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8th Seminar on Neural Network Applications in Electrical Engineering, Neurel-2006

作者： Todorovic, Nenad Klán, Petr Czech Technical University in Prague Faculty of Mechanical Engineering Department of Instrumentation and Control Engineering Institute of Computer Science Prague Czech Republic

ISBN: (纸本)1424404339

This paper covers the state of the art in nonlinear dynamical system identification using Artificial Neural Networks (ANN). The main approaches in the last two decades are presented in unified framework. ANN have unique characteristics, which enable them to model nonlinear dynamical systems. The main problems with the choice of ANN model structure are considered and commonly used identification schemes are proposed. A procedure for derivation of parameter estimation law using Lyapunov synthesis approach, which guarantees stability and convergence of the overall identification scheme, is presented. © 2006 IEEE.

关键词： Neural networks

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Application of interpretability method based on FN-v8 in defect detection of photovoltaic panels 3

Application of interpretability method based on FN-v8 in def...

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3rd International Conference on Electronics, Electrical and Information Engineering, ICEEIE 2023

作者： Zhou, Li Sun, Xueliang Liu, Yiming Faculty of Electrical and Control Engineering Liaoning Technical University Huludao China Information Science Department of Beijing University of Technology Beijing China

ISBN: (纸本)9781510671423

Aiming at the problems of poor interpretability of traditional neural network and bloated traditional model in photovoltaic panel defect detection. Firstly, an FN-v8 model was proposed for detecting various types of defects in photovoltaic panels. The FN-v8 model reduces redundant computing and memory access, while also reducing the GFLOPs of the model. Then, Grad-CAM was used to further demonstrate the stronger interpretability of the new model through passive interpretation. Compared with other traditional models with relatively bulky structures, the proposed new model has smaller parameter quantities and stronger interpretability. Experiments were conducted on devices with the same performance and the same dataset of photovoltaic panel defects. The results showed that the proposed new model has a smaller volume, faster calculation speed, and higher accuracy than the original model. The GFLOPs of the new model have decreased by 18.25% compared to the original model, significantly reducing the computational complexity, and the visualization of the thermodynamic diagram is more accurate, further indicating an improvement in interpretability. Moreover, the new model has better detection accuracy for photovoltaic panel detection, with an average detection accuracy( mAP@0.5 )Improved by 2.1% compared to the original model. © 2023 SPIE.

关键词： Defects

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A novel deep learning-based spider wasp optimization approach for enhancing brain tumor detection and physical therapy prediction

Computer Methods and Programs in Biomedicine Update

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Computer Methods and Programs in Biomedicine Update 2025年 7卷

作者： Suleiman Daoud Ahmad Nasayreh Khalid M.O. Nahar Wlla k. Abedalaziz Salem M. Alayasreh Hasan Gharaibeh Ayah Bashkami Amer Jaradat Sultan Jarrar Hammam Al-Hawamdeh Absalom E. Ezugwu Raed Abu Zitar Aseel Smerat Vaclav Snasel Laith Abualigah Neurosurgery Department Faculty of Medicine Jordan University of Science and Technology Irbid 22110 Jordan Artificial Intelligence and Data Innovation Office King Hussein Cancer Center Amman Jordan Department of Information Technology and Computer Sciences Yarmouk University Irbid 211633 Jordan Department of Rehabilitation science Faculty of applied medical science Jordan University of Science and Technology King Abdullah University Hospital Irbid Jordan Department of Medical Laboratory Sciences Al Balqa Applied University Salt Jordan Unit for Data Science and Computing North-West University 11 Hofman Street Potchefstroom 2520 South Africa College of Engineering and Computing Liwa University Abu Dhabi United Arab Emirates Faculty of Educational Sciences Al-Ahliyya Amman University Amman 19328 Jordan Computer Technologies Engineering Mazaya University College Nasiriyah Iraq Faculty of Electrical Engineering and Computer Science VŠB-Technical University of Ostrava 70800 Poruba-Ostrava Czech Republic Computer Science Department Al al-Bayt University Mafraq 25113 Jordan

A brain tumor, one of the deadliest disorders, is characterized by the abnormal growth of synapses in the brain. Early detection can improve brain tumor diagnosis, and accurate diagnosis is essential for effective treatment. Researchers have developed several deep-learning classification methods to diagnose brain tumors. Moreover, these types of tumorscan significantly impair physical activity, presenting a broad spectrum of symptoms. As a result, each patient requires an individualized physical therapy treatment plan tailored to their specific needs. However, some challenges remain, including the need for a competent expert in classifying brain tumors using deep learning models, as well as the challenge of creating the most accurate deep learning model for brain tumor classification. To address these challenges, we present a highly accurate and efficient methodology based on advanced metaheuristic algorithms and deep learning. To identify different types of pediatric brain tumors, we specifically develop an optimal residual learning architecture. We also present the Spider Wasp Optimization (SWO) algorithm, which aims to improve performance by feature selection. The algorithm enhances the effectiveness of optimization by balancing the speed of convergence and diversity of solutions. We first convert the algorithm from continuous to binary, combine it with the K-Nearest Neighbor (KNN) algorithm for classification, and evaluate it on a dataset of brain MRI images collected from King Abdullah Hospital. Our analysis revealed that in terms of metrics such as accuracy, sensitivity, specificity, and f1-score, it outperformed other conventional algorithms. We demonstrate the overall effectiveness of the proposed model by using it to select the optimal features extracted from the Resnet50V2 model for pediatric brain tumor detection. We compared the proposed SWO+KNN model with other deep learning architectures such as MobileNetV2, Resnet50V2, and machine learning algorithms

关键词： Brain tumor Pediatric Machine learning Rehabilitation Physical therapy Deep learning Metaheuristic optimization Feature selection

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An enhanced Walrus Optimizer with opposition-based learning and mutation strategy for data clustering

Array

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Array 2025年 26卷

作者： Laith Abualigah Saleh Ali Alomari Mohammad H. Almomani Raed Abu Zitar Hazem Migdady Kashif Saleem Aseel Smerat Vaclav Snasel Absalom E. Ezugwu Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Faculty of Science and Information Technology Jadara University Irbid 21110 Jordan Department of Mathematics Facility of Science The Hashemite University P.O box 330127 Zarqa 13133 Jordan Faculty of Engineering and Computing Liwa College Abu Dhabi United Arab Emirates CSMIS Department Oman College of Management and Technology 320 Barka Oman Department of Computer Science & Engineering College of Applied Studies & Community Service King Saud University Riyadh 11362 Saudi Arabia Faculty of Educational Sciences Al-Ahliyya Amman University Amman 19328 Jordan Computer Technologies Engineering Mazaya University College Nasiriyah Iraq Faculty of Electrical Engineering and Computer Science VŠB-Technical University of Ostrava 70800 Poruba-Ostrava Czech Republic Unit for Data Science and Computing North-West University 11 Hofman Street Potchefstroom 2520 South Africa

Data clustering plays a crucial role in various domains, such as image processing, pattern recognition, and data mining. Traditional clustering techniques often suffer from limitations like sensitivity to initialization, poor convergence, and entrapment in local optima. To address these challenges, this paper proposes an Enhanced Walrus Optimizer (IWO) tailored for clustering tasks. The proposed IWO integrates two powerful strategies–Opposition-Based Learning (OBL) and Mutation Search Strategy (MSS)–to improve population diversity and prevent premature convergence, thereby enhancing both exploration and exploitation capabilities. These enhancements enable more accurate and stable identification of cluster centers. The effectiveness of IWO is validated through extensive experiments on multiple benchmark clustering datasets and compared against several state-of-the-art metaheuristic algorithms, including PSO, GWO, AOA, and others. The results demonstrate that IWO achieves better results, indicating improved compactness and separation of clusters. Statistical validation using p-values and ranking scores further confirms the superiority of the proposed method. These findings suggest that IWO offers a robust and flexible framework for solving complex clustering problems. Future work will explore hybrid deep learning-integrated models and parallel implementations to enhance scalability.

关键词： Optimization-based clustering Metaheuristic algorithms Improved Walrus Optimizer Benchmark datasets Swarm intelligence

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One Solution for Deterministic Scheduling on GPU for Automotive Algorithms

One Solution for Deterministic Scheduling on GPU for Automot...

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2021 Zooming Innovation in Consumer Technologies Conference, ZINC 2021

作者： Kenjic, Dusan Milosevic, Milena Antic, Marija Teslic, Nikola University of Novi Sad Faculty of Technical Sciences Department of Computing and Control Engineering Novi Sad Serbia RT-RK Institute for Computer Based Systems Novi Sad Serbia

ISBN: (纸本)9781665404174

In order for higher levels of autonomous driving to be achieved, the demanding environmental perception and decision-making algorithms are developed. Meeting real-time system requirements is a mandatory task for all of these algorithms to be properly utilized. In order to meet these requirements, GPUs are widely used, and one of the biggest challenges is providing the deterministic scheduling of different tasks on GPU based on their priority classes. The complete determinism which ensures the absolute control of processes on GPU has still not been achieved. In this paper, we propose and evaluate one method that allows scheduling of tasks with different types of time constraints. © 2021 IEEE.

关键词： Scheduling

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One solution of a Distributed System intended for communication between ADAS and IVI Systems

One solution of a Distributed System intended for communicat...

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2023 IEEE Zooming Innovation in Consumer Technologies Conference, ZINC 2023

作者： Cabrilo, Vuk Kenjic, Dusan Zlatkovic, Radomir Antic, Marija University of Novi Sad Faculty of Technical Sciences Department of Computing and Control Engineering Novi Sad Serbia RT-RK Institute of Computer Based Systems Novi Sad Serbia

ISBN: (纸本)9798350347722

Current trends of digitalization and automation in the automotive industry have increased the complexity of computer clusters in different domains of vehicular ecosystems, which are required to support new features. This has led to redundancy in resource usage in specific use cases, resulting in increased production costs and manufacturer suggested retail price (MSRP) markups for the finalized products. The average retail price of a new vehicle in the United States increased by 13.4% between 2015 and 2020. Considering the global chip shortage of 2020 and current global inflation, the average price of a new vehicle in the United States reached 49,500 in 2023 [1]. With the electrification of current automotive platforms underway, as well as the move to centralized system architectures, new approaches to inter-domain communication are expected. This paper proposes a solution of a distributed AUTOSAR Adaptive system that can establish communication with the In-Vehicle Infotainment (IVI) domain. The proof of concept implemented in a controlled environment proves viable for inter-domain communication, as it provides good scalability and adaptability to different scenarios. The use of a distributed approach facilitates the applicability of distributed system principles, allowing for the implementation of a stable system. © 2023 IEEE.

关键词： Vehicles

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Awareness to Deepfake: A resistance mechanism to Deepfake

Awareness to Deepfake: A resistance mechanism to Deepfake

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2021 International Congress of Advanced Technology and Engineering, ICOTEN 2021

作者： Ahmed, Mohammad Faisal Bin Miah, M. Saef Ullah Bhowmik, Abhijit Sulaiman, Juniada Binti University of Munich Department of Informatics Technical Munich Germany University Malaysia Pahang Faculty of Computing Pekan Malaysia American International University-Bangladesh Faculty of Science and Technology Dhaka Bangladesh

ISBN: (纸本)9781665412247

The goal of this study is to find whether exposure to Deepfake videos makes people better at detecting Deepfake videos and whether it is a better strategy against fighting Deepfake. For this study a group of people from Bangladesh has volunteered. This group were exposed to a number of Deepfake videos and asked subsequent questions to verify improvement on their level of awareness and detection in context of Deepfake videos. This study has been performed in two phases, where second phase was performed to validate any generalization. The fake videos are tailored for the specific audience and where suited, are created from scratch. Finally, the results are analyzed, and the study's goals are inferred from the obtained data. © 2021 IEEE.

关键词： Cybersecurity

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Data-Driven Process Monitoring and Fault Tolerant control in Wind Energy Conversion System with Hydraulic Pitch System

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Journal of Shanghai Jiaotong university(science) 2015年第4期20卷 489-494页

作者：王凯罗浩 KRUEGER M DING S X 杨旭 JEDSADA S Institute for Automatic Control and Complex Systems University of Duisburg-Essen Baotou Vocational & Technical Collage School of Automation and Electrical Engineering University of Science and Technology Beijing Department of Electrical Engineering Faculty of Engineering Burapha University

Wind energy is one of the widely applied renewable energies in the world. Wind turbine as the main wind energy converter at present has very complex technical system containing a huge number of components,actuators and sensors. However, despite of the hardware redundancy, sensor faults have often affected the wind turbine normal operation and thus caused energy generation loss. In this paper, aiming at the wind turbine hydraulic pitch system, data-driven design of process monitoring(PM) and diagnosis has been realized in the wind turbine benchmark. Fault tolerant control(FTC) strategies focused on sensor faults have also been presented here, where with the implementation of soft sensor the sensor fault can be handled and the performance of the system is improved. The performance of this method is demonstrated with the wind turbine benchmark provided by Math Works.

关键词： data-driven process monitoring(PM) fault tolerant control(FTC) soft sensor wind turbine

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Improving finite-difference time-domain memory bandwidth by using block floating-point arithmetic 31

Improving finite-difference time-domain memory bandwidth by ...

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31st Telecommunications Forum, TELFOR 2023

作者： Pijetlovic, Stefan Kastelan, Ivan Subotic, Milos Pjevalica, Nebojsa RT-RK Institute for Computer Based Systems Novi Sad Serbia University of Novi Sad Faculty of Technical Sciences Department of Computing and Control Engineering Novi Sad Serbia

ISBN: (纸本)9798350303131

Finite-difference time-domain is a numerical method used for modelling of computational electrodynamics. The method is resource intensive, especially regarding memory usage since multiple memory accesses are required per single computation. In other words, memory, or more precisely its speed, is a limiting factor for the overall performance. Existing solutions focus either on structures with great parallelism such as graphical processing units or custom designs. The former have issues with underutilized resources, while the latter require significant time and effort to achieve similar or slightly better performances. The approach taken during this research was to target the memory itself and find a way to improve the efficiency of memory usage across multiple platforms. The main contribution of the paper is using block floating-point in a novel way regarding the finite-difference time-domain method. The presented solution is a verification of earlier research, proving that the performance of this algorithm can indeed be improved using the suggested methods. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Lightweight Transfer Learning Models for Ultrasound-Guided Classification of COVID-19 Patients

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Computers, Materials & Continua 2021年第11期69卷 2295-2312页

作者： Mohamed Esmail Karar Omar Reyad Mohammed Abd-Elnaby Abdel-Haleem Abdel-Aty Marwa Ahmed Shouman College of Computing and Information Technology Shaqra UniversityShaqra11961Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering(FEE)Menoufia UniversityMenouf32952Egypt Department of Mathematics and Computer Science Faculty of ScienceSohag UniversitySohag82524Egypt Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Physics College of SciencesUniversity of BishaBisha61922Saudi Arabia Department of Physics Faculty of ScienceAl-Azhar UniversityAssiut71524Egypt Department of Computer Science and Engineering Faculty of Electronic Engineering(FEE)Menoufia UniversityMenouf32952Egypt

Lightweight deep convolutional neural networks(CNNs)present a good solution to achieve fast and accurate image-guided diagnostic procedures of COVID-19 ***,advantages of portable Ultrasound(US)imaging such as simplicity and safe procedures have attracted many radiologists for scanning suspected COVID-19 *** this paper,a new framework of lightweight deep learning classifiers,namely COVID-LWNet is proposed to identify COVID-19 and pneumonia abnormalities in US *** to traditional deep learning models,lightweight CNNs showed significant performance of real-time vision applications by using mobile devices with limited hardware *** main lightweight deep learning models,namely MobileNets,ShuffleNets,MENet and MnasNet have been proposed to identify the health status of lungs using US *** image dataset(POCUS)was used to validate our proposed COVID-LWNet framework *** classes of infectious COVID-19,bacterial pneumonia,and the healthy lung were investigated in this *** results showed that the performance of our proposed MnasNet classifier achieved the best accuracy score and shortest training time of 99.0%and 647.0 s,*** paper demonstrates the feasibility of using our proposed COVID-LWNet framework as a new mobilebased radiological tool for clinical diagnosis of COVID-19 and other lung diseases.

关键词： Coronavirus medical image processing artificial intelligence ultrasound

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