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3rd International Conference on Electronic engineering and Renewable Energy, ICEERE 2022

作者： Mellit, Adel Boubaker, Sahbi Department of Electronics Faculty of Science and Technology University of Jijel Jijel Algeria Department of Computer and Network Engineering College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia

ISBN: (纸本)9789811962226

In this paper, a novel fault diagnosis method for photovoltaic (PV) arrays is proposed. The method combines three machine learning (ML) algorithms: the first one is an unsupervised ML algorithm (principal component analysis, ‘PCA’) used for features reduction;the second one is a kind of a recurrent neural networks (long short-term memory, ‘LSTM’) employed to predict the PV power;and the third one is a supervised ensemble learning categorical boosting algorithm (CatBoost), used mainly for fault classification improvement. Four types of faults (dust deposit on PV modules surface, partial shading effect, shunted diode and open-circuit diode) are examined in this study. Results showed the effectiveness of the proposed method in terms of detection and classification accuracy, 98.70% and 98.58% respectively, and demonstrate the superiority of this method over other investigated ML algorithms (e.g. decision tree, support vector machine, k-nearest neighbours, and random forest). © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Failure analysis

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Industrial Teleassistance with the Help of Augmented Reality 23

Industrial Teleassistance with the Help of Augmented Reality

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23rd RoEduNet Conference: Networking in Education and Research, RoEduNet 2024

作者： Bobeica, Valentin Popescu, Dan Angelescu, Nicoleta Ichim, Loretta National University of Science and Technology Politehnica Bucharest Faculty of Automatic Control and Computer Science Bucharest Romania Valahia University of Targoviste Faculty of Electrical Engineering Electronics and Information Technology Targoviste Romania

ISBN: (纸本)9798331540388

This paper details the integration of augmented reality (AR) in a new factory, focusing on enhancing maintenance efficiency and optimization. The innovative approach involves combining AR technology, an Android application, and Siemens PLCs (Programmable Logic Controller), for efficient automation. The robustness of the PLC enables seamless integration within manufacturing environments, accommodating diverse programming paradigms. The software aspect features Human Machine Interface (HMI) configuration using Siemens' WinCC software, offering dynamic visualizations, alarm handling, and data logging. WinCC's scalability suits applications of varying sizes. The HMI architecture is designed for easy parameter display. The Android application, developed with Java and Python, leverages their respective strengths in Android app development and image processing. Data exchange is facilitated by OPC-UA, supporting multiple programming languages for seamless communication. The AR Teleassistance is highlighted as a revolutionary solution, fostering real-Time communication among global experts, technicians, and operators. Beyond gaming origins, AR teleassistance enhances efficiency in various sectors, minimizing downtime and facilitating global knowledge transfer. The integration of Teleassistance promotes effective collaboration between human expertise and automated systems, crucial for optimal performance in modern industrial settings. Continuous development is suggested through the addition of more 3D mechanical drawings and real-Time stock information for parts in the application, aiming for future optimization and performance enhancement. © 2024 IEEE.

关键词： augmented reality data exchange global knowledge image processing manufacturing environment production processes smart industrial

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Optimization of Electrocardiogram Classification Using Dipper Throated Algorithm and Differential Evolution

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computers, Materials & Continua 2023年第2期74卷 2379-2395页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Faten Khalid Karim Sameer Alshetewi Abdelhameed Ibrahim Abdelaziz A.Abdelhamid D.L.Elsheweikh Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt General Information Technology Department Ministry of DefenseThe Executive AffairsExcellence Services DirectorateRiyadh11564Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Specific EducationMansoura UniversityEgypt

Electrocardiogram(ECG)signal is a measure of the heart’s electrical ***,ECG detection and classification have benefited from the use of computer-aided systems by *** goal of this paper is to improve the accuracy of ECG classification by combining the Dipper Throated Optimization(DTO)and Differential Evolution Algorithm(DEA)into a unified algorithm to optimize the hyperparameters of neural network(NN)for boosting the ECG classification *** addition,we proposed a new feature selection method for selecting the significant feature that can improve the overall *** prove the superiority of the proposed approach,several experimentswere conducted to compare the results achieved by the proposed approach and other competing ***,statistical analysis is performed to study the significance and stability of the proposed approach using Wilcoxon and ANOVA *** results confirmed the superiority and effectiveness of the proposed *** classification accuracy achieved by the proposed approach is(99.98%).

关键词： Electrocardiogram differential evolution algorithm dipper throated optimization neural networks

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Estimating the AGV load and a battery lifetime based on the current measurement and random forest application

Estimating the AGV load and a battery lifetime based on the ...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Hanzel, Krzysztof Grzechca, Damian Ziebinski, Adam Chruszczyk, Lukas Janus, Artur Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Telecommunications and Teleinformatics Gliwice Poland Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Electronics Electrical Engineering and Microelectronics Gliwice Poland Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Distributed Systems and Informatics Devices Gliwice Poland Silesian University of Technology Student of Electrical EngineeringFaculty of Electrical Engineering Gliwice Poland

ISBN: (纸本)9798350324457

The paper describes the energy consumption from the battery based on the current measurements for various cases, i.e., speed (PWL adjustment) and loads. The main purpose of the research is to have additional and reliable information about power consumption and battery life estimation for autonomous guided vehicles (AGV). The authors propose a two-step algorithm. In the first step, a linear classifier was proposed. Then, the KNN classifier was tested;however, it did not give satisfactory results, so it was finally decided to use the random forest to estimate the load and PWL. The time domain current measurement is evaluated, and the beforementioned algorithms process the selected statistical measures. It has been proven that a two-step algorithm allows for achieving high accuracy. Based on the current observation, the paper is a good starting point for further investigation of the AGV because it is usually implemented in the AGV - so it does not require additional hardware. Moreover, it can lead to better energy management and increase battery lifetime. © 2023 IEEE.

关键词： AGV load estimation machine learning Prediction of energy consumption random forest

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Enhancing Early Detection of Lung Cancer through Advanced Image Processing Techniques and Deep Learning Architectures for CT Scans

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computers, Materials & Continua 2024年第10期81卷 271-307页

作者： Nahed Tawfik Heba M.Emara Walid El-Shafai Naglaa F.Soliman Abeer D.Algarni Fathi EAbd El-Samie Computers and Systems Department Electronics Research InstituteEl NozhaHuckstep Cairo12622Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Lung cancer remains a major concern in modern oncology due to its high mortality rates and multifaceted origins,including hereditary factors and various clinical *** stands as the deadliest type of cancer and a significant cause of cancer-related deaths *** diagnosis enables healthcare providers to administer appropriate treatment measures promptly and accurately,leading to improved prognosis and higher survival *** significant increase in both the incidence and mortality rates of lung cancer,particularly its ranking as the second most prevalent cancer among women worldwide,underscores the need for comprehensive research into efficient screening *** in diagnostic techniques,particularly the use of computed tomography(CT)scans,have revolutionized the identification of lung *** scans are renowned for their ability to provide high-resolution images and are particularly effective in detecting small,calcified areas,crucial for identifying earlystage lung ***,there is growing interest in enhancing computer-aided detection(CAD)*** algorithms assist radiologists by reducing false-positive interpretations and improving the accuracy of early cancer *** study aims to enhance the effectiveness of CAD systems through various ***,the Contrast Limited Adaptive Histogram Equalization(CLAHE)algorithm is employed to preprocess CT scan images,thereby improving their visual *** refinement is achieved by integrating different optimization strategies with the CLAHE *** CutMix data augmentation technique is applied to boost the performance of the proposed model.A comparative analysis is conducted using deep learning architectures such as InceptionV3,ResNet101,Xception,and *** study evaluates the performance of these architectures in image classification tasks,both with and without the implementation of the CLAHE *** empirical findings of the study demonst

关键词： Lung cancer detection CLAHE algorithm optimization deep learning classification feature extraction healthcare applications

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Comparative Study between KNN and CNN's Techniques for Kidney Stone Detection

Comparative Study between KNN and CNN's Techniques for Kidne...

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2023 International Conference on Smart Computing and Application, ICSCA 2023

作者： Al-Tbenawey, Fahdah M. Alotaibi, Shoayee Salem, Marwa Mir, Shamaila Mir, Zahida Computer Science and Engineering College University of Ha'il Department of Computer Science and Information Hail Saudi Arabia Faculty of Engineering Department of Electrical Communication and Electronics Systems Engineering Cairo Egypt

ISBN: (纸本)9798350347050

This paper aims to determine the better technique for kidney stone detection between K-Nearest Neighbor (KNN) and Convolutional Neural Networks (CNNs). As well known, the presence of kidney stones is an important topic that deserves discussion regarding expected medical outcomes. Most of the detection measures comprise CT scans, ultrasounds, and abdominal X-rays. These measures have varying effectiveness levels, hence used in the detection to prepare patients for specific treatment options. Patients' treatment options comprise percutaneous nephrolithotomy (PCNL), extracorporeal shockwave lithotripsy (ESWL), and ureteroscopy. The different detection techniques carried out for the patients are essential to test whether the patient has small or large kidney stones. One essential technique is using conventional neural networks (CNNs) technology. In this paper kidney, stone detection will be carried out using both of K-Nearest Neighbor (KNN) and CNN's techniques. A comparison between both techniques will be carried out to figure out the better. All of the simulations are carried out using MATLAB. © 2023 IEEE.

关键词： MATLAB

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A novel approach to deriving adaboost classifier weights using squared loss function for overlapping speech detection

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Multimedia Tools and Applications 2025年 1-28页

作者： Asbai, Nassim Bounazou, Hadjer Zitouni, Sihem Department of Electronics Faculty of Sciences and Technology Mohamed El Bachir El Ibrahimi University Bordj Bou Arreridj City34030 Algeria Speech Com. & Signal Proc. Lab. Faculty of Electrical Engineering USTHB Bab Ezzouar City16111 Algeria Department of Computer Science Faculty of Mathematics and Computer Science Mohamed El Bachir El Ibrahimi University Bordj Bou Arreridj City34030 Algeria

In this paper, we address the complex problem of detecting overlapping speech segments, a key challenge in speech processing with applications in speaker diarization, automatic transcription, and multi-speaker recognition systems. Traditional approaches, often relying on exponential loss functions within the AdaBoost framework, struggle to maintain robustness in noisy or imbalanced data environments. To enhance detection accuracy, we propose a novel AdaBoost variant—SLFAdaBoost—utilizing a squared loss function specifically tailored for overlapping speech detection. This model demonstrates superior stability and convergence, significantly outperforming conventional methods. Experimental evaluation on the NIST 2005 corpus revealed that SLFAdaBoost achieves an accuracy range of 92.5% to 98.6%, with F1 scores between 95.4% and 99.2%, notably surpassing standard AdaBoost and Random Forest classifiers. Additionally, our model shows resilience in noisy conditions, maintaining precision and recall metrics at higher noise levels than other classifiers. These results underscore SLFAdaBoost's capacity to handle intricate data patterns, offering a more robust solution for real-world speech processing applications where overlapping segments are prevalent. This contribution provides an efficient, high-performing model, advancing the capabilities of ensemble methods in complex audio environments. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Speech recognition

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Development of Automotive-mobility Training in the TRIREME project 30

Development of Automotive-mobility Training in the TRIREME p...

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30th IEEE International Symposium for Design and Technology in Electronic Packaging, SIITME 2024

作者： Medgyes, Balint Farkas, Csaba Spanyik, Marek Danieli, Stefano Cantiani, Elena Stolfa, Jakub Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Electronics Technology Budapest Hungary VSB-Technical University of Ostrava Faculty of Electrical Engineering and Computer Science Department of Computer Science Ostrava Czech Republic Mylia-Adecco Formazione Milan Italy

ISBN: (纸本)9798331539511

To achieve climate neutrality by 2050, the consistent transformation of the mobility sector is one of the most important pillars of the European Green deal. As a result of green and sustainable approaches, novel automotive concepts and massive digitalisation in the mobility ecosystem is undergoing a continuous development towards eco-friendly solutions and novel business models, and the changing landscape of automotive industry comes with a significant social impact as well. The overall horizontal collaboration on skills agenda in the Automotive-Mobility Ecosystem is assured throughout the Automotive Skills Alliance (ASA) [1], which is the Large-scale Pact for Skills Partnership. The latest strategic project of the partnership to support its activities is the Digital & Green Skills Towards Future of the Mobility Ecosystem (TRIREME) which promotes a collaboration between industry stakeholders and training providers including Vocational education Trainings (VETs), Higher Educations (HEs) and universities), moreover, also emphasizing the widespread public representation of different project segments and evaluating the social impacts and regional impact of the changes in the Automotive-Mobility Ecosystem. In parallel to the development of sectoral skills strategy, our department (Department of electronics Technology-ETT) is contributing to deliver specific training materials and courses within Work Package 4 (WP4), which is based on the gathered and processed intelligence from previous desk research, survey activity and participation in professional events. The methodology of course development relies on the concept of modularity, our professional competences in the field of Automotive-mobility, and the fulfillment of requirements to cover the needed skills, the competence and the occupational profile subsets. © 2024 IEEE.

关键词： Automotive Mobility Modular Courses Skills Training Development

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Evaluation and Design Criterion for Pulse-shaped AFDM

Evaluation and Design Criterion for Pulse-shaped AFDM

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Yin, Haoran Tang, Yanqun Li, Shuangyang Zhou, Yu Yi, Cong Sun Yat-sen University School of Electronics and Communication Engineering Shenzhen China Technical University of Berlin Faculty of Electrical Engineering and Computer Science Berlin Germany

ISBN: (纸本)9798350351255

Affine frequency division multiplexing (AFDM) is a promising chirp-based waveform designed for communications in high-mobility scenarios. In this paper, the pulse shaping for AFDM over doubly selective channels (DSC) is investigated. We first develop the pulse-shaped AFDM (PS-AFDM) system, where different transmit pulses and receive pulses can be used for each chirp carrier. Based on that, we formulate the impacts of pulse shaping on the input-output relationship of PS-AFDM system with fractional delay and fractional Doppler shifts. In particular, we reveal that there exists inter-pulse interference (IPI) within the pilot region and inter-region interference (IRI) between the pilot region and the data region in the AFDM/PS-AFDM received symbols. To provide an instructive guideline for interference suppression, we elaborate how the adopted transmit and receive pulses determine the IPI and IRI. Furthermore, we demonstrate that applying the pulse-shaping window with low sidelobe levels in PS-AFDM can suppress the IPI and IRI, facilitating the channel estimation and signal detection processes significantly. Simulations verify that the proposed PS-AFDM systems can achieve lower overhead and higher accuracy channel estimation compared to the conventional AFDM systems. © 2024 IEEE.

关键词： Channel estimation

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Hybrid Dipper Throated and Grey Wolf Optimization for Feature Selection Applied to Life Benchmark Datasets

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computers, Materials & Continua 2023年第2期74卷 4531-4545页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Faten Khalid Karim Mostafa Abotaleb Abdelhameed Ibrahim Abdelaziz A.Abdelhamid D.L.Elsheweikh Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of System Programming South Ural State UniversityChelyabinsk454080Russia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Specific EducationMansoura UniversityEgypt

Selecting the most relevant subset of features from a dataset is a vital step in data mining and machine *** feature in a dataset has 2n possible subsets,making it challenging to select the optimum collection of features using typical *** a result,a new metaheuristicsbased feature selection method based on the dipper-throated and grey-wolf optimization(DTO-GW)algorithms has been developed in this *** can result when the selection of features is subject to metaheuristics,which can lead to a wide range of ***,we adopted hybrid optimization in our method of optimizing,which allowed us to better balance exploration and harvesting chores more *** propose utilizing the binary DTO-GW search approach we previously devised for selecting the optimal subset of *** the proposed method,the number of features selected is minimized,while classification accuracy is *** test the proposed method’s performance against eleven other state-of-theart approaches,eight datasets from the UCI repository were used,such as binary grey wolf search(bGWO),binary hybrid grey wolf,and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hysteresis optimization(bHy),and binary hysteresis optimization(bHWO).The suggested method is superior 4532 CMC,2023,vol.74,no.2 and successful in handling the problem of feature selection,according to the results of the experiments.

关键词： Metaheuristics dipper throated optimization grey wolf optimization binary optimizer feature selection

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