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Performance Assessment of a Real PV System Connected to a Low-Voltage Grid

Energy engineering 2024年第1期121卷 13-26页

作者： Gaber Magdy Mostafa Metwally Adel A.Elbaset Esam Zaki Electrical Engineering Department Faculty of Energy EngineeringAswan UniversityAswanEgypt `2Faculty of Engineering King Salman International UniversityEl-Tor46511Egypt Electrical Engineering Department Faculty of EngineeringMinia UniversityMiniaEgypt Department of Electromechanics Engineering Faculty of EngineeringHeliopolis UniversityCairoEgypt Computer Department Faculty of EngineeringModern Academy of Engineering&TechnologyCairoEgypt

The generation of photovoltaic(PV)solar energy is increasing continuously because it is renewable,unlimited,and clean *** the past,generation systems depended on non-renewable sources such as oil,coal,and ***,this paper assesses the performance of a 51 kW PV solar power plant connected to a low-voltage grid to feed an administrative building in the 6th of October City,*** performance analysis of the considered grid-connected PV system is carried out using power system simulator for engineering(PSS/E)*** the PSS/E program,monitors and uses the power analyzer that displays the parameters and measures some parameters such as current,voltage,total power,power factor,frequency,and current and voltage harmonics,the used inverter from the type of grid inverter for the considered *** results conclude that when the maximum solar radiation is reached,the maximum current can be obtained from the solar panels,thus obtaining the maximum power and power *** total voltage harmonic distortion,a current harmonic distortion within permissible limits using active harmonic distortion because this type is fast in processing up to 300 *** connection between solar stations and the national grid makes the system more efficient.

关键词： Low-voltage grid photovoltaic(PV)system total harmonic distortion grid-connected PV system

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Wideband Matched Filtering Antennas with Absorptive Resonators

Wideband Matched Filtering Antennas with Absorptive Resonato...

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2023 International Applied Computational Electromagnetics Society Symposium, ACES-China 2023

作者： Cui, Jiaqian Zhu, Lei University of Macau Faculty of Science and Technology Department of Electrical and Computer Engineering China

ISBN: (纸本)9781733509657

This paper presents a design method of filtering antennas with wide matching bands to realize the integration of multiple functions such as absorbing, filtering, and radiating, and protect the system as well. In our design, an absorptive stub is employed to absorb the out-of-band signals. As such, a cascaded topology with lossy stubs, lossless stubs, and the resonant antenna is proposed to acquire the absorptive filtering antenna. The characteristic of the absorptive stub, and the response of the equivalent circuit are specifically analyzed here. Finally, an absorptive filtering slot antenna is implemented to prove that the method can be used to achieve a matching bandwidth far beyond that of a conventional resonant antenna. © 2023 Applied Computational Electromagnetics Society (ACES).

关键词： Topology

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A Comprehensive Review on Data Visualization Techniques for Real-Time Information 7

A Comprehensive Review on Data Visualization Techniques for ...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Choudhary, Vishal Kumar Kumar, Gulshan Suman, Saurabh Kumar, Abhishek Faculty of Engineering And Technology Department of Computer Science And Medical Engineering Maharashtra Wardha India Faculty of Engineering And Technology Department of Computer Science And Design Maharashtra Wardha India

ISBN: (纸本)9798350359299

Research on real-time data visualization methods is necessary to achieve the most accurate and clear representation of information. Creating specific boards and modifying current platforms are two key tasks in performing an appropriate information visualization. An overview of the studies on dashboard platforms, data visualization methods, and accessible strategies is given in this article. To evaluate the effects of the various data visualization approaches on the finished dashboard, a generic and dynamic dashboard based on real-time data was created. Consequently, a preliminary collection of cues, charts, tables, and reports will allow users to interact with the data. produced by the Dashboard itself. Making informed decisions across a variety of businesses requires the capacity to acquire and comprehend real-time information in today's data-driven environment. Real-time data visualization techniques are now essential for turning complex data into insights that can be put to use. This study examines the value of real-time data visualization and offers a summary of strategies and equipment that enable businesses to efficiently use real-time data. © 2024 IEEE.

关键词： Data visualization

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Enhanced MADM Strategy with Heptapartitioned Neutrosophic Distance Metrics

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Neutrosophic Sets and Systems 2025年 78卷 74-96页

作者： Eassa, Mohamed Myvizhi, M. Elkholy, Mohamed Abdelhafeez, Ahmed Elbehiery, Hussam Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Mathematics KPR Institute of Engineering and Technology Tamilnadu Coimbatore India Faculty of Computer Science& Engineering Alamein International University Egypt

In this work, a novel methodological approach to multi-attribute decision-making problems is developed and the notion of Heptapartitioned Neutrosophic Set Distance Measures (HNSDM) is introduced. By averaging the Penta-Partitioned Neutrosophic Distance Measures under particular conditional criteria, the suggested technique ranks the options. Additionally, we explore the fundamental characteristics of HNSDM and provide several illustrative theorems. The Heptapartitioned Neutrosophic Set Distance Measures are formulated based on the Hepta Neutrosophic Set. Lastly, a practical numerical example is provided to illustrate the efficacy of the suggested methodology. © 2025, University of New Mexico. All rights reserved.

关键词： Heptapartitioned Neutrosophic set Heptapartitioned Neutrosophic set and MADM strategy Heptapartitioned set Neutrosophic sine distance measure

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Investigation of DNA discontinuity for detecting tuberculosis

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 1149-1163页

作者： Nimmy, Sonia Farhana Sarowar, Md. Golam Dey, Nilanjan Ashour, Amira S. Santosh, K.C. Department of Computer Science and Engineering Notre Dame University Dhaka Bangladesh Department of Computer Science and Engineering East West University Dhaka Bangladesh Department of Information Technology Techno India College of Technology Kolkata India Department of Electronics and Electrical Communications Engineering Faculty of Engineering Tanta University Tanta Egypt Vermillion United States

Discontinuity in long Deoxyribonucleic Acid (DNA) sequences creates harmful diseases. Changes in the DNA structure refers to changes in the human immunity system. Tuberculosis is a critical disease that causes coughing, fatigue, unintentional weight loss and fever on aged people due to the disorder in the DNA. Breaks or mutations over long DNA sequences are the pivotal reasons for this fatal disease. This study developed an automated machine learning technique to assess the total number of such breaks in the long DNA sequences. Data cleansing and deep neural network techniques are applied to handle this big data. The National Center for Biotechnology Information (NCBI) database has been used to extract the amino acid sequences for Tuberculosis disease from the big DNA datasets. Results reveal that the proposed automated approach is significantly effective for the determination of DNA sequence breaks for the tuberculosis diseases due to the high sensitivity of Markov chain as well as the effective normalization techniques. This approach fixed the size of the training datasets and recursively divide the whole dataset into certain length. The study also adopts multiple predictions approaches, such as the hidden Markov chain, Box-Cox transformation and linear transformation to forecast about the breaks for any long positions of the training and testing datasets. The results demonstrated that hidden the Markov chain model provided faster analysis with more accurate and reliable results. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： DNA

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Powdery Mildew Disease Classification in Laboratory and Real-Field Images using Convolutional Neural Networks for Precision Agriculture 1

Powdery Mildew Disease Classification in Laboratory and Real...

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1st International Conference on Innovative and Intelligent Information Technologies, IC3IT 2024

作者： Bourzig, Dounia Kawther Dihya Abed, Mansour Merah, Mostefa Faculty of Science and Technology Electrical Engineering Department Mostaganem Algeria

ISBN: (纸本)9798331541064

Precision agriculture encounters challenges in detecting plant diseases. Powdery mildew is a fungal disease that impacts various crops. Accurately identifying this disease in agricultural field images is crucial for effective crop management. This research presents a systematic approach to assess the generalization capabilities of convolutional neural network (CNN) models, specifically DenseNet121, EfficientNetV2B0, InceptionResNetV2, InceptionV3, MobileNet, and ResNet50V2, in classifying powdery mildew disease when transitioning from laboratory images to real-field images. The experiment is organized into three steps, utilizing distinct subsets derived from the PlantVillage and Kaggle plant disease recognition datasets. PlantVillage supplies laboratory images, while the plant disease recognition dataset provides real-field images. The data is divided into five subsets, allowing for a comprehensive analysis across six training and evaluation scenarios. The results indicate that while all models achieve perfect accuracy in controlled laboratory conditions, their performance significantly declines when assessed on real-field data, underscoring the challenges of generalizing to complex, real-world environments. However, models trained on real-field data, or a combination of both datasets, demonstrate strong performance in real-field applications, with some CNN models achieving perfect accuracy. InceptionResNetV2 and InceptionV3 consistently excelled across various scenarios, positioning them as promising candidates for smart farming applications aimed at accurately classifying powdery mildew disease. This approach holds substantial potential for the agriculture industry, establishing a solid foundation for future studies. © 2024 IEEE.

关键词： Convolutional neural networks

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New Antenna Array Beamforming Techniques Based on Hybrid Convolution/Genetic Algorithm for 5G and Beyond Communications

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computer Modeling in engineering & sciences 2024年第3期138卷 2749-2767页

作者： Shimaa M.Amer Ashraf A.M.Khalaf Amr H.Hussein Salman A.Alqahtani Mostafa H.Dahshan Hossam M.Kassem Electronics and Communications Engineering Department Higher Institute of Engineering and TechnologyKafr ElsheikhEgypt Electronics and Electrical Communications Engineering Department Faculty of EngineeringMinia UniversityMiniaEgypt Electronics and Electrical Communications Engineering Department Faculty of EngineeringTanta UniversityTantaEgypt Electronics and Electrical Communications Engineering Department Faculty of EngineeringHorus University EgyptNew DamiettaEgypt Department of Computer Engineering College of computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia School of Computing Mathematics and EngineeringCharles Sturt UniversityBathurstNSW2795Australia

Side lobe level reduction(SLL)of antenna arrays significantly enhances the signal-to-interference ratio and improves the quality of service(QOS)in recent and future wireless communication systems starting from 5G up to ***,it improves the array gain and directivity,increasing the detection range and angular resolution of radar *** study proposes two highly efficient SLL reduction *** techniques are based on the hybridization between either the single convolution or the double convolution algorithms and the genetic algorithm(GA)to develop the Conv/GA andDConv/GA,*** convolution process determines the element’s excitations while the GA optimizes the element *** M elements linear antenna array(LAA),the convolution of the excitation coefficients vector by itself provides a new vector of excitations of length N=(2M−1).This new vector is divided into three different sets of excitations including the odd excitations,even excitations,and middle excitations of lengths M,M−1,andM,*** the same element spacing as the original LAA is used,it is noticed that the odd and even excitations provide a much lower SLL than that of the LAA but with amuch wider half-power beamwidth(HPBW).While the middle excitations give the same HPBWas the original LAA with a relatively higher *** the increased HPBWof the odd and even excitations,the element spacing is optimized using the ***,the synthesized arrays have the same HPBW as the original LAA with a two-fold reduction in the ***,for extreme SLL reduction,the DConv/GA is *** this technique,the same procedure of the aforementioned Conv/GA technique is performed on the resultant even and odd excitation *** provides a relatively wider HPBWthan the original LAA with about quad-fold reduction in the SLL.

关键词： Array synthesis convolution process genetic algorithm(GA) half power beamwidth(HPBW) linear antenna array(LAA) side lobe level(SLL) quality of service(QOS)

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ELCD-NSC2: a novel early lung cancer detection and non-small cell classification framework

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Neural Computing and Applications 2024年第24期36卷 15149-15164页

作者： Helaly, Hadeer A. Badawy, Mahmoud El-Gendy, Eman M. Haikal, Amira Y. Electrical Engineering Department Faculty of Engineering Damietta University Damietta Egypt Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt Department of Computer Science and Informatics Taibah University Medina Saudi Arabia

The survival rate of lung cancer relies significantly on how far the disease has spread when it is detected, how it reacts to the treatment, the patient’s overall health, and other factors. Therefore, the earlier the lung cancer diagnosis, the higher the survival rate. For radiologists, recognizing malignant lung nodules from computed tomography (CT) scans is a challenging and time-consuming process. As a result, computer-aided diagnosis (CAD) systems have been suggested to alleviate these burdens. Deep-learning approaches have demonstrated remarkable results in recent years, surpassing traditional methods in different fields. Researchers are currently experimenting with several deep-learning strategies to increase the effectiveness of CAD systems in lung cancer detection with CT. This work proposes a deep-learning framework for detecting and diagnosing lung cancer. The proposed framework used recent deep-learning techniques in all its layers. The autoencoder technique structure is tuned and used in the preprocessing stage to denoise and reconstruct the medical lung cancer dataset. Besides, it depends on the transfer learning pre-trained models to make multi-classification among different lung cancer cases such as benign, adenocarcinoma, and squamous cell carcinoma. The proposed model provides high performance while recognizing and differentiating between two types of datasets, including biopsy and CT scans. The Cancer Imaging Archive and Kaggle datasets are utilized to train and test the proposed model. The empirical results show that the proposed framework performs well according to various performance metrics. According to accuracy, precision, recall, F1-score, and AUC metrics, it achieves 99.60, 99.61, 99.62, 99.70, and 99.75%, respectively. Also, it depicts 0.0028, 0.0026, and 0.0507 in mean absolute error, mean squared error, and root mean square error metrics. Furthermore, it helps physicians effectively diagnose lung cancer in its early stages and allows spe

关键词： Image classification

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A novel image descriptor based on dot-diffused block truncation coding with bit probability

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Journal of Ambient Intelligence and Humanized Computing 2023年第11期14卷 14841-14858页

作者： Riyono, Dwi Sun, Chi-Chia Guo, Jing-Ming Wong, KokSheik Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Electrical Engineering National Formosa University Yunlin Huiwei Taiwan Faculty of Computer Science and Information Technology Monash University-Malaysia Campus Kuala Lumpur Malaysia

With the advent of mobile smart devices and ubiquitous network connections, digital images can now be conveniently captured, edited, and shared online worldwide. The ever-increasing number of pictures poses a technically challenging, yet unavoidable problem of efficiently and accurately finding a desired image. In this work, we develop a new image retrieval system based on an image compression technique, namely dot-diffused block truncation coding (DDBTC) with bit probability. Specifically, the color feature derived from color distribution and the bitmap feature (BF) derived from both edges and textures jointly describe the image. The degree of similarity between two images is then measured by their respective color and BFs by using the modified Canberra distance metric. Experimental results show that the proposed feature descriptor achieves superior image retrieval performance as compared to the former DDBTC image retrieval feature and conventional non-DDBTC based features. Additional experiments on image classification verify that the proposed feature descriptor outperforms conventional image classification methods. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Color

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Inductive Lottery Ticket Learning for Graph Neural Networks

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Journal of computer science & technology 2024年第6期39卷 1223-1237页

作者： Yong-Duo Sui Xiang Wang Tianlong Chen Meng Wang Xiang-Nan He Tat-Seng Chua School of Data Science University of Science and Technology of ChinaHefei 230027China Department of Electrical and Computer Engineering The University of Texas at AustinAustin 78712U.S.A School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230009China School of Computing National University of SingaporeSingapore

Graph neural networks (GNNs) have gained increasing popularity, while usually suffering from unaffordable computations for real-world large-scale applications. Hence, pruning GNNs is of great need but largely unexplored. The recent work Unified GNN Sparsification (UGS) studies lottery ticket learning for GNNs, aiming to find a subset of model parameters and graph structures that can best maintain the GNN performance. However, it is tailed for the transductive setting, failing to generalize to unseen graphs, which are common in inductive tasks like graph classification. In this work, we propose a simple and effective learning paradigm, Inductive Co-Pruning of GNNs (ICPG), to endow graph lottery tickets with inductive pruning capacity. To prune the input graphs, we design a predictive model to generate importance scores for each edge based on the input. To prune the model parameters, it views the weight’s magnitude as their importance scores. Then we design an iterative co-pruning strategy to trim the graph edges and GNN weights based on their importance scores. Although it might be strikingly simple, ICPG surpasses the existing pruning method and can be universally applicable in both inductive and transductive learning settings. On 10 graph-classification and two node-classification benchmarks, ICPG achieves the same performance level with 14.26%–43.12% sparsity for graphs and 48.80%–91.41% sparsity for the GNN model.

关键词： lottery ticket hypothesis graph neural networks neural network pruning

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