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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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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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SINR-Delay Constrained Node Localization in RIS-Assisted Time-Varying IoT Networks Using ML Frameworks

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IEEE Transactions on Network and Service Management 2025年第2期22卷 1544-1557页

作者： Bhardwaj, Vikash Kumar Mundada, Gagan Sahoo, Omm Prakash Shukla, Mahendra K. Pandey, Om Jee Varanasi Dept. of Electronics Engineering 221005 India University of California San Diego Computer Science and Engineering Dept United States Varanasi Dept. of Electrical Engineering 221005 India ABV-IIITM Gwalior Dept. of Information Technology India

Node localization in time-varying Internet of Things (IoT) networks is an essential problem due to increased delay and poor Signal-to-Interference plus Noise Ratio (SINR) at the Base Station (BS). To improve the received signal strength at the BS, Reconfigurable Intelligent Surface (RIS) has recently been used between transmitter and receiver. Additionally, novel phase prediction methods and optimal weight assignment frameworks have been proposed over RIS and BSs, respectively. Nevertheless, these methods suffer from poor performance due to their heuristic approach, resulting in more time consumption and poor SINR. Motivated by the aforementioned challenges, we propose a novel node localization method over a RIS-assisted time-varying IoT network using Machine Learning (ML) frameworks in this work. Firstly, the method computes the optimal phase configuration over the RIS corresponding to each element using coeff2phaseNN, which has been trained on channel coefficients among the transmitter, receiver, and RIS. Subsequently, the weight of the individual antenna element at the BS is optimized using the proposed VectorSync model. The results confirm that the coeff2phaseNN method demonstrates a reduction of 89.79% in total MSE loss compared to the Artificial Neural Network-RIS (ANN-RIS) method. Additionally, it demonstrates a 71.04% reduction in the absolute RIS phase prediction deviation from the optimal phase compared to the ANN-RIS method. Moreover, the proposed VectorSync method attains a 79.28% and 92.29% reduction in time required for optimal weight assignment compared to the Bartlett and Capon methods, respectively. Finally, the Localization Error(LR) using the proposed method is compared to conventional methods in a time-varying experimental scenario and found to be the minimum, i.e., 6.156%. © 2004-2012 IEEE.

关键词： Signal to noise ratio

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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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Solution to Mitigate the Disadvantages in the Design of LLC Resonant Converters for Electric Vehicle Fast Chargers with High Output Power and Wide Output Voltage Range

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Transactions of the Korean Institute of Electrical Engineers 2025年第2期74卷 316-325页

作者： Park, Ha-Min Lee, Hyung-Woo Park, Chan-Bae Guo, Youguang Lee, Jae-Bum Dept. of Railway Vehicle & Operation System Engineering Korea National University of Transportation Korea Republic of Dept. of Electrical and Data Engineering University of Technology Sydney Australia Dept. of Railroad Electrical and Information Engineering Korea National University of Transportation Korea Republic of

As the number of electric vehicles grows, the development of fast chargers is advancing rapidly. A DC-DC converter that can deliver high output power, a fixed input voltage, and a wide output voltage range is critical for electric vehicle fast chargers. While the LLC resonant converter is commonly used in these converters, it requires a low magnetizing inductance, which increases the transformer air gap, causing additional AC losses in the windings due to the fringing effect. To mitigate these issues, this study explores design solutions in LLC resonant converters. By using two transformers per module, with the primary sides connected in series and the secondary sides in parallel, the number of primary turns can be reduced, minimizing the air gap. This reduction also allows for structural changes in the windings, improving heat dissipation. Magnetic field analysis and circuit simulations were conducted to validate the proposed DC-DC converter design. Copyright © The Korean Institute of Electrical Engineers.

关键词： Transformer windings

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IoT Based Night Patrol Robot Leveraging Voice technology for Superior Surveillance

IoT Based Night Patrol Robot Leveraging Voice Technology for...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Libina, M. Dhevendhiran, R. Praveenkumar, B. Ifet College of Engineering Dept. of Information Technology Villupuram India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509828

It designs and implements an IoT-based night patrol robot that has improved surveillance capabilities by combining voice technology. The patrol robot system is remote-controlled, patrols designated areas at night, and provides real-time monitoring through onboard sensors and live video feed. This robot provides an easy interface to human operators that can give voice commands. There is no need for physical interactions in terms of remote control or status updates that can run on the desired location from instructions from the control room. The night patrol human risks lead to reduced awareness, vulnerabilities, physical health, and work-life balance. Motion detection, along with the sensing of environmental conditions, and secure wireless communication ensure real-time alerts for suspicious activities in areas like cities, critical areas, and construction sites. The patrol robot is efficient, scalable, and cost-effective in enhancing traditional security systems. The robot can run in a crucial area that human patrol cannot do, ensuring reliability, flexibility, and adaptability to the environment it roams. It includes technologies like motion detection, location tracking, and voice controls that can reduce human intervention while providing improvements in safety in high-risk environments. The robot can increase the emergency patrol assistant by monitoring live video streaming to control rooms. The results from prototype testing indicate effectiveness in performing patrol duties and in responding to voice-based instructions in varying environmental conditions. © 2025 IEEE.

关键词： Remote control

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Comparative Study of Asymmetric Key Cryptographic Algorithms in Image Encryption

Comparative Study of Asymmetric Key Cryptographic Algorithms...

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2025 International Conference on Computer, Electrical and Communication engineering, ICCECE 2025

作者： Maiti, Pubali Paul, M. Atanu Kumar Acharya, Meenakshi Government College of Engineering and Ceramic Technology Dept. of Information Technology Kolkata India

ISBN: (纸本)9798331513894

In the realm of information security, ensuring the confidentiality and integrity of image communication has become a critical concern, especially in the context of modern technological advancements. This paper presents an in-dept. analytical study of image encryption using asymmetric key cryptography algorithms such as RSA, ElGamal, Elliptic Curve Cryptography (ECC), and LUC. The study explores the encryption and decryption mechanisms of these algorithms, assesses their computational efficiency, and evaluates their strengths and weaknesses in the context of secure image transmission. The paper concludes with a detailed comparison of these algorithms, identifying the most suitable ones for practical application in secure communication systems. Finally, the study highlights the need for algorithms that are not only secure but also capable of efficiently handling the growing challenges, ensuring minimal computation overhead and no compromise in encryption and decryption speeds. © 2025 IEEE.

关键词： Decryption EIGamal Elliptic Curve Cryptography (ECC) Encryption LUC Rivest-Shamir-Adleman (RSA)

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