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Performance improvement of the hybrid switch reluctance motor by notching method

Scientia Iranica 2024年第10期31卷 790-797页

作者： Hasanzadeh, Saeed Rezaei, Hossein Taheri, Hadis Department of Electrical and Computer Engineering Qom University of Technology Qom Iran Department of Electrical and Computer Engineering Babol Noshirvani University of Technology Babol Iran Mazandaran Regional Electric Company Sari Iran

Hybrid switch reluctance motors are the family of switch reluctance motors (SRMs) that attenuate the magnetic saturation and increase the air gap magnetic flux by exploiting permanent magnets. The permanent magnet auxiliary air gap flux can affect the average torque and ripple. Commonly, the torque ripples reduction comes with the average torque drop. In this paper, the torque ripple reduction and average torque improvement are achieved for a 6/10 pole hybrid switch reluctance motor by inserting two symmetrical notches on its rotor. Also, the lengths of magnet and rectangular notches are optimized by the finite element method and sensitivity analysis. The comparison of the optimized design with the initial one carried out by finite element proves the efficiency of the proposed model. © 2024 Sharif University of technology. All rights reserved.

关键词： Sensitivity analysis

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Hybrid millimeter wave heterogeneous networks with spatially correlated user equipment

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Digital Communications and Networks 2024年第4期10卷 904-917页

作者： Arif Ullah Ziaul Haq Abbas Ghulam Abbas Fazal Muhammad Jae-Mo Kang Department of Computer Engineering College of IT ConvergenceChosun UniversityGwangju61452South Korea Faculty of Electrical Engineering GIK Institute of Engineering Sciences and TechnologyTopi23640Pakistan Faculty of Computer Science and Engineering GIK Institute of Engineering Sciences and TechnologyTopi23640Pakistan Department of Electrical Engineering University of Engineering and TechnologyMardan23200Pakistan Department of Artificial Intelligence Kyungpook National UniversityDaegu41566South Korea

In this paper,we analyze a hybrid Heterogeneous Cellular Network(HCNet)framework by deploying millimeter Wave(mmWave)small cells with coexisting traditional sub-6GHz macro cells to achieve improved coverage and high data *** consider randomly-deployed macro base stations throughout the network whereas mmWave Small Base Stations(SBSs)are deployed in the areas with high User Equipment(UE)*** user centric deployment of mmWave SBSs inevitably incurs correlation between UE and *** a realistic scenario where the UEs are distributed according to Poisson cluster process and directional beamforming with line-of-sight and non-line-of-sight transmissions is adopted for mmWave *** using tools from stochastic geometry,we develop an analytical framework to analyze various performance metrics in the downlink hybrid HCNets under biased received power *** UE clustering we considered Thomas cluster process and derive expressions for the association probability,coverage probability,area spectral efficiency,and energy *** also provide Monte Carlo simulation results to validate the accuracy of the derived ***,we analyze the impact of mmWave operating frequency,antenna gain,small cell biasing,and BSs density to get useful engineering insights into the performance of hybrid mmWave *** results show that network performance is significantly improved by deploying millimeter wave SBS instead of microwave BS in hot spots.

关键词： Downlink cell association Heterogeneous cellular networks Integrated sub-6GHz and mmWave networks Millimeter wave communications Poisson cluster process

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Monitoring COVID-19 environment: a real-time facial mask detection using YOLO models

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Research on Biomedical engineering 2025年第1期41卷 1-18页

作者： Pandiyan, P. Thangaraj, Rajasekaran V, Balasubramanian V. K, Manavalasundaram S, Balakrishnan Department of Electrical and Electronics Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering Nandha Engineering College Erode India Department of Information Technology Velalar College of Engineering and Technology Erode India Department of Computer Science and Engineering Aarupadai Veedu Institute of Technology Chennai India Department of Computer Science and Engineering Sri Ramakrishna Institute of Technology Coimbatore India

Purpose: The rapid spread of COVID-19 has resulted in significant harm and impacted tens of millions of people globally. In order to prevent the transmission of the virus, individuals often wear masks as a protective measure for themselves and others. Coronavirus protection guidelines have been published by the World Health Organization (WHO). According to WHO standards, COVID-19 can be prevented by wearing a mask in public places and congested regions. In these places, it is very difficult to personally check to see if people are wearing face masks or not. Methods: The objective of this research work is to build a powerful, efficient, and real-time approach for detecting people not wearing masks. Three cutting-edge object identification models, namely YOLOv4, Tiny-YOLOv4, and YOLOv5, are employed in this study for the identification of masked faces. Result: The proposed YOLOv5 model is evaluated using real-time images captured using a smartphone or tablet. The test images include both single and multiple people with and without masks. The YOLOv5 model achieved recognition accuracy of 88.90% with an average detection speed of 0.0316 s per image, whereas the YOLOv4 and Tiny-YOLOv4 produced recognition accuracy of 82.24% and 74.80% with an average detection speed of 0.0530 s and 0.0541 s per image, respectively. Conclusion: The comparative performance suggests that the YOLOv5 model has a maximum recognition accuracy of 88.90% in face mask identification tasks compared to other models such as the YOLOv4 and Tiny-YOLOv4. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering 2025.

关键词： COVID-19

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Formalization of the Telegrapher’s Equations using Higher-Order-Logic Theorem Proving

Journal of Applied Logics

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Journal of Applied Logics 2024年第2期11卷 197-236页

作者： Deniz, Elif Rashid, Adnan Hasan, Osman Tahar, Sofiène Department of Electrical and Computer Engineering Concordia University MontrealQC Canada School of Electrical Engineering and Computer Science National University of Sciences and Technology Islamabad Pakistan

The telegrapher’s equations constitute a set of linear partial differential equations that establish a mathematical correspondence between the electrical current and voltage within transmission lines, taking into account factors, such as distance and time. These equations find wide applications in the design and analysis of various systems, including integrated circuits and antennas. This paper proposes the utilization of higher-order-logic theorem proving for a formal analysis of the telegrapher’s equations, also referred to as the transmission line equations. Specifically, we present a formal model of the telegrapher’s equations in both time and phasor domains. Subsequently, we employ the HOL Light theorem prover to formally verify the solutions of the telegrapher’s equations in the phasor domain. Furthermore, we established a connection between phasor and time-domain functions to formally verify the general solutions for the time-domain partial differential equations for the current and voltage in an electric transmission line. To demonstrate the practical effectiveness of our proposed formalization, we conduct a formal analysis of a terminated transmission line and its special cases, i.e., short-and open-circuited transmission lines commonly used in antenna design, by formally verifying the load impedance and the voltage reflection coefficient. © 2024, College Publications. All rights reserved.

关键词： Electric lines

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Multi-agent Path Planning Based on Conflict-Based Search (CBS) Variations for Heterogeneous Robots

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Journal of Intelligent and Robotic Systems: Theory and Applications 2025年第1期111卷 1-12页

作者： Bai, Yifan Kotpalliwar, Shruti Kanellakis, Christoforos Nikolakopoulos, George Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå797187 Sweden

This article introduces a novel Multi-agent path planning scheme based on Conflict Based Search (CBS) for heterogeneous holonomic and non-holonomic agents, designated as Heterogeneous CBS (HCBS). The proposed methodology employs a hybrid A∗ algorithm for non-holonomic car-like robots and a conventional A∗ algorithm for holonomic robots. Following this, a body conflict detection strategy is utilized to construct the conflict tree, bridging the initial path planning with the resolution of conflicts among agents. Moreover, we present two variants of HCBS: the Enhanced Conflict-Based Search (EHCBS) and the Depth-First Conflict-Based Search (DFHCBS). We evaluate the efficacy of our proposed algorithms—HCBS, EHCBS, and DFHCBS—against a standard prioritized planning algorithm, focusing on success rates and computational efficiency in environments with varying numbers of agents and obstacles. The empirical results demonstrate that EHCBS exhibits superior computational efficiency in small, dense environments, while DFHCBS performs well in larger-scale environments. This highlights the adaptability of our proposed approaches in various settings, proving the computational advantage of EHCBS and DFHCBS over traditional methods. © The Author(s) 2025.

关键词： Multi agent systems

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Cavity-agnostic acoustofluidic manipulations enabled by guided flexural waves on a membrane acoustic waveguide actuator

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Microsystems & Nanoengineering 2024年第2期10卷 153-171页

作者： Philippe Vachon Srinivas Merugu Jaibir Sharma Amit Lal Eldwin J.Ng Yul Koh Joshua E.-Y.Lee Chengkuo Lee Department of Electrical and Computer Engineering National University of SingaporeSingaporeSingapore Institute of Microelectronics Agency for ScienceTechnology and Research(A*STAR)SingaporeSingapore SonicMEMS Laboratory School of Electrical and Computer EngineeringCornell UniversityIthacaNYUSA School of Electrical and Data Engineering University of Technology SydneyUltimoNSWAustralia

This article presents an in-depth exploration of the acoustofluidic capabilities of guided flexural waves(GFWs)generated by a membrane acoustic waveguide actuator(MAWA).By harnessing the potential of GFWs,cavity-agnostic advanced particle manipulation functions are achieved,unlocking new avenues for microfluidic systems and lab-on-a-chip *** localized acoustofluidic effects of GFWs arising from the evanescent nature of the acoustic fields they induce inside a liquid medium are numerically investigated to highlight their unique and promising *** traditional acoustofluidic technologies,the GFWs propagating on the MAWA’s membrane waveguide allow for cavity-agnostic particle manipulation,irrespective of the resonant properties of the fluidic ***,the acoustofluidic functions enabled by the device depend on the flexural mode populating the active region of the membrane *** demonstrations using two types of particles include in-sessile-droplet particle transport,mixing,and spatial separation based on particle diameter,along with streaming-induced counter-flow virtual channel generation in microfluidic PDMS *** experiments emphasize the versatility and potential applications of the MAWA as a microfluidic platform targeted at lab-on-a-chip development and showcase the MAWA’s compatibility with existing microfluidic systems.

关键词： waveguide fluid cavity

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Halo reduction multi-exposure image fusion technique

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Multimedia Tools and Applications 2025年第13期84卷 12347-12370页

作者： Sharif, Rizwan Amin, Benish Sukhia, Komal Nain Department of Electrical Engineering Institute of Space Technology Islamabad Pakistan Department of Computer Science Institute of Space Technology Islamabad Pakistan

Multi-exposure image fusion (MEF) involves combining images captured at different exposure levels to create a single, well-exposed fused image. MEF has a wide range of applications, including low light, low contrast, night photography, medical imaging, and remote sensing. However, MEF methods often face issues like artifacts, halos around edges, color inconsistencies, noise amplification, and difficulty in preserving fine details. Moreover, assessing the quality of fused images objectively is complex due to the subjective nature of human perception. Solving the challenges is essential to developing efficient MEF techniques that produce high-quality results across various scenarios. The proposed technique introduces an approach to handling halo artifacts and implementing MEF. The Dense Scale-Invariant Feature Transform (DSIFT) is used to capture vital information about image brightness, texture, and edges from source images. Three weight maps are computed from the local mean, signal strength, and the global gradient for initial weight estimation. The local mean represents the brightness of specific image areas, signal strength preserves essential details like textures and edges while reducing image noise, and global gradient helps identify regions with significant pixel value shifts across multiple exposure images. The weight maps are then combined using a weighted average and a Gaussian smoothing filter is applied to reduce inherent noise and discontinuities in the original weights. Subsequently, pyramid decomposition is performed to generate a fused image. The efficiency of the proposed method is extensively tested on challenging multi-exposure image sequences. The results of the proposed approach demonstrate its superiority in both subjective evaluation and objective metrics like the MEF-Structural Similarity Index (MEF-SSIM), Natural Image Quality Evaluator (NIQE) and Gradient based performance measure (QAB/f). © The Author(s), under exclusive licence to Springer

关键词： Image fusion

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Clustered Reinforcement Learning

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Frontiers of computer Science 2025年第4期19卷 43-57页

作者： Xiao MA Shen-Yi ZHAO Zhao-Heng YIN Wu-Jun LI National Key Laboratory for Novel Software Technology Department of Computer Science and TechnologyNanjing UniversityNanjing 210023China Department of Electrical Engineering and Computer Sciences University of CaliforniaBerkeleyCA 94720-1770USA

Exploration strategy design is a challenging problem in reinforcement learning(RL),especially when the environment contains a large state space or sparse *** exploration,the agent tries to discover unexplored(novel)areas or high reward(quality)*** existing methods perform exploration by only utilizing the novelty of *** novelty and quality in the neighboring area of the current state have not been well utilized to simultaneously guide the agent’s *** address this problem,this paper proposes a novel RL framework,called clustered reinforcement learning(CRL),for efficient exploration in *** adopts clustering to divide the collected states into several clusters,based on which a bonus reward reflecting both novelty and quality in the neighboring area(cluster)of the current state is given to the *** leverages these bonus rewards to guide the agent to perform efficient ***,CRL can be combined with existing exploration strategies to improve their performance,as the bonus rewards employed by these existing exploration strategies solely capture the novelty of *** on four continuous control tasks and six hard-exploration Atari-2600 games show that our method can outperform other state-of-the-art methods to achieve the best performance.

关键词： deep reinforcement learning exploration count-based method clustering K-means

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Power quality assessment in different wind power plant models considering wind turbine wake effects

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Clean Energy 2023年第4期7卷 843-858页

作者： Mohsen Khatamiaghda Saeed Bahraminejad Roohollah Fadaeinedjad Department of Electrical and Computer Engineering Graduate University of Advanced TechnologyKermanIran

The intense increase in the installed capacity of wind farms has required a computationally efficient dynamic equivalent model of wind *** types of wind-farm modelling aim to identify the accuracy and simulation time in the presence of the power *** this study,dynamic simulation of equivalent models of a sample wind farm,including single-turbine representation,multiple-turbine representation,quasi-multiple-turbine representation and full-turbine representation models,are performed using a doubly-fed induction generator wind turbine model developed in DIgSILENT *** developed doubly-fed induction generator model in DIgSILENT is intended to simulate inflow wind turbulence for more accurate *** wake effects between wind turbines for the fullturbine representation and multiple-turbine representation models have been considered using the Jensen *** developed model improves the extraction power of the turbine according to the layout of the wind *** accuracy of the mentioned methods is evaluated by calculating the output parameters of the wind farm,including active and reactive powers,voltage and instantaneous flicker *** study was carried out on a sample wind farm,which included 39 wind *** simulation results confirm that the computational loads of the single-turbine representation(STR),the multiple-turbine representation and the quasi-multiple-turbine representation are 1/39,1/8 and 1/8 times the full-turbine representation model,*** the other hand,the error of active power(voltage)with respect to the full-turbine representation model is 74.59%(1.31%),43.29%(0.31%)and 7.19%(0.11%)for the STR,the multiple-turbine representation and the quasi-multiple representation,respectively.

关键词： wind-farm models DIgSILENT software flicker intensity wake effect DFIG turbine

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Skin Lesion Classification via ensemble method on deep learning

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Multimedia Tools and Applications 2025年第18期84卷 19379-19397页

作者： Amiri, Sekineh Asadi Nasrolahzadeh, Mahda Mohammadpoory, Zeynab Kordkheili, Amir Hossein Zare Department of Computer Engineering University of Mazandaran Babolsar Iran Department of Electrical Engineering University of Torbat Heydarieh Torbat Heydarieh Iran Department of Electrical Engineering Shahrood University of Technology Shahrood Iran

Deep neural networks have shown promising results in the classification of skin lesion images, particularly when they focus on the most significant regions of an image. However, the identification of melanoma continues to pose a significant challenge, primarily because of the substantial variability both within and between classes in images of moles. A new framework is proposed in this paper to develop an innovative method for effective melanoma diagnosis, presenting a new approach for Skin Lesion Classification (SLC) that combines a modified Inception ResNet v2 and Efficient Net-B4 in an ensemble. In the modified Inception ResNet v2, we incorporate a Soft-Attention (SA) mechanism. The goal is to enhance the network’s performance by using the SA mechanism to highlight informative features and suppress those that induce noise. Given the diversity of skin lesions in terms of type, texture, color, shape, and distribution, our proposed method extracts crucial features from the images by employing both the SA and merging the features of two layers of Inception ResNet ***, we have modified EfficientNet-B4 by adding two dense layers to improve its classification capabilities. After classifying the images with these two models, we employ soft voting for the final classification. We evaluated the performance of our proposed method on the ISIC-2017 and ISIC-2018 datasets, considering metrics such as Accuracy, Precision, Recall, and F-measures. Experimental results and a comparison with existing techniques demonstrate that our method achieves higher accuracy than current state-of-the-art methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep neural networks

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