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A Mathematical and Dynamical Model for Analyzing H-Shaped PM Vernier Motor for Electric Motorcycle Mid-Drive Applications

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IEEE Transactions on Energy Conversion 2025年第1期40卷 529-543页

作者： Ghods, Mehrage Faiz, Jawad Bazrafshan, Mohammad Amin Gorginpour, Hamed Toulabi, Mohammad Sedigh University of Tehran School of Electrical and Computer Engineering College of Engineering Tehran*** Iran Persian Gulf University Department of Intelligent Systems Engineering and Data Science Bushehr*** Iran Oakland University Department of Electrical and Computer Engineering RochesterMI48309 United States

The permanent magnet (PM) Vernier machines enhance torque density and decrease cogging torque compared to conventional permanent magnet synchronous motor. This paper presents a novel fractional-slot H-shaped PM Vernier machines, which incorporates unique design for the PM housing and rotor core construction. These designs are specifically optimized for the small-scale electric vehicles application. The choice of the PM arrangement is based on the combination of V-shaped and spoke-array PM topologies. The aim is to achieve a higher torque density than V-shaped PM machines, while reducing cogging torque compared to spoke-array PM machines. By using this methodology, leakage fluxes in the terminal region of the PMs reduce, resulting in an augmentation of both flux-linkage and power factor. A prototype 680 W motor was fabricated with 12 slots and 8 poles to verify the accuracy and effectiveness of the proposed model. Mathematical model and dynamical analysis are proposed for the analytical prediction of machine performance. Due to the unique shape feature of the machine, the conformal mapping approach is applied to design the rotor core. To validate the model, the predicted performance of the motor was compared with those obtained by finite element analysis and experiments. © 1986-2012 IEEE.

关键词： Permanent magnets

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Predicting heart disease based on an intelligent healthcare monitoring system using HPM-NIA

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Multimedia Tools and Applications 2025年第13期84卷 11475-11501页

作者： Alharbi, Meshal Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia

Before a heart attack happens, treating cardiac patients effectively depends on precise heart disease prediction. A heart disease prediction system for the determination of whether the patient has a heart disease condition or not is to be developed. The paper employs a diverse array of models within the heart disease prediction system, each serving a specific purpose to enhance accuracy and efficiency. Data pre-processing techniques, including normalization, standardization, and missing value removal, ensure the quality and consistency of the dataset. Feature extraction methods such as mean, median, standard deviations, and higher-order statistical features contribute to extracting informative features crucial for prediction. The Multi-Objective Forest Particle Swarm Optimization (MOFPSO) algorithm (hybrid version of Forest Optimization (FO) and Particle Swarm Optimization (PSO)) is introduced for efficient feature selection, balancing predictive accuracy and model complexity. Finally, the prediction model, Naïve Bayes trained with Meliorated Ant Colony Optimization algorithm (NB-MACO), is implemented for its simplicity and effectiveness in handling medical datasets. This integration fine-tunes the Naïve Bayes classifier’s hyperparameters, optimizing its performance and resulting in an accuracy of 91.974%. The collective utilization of these models and techniques ensures the development of a robust and accurate heart disease prediction system. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Forecasting

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Optimizing IoT Network Intrusion Detection: A Deep Learning Approach 7

Optimizing IoT Network Intrusion Detection: A Deep Learning ...

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7th Conference on Cloud and Internet of Things, CIoT 2024

作者： Alam, Khorshed Monir, Md Fahad Hassan, Zoheb Habib, Md Tarek Department of Computer Science & Engineering Bangladesh Virginia Tech Department of Electrical and Computer Engineering United States Université Laval Department of Electrical and Computer Engineering Canada

ISBN: (纸本)9798350356717

Network Intrusion Detection System (NIDS) serves as a essential component in data protection by monitoring computer networks for threats that can bypass conventional defenses such as malware and hackers. Deep learning (DL) techniques provide a promising approach for analyzing raw IoT network data to identify subtle patterns indicative of intrusion attempts. This study addresses a crucial research gap by developing a Deep Convolutional Neural Network (DCNN) model specifically designed for the efficient detection of stealthy and polymorphic variants while reducing false positives. Utilizing the NF-ToN-IoT dataset, the proposed model achieves outstanding performance metrics on test data, with an accuracy of 0.9923, precision of 0.9925, recall of 0.9979, and F1 score of 0.9952. To comprehensively evaluate the robustness of the model, a multi-dataset validation strategy is employed. The model is retrained and assessed on established benchmark datasets on IoT Networks, including NF-UNSW-NB15, NF-UNSW-NB15-v2 and NF-BoTIoT, demonstrating exceptional performance. Furthermore, the significance of the contribution is validated by comparing the proposed model against previously established architectures such as CNN+BiLSTM, DNN, GRU+RNN, and CNN+LSTM using the NF-ToN-IoT dataset. The proposed model consistently outperforms these prior models, highlighting its efficacy and advancements in the field. Additionally, an ablation study is conducted to analyze the individual components of the Deep CNN model, providing insights into their contributions towards detecting malware traffic and offering guidance for optimizing future NIDS models in the cybersecurity domain. Making our work available open-source on https://***/codewithkhurshed/IDSIUB can enhance its accessibility and promote future research opportunities in Network Intrusion Detection. © 2024 IEEE.

关键词： Network intrusion

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Alkali Metal Doping on Sol-Gel-Derived Copper Oxide for Performance Enhancement of Thin-Film Transistors

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible Electronics 2024年第6期3卷 228-233页

作者： Baek, Seokhyeon Choi, Jun-Gyu Park, Sungjun Ajou University Department of Intelligence Semiconductor and Engineering Suwon16449 Korea Republic of Ajou University Department of Electrical and Computer Engineering Suwon16449 Korea Republic of Ajou University The Department of Electrical and Computer Engineering Suwon16449 Korea Republic of

In recent years, copper oxide (CuxO) has emerged as a promising p-type oxide semiconductor owing to its high Hall mobility. However, its inherent drawbacks, such as the substantial native defects and uncontrolled stoichiometry, limit its application in thin-film transistors (TFTs) for energy-efficient complementary devices. In this study, we employ the sol-gel synthetic approach for facile doping to investigate the doping effects of alkali metals (Li, Na, and K) on the electrical performance of CuxO TFTs. The results demonstrate that doping, particularly with lithium (Li), significantly improves the electrical performance of CuxO TFTs. The similarity in ionic radius facilitates efficient hole transport, which considerably enhances the field-effect mobility (4.7×10-3 cm2/Vs), subthreshold swing (SS) (8.3 V/dec), and on-off current ratio (∼104) with minimized hysteresis. The structural analysis of the Li-doped CuxO films using X-ray diffraction (XRD) does not exhibit any significant lattice distortion and an increase in the grain size implies the reduction of trap sites. Consequently, the successful fabrication of Li-doped CuxO TFTs on polyimide substrates, using ZrOxO as a gate dielectric layer, demonstrates its compatibility with flexible electronics. This approach enhances the electrical performance of p-type CuxO TFTs and presents a scalable and efficient pathway for the development of advanced TFT technology in flexible electronics. © 2022 IEEE.

关键词： Sol-gel process

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Learning from Mistakes: A Multi-level Optimization Framework

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第6期6卷 1651-1663页

作者： Zhang, Li Garg, Bhanu Sridhara, Pradyumna Hosseini, Ramtin Xie, Pengtao University of California Department of Electrical and Computer Engineering San DiegoCA92093 United States

Bi-level optimization methods in machine learning are popularly effective in subdomains of neural architecture search, data re-weighting, etc. However, most of these methods do not factor in variations in learning difficulty, which limits their performance in real-world applications. To address the above problems, we propose a framework that imitates the learning process of humans. In human learning, learners usually focus more on the topics where mistakes have been made in the past to deepen their understanding and master the knowledge. Inspired by this effective human learning technique, we propose a multi-level optimization framework, Learning From Mistakes (LFM), for machine learning. We formulate LFM as a three-stage optimization problem: 1) the learner learns, 2) the learner re-learns based on the mistakes made before, and;3) the learner validates his learning. We develop an efficient algorithm to solve the optimization problem. We further apply our method to differentiable neural architecture search and data re-weighting. Extensive experiments on CIFAR-10, CIFAR-100, ImageNet, and other related datasets powerfully demonstrate the effectiveness of our approach. The code of LFM is available at: https://***/importZL/LFM. © 2020 IEEE.

关键词： Adversarial machine learning

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Obstacle Avoidance Capability for Multi-Target Path Planning in Different Styles of Search

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computers, Materials & Continua 2024年第10期81卷 749-771页

作者： Mustafa Mohammed Alhassow Oguz Ata Dogu Cagdas Atilla Department of Electrical and Computer Engineering Altinbas UniversityIstanbul34217Turkey Department of Software Engineering Altinbas UniversityIstanbul34217Turkey

This study investigates robot path planning for multiple agents,focusing on the critical requirement that agents can pursue concurrent pathways without *** agent is assigned a task within the environment to reach a designated *** the map or goal changes unexpectedly,particularly in dynamic and unknown environments,it can lead to potential failures or performance degradation in various ***,priority inheritance plays a significant role in path planning and can impact *** study proposes a ConflictBased Search(CBS)approach,introducing a unique hierarchical search mechanism for planning paths for multiple *** study aims to enhance flexibility in adapting to different *** scenarios were tested,and the accuracy of the proposed algorithm was *** the first scenario,path planning was applied in unknown environments,both stationary and mobile,yielding excellent results in terms of time to arrival and path length,with a time of 2.3 *** the second scenario,the algorithm was applied to complex environments containing sharp corners and unknown obstacles,resulting in a time of 2.6 s,with the algorithm also performing well in terms of path *** the final scenario,the multi-objective algorithm was tested in a warehouse environment containing fixed,mobile,and multi-targeted obstacles,achieving a result of up to 100.4 *** on the results and comparisons with previous work,the proposed method was found to be highly effective,efficient,and suitable for various environments.

关键词： Conflict algorithm dynamic environment mobile robot omnidirectional mobile robot unknown environment warehouse

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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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Energy-Efficient and Robust Communication in Multi-Connectivity Enabled mmWave Ultra-Dense Networks

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2025年第2期9卷 621-634页

作者： Kim, Jungjun Lee, Jihwan Bahk, Saewoong Seoul National University Department of Electrical and Computer Engineering INMC Seoul08826 Korea Republic of

The ultra-dense network (UDN) concept with multi-connectivity (MC) has emerged as a promising scenario for millimeter-wave (mmWave) communications due to its synergistic effect. However, mmWave UDNs with MC face challenges such as escalated energy consumption and computational complexity for traffic splitting decisions. To save energy and reduce the need for frequent traffic splitting decisions in the considered scenario, we formulate the problem as a mixed integer programming (MIP). Our objective is to determine which base stations (BSs) to enable for energy saving and how to split user traffic to maintain robust communication in MC enabled UDNs. To this end, this paper proposes energy-efficient and robust communication (EERC), an ensemble of heuristic methods and convex optimization, to achieve our goal without directly solving the computationally expensive MIP. In addition, we consider greedy algorithm that potentially offers enhanced optimality. We analyze the complexity of each scheme and demonstrate that EERC is the most viable option considering both performance and complexity. Through extensive simulations, we demonstrate that EERC achieves over 98% optimality for small-sized networks. More importantly, we evaluate EERC in realistic scenarios using a 3-D ray-tracing simulator and confirm that EERC performs very well even in urban terrain environments with buildings. © 2017 IEEE.

关键词： Convex optimization

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Coordinated Frequency Control for Isolated Power Systems with High Penetration of DFIG-Based Wind Power

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CSEE Journal of Power and Energy Systems 2024年第4期10卷 1399-1414页

作者： Xin Ding Wei Lin Jian Xu Yuanzhang Sun Liangzhong Yao Beilin Mao School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072China Department of Electrical Engineering and Computer Science Case Western Reserve UniversityClevelandOH 44106 USA

This paper proposes a coordinated frequency control scheme for emergency frequency regulation of isolated power systems with a high penetration of wind *** proposed frequency control strategy is based on the novel nonlinear regulator theory,which takes advantage of nonlinearity of doubly fed induction generators(DFIGs)and generators to regulate the frequency of the power *** deviations and power imbalances are used to design nonlinear feedback controllers that achieve the reserve power distribution between generators and DFIGs,in various wind speed *** effectiveness and dynamic performance of the proposed nonlinear coordinated frequency control method are validated through simulations in an actual isolated power grid.

关键词： Frequency control isolated power systems nonlinear feedback nonlinear regulator theory wind power

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Deep learning for object recognition: A comprehensive review of models and algorithms

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International Journal of Cognitive Computing in engineering 2025年 6卷 298-312页

作者： Tsirtsakis, Paschalis Zacharis, Georgios Maraslidis, George S. Fragulis, George F. Department of Electrical and Computer Engineering University of Western Macedonia ZEP Campus Kozani50100 Greece

The rapid advancements in artificial intelligence (AI) and machine learning (ML) have significantly enhanced progress in computer vision, opening doors to innovative technological possibilities and enabling a range of real-world applications. Despite these developments, object recognition remains a complex domain with persistent challenges and limitations. This work seeks to address these challenges by investigating the effectiveness of deep learning (DL) methods in object detection tasks. Leveraging DL allows for the direct learning of feature representations from image data, resulting in advanced performance. This review provides a comprehensive examination of fundamental models and algorithms, with a particular focus on neural network (NN) frameworks utilized for feature extraction. Additionally, it evaluates the benchmark datasets commonly employed to assess their performance in object recognition tasks. © 2025 The Authors

关键词： Contrastive Learning

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