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Pulsed Electromagnetic Field Interaction With a Transmission Line: An Analytical Traveling-Wave Approach Based on Reciprocity

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IEEE Transactions on Antennas and Propagation 2025年第6期73卷 3783-3791页

作者： Stumpf, Martin Antonini, Giulio Lager, Ioan E. Ekman, Jonas The Czech Republic Lerch Laboratory of em Research Department of Radio Electronics Feec Brno University of Technology Technická Brno3082/12 616 00 Czech Republic Luleå University of Technology Department of Computer Science Electrical and Space Engineering Eislab Luleå971 87 Sweden University of l'Aquila UAq Emc Laboratory Department of Industrial and Information Engineering and Economics L'Aquila671 00 Italy Delft University of Technology Terahertz Sensing Group Faculty of Electrical Engineering Mathematics and Computer Science Delft2628 CD Netherlands

Pulsed electromagnetic (EM) field signal transfer from a general EM source distribution to a transmission line (TL) is analyzed with the aid of Lorentz's reciprocity theorem. In this fashion, the transient voltage induced by the impulsive EM source is expressed through the EM fields as radiated by the TL. These transmitted EM fields are expressed in closed form using an analytical procedure that resembles the Cagniard-DeHoop (CdH) technique. The validity of the proposed reciprocity-based methodology is verified with the aid of an alternative analytical solution describing the EM field signal transfer excited by an impulsive vertical electric dipole (VED). Illustrative numerical examples are presented. © 1963-2012 IEEE.

关键词： Time domain analysis

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Experimental Analysis of Methods Used to Solve Linear Regression Models

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computers, Materials & Continua 2022年第9期72卷 5699-5712页

作者： Mua’ad Abu-Faraj Abeer Al-Hyari Ziad Alqadi Computer Information Systems Department The University of JordanAqaba77110Jordan Electrical Engineering Department Al-Balqa Applied UniversityAs Salt19117Jordan Computers and Networks Engineering Department Amman15008Jordan

Predicting the value of one or more variables using the values of other variables is a very important process in the various engineering experiments that include large data that are difficult to obtain using different measurement *** is one of the most important types of supervised machine learning,in which labeled data is used to build a prediction model,regression can be classified into three different categories:linear,polynomial,and *** this research paper,different methods will be implemented to solve the linear regression problem,where there is a linear relationship between the target and the predicted *** methods for linear regression will be analyzed using the calculated Mean Square Error(MSE)between the target values and the predicted outputs.A huge set of regression samples will be used to construct the training dataset with selected sizes.A detailed comparison will be performed between three methods,including least-square fit;Feed-Forward Artificial Neural Network(FFANN),and Cascade Feed-Forward Artificial Neural Network(CFFANN),and recommendations will be *** proposed method has been tested in this research on random data samples,and the results were compared with the results of the most common method,which is the linear multiple regression *** should be noted here that the procedures for building and testing the neural network will remain constant even if another sample of data is used.

关键词： Linear regression ANN CFFANN FFANN MSE training cycle training set

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Machine Learning Data Suitability and Performance Testing Using Fault Injection Testing Framework 8th

Machine Learning Data Suitability and Performance Testing U...

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8th International Conference on engineering of computer-Based Systems, ECBS 2023

作者： Rahal, Manal Ahmed, Bestoun S. Samuelsson, Jörgen Department of Mathematics and Computer Science Karlstad University Karlstad Sweden Department of Computer Science Faculty of Electrical Engineering Czech Technical University in Prague Prague16627 Czech Republic Department of Engineering and Chemical Sciences Karlstad University Karlstad Sweden

ISBN: (纸本)9783031492518

Creating resilient machine learning (ML) systems has become necessary to ensure production-ready ML systems that acquire user confidence seamlessly. The quality of the input data and the model highly influence the successful end-to-end testing in data-sensitive systems. However, the testing approaches of input data are not as systematic and are few compared to model testing. To address this gap, this paper presents the Fault Injection for Undesirable Learning in input Data (FIUL-Data) testing framework that tests the resilience of ML models to multiple intentionally-triggered data faults. Data mutators explore vulnerabilities of ML systems against the effects of different fault injections. The proposed framework is designed based on three main ideas: The mutators are not random;one data mutator is applied at an instance of time, and the selected ML models are optimized beforehand. This paper evaluates the FIUL-Data framework using data from analytical chemistry, comprising retention time measurements of anti-sense oligonucleotide. Empirical evaluation is carried out in a two-step process in which the responses of selected ML models to data mutation are analyzed individually and then compared with each other. The results show that the FIUL-Data framework allows the evaluation of the resilience of ML models. In most experiments cases, ML models show higher resilience at larger training datasets, where gradient boost performed better than support vector regression in smaller training sets. Overall, the mean squared error metric is useful in evaluating the resilience of models due to its higher sensitivity to data mutation. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Oligonucleotides

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Comparative Analysis of Large Language Models for OCR Post-Processing in Persian: From ParsBERT to GPT 29

Comparative Analysis of Large Language Models for OCR Post-P...

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29th International computer Conference, computer Society of Iran, CSICC 2025

作者： Valizadeh, Fatemeh Ghasemian, Fahimeh Shabaninia, Elham Department of Computer Engineering Shahid Bahonar University of Kerman Kerman Iran Department of Applied Mathematics Graduate University of Advanced Technology Kerman Iran

ISBN: (纸本)9798331523114

Optical Character Recognition (OCR) refers to the automatic identification of text in images and its conversion into searchable and editable formats. Due to its extensive applications, OCR is considered a crucial and challenging topic in the field of computer vision. In Persian, the unique characteristics of the script often result in OCR outputs with significant errors, which can compromise readability and comprehension of the content, emphasizing the need for error correction, particularly in terms of spelling. This study investigates the performance of four large language models-ParsBERT, LLaMA, Mistral, and GPT-for enhancing OCR outputs. Additionally, an innovative approach that integrates ParsBERT with other models is introduced. These models were evaluated on three corpora of varying sizes and complexities using diverse evaluation metrics, including precision, accuracy, recall, and others. Our comparative analysis highlights the strengths and weaknesses of each model across different scenarios, providing deeper insights into their performance. Furthermore, the results demonstrate that the proposed hybrid approaches outperform standalone models across all corpora, significantly reducing error rates and improving key metrics such as precision, accuracy, and recall. © 2025 IEEE.

关键词： Natural language processing systems

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Determination of Parameters Affecting the Estimation of Iceberg Draft

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China Ocean engineering 2023年第1期37卷 62-72页

作者： Hamed AZIMI Masoud MAHDIANPARI Hodjat SHIRI Civil Engineering Department Faculty of Engineering and Applied ScienceMemorial University of NewfoundlandA1B 3X5St.John’sNLCanada Department of Electrical and Computer Engineering Faculty of Engineering and Applied ScienceMemorial University of NewfoundlandA1B 3X5St.John’sNLCanada C-CORE A1B 3X5St.John’sNLCanada

Recent offshore oil and gas loading facilities developed in the Arctic area have led to a considerable awareness of the iceberg draft approximation, where deep keel icebergs may gouge the ocean floor, and these submarine infrastructures would be damaged in the shallower waters. Developing reliable solutions to estimate the iceberg draft requires a profound understanding of the problem’s dominant parameters. As such, the dimensionless groups of the parameters affecting the iceberg draft estimation were determined for the first time in the present study. Using the dimensionless groups recognized and the linear regression(LR) analysis, nine LR models(i.e., LR 1 to LR 9) were developed and then validated using a comprehensive dataset, which has been constructed in this study. A sensitivity analysis distinguished the premium LR models and important dimensionless groups. The best LR model, as a function of all dimensionless parameters, was able to estimate the iceberg draft with the highest level of precision and correlation along with the lowest degree of complexity. The ratio of iceberg length to iceberg height as the “iceberg length ratio” and the ratio of iceberg width to iceberg height as the “iceberg width ratio” was detected as the important dimensionless groups in the estimation of the iceberg draft. An uncertainty analysis demonstrated that the best LR model was biased towards underestimating the iceberg drafts. The premium LR model outperformed the previous empirical ***, a set of LR-based relationships were derived for estimating the iceberg drafts for practical engineering applications, e.g., the early stages of the iceberg management projects.

关键词： dimensional analysis iceberg draft subsea assets linear regression(LR) sensitivity analysis

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Enhancing Power Grid Stability: Design and Integration of a Fast Bus Tripping System in Protection Relays

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 561-570页

作者： Ghiasi, Mohammad Wang, Zhanle Mehrandezh, Mehran Alhelou, Hassan Haes Ghadimi, Noradin University of Regina Faculty of Engineering and Applied Science ReginaSKS4S 0A2 Canada School of Engineering CambridgeMA United States Monash University Department of Electrical and Computer System Engineering Australia Islamic Azad University Young Researchers and Elite Club Ardabil Branch Ardabil Iran

This article introduces a novel method for efficiently and promptly operating protection relays within a power system, with a specific emphasis on adaptive overcurrent (OC) protection in a power grid. The approach utilizes SEL751 and SEL751A relays that communicate through the IEC-61850 GOOSE protocol, establishing connections using intelligent electronic devices (IED) at several locations in the system. The power system, consisting of transformers, bus bars, and feeders, employs protection relays to identify OC faults based on time and current parameters. Relay configurations are tested in a case study at the Omicron Lab involving a 138/25kV substation. The test encompasses various fault scenarios on the low-voltage (LV) and high-voltage (HV) sides, incorporating different current values and fault times, utilizing the chosen time-overcurrent U3 (very inverse) curve. The proposed approach exhibits the ability to promptly remove faults in transformers and substations at both the HV and the LV levels. Furthermore, the experimental results closely match the theoretical calculations, validating the effectiveness of the proposed approach in the protection of AC grids. Additionally, built-in cybersecurity measures in SEL relays ensure secure and reliable operation, mitigating the risks of cyber-related relay failures. © 1975-2011 IEEE.

关键词： Relay protection

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Transformer-Based Embeddings for Greek Language Categorization 24

Transformer-Based Embeddings for Greek Language Categorizati...

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24th IEEE/ACIS International Conference on computer and Information Science, ICIS 2024

作者： Liapis, Charalampos M. Kyritsis, Konstantinos Perikos, Isidoros Paraskevas, Michael University of Patras Computer Technology Institute & Press 'Diophantus' Mathematics Dept. Patras26504 Greece University of Peloponnese Electrical and Computer Engineering Dept. Patras Greece University of Patras Computer Engineering and Informatics Department Patras Greece University of Peloponnese Computer Technology Institute and Press 'Diophantus' Electrical and Computer Engineering Dept. Patras Greece

ISBN: (纸本)9798350376647

The Greek School Network (GSN) provides support to students, teachers, and school units in secondary education across Greece. Handling numerous user queries manually can be challenging, necessitating the development of an automated system for accurate categorization of these queries. This paper presents a comparative study of various transformer-based models for multi-class text categorization of Greek language queries submitted to the GSN helpdesk. We introduce a new experimental balanced dataset and extract vector representations from eleven transformer-based models. These representations are evaluated using ten classic machine learning classifiers. Our findings highlight the superior performance of the Multilingual E5 Text Embeddings model, particularly when paired with the extreme gradient-boosting classifier. This combination demonstrates a clear advantage in accurately categorizing user queries, paving the way for more efficient automated helpdesk systems. © 2024 IEEE.

关键词： Contrastive Learning

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Prediction of Geopolymer Concrete Compressive Strength Using Convolutional Neural Networks

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computer Modeling in engineering & Sciences 2024年第5期139卷 1455-1486页

作者： Kolli Ramujee Pooja Sadula Golla Madhu Sandeep Kautish Abdulaziz S.Almazyad Guojiang Xiong Ali Wagdy Mohamed Department of Civil Engineering VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Information Technology VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Computer Science LBEF Campus(Asia Pacific University of Technology&InnovationMalaysia)Kathmandu44600Nepal Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmmanJordan

Geopolymer concrete emerges as a promising avenue for sustainable development and offers an effective solution to environmental *** attributes as a non-toxic,low-carbon,and economical substitute for conventional cement concrete,coupled with its elevated compressive strength and reduced shrinkage properties,position it as a pivotal material for diverse applications spanning from architectural structures to transportation *** this context,this study sets out the task of using machine learning(ML)algorithms to increase the accuracy and interpretability of predicting the compressive strength of geopolymer concrete in the civil engineering *** achieve this goal,a new approach using convolutional neural networks(CNNs)has been *** study focuses on creating a comprehensive dataset consisting of compositional and strength parameters of 162 geopolymer concrete mixes,all containing Class F fly *** selection of optimal input parameters is guided by two distinct *** first criterion leverages insights garnered from previous research on the influence of individual features on compressive *** second criterion scrutinizes the impact of these features within the model’s predictive *** to enhancing the CNN model’s performance is the meticulous determination of the optimal *** a systematic trial-and-error process,the study ascertains the ideal number of epochs for data division and the optimal value of k for k-fold cross-validation—a technique vital to the model’s *** model’s predictive prowess is rigorously assessed via a suite of performance metrics and comprehensive score ***,the model’s adaptability is gauged by integrating a secondary dataset into its predictive framework,facilitating a comparative evaluation against conventional prediction *** unravel the intricacies of the CNN model’s learning trajectory,a loss plot is deployed to elucidate its learning rat

关键词： Class F fly ash compressive strength geopolymer concrete prediction deep learning convolutional neural network

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A Sweeping Process Control Problem Subject To Mixed Constraints 22

A Sweeping Process Control Problem Subject To Mixed Constrai...

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22nd IFAC World Congress

作者： Cortez, Karla L. Khalil, Nathalie T. Solís, Julio E. Department of Mathematics Universidad Autónoma Metropolitana-Iztapalapa Mexico Department of Electrical and Computer Engineering University of Porto Portugal

ISBN: (纸本)9781713872344

In this study, we investigate optimal control problems that involve sweeping processes with a drift term and mixed inequality constraints. Our goal is to establish necessary optimality conditions for these problems. We address the challenges that arise due to the combination of sweeping processes and inequality mixed constraints in two contexts: regular and non-regular. This requires working with different types of multipliers, such as finite positive Radon measures for the sweeping term and integrable functions for regular mixed constraints. For non-regular mixed constraints, the multipliers correspond to purely finitely additive set functions. Copyright © 2023 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： necessary optimality conditions optimal control theory regular and non-regular mixed constraints sweeping process

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MOCBOA:Multi-Objective Chef-Based Optimization Algorithm Using Hybrid Dominance Relations for Solving engineering Design Problems

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computer Modeling in engineering & Sciences 2025年第4期143卷 967-1008页

作者： Nour Elhouda Chalabi Abdelouahab Attia Abdulaziz S.Almazyad Ali Wagdy Mohamed Frank Werner Pradeep Jangir Mohammad Shokouhifar Computer Science Department University of M’silaIchebiliaM’sila28000Algeria Computer ScienceDepartment Mohamed El Bachir El IbrahimiUniversity of Bordj BouArreridjBordj BouArreridj34000Algeria Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11931Jordan Faculty of Mathematics Otto-von-Guericke UniversityMagdeburg39016Germany University Centre for Research and Development Chandigarh UniversityMohali140413India Department of Electrical and Computer Engineering Shahid Beheshti UniversityTehran14399Iran DTU AI and Data Science Hub(DAIDASH) Duy Tan UniversityDa Nang550000Vietnam

Multi-objective optimization is critical for problem-solving in engineering,economics,and *** study introduces the Multi-Objective Chef-Based Optimization Algorithm(MOCBOA),an upgraded version of the Chef-Based Optimization Algorithm(CBOA)that addresses distinct *** approach is unique in systematically examining four dominance relations—Pareto,Epsilon,Cone-epsilon,and Strengthened dominance—to evaluate their influence on sustaining solution variety and driving convergence toward the Pareto *** comparison investigation,which was conducted on fifty test problems from the CEC 2021 benchmark and applied to areas such as chemical engineering,mechanical design,and power systems,reveals that the dominance approach used has a considerable impact on the key optimization measures such as the hypervolume *** paper provides a solid foundation for determining themost effective dominance approach and significant insights for both theoretical research and practical applications in multi-objective optimization.

关键词： Multi-objective optimization chef-based optimization algorithm(CBOA) pareto dominance epsilon dominance cone-epsilon dominance strengthened dominance

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