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Method of Classifying Motor Failure Conditions Using an Auto-Encoder-Based Ensemble Technique

Transactions of the Korean Institute of Electrical Engineers 2025年第2期74卷 273-279页

作者： Choi, Eui-Jin Han, Ji-Hoon Song, Seung-Min Park, Jong-Hoon Hong, Sun-Ki Dept. of System and Control Engineering Hoseo University Korea Republic of Dept. of Information Control Engineering Hoseo University Korea Republic of

In the field, unsupervised learning-based fault diagnosis is necessary due to the lack of fault data. However, conventional auto-encoder models face challenges in fault classification. To address this issue, this study proposes an unsupervised learning-based auto-encoder model enhanced with an ensemble approach. The proposed model performs fault classification using gear, bearing, and eccentricity fault data, and employs the Layer-wise Relevance Propagation (LRP) technique to improve fault classification. Through the LRP technique, the key frequency bands relevant to each fault are identified and incorporated into the learning process, allowing for frequency band-based training. As a result, the proposed auto-encoder model improves classification performance and effectively distinguishes between different types of faults. © The Korean Institute of Electrical Engineers.

关键词： Unsupervised learning

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Study on Improving Hysteresis Motor Efficiency by Over-Excitation Based on Finite Element Analysis

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Transactions of the Korean Institute of Electrical Engineers 2025年第1期74卷 49-55页

作者： Park, Jong-Hoon Han, Ji-Hoon Choi, Eui-Jin Hong, Sun-Ki Dept. of Information Control Engineering Hoseo University Korea Republic of Dept. of System and Control Engineering Hoseo University Korea Republic of

A hysteresis motor is a motor characterized by an increase in the efficiency of the output to input ratio during an over-excitation in which a higher voltage is applied at startup than when the rated voltage is applied, and the voltage is reduced over a period of time after entering synchronous speed. However, due to the specificity of the rotor material, finite element analysis of hysteresis motors is difficult, so the design was conducted by analyzing only the rated voltage application condition through an equivalent circuit. As a result, the improved efficiency and torque that can be achieved by over-excitation were not verified, which was confirmed by experiments after the design was completed. However, recently, finite element analysis simulation that can express the hysteresis loop behavior has become possible, so finite element analysis of hysteresis motors has become possible, and the improved efficiency and torque that can be obtained through over-excitation can be confirmed. Therefore, this paper compares the output and efficiency of hysteresis motors under over-excitation and rated voltage application, and confirms the necessity and feasibility of designing hysteresis motors considering over-excitation. Copyright © The Korean Institute of Electrical Engineers.

关键词： Equivalent circuits

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Dynamic Bidirectional Feature Enhancement Network for Thin Cloud Removal in Remote Sensing Images

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 13281-13295页

作者： Wang, Yu Chen, Hao Zhang, Ye Li, Guozheng Harbin Institute of Technology Dept. of Information Engineering Harbin China

Existing thin cloud removal methods primarily rely on generative paradigms or discriminative paradigms. Generative paradigms often suffer from training instability, while discriminative paradigms exhibit insufficient feature representation, and their loss strategies lack physical consistency, resulting in suboptimal performance. To address these issues, we propose a dynamic bidirectional feature enhancement network for thin cloud removal in optical remote sensing images. Firstly, we design a multidimensional attention module comprising skip-recursive dilated convolution-based spatial attention module and frequency-domain-driven channel attention module. These modules efficiently capture long-range contextual dependencies and suppress cloud noise interference. Next, an adaptive local feature enhancement block is constructed using cross-fusion and adjacent feature propagation between dynamic convolutions, aimed at enhancing the ability of model to recover details. Then, we present a dynamic enhancement-based bidirectional information flow module to model the dynamic interaction between multi-task features, guiding detail recovery and feedback for optimized cloud removal features. Finally, we design a physics-aware joint loss function incorporating atmospheric light consistency constraints to ensure the physical authenticity of cloud-free images. Evaluation on the RICE and WHUS2-CR datasets demonstrates that the proposed method is superior to compared methods in thin cloud removal and can improve the performance of baseline. © 2008-2012 IEEE.

关键词： Adaptive optics

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Automatic Traffic Monitoring using Spatial Transformer Network and Monocular dept. Estimation 2

Automatic Traffic Monitoring using Spatial Transformer Netwo...

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2nd IEEE International Conference on Mobility, Operations, Services and Technologies, MOST 2024

作者： Kurti, Adam Szemenyei, Marton Budapest University of Technology and Economics Dept. of Control Engineering and Information Technology Budapest Hungary

ISBN: (纸本)9798350307733

Automatic license plate recognition and vehicle speed measurement are two of the critical tasks in traffic monitoring that face several challenges. Existing CNN and RNN-based license plate recognition systems must manage significantly distorted input images or possess complex architecture leading to slower training and inference times. Speed measurement performed with dedicated hardware like various lasers, and Doppler radars have significant acquisition costs, while existing single camera solutions that use the geometric information of the road or homographic mapping have often cumbersome calibration methods and may not always provide accurate results. This paper proposes a license plate recognition solution based on a fully convolutional deep neural network combined with a spatial transformer network, which enables segmentation-free recognition of license plates while addressing the spatial weaknesses of convolutional networks. Additionally, a monocular camera-based speed measurement solution is proposed that builds on metric dept. estimation algorithms and uses 3D reconstruction to estimate vehicle speed. The license plate recognition results show competitive accuracy and inference speed compared to other existing solutions while the proposed speed estimation method shows promising potential as an easy to calibrate and cost-effective solution. © 2024 IEEE.

关键词： Deep neural networks

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Hybrid CNN-ViT architecture to exploit spatio-temporal feature for fire recognition trained through transfer learning

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Multimedia Tools and Applications 2025年第8期84卷 4703-4732页

作者： Shahid, Mohammad Wang, Hong-Cyuan Chen, Yung-Yao Hua, Kai-Lung Dept. of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan Dept. of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan

Fires are becoming one of the major natural hazards that threaten the ecology, economy, human life and even more worldwide. Therefore, early fire detection systems are crucial to prevent fires from spreading out of control and causing destruction. Based on vision sensors, many fire detection techniques have evolved with the recent surge of curiosity in deep learning, which exploits the spatial features of individual images. However, fire can take different forms, scales, and combustion materials can produce different colors, making accurate fire detection from an image challenging. Small fires captured from long-distance cameras lack salient features, further complicating detection. This paper proposes a hybrid structure that uses attention-enhanced convolutional neural networks and vision transformers (CNN-ViT) to detect fire. The proposed CNN-ViT first pays spatial attention to every frame and then aggregates temporal contextual information from neighboring frames to improve detection performance. Due to the limited availability of training fire datasets, the study employs deep transfer learning for feature extraction using pre-trained CNN. We used various metrics to examine the efficacy of the proposed approach. The results showed that the CNN-ViT method outperformed previous models based on spatial-temporal characteristics by achieving a relative improvement in accuracy and F1 score. The satisfactory results on images contaminated with different intensities of noise confirm the robustness of the approach. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Fires

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A Study on the Performance of Bearingless PMSM Based on Rotor Permanent Magnet Arrangement

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Transactions of the Korean Institute of Electrical Engineers 2025年第4期74卷 739-746页

作者： Noh, Min-Soo Kim, Dong-Hyeon Guo, Youguang Lee, Ho-Joon Dept. of Electrical and Control Engineering Cheonju University Korea Republic of Dept. of Electronics and Electrical Engineering Dankook University Korea Republic of Dept. of Electronic Engineering Cheongju University Korea Republic of School of Electrical and Data Engineering University of Technology Sydney Australia

Magnetic bearing motors are in high demand due to their ability to operate in special environments without mechanical friction and lubrication, effectively avoiding wear and contamination. To overcome the complexity and maximum speed limitations, bearingless PMSMs (Permanent Magnet Synchronous Motors) have been developed. This study explains the electromagnetic driving principles of bearingless PMSMs based on generation conditions and the components of suspension forces. Furthermore, the permanent magnets arranged on the rotor are designed to alter the magnetic flux distribution using various magnetization patterns. FEA(Finite Element Analysis) was conducted for each model, and the derived results were compared and analyzed. This study investigates the performance of bearingless PMSMs based on different rotor permanent magnet arrangements, and draws a comprehensive conclusion. © The Korean Institute of Electrical Engineers.

关键词： Permanent magnets

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Multi-Objective Cooperative control of Vehicle Platoon Based on Fuzzy MPC 26

Multi-Objective Cooperative Control of Vehicle Platoon Based...

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26th International Conference on Industrial technology, ICIT 2025

作者： Zhu, Wanying Yin, Yunfei Xu, Zhanguo Wei, Yuanlong Zeng, Shenjun Harbin Institute of Technology Dept. of Traffic Information and Control Engineering Harbin China Jilin University Dept. of Traffic Information and Control Engineering Changchun China

ISBN: (纸本)9798331521950

To simultaneously optimize multiple performances of the cooperative adaptive cruise control (CACC) system, a fuzzy MPC control strategy is proposed to achieve multi-objective cooperative control for electric vehicle platoons. The longitudinal kinematic analysis of the vehicle platoon is conducted to establish the platoon model. Based on the analysis of system performance, safety, comfort, and following performance are selected as the system's performance indexes, and a comprehensive performance function for the MPC is constructed. Fuzzy control theory is employed to optimize the weight parameters, enabling the CACC system to adapt to different conditions. The effectiveness and reliability of the fuzzy MPC control strategy are verified through co-simulation using Carsim and Simulink. © 2025 IEEE.

关键词： control system analysis

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J-NECORA: A Framework for Optimal Resource Allocation in Cloud-Edge-Things Continuum for Industrial Applications With Mobile Nodes

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IEEE Internet of Things Journal 2025年第11期12卷 16525-16542页

作者： Pettorali, Marco Righetti, Francesca Vallati, Carlo Das, Sajal K. Anastasi, Giuseppe Univ. of Pisa Dept. of Information Engineering Italy Missouri Univ. of Science and Technology Dept. of Computer Science RollaMO United States

In the Industrial Internet of Things (IIoT) landscape, where the Cloud-to-Things Continuum (C2TC) paradigm is now a reality, industrial applications need to cope with highly heterogeneous network and computing resources. Moreover, many industrial applications also involve Mobile Nodes (MNs). Efficient allocation of network and computing resources to meet the stringent requirements of such applications is often a very challenging task. In this paper, we propose J-NECORA (Joint NEtwork and COmputing Resource Allocation), a comprehensive analytical framework to derive the optimal joint allocation of network and computing resources in the C2TC, that guarantees the application requirements, even in the presence of MNs. If the optimal allocation does not exist, J-NECORA provides a best-effort allocation that minimizes the delay. The proposed framework is highly modular and flexible, and can be easily customized to different scenarios. We validated its effectiveness through simulation experiments, focusing on a specific use case. For the considered scenarios, the performance predicted by our framework matches quite closely the simulation results. © 2014 IEEE.

关键词： Cloud platforms

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EEG Data Enhancement and Emotion Recognition Based on Generative Adversarial Networks 6

EEG Data Enhancement and Emotion Recognition Based on Genera...

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6th IEEE International Conference on Electronic information and Communication technology, ICEICT 2023

作者： Wang, Tianyu Sun, Jie Li, Zhengyan Zhang, Zhanwang Engineering Qingdao University of Technology Dept. School of Information and Control Qingdao China

ISBN: (纸本)9798350399059

In emotion recognition, the requirement for authenticity and real-time of the medium under study has always been a primary consideration. Thus electroencephalogram (EEG) is most suitable as the primary medium for emotion recognition research. However, there needs to be more high-quality training data for building EEG-based emotion recognition models through machine learning or deep learning methods, and manual generation of high-quality data is an effective way to overcome this problem. Generative adversarial networks (GAN) have recently succeeded in generative applications involving images. They are beginning to be applied to EEG data enhancement, so this paper generates high-quality artificial data through generative adversarial networks. © 2023 IEEE.

关键词： Emotion Recognition

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QoE-Driven Optimization Strategies for Dynamic Adaptive Streaming Media 7

QoE-Driven Optimization Strategies for Dynamic Adaptive Stre...

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7th IEEE International Conference on Smart Internet of Things, SmartIoT 2023

作者： Aiguo, Ji Yunzhe, Luan Xuebo, Liu Dept. Qingdao University of Technology School of Information and Control Engineering Qingdao China

ISBN: (纸本)9798350316575

Dynamic adaptive streaming over HTTP can adaptively select the most appropriate video bitrate for video users. Mobile edge computing (MEC) can effectively improve video quality by providing computing and storage capabilities. In order to improve the quality of video user experience, a QoE-driven DASH optimization scheme is proposed, which includes an adaptive bitrate algorithm deployed on video clients and a video caching strategy deployed on edge servers. First of all, we propose an adaptive bitrate algorithm that can cyclically accumulate or consume video buffer to intermittently increase video bitrate. Besides, we design a video caching and replacement strategy that works on edge servers, which treats the video blocks instead of complete videos as the basic storage unit of edge servers. The simulation results show that the proposed adaptive bitrate algorithm can effectively improve the average video bitrate and the smoothness of video playback. The proposed video caching strategy can effectively improve the cache hit rate of edge servers and reduce the average request response delay, significantly improving the quality of user experience. © 2023 IEEE.

关键词： Mobile edge computing

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