective anomaly detection and support for a dynamic adaptive approach. In the context of the article, the issues of privacy and data security are also considered, as well as the possibilities of applying the developed methods in various fields, including finance, medicine and the Internet of Things. The results of the study represent a significant contribution to the field of digital. © 2024 IEEE.

关键词： biometric authentication deep learning digital security identification Neural networks

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Experimentally Adjusted Modelling and Simulation Technique for a Catamaran Autonomous Surface Vessel

Experimentally Adjusted Modelling and Simulation Technique f...

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2022 IEEE International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022

作者： Astrov, Igor Udal, Andres Molder, Heigo Jalakas, Tanel Moller, Taavi Tallinn University of Technology Finest Centre for Smart Cities Tallinn Estonia Tallinn University of Technology Department of Software Science Tallinn Estonia Tallinn University of Technology Department of Electrical Power Engineering and Mechatronics Tallinn Estonia

ISBN: (数字)9781665470872

ISBN: (纸本)9781665470872

Efficient and reliable computer control of different autonomous vehicles has become one of the high priority tasks of global technological development. So far, research of Autonomous Surface Vessels (ASVs) has been 4-6 times less intensive compared research of Unmanned Aerial Vehicles (UAVs) but interest in research of Unmanned Aerial Vehicles (UAVs) but interest in watercraft modelling has shown an accelerating trend in recent methodology of a realistic and practical mathematical model of catamaran catamaran ASV 'Nymo' of 200 kg weight class developed in Tallinn University of technology. Applicability of vessel model is maneuvers where ASV is controlled by a PID controller. The maneuvers where ASV is controlled by a PID controller. The coefficients are discussed. © 2022 IEEE.

关键词： Three term control systems

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Identification of Abnormal Behavior in Electric power Operations Using the Human Skeleton's Key Features 8

Identification of Abnormal Behavior in Electric Power Operat...

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8th International Conference on Image, Vision and Computing, ICIVC 2023

作者： Zhou, Yang Wang, Xingjian Hu, Guoqiang Luo, Yong Chai, Zheng Xu, Wei Liu, Qiuming State Grid Jiangxi Electric Power Co Information and Communication Branch Nanchang330095 China Computer College Nanjing University of Posts and Telecomunications Nanjing210032 China State Grid Nanchang Power Supply Company Nanchang330095 China School of Software Jiangxi Normal University Nanchang330022 China School of Software Engineering Jiangxi University of Science and Technology Nanchang330013 China

ISBN: (纸本)9798350335231

At present, the safety monitoring of electric power sites mainly monitors the monitoring video through personnel, which has problems such as heavy workload, missed detection, and false alarms. In order to realize the intelligent recognition of the behavior of personnel at the electric power operation site, an abnormal behavior recognition method based on the key points of human skeleton is proposed, which can realize the real-time detection of the behavior of personnel at the operation site. First, the backbone network of YOLOv5s is improved for target detection, and the trajectory of the target is obtained by combining the DeepSort target tracking algorithm;then, the AlphaPose attitude estimation method is used to extract the target skeleton sequence from the trajectory;finally, the multi-partition spatiotemporal graph convolutional network is used to track the personnel Abnormal behavior identification. The experimental results show that the recognition accuracy rate of the algorithm reaches 93.5%, and the processing speed reaches 18.32 fps, which can identify abnormal behaviors of electric power workers in real time and accurately. © 2023 IEEE.

关键词： Personnel

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Short-term PV power Prediction Model Based on VMD-CNN-IBiLSTM 3

Short-term PV Power Prediction Model Based on VMD-CNN-IBiLST...

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2023 3rd International Conference on power Grid Systems and Green Energy, PGSGE 2023

作者： Tao, Yufeng Liu, Linping Jiang, Shuting Mao, Shuangshuang Ju, Xiaoming Lishui Power Supply Company of State Grid Zhejiang Electric Power Co. Ltd. Lishui323000 China Jingning County Power Supply Company of State Grid Zhejiang Electric Power Co. Ltd. Lishui323000 China School of Software Engineering East China Normal University Shanghai200000 China Zhejiang Jierui Electric Power Technology Co. LTD Ningbo315000 China

Because PV power's generation is unpredictable, so timely and accurate prediction of PV power has great practical significance and research value for daily grid dispatch and power system security. The combined forecast model presented in our study is dependent on variational modal decomposition-convolutional neural network-improved bidirectional long short-term memory neural network (VMD-CNN-IBiLSTM) with PV power as the research object. The model uses VMD to decompose PV power sequences, thus reducing the non-smoothness of the sequences;CNN to extract the essential features of the information;BiLSTM with peephole connections to learn the forward and backward features of the temporal data;and genetic algorithm to enhance the model's variables. For validation, actual data from the Australian DKASC testbed are used. According to the experimental findings, our study's combined model is more precise than the classical BP neural network, SVM model, and LSTM model at making predictions. © Published under licence by IOP Publishing Ltd.

关键词： Long short-term memory

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TOWARDS PRAGMATIC INTEROPERABILITY: A CONCEPTUAL FRAMEWORK FOR AN ENERGY IOT PLATFORM IN THE GERMAN ENERGY SECTOR

TOWARDS PRAGMATIC INTEROPERABILITY: A CONCEPTUAL FRAMEWORK F...

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CIRED 2024 Vienna Workshop

作者： Karmann, Alexandra Kilthau, Maximilian Derksen, Christian Strobel, Florian Bittermann, Lukas Korotkiewicz, Kamil Ostmeyer, Jonas Fay, Alexander Institute of Automation Technology Helmut-Schmidt-University Hamburg Germany Research Group for Business Informatics and Software Engineering University Duisburg-Essen Essen Germany Institute of Electrical Power and Energy Technology University of Technology Hamburg Hamburg Germany PSI GridConnect GmbH Karlsruhe Germany PSI FLS Fuzzy Logik & Neuro Systeme GmbH Dortmund Germany Ruhr-University Bochum Germany

ISBN: (纸本)9781837242672

This paper introduces an innovative conceptual framework for an Energy Internet of Things (eIoT) platform designed to address challenges related to grid congestion, voltage limit violations, and the growing demands on low-voltage grids due to increased electrification in the energy and heating sectors. The proposed platform seamlessly integrates cutting-edge technologies including advanced data collection, state estimation algorithms, AI-driven decision-making processes, and grid control functionalities. Its primary objective is to facilitate proactive measures aimed at enhancing energy distribution efficiency and effectively managing consumption devices while ensuring compliance with regulatory standards such as §14a of the Energy Industry Act (EnWG) in Germany. © The Institution of engineering & technology 2024.

关键词： Internet of Energy

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Empirical Cointegration Nexus among Electricity consumption, Gross Domestic Product, Population and CO2 emission in Pakistan: By Granger Causality Method 2

Empirical Cointegration Nexus among Electricity consumption,...

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2nd International Conference on Sustainable technology and Management, ICSTM 2023

作者： Jan, Ali Zhao, Xin-Gang Brohi, Ali Anwar Ibrar, Muhammad School of Economics and Management North China Electric Power University Beijing China Beijing China Mehran University of Engineering and Technology SZAB Campus Sindh Khairpur Mirs Pakistan Software College Shenyang Normal University Liaoning Shenyang China

ISBN: (纸本)9781510668645

The central goal of this study to explores the correlations amongst the electricity consumption, gross domestic product, population and carbon emissions by utilizing annual data from time span of 1970 to 2017 by applying Johansen cointegration test along with the Granger causality approaches Pakistan’s economy. The outcomes shown for Johansen cointegration test that the correlations of long term cointegration exists among these research variables and findings for Granger causality revealed that the bi-variate causal of direction existing between electricity consumptions and carbon emission, and GDP growth and carbon emission, while uni-variate causal of direction exists between population rate and carbons emission in Pakistan. As a result, the research suggests that Pakistan’s economic policies should be updated in order to combat environmental deterioration. © 2023 SPIE.

关键词： Electric power utilization

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Characterization and Control of a Superconducting Homopolar Motor with Cryogenic GaN Inverter for Electric Aircrafts

Characterization and Control of a Superconducting Homopolar ...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Gliese, Felix Pearce, Matthew G. S. Wadsworth, Aaron Siamaki, Mohammad Storey, James Lumsden, Grant Thrimawithana, Duleepa J. Badcock, Rod Roth-Stielow, Joerg Neuburger, Martin Esslingen University Faculty of Mobility and Technology Germany The University of Auckland Dept. of Electrical Computer and Software Engineering New Zealand OpenStar Technologies New Zealand Victoria University of Wellington Robinson Research Institute New Zealand University of Stuttgart Institute for Power Electronics and Electrical Drives Germany

ISBN: (纸本)9798350376067

In the quest for a zero-emissions future, the aviation sector is facing the challenge of reducing its carbon footprint. This paper presents a characterization of a superconducting homopolar motor designed to meet the stringent power and efficiency requirements of all-electric aircraft. The control system, which features field-oriented control and a multi-layer architecture, ensures precise operation of the motor under various load conditions. The motor uses a stationary cryogenic superconducting DC coil to generate the rotor's magnetic field. Utilizing superconducting materials helps produce the high power densities critical for aviation. The characterization of the key parameters of the motor, such as inductances, stator resistance and flux, are presented. A description of the cryogenic GaN power electronic inverter power stage immersed in liquid nitrogen is provided. This design enables future integration next to the motor, and improved power density and efficiency. This paper outlines the design, implementation, and initial testing of the superconducting homopolar motor and its power electronics, highlighting its potential for electric propulsion in aviation by providing a viable solution for long-range flights. © 2024 IEEE.

关键词： Gallium nitride

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Neural Machine Translation by Fusing Key Information of Text

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Computers, Materials & Continua 2023年第2期74卷 2803-2815页

作者： Shijie Hu Xiaoyu Li Jiayu Bai Hang Lei Weizhong Qian Sunqiang Hu Cong Zhang Akpatsa Samuel Kofi Qian Qiu Yong Zhou Shan Yang School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu610054China Science and Technology on Altitude Simulation Laboratory Sichuan Gas Turbine Establishment Aero Engine Corporation of ChinaMianyang621000China School of Power and Energy Northwestern Polytechnical UniversityXi’an710072China School of Computer Science Southwest Petroleum UniversityChengdu610500China Department of Chemistry Physics and Atmospheric SciencesJackson State UniversityJacksonMS 39217USA

When the Transformer proposed by Google in 2017,it was first used for machine translation tasks and achieved the state of the art at that *** the current neural machine translation model can generate high quality translation results,there are still mistranslations and omissions in the translation of key information of long *** the other hand,the most important part in traditional translation tasks is the translation of key *** the translation results,as long as the key information is translated accurately and completely,even if other parts of the results are translated incorrect,the final translation results’quality can still be *** order to solve the problem of mistranslation and missed translation effectively,and improve the accuracy and completeness of long sentence translation in machine translation,this paper proposes a key information fused neural machine translation model based on *** model proposed in this paper extracts the keywords of the source language text separately as the input of the *** the same encoding as the source language text,it is fused with the output of the source language text encoded by the encoder,then the key information is processed and input into the *** incorporating keyword information from the source language sentence,the model’s performance in the task of translating long sentences is very *** order to verify the effectiveness of the method of fusion of key information proposed in this paper,a series of experiments were carried out on the verification *** experimental results show that the Bilingual Evaluation Understudy(BLEU)score of the model proposed in this paper on theWorkshop on Machine Translation(WMT)2017 test dataset is higher than the BLEU score of Transformer proposed by Google on the WMT2017 test *** experimental results show the advantages of the model proposed in this paper.

关键词： Key information transformer fusion neural machine translation

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