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A Review of Grid-Connected Inverters and Control Methods Under Unbalanced Grid Conditions 16

A Review of Grid-Connected Inverters and Control Methods Und...

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16th Annual Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2025

作者： Marangalu, Milad Ghavipanjeh Rehman, Fazal Ur Kurkdjian, Naser Vosoughi Computer Queen's University Dep. Of Electrical and Computer Engineering KingstonON Canada University of L'Aquila Dep. Information Engineering Computer Science and Mathematics L'Aquila Italy San Diego State University Faculty of Electrical and Computer Engineering San DiegoCA92182-0001 United States

ISBN: (纸本)9798331533946

Grid-connected inverters play a pivotal role in integrating renewable energy sources into modern power systems. However, the presence of unbalanced grid conditions poses significant challenges to the stable operation of these inverters. This review paper provides a comprehensive overview of grid-connected inverters and control methods tailored to address unbalanced grid conditions. Beginning with an introduction to the fundamentals of grid-connected inverters, the paper elucidates the impact of unbalanced grid voltages on their performance. Various control strategies, including voltage and current control methods, are examined in detail, highlighting their strengths and limitations in mitigating the effects of grid imbalance. Lastly, the review identifies emerging trends and future research directions in the field, emphasizing the need for innovative solutions to address the evolving challenges posed by unbalanced grid conditions in the context of renewable energy integration. © 2025 IEEE.

关键词： control strategy fault conditions grid-connected inverters Photovoltaic systems

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Translation invariant diagonal frame decomposition for the Radon transform

Translation invariant diagonal frame decomposition for the R...

引用

2023 International Conference on Sampling Theory and Applications, SampTA 2023

作者： Goppel, Simon Haltmeier, Markus Frikel, Jurgen Universität Innsbruck Dep. of Mathematics Innsbruck Austria Oth Regensburg Dep. of Computer Science and Mathematics Regensburg Germany

ISBN: (纸本)9798350328851

In this article, we address the challenge of solving the ill-posed reconstruction problem in computed tomography using a translation invariant diagonal frame decomposition (TI-DFD). First, we review the concept of a TI-DFD for general linear operators and the corresponding filter-based regularization. We then introduce the TI-DFD for the Radon transform on L2\left( R2 ) and provide an exemplary construction using the TI wavelet transform. Presented numerical results clearly demonstrate the benefits of our approach over non-translation invariant counterparts. © 2023 IEEE.

关键词： Inverse problems

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MPI Impact in C+L+S Multiband Transmission Reach

MPI Impact in C+L+S Multiband Transmission Reach

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2024 Photonic Networks and Devices, NETWORKS 2024

作者： Cancela, Luís G. Pires, João O. Instituto de Telecomunicações Dep. Information Science and Technology Iscte Instituto Universitário de Lisboa Portugal Instituto de Telecomunicações Dep. Electrical and Computer Engineering Instituto Superior Técnico Portugal

Multipath interference (MPI) impact is assessed in a C+L+S multiband transmission scenario. For a typical -34 dB/span MPI, transmission reach in the L-band suffers a 20% reach penalty considering the QPSK modulation f... 详细信息

关键词： C++ (programming language)

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Deep Feature Extraction Framework Based on DNN for Enhancing Mirai Attachment Classification in Machine Learning 2

Deep Feature Extraction Framework Based on DNN for Enhancing...

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2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Gharaibeh, Hasan Aljaidi, Mohammad Nasayreh, Ahmad Al-Na'amneh, Qais Jaradat, Ameera S. Samara, Ghassan Emhamed Al Mamlook, Rabia Yarmouk University Dep. Computer Science Irbid Jordan Zarqa University Dep. of Computer Science Zarqa Jordan Applied Science Private University Dep. of Cyber Security Amman Jordan Dep. of Business Administration Trine University IN United States Department of Mechanical and Industrial Engineering University of Zawia Tripoli Libya

ISBN: (纸本)9798350373363

Nowadays, the rapid growth of Internet of Things (IoT) devices has changed many parts of our lives by providing seamless connectivity and automation. However, this expansion has created new challenges and vulnerabilities, especially with regard to security. Mirai botnet, a strain of malware that has severely damaged the IoT ecosystem, is one of the prominent risks to IoT security. This research examines the impact of the Mirai botnet on IoT devices. In this study, we present a hybrid model to detect Mirai attacks. Deep neural networks (DNNs) are used to extract significant deep features, which are subsequently passed to the (Light Gradient Boosting Machine) LGBM algorithm for classification. A dataset from the Canadian Institute 2023 that included several kinds of Internet of Things assaults was used to assess the model. The model achieved excellent feature extraction and promising results with accuracy, recall, precision, and F1-score scoring up to 95 % for all. These accuracy results demonstrated the superior performance of the suggested model over competing techniques, which had scores of 71 %, 85%, and 52%, respectively, for Support Vector Machine (SVM), Light Gradient Boosting Machine (LGBM), and Stochastic Gradient Descent (SGD). Furthermore, the model outperforms the DNN model, which received a score of 65%. © 2023 IEEE.

关键词： Deep neural networks

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An Enhanced Transformer-Based Approach with Meta-Ensemble Learning for Arabic Sentiment Analysis 3

An Enhanced Transformer-Based Approach with Meta-Ensemble Le...

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3rd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2023

作者： Ashraf, Mohamed Marzouk, Mustafa Kora, Rania Mohammed, Ammar Msa University Faculty of Computer Science Giza Egypt Cairo University Dep. of Computer Science Egypt

ISBN: (纸本)9798350306231

Sentiment analysis represents a fundamental task in natural language processing (NLP) critical for extracting and analyzing emotions and opinions represented in textual data. The complexity of the Arabic language presents unique obstacles to accurate sentiment categorization in Arabic sentiment analysis. As cutting-edge NLP models, transformers have demonstrated exceptional performance on various tasks, including sentiment analysis. However, because of the multiplicity of pre-trained transformer models available, fine-tuning for best performance is required, making the procedure time-consuming and demanding significant knowledge. To overcome these challenges, this research paper introduces an ensemble learning approach consolidating multiple transformer models into a unified framework. In the initial stage, a set of baseline transformer models, comprising AraBERT, CAMeIBERT, and Roberta, undergo training and fine-tuning. Subsequently, three distinct ensemble fusion techniques-voting, weighted voting, and meta-learning-are applied to aggregate the predictions generated by these baseline transformer models. Conducting comprehensive experiments on the Egyptian Arabic Corpus, the paper reveals transformer models' superiority over existing methods applied to the corpus. Furthermore, the proposed ensemble approach exhibits enhanced performance for these transformer models, resulting in even more promising outcomes in sentiment classification tasks. © 2023 IEEE.

关键词： Sentiment analysis

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Enhanced Diagnosis of Liver Cancer Subtypes Through Deep Learning Techniques 6

Enhanced Diagnosis of Liver Cancer Subtypes Through Deep Lea...

引用

6th International Conference on Computing and Informatics, ICCI 2024

作者： Khaled, Nada Tarek, Hanan Makram, Manal Mohammed, Ammar Faculty of Computer Science MSA University Cairo Egypt Dep. of Computer Science FGSSR Cairo University Cairo Egypt Cairo University Egypt

ISBN: (纸本)9798350373875

Liver cancer presents a substantial threat to life and is recognized as one of the rapidly growing forms of cancer globally. According to a 2020 World Health Organization study, Egypt ranks second internationally in liver cancer mortality, accounting for 4.57% of total fatalities. Liver cancer may be categorized into several categories, with the detection and treatment of each stage of differentiation being critical in deciding patients' survival rate and duration. In the last few years, the invention of a computer-assisted imaging procedure that employs deep learning has become critical for evaluating liver illnesses, consequently improving medical diagnosis. This paper makes several contributions. First, it presents a significant effort in creating a multi-class liver tumor dataset sourced from Ain Shams University Specialized Hospital (ASUSH) in Egypt and annotated by two specialists. The dataset comprises a total of 8237 CT images categorized into eight distinct classes. Furthermore, the paper benchmarks the dataset to multiple deep learning models, including a proposed CNN, as well as fine-tuned pre-trained models like VGG16, VGG19, ResNet50, ResNetlOl, Xception, and Inception-V3. The results of the experiment demonstrate that the suggested CNN surpasses the fine-tuned networks, obtaining an average accuracy of 99.76% in predicting eight-class liver cancer variations. © 2024 IEEE.

关键词： Diseases

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A Framework for Reproducible Parallel DNA String Matching

A Framework for Reproducible Parallel DNA String Matching

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18th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2025

作者： Chaves, Ricardo Regis Cavalcante Alves de Melo, Alba Cristina Magalhaes Dep. of Computer Science Campus Universitario Darcy Ribeiro University of Brasilia Brasilia Brazil

In this paper, we propose an output reproducible framework that executes parallel sequence comparison algo rithms, computing the edit distance. The framework generates tables/graphics and linear regressions that can be used to predict the execution times. We also propose parallel OpenMP versions of serial algorithms (DP and UK)used to compute the edit distance. Our parallel DP is antidiagonal block-based, where the blocks that belong to the same set of antidiagonals are assigned to different threads, which compute them simultaneously. Due to data dep.ndencies presented by UK, we opted to compute each antidiagonal in parallel. Our results with synthetic and real sequences show that the parallel UK version presents the best execution times in most cases. We also show that the linear regressions generated by our tool have errors below 10%, on average. © 2025 by SCITEPRESS– science and technology Publications, Lda.

关键词： Linear regression

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Biological age estimation from ECG using Artificial Intelligence approaches 2

Biological age estimation from ECG using Artificial Intellig...

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2nd Jordanian International Biomedical Engineering Conference, JIBEC 2024

作者： Sleiman, Jana Daher, Mohamad Khalil, Mohamad Diab, Ahmad Kassem, Abdallah Lebanese University Computer and Communications Engineering Tripoli Lebanon Faculty of Science Faculty of Applied Sciences & Technology Dep. of Computer Science Dep. of Computer Science & Information Technology Tripoli Lebanon Lebanese University Electrical Engineering Tripoli Lebanon Lebanese International University Biomedical Engineering Beirut Lebanon Notre Dame University Faculty Of Engineering ECCE Dept. Zouk Mosbeh Lebanon

ISBN: (纸本)9798331539542

This paper explores the application of Artificial Intelligence (AI) techniques in healthcare, specifically focusing on electrocardiogram (ECG) data analysis and biological age estimation. The study begins with an overview of ECG as a diagnostic tool and discusses the integration of Deep Learning (DL) models, particularly Convolutional Neural Networks (CNNs), in healthcare for various applications. Previous studies reviewed in this context consistently identify CNNs as the superior model for ECG analysis and prediction tasks. The primary research investigates the use of Machine Learning (ML) algorithms to estimate biological age from ECG data, employing metrics such as Mean Absolute Error (MAE) and variance to evaluate model performance. Subsequently, DL models are implemented and compared, demonstrating superior accuracy in estimating the biological age of new patients. While both ML and DL approaches were initially explored, this paper primarily focuses on deep learning methodologies due to their demonstrated superiority in healthcare applications. The findings highlight the efficacy of deep learning over traditional ML methods, underscoring its potential for enhancing diagnostic accuracy and personalized patient care. For instance, the most accurate deep learning model, the Convolutional Neural Network (CNN), achieved a Mean Absolute Error (MAE) of 1.49 and an R2 score of 0.53, whereas the best-performing traditional ML model, Random Forest, achieved an MAE of 1.54 and an R2 score of 0.55. These results clearly indicate the advantage of deep learning models in capturing complex patterns in ECG data, leading to more precise and reliable estimations of biological age. © 2024 IEEE.

关键词： Convolutional neural networks

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Genetic Optimization Techniques for Enhancing Web Attacks Classification in Machine Learning

Genetic Optimization Techniques for Enhancing Web Attacks Cl...

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2023 IEEE International Conference on dep.ndable, Autonomic and Secure Computing, 2023 International Conference on Pervasive Intelligence and Computing, 2023 International Conference on Cloud and Big Data Computing, 2023 International Conference on Cyber science and technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Jaradat, Ameera S. Nasayreh, Ahmad Al-Na'amneh, Qais Gharaibeh, Hasan Al Mamlook, Rabia Emhamed Yarmouk University Dep. of Computer Science Irbid Jordan Yarmouk University Dep. of Computer Science Irbid211633 Jordan Applied Science Private University Dep. of Cyber Security and Cloud Computing Amman Jordan Trine University Dep. of Business Administration IN United States University Zawia Department of Industrial Engineering Tripoli Libya

ISBN: (纸本)9798350304602

Web-based applications are now the preferred approach for delivering a variety of services via the Internet. As a result of the globalization of commerce, web applications have been growing quickly and becoming increasingly complicated. Such applications have a significant security vulnerability in the online environment since they were developed with little experience and without testing or validation. Numerous attackers use this security vulnerability to take control of the program, modify the data, and steal the most crucial information. They may also access all internal, unauthorized items. This work presents a hybrid model that classifies website attacks as benign through the integration of four machine learning algorithms: gradient boost (GB), multilayer perceptron (MLP), logistic regression (LR) and K nearest neighbor (KNN). The work dep.oyed Tree-based Pipeline Optimization Tool (TPOT) that utilizes the Genetic algorithm (GA) to extract the optimal parameter and consequently enhance the model performance. The model underwent evaluation utilizing a data set from the Canadian Institute 2023 that contains various types of attacks. Among these algorithms, GB achieved the best accuracy scores of 95%, 94% and 95% for accuracy, recall and F1-score, respectively. © 2023 IEEE.

关键词： Genetic algorithms

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Optimization of Methods for Querying Formal Ontologies in Natural Language Using a Neural Network 16

Optimization of Methods for Querying Formal Ontologies in Na...

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16th International Conference on Knowledge Engineering and Ontology Development, KEOD 2024 as part of 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2024

作者： Ondo, Anicet Lepetit Capus, Laurence Bousso, Mamadou Dep. of Computer and Software Engineering Laval University QuebecQCG1V 0A6 Canada Dep. Computer Science Iba Der Thiam University Thies967 Senegal

ISBN: (纸本)9789897587160

A well-designed ontology must be capable of addressing all the needs it is intended to satisfy. This complex task involves gathering all the potential questions from future users that the ontology should answer in order to respond precisely to these requests. However, variations in the questions asked by users for the same need complicate the interrogation process. Consequently, the use of a question-answering system seems to be a more efficient option for translating user queries into the formal SPARQL language. Current methods face significant challenges, including their reliance on predefined patterns, the quality of models and training data, ontology structure, resource complexity for approaches integrating various techniques, and their sensitivity to linguistic variations for the same user need. To overcome these limitations, we propose an optimal classification approach to classify user queries into corresponding SPARQL query classes. This method uses a neural network based on Transformer encoder-decoder architectures, improving both the understanding and generation of SPARQL queries while better adapting to variations in user queries. We have developed a dataset on estate liquidation and Python programming, built from raw data collected from specialist forums and websites. Two transformer models, GPT-2 and T5, were evaluated, with the basic T5 model obtaining a satisfactory score of 97.22%. © 2024 by SCITEPRESS – science and technology Publications, Lda.

关键词： Structured Query Language

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