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High Accurate PMSM Computation Model Based on Strongly Coupled Magnetic Field and Multi-Turn Electric Winding Circuits Using the Time-Stepping Finite Element

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Progress in Electromagnetics Research C 2025年 153卷 13-23页

作者： Merzouki, Tarik Rachek, M’Hemed Department of Electrotechnics Faculty of Electrical and Computer Engineering Mouloud Mammeri University Algeria

The work presented in this paper has great significance in improving electromagnetic models based on the strong coupling between the magnetic and electric fields transient equations while considering a realistic random multi-turn stranded winding where eddy currents, proximity and skin effects occur. The space-time partial differential equations of electromagnetic field expressed in terms of magnetic vector potential under nonlinear (B-H) magnetic materials curves handled by the iterative Newton-Raphson (NR) algorithm are simultaneously coupled with the voltage fed multi-turns electric circuits equations based on Kirchhoff’s voltage law for each turn coil current loop. The magnetic field-multi-turn electric circuit coupled model solved using the time-stepping finite element method (FEM) formulation is dedicated to highly accurate computation of electromagnetic-mechanical devices. The developed FEM tools implemented under Matlab software are used to the modeling of the permanent magnet synchronous motor (PMSM) behavior through the physical quantities such as magnetic flux density, electric current, electromagnetic torque, and angular velocity. © 2025, Electromagnetics Academy. All rights reserved.

关键词： MATLAB

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Impact of machine learning-based imputation techniques on medical datasets- a comparative analysis

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Multimedia Tools and Applications 2025年第9期84卷 5905-5925页

作者： Tiwaskar, Shweta Rashid, Mamoon Gokhale, Prasad Department of Computer Engineering Faculty of Science and Technology Vishwakarma University Pune India

In the realm of medical datasets, particularly when considering diabetes, the occurrence of data incompleteness is a prevalent issue. Unveiling valuable patterns through medical data analysis is crucial for early and precise medical predictions. However, the quality of data and the proper handling of missing data hold significant significance. To address this challenge, imputation stands as a robust approach. The main goal of this paper aims to provide a comprehensive investigation into the effects brought about by Machine Learning (ML) based imputation techniques, specifically K Nearest Neighbor Imputation (KNNI), Multiple Imputation by Chained Equations (MICE), and MissForest. Results of all three techniques are compared with the complete dataset for five missing rates (10% to 50%), and evaluated using four categories of evaluation criteria i.e. (1) model performance, (2) imputation error rate (Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Coefficient of Determination (R^2) values), (3) Pearson correlation analysis and, (4) model selection basis (Bayesian information criterion (BIC), Akaike information criterion (AIC), values). Model performance includes accuracy, precision, recall, F1 score, and Matthews Correlation Coefficient (Mcoff) score of four ML classifiers viz. (a) Random Forest (RF), (b) Support vector machine (SVM), (c) AdaBoost, (d) XGBoost (XGB). For all missing rate cases, the MissForest technique is better than the KNNI and MICE in accuracy and Mcoff in 80% of cases, precision in 40% of cases, recall in 60% of cases, F1 score, MAE, RMSE, R^2 in 100% of cases, AIC in 80% of cases, and BIC values in 100% of cases. Also, the correlation analysis confirms that the MissForest imputation preserves association between the variables, like the complete dataset. Overall, our findings suggest that MissForest is a better machine learning-based imputation technique for handling missing data in diabetes research. © The Author(s), under exclusive lice

关键词： Adaptive boosting

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Optimized COVID-19 detection using sparse deep learning models from multimodal imaging data

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Multimedia Tools and Applications 2025年第13期84卷 11451-11474页

作者： Moradi, MohammadMahdi Hassanzadeh, Alireza Haghanifar, Arman Ko, Seok Bum Faculty of Computer Science and Software Engineering Concordia University MontrealQC Canada Faculty of Electrical Engineering Shahid Beheshti University Tehran Tehran Iran Department of Electrical & Computer Engineering University of Saskatchewan SaskatoonSK Canada

This paper explores the global spread of the COVID-19 virus since 2019, impacting 219 countries worldwide. Despite the absence of a definitive cure, the utilization of artificial intelligence (AI) methods for disease diagnosis has demonstrated commendable effectiveness in promptly diagnosing patients and curbing infection transmission. The study introduces a deep learning-based model tailored for COVID-19 detection, leveraging three prevalent medical imaging modalities: computed tomography (CT), chest X-ray (CXR), and Ultrasound. Various deep Transfer Learning Convolutional Neural Network-based (CNN) models have undergone assessment for each imaging modality. For each imaging modality, this study has selected the two most accurate models based on evaluation metrics such as accuracy and loss. Additionally, efforts have been made to prune unnecessary weights from these models to obtain more efficient and sparse models. By fusing these pruned models, enhanced performance has been achieved. The models have undergone rigorous training and testing using publicly available real-world medical datasets, focusing on classifying these datasets into three distinct categories: Normal, COVID-19 Pneumonia, and non-COVID-19 Pneumonia. The primary objective is to develop an optimized and swift model through strategies like Transfer Learning, Ensemble Learning, and reducing network complexity, making it easier for storage and transfer. The results of the trained network on test data exhibit promising outcomes. The accuracy of these models on the CT scan, X-ray, and ultrasound datasets stands at 99.4%, 98.9%, and 99.3%, respectively. Moreover, these models’ sizes have been substantially reduced and optimized by 51.93%, 38.00%, and 69.07%, respectively. This study proposes a computer-aided-coronavirus-detection system based on three standard medical imaging techniques. The intention is to assist radiologists in accurately and swiftly diagnosing the disease, especially during the screen

关键词： COVID-19

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VoteDroid: a new ensemble voting classifier for malware detection based on fine-tuned deep learning models

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Multimedia Tools and Applications 2025年第12期84卷 10923-10944页

作者： Bakır, Halit Faculty of Engineering and Natural Sciences Department of Computer Engineering Sivas University of Science and Technology Sivas Turkey

In this work, VoteDroid a novel fine-tuned deep learning models-based ensemble voting classifier has been proposed for detecting malicious behavior in Android applications. To this end, we proposed adopting the random search optimization algorithm for deciding the structure of the models used as voter classifiers in the ensemble classifier. We specified the potential components that can be used in each model and left the random search algorithm taking a decision about the structure of the model including the number of each component that should be used and its location in the structure. This optimization method has been used to build three different deep learning models namely CNN-ANN, pure CNN, and pure ANN. After selecting the best structure for each DL model, the selected three models have been trained and tested using the constructed image dataset. Afterward, we suggested hybridizing the fine-tuned three deep-learning models to form one ensemble voting classifier with two different working modes namely MMR (Malware Minority Rule) and LMR (Label Majority Rule). To our knowledge, this is the first time that an ensemble classifier has been fine-tuned and hybridized in this way for malware detection. The results showed that the proposed models were promising, where the classification accuracy exceeded 97% in all experiments. © The Author(s) 2024.

关键词： Android malware

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A dual-band tunable terahertz bandpass filter using an E-shaped graphene resonator

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Optical and Quantum Electronics 2025年第1期57卷 1-19页

作者： Mohammadi, Ghader Orouji, Ali Asghar Danaie, Mohammad Faculty of Electrical and Computer Engineering Semnan University Semnan Iran

This paper presents a tunable terahertz (THz) bandpass filter (BPF) based on graphene, designed and simulated for dual-mode operation. The filter topology features two input/output waveguides and a novel E-shaped resonator, enabling dual-mode performance. An optimized gap between the resonator and waveguides effectively minimizes harmonics and unwanted modes. Simulations were conducted using three-dimensional finite-difference time-domain (3D-FDTD) and coupled-mode theory (CMT) methods. The results demonstrate that the graphene-based dual-band filter achieves mode 1 at 7 THz, with a quality factor (Q-factor) of 32, a full width at half maximum (FWHM) of 0.22 THz, and a transmission efficiency of 98%. For mode 2, the filter operates at 11.8 THz, exhibiting a Q-factor of 118, an FWHM of 0.1 THz, and a transmission efficiency of 97%. The free spectral range (FSR) for both modes is approximately 24 THz. Additionally, tuning the chemical potential of graphene induces a shift of about 5 THz in the transmission spectrum for the combined modes. The proposed filter indicates significant potential as a highly compact dual-band BPF for use in integrated photonic circuits. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Q factor measurement

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Investigating the influence of material composition and design parameters on optical loss in hollow core fibers at 9.5 µm

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Optical and Quantum Electronics 2025年第3期57卷 1-19页

作者： Hummam, Zahraa Vahed, Hamid Pourziad, Ali Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran

This study examines a novel design for hollow core metal fibers HCMF by systematically altering geometric factors. Various materials, such as gold, silver, aluminum, graphene, and silicon nitride Si3N4, were examined as substitutes for conventional metallic components to assess their influence on fiber performance at a wavelength of 9.5 µm. The optical losses of each material were studied using Lumerical Ansys 2023 and the finite-difference time-domain approach. Results demonstrated that Si₃N₄ had outstanding optical characteristics, with negligible optical loss approaching zero at the specified wavelength, rendering it suitable for low-attenuation applications;regarding the selection of Si₃N₄ as the material for the metal wires in the hollow-core fiber, subsequent modifications to the geometry, such as wire widths and the spacing between the wires and the core, enhanced fiber performance, achieving an optical confinement loss as minimal as 10−18 dB/m. This illustrates Si₃N₄ capability as an exceptional material for highly efficient hollow-core fiber configurations. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Silicon nitride

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Proposing a digital twin-based sustainable water governance system for rural Indian villages

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International Journal of Information Technology (Singapore) 2025年第3期17卷 1777-1783页

作者： Singh, Dineshkumar Sharma, Vishnu Department of Computer Science and Engineering Faculty of Computer Science & Engineering (FCE) Poornima University Jaipur India Department of Computer Science and Application Faculty of Computer Science & Engineering (FCE) Poornima University Jaipur India

A sustainably governed water-ecosystem at village-level is crucial for the community's well-being. It requires understanding natures’ limits to store and yield water and balance it with the stakeholders’ needs, in the context of population growth, climate change, and unsustainable agricultural practices. Maharashtra villages like Randullabad and Karanjkhop are impacted by limited groundwater availability due to the geological structure of the Deccan Traps basaltic rock formation and are recharged by the most rainfall during the monsoon season. Traditional water management practices are inadequate, with old wells causing conflicts among villagers due to fluctuating water levels. In order to achieve a sustainable water system for rural Indian villages, this paper proposes a Digital Twin-Based Water Governance System (DToWGS) with the aim of balancing nature's boundaries with stakeholders' interests to provide real-time water insights, encourage community engagement, transparency in water allocation and decision-making, and promote sustainable practices. These measured indicators can potentially link local governance outcomes to global goals like Viksit Bharat 2047 and UN Sustainable Development Goal 6 (SDG 6). © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Digital twin Empathy map Remote sensing SDG6 Stakeholders Village water environment

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A novel data-driven algorithm for object detection, tracking, distance estimation, and size measurement in stereo vision systems

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Multimedia Tools and Applications 2025年第12期84卷 11041-11061页

作者： Taromi, Amirhossein Dadashzadeh Klidbary, Sajad Haghzad Department of Engineering University of Zanjan Zanjan Iran Faculty of Engineering Department of Electrical and Computer Engineering University of Zanjan Zanjan Iran

Distance and size estimation of objects of interests is an inevitable task for many navigation and obstacle avoidance algorithms mainly used in autonomus and robotic systems. Stereo vision systems, inspired by human visual perception, can infer depth from images as a cheap and accessible solution. On one hand, accurately calibrating cameras is a challenging task and the main source of error in current stereo vision based distance and size estimation algorithms. On the other hand, considering the recent advancements in Deep Learning, alongside the fact that human eyes do not need calibration but human brain can estimate the distance and size of objects fairly accurate was the main motivation behind this study. The proposed algorithm uses YOLOv8 as the object detector, and an MLP to learn the relation between distance, size, and disparity from collected data in a stereo vision system. In our experiments, conducted at distances ranging from 50 to 200 centimeters with calibrated and uncalibrated cameras, our proposed algorithm showcased accurate performance in both scenarios. It achieved distance measurements with an accuracy of up to 99.99% in select cases and maintained the mean accuracy of 98.15% for distance, 92.87% for width, and 93.92% for height estimations. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Object detection

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ES-Net: Unet-based model for the semantic segmentation of Iris

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Multimedia Tools and Applications 2025年第13期84卷 12395-12416页

作者： Pourafkham, Behnam Khotanlou, Hassan RIV Lab. Department of Computer Engineering Faculty of Engineering Bu-Ali Sina University Hamedan Iran

The segmentation of the iris is crucial and holds great importance within the medical image recognition area. Researchers have introduced many architectures and applied them to iris image segmentation, However, they are involved with some critical challenges like low accuracy, slow runtime, and high computational complexity. In this paper, we present ES-Net which is a fast and precise deep-learning architecture for the semantic segmentation of Iris. The U-Net model serves as the foundation for the proposed method. Furthermore, our primary objectives are to develop a lightweight neural network architecture capable of accurately segmenting irises in diverse environments. To accomplish this, we employ two key strategies. Firstly, we integrate an attention mechanism to enhance the accuracy of our model. This mechanism allows the network to focus on crucial iris features, improving segmentation precision. Secondly, we incorporate the efficient spatial pyramid (ESP) block, which effectively reduces the time complexity of the overall model. The performance of our model is evaluated on the CASIA-Iris-V4-Interval, IIT Delhi, and UBIRIS.v2 databases, along with, our model achieving MIOU of 96.29%, 96.02%, and 93.61% respectively. Our research reveals that the proposed network delivers exceptional levels of accuracy with high computational efficiency. Furthermore through comparative analysis with other models and methods, we established that our model is more accurate and cost-efficient than other state-of-the-art semantic segmentation architectures and methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Semantic Segmentation

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Customer satisfaction optimisation in a dynamic closed-loop supply chain under uncertainty

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International Journal of Industrial and Systems engineering 2025年第1期49卷 18-56页

作者： Shambayati, Hanieh Nikabadi, Mohsen Shafiei Rahmanimanesh, Mohammad Faculty of Economic Management and Administrative Sciences Semnan University Iran Electrical and Computer Engineering Faculty Semnan University Iran

Optimising the management of the closed-loop supply chain (CLSC) has attracted considerable attention over the past few years. However, most researches in this area have only considered the cost and profit functions. In this research, the optimisation of multi-product CLSC considering customer satisfaction with dimensions such as quality, service level, lead time, and environmental pollution along with the profit function in different periods is considered. The uncertainty of demand in the form of grey numbers is considered. To optimise this NP-hard problem, a multi-objective meta-heuristic pareto-based enhanced firefly algorithm was used. The purpose of the proposed model is to determine the optimal production quantities of each product and finding the location of the warehouse at each stage and period in the CLSC. Finally, for the validity and analysis of the model, a numerical example has been considered. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Sales

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