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A framework for locating multiple RFID tags using RF hologram tensors

Digital Communications and Networks 2025年第2期11卷 337-348页

作者： Xiangyu Wang Jian Zhang Shiwen Mao Senthilkumar CG Periaswamy Justin Patton Dept. of Electrical and Computer Engineering Auburn University RFID Lab Auburn University Department of Electrical and Computer Engineering Kennesaw State University

In this paper, we present a Deep Neural Network(DNN) based framework that employs Radio Frequency(RF) hologram tensors to locate multiple Ultra-High Frequency(UHF) passive Radio-Frequency Identification(RFID) tags. The RF hologram tensor exhibits a strong relationship between observation and spatial location, helping to improve the robustness to dynamic environments and equipment. Since RFID data is often marred by noise, we implement two types of deep neural network architectures to clean up the RF hologram tensor. Leveraging the spatial relationship between tags, the deep networks effectively mitigate fake peaks in the hologram tensors resulting from multipath propagation and phase wrapping. In contrast to fingerprinting-based localization systems that use deep networks as classifiers, our deep networks in the proposed framework treat the localization task as a regression problem preserving the ambiguity between fingerprints. We also present an intuitive peak finding algorithm to obtain estimated locations using the sanitized hologram tensors. The proposed framework is implemented using commodity RFID devices, and its superior performance is validated through extensive experiments.

关键词： Radio-frequency identification (RFID) Ultra-high frequency (UHF) passive RFID tag RF hologram tensor Indoor localization Deep learning (DL) Swin Transformer Self-supervised learning

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A Study on Prediction and Accuracy Improvement of SVR Voltage Control in Distribution System Based on Machine Learning

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Transactions of the Korean Institute of electrical Engineers 2025年第1期74卷 7-14页

作者： Yoo, Byungchan Choi, Wonna Jung, Seungmin Dept. of Electrical and Computer Engineering University of Seoul Korea Republic of

The proportion of distributed energy sources in the distribution network is rapidly increasing. Along with this, voltage management is becoming more difficult, and stability issues in the power system are being reported. Until recently, the voltage of distribution lines has generally been regulated using voltage regulators, predominantly deployed in sections where rapid voltage drops occur. However, as the characteristics of distributed energy sources are reflected, the efficiency of the voltage regulator diminishes. Therefore, cooperation with substations is necessary for stable operation of the distribution network. This paper aims to predict the operation of a machine learning-based voltage regulator to evaluate voltage fluctuations occurring in distribution lines and the corresponding control operations in advance. Long Short-Term Memory is capable of prediction on time scales. Therefore, the output characteristics of distributed energy sources can be considered. Additionally, to improve prediction accuracy, cases are generated in the OpenDSS-Python environment and additional training is performed on the LSTM model. As a result, it was confirmed that the prediction accuracy improved when the proposed method was applied. The machine learning model was verified using four evaluation indices. © 2025 Korean Institute of electrical Engineers. All rights reserved.

关键词： Power distribution lines

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Analysis of Iron Loss Characteristics of PM-Assisted and Fluid-Shaped Synchronous Reluctance Machines for Micro EVs

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Transactions of the Korean Institute of electrical Engineers 2025年第3期74卷 425-431页

作者： Choi, Jaesung Choi, Gilsu Dept. of Electrical and Computer Engineering Inha University Korea Republic of

This paper examines the iron loss characteristics of a permanent magnet-assisted synchronous reluctance machine (PMa-SynRM) and a fluid-shape synchronous reluctance machine (FS-SynRM) for micro EV applications, emphasizing the impact of DC bias fields and minor loops on iron loss estimation. Iron losses are comparatively evaluated using three methods: the Steinmetz method with constant coefficients, the loss separation method with varying coefficients and FFT, and the hysteresis-based model that accounts for minor loops and DC bias fields. The results show that iron losses per unit stack length are higher in the PMa-SynRM, where the effects of DC bias fields and minor loops are more pronounced. Moreover, the Steinmetz and loss separation methods, which neglect these effects, overestimate motor efficiency by up to 1.5% and 0.3%, respectively. This study highlights the importance of incorporating DC bias and minor loops in iron loss and efficiency calculations for micro EV traction machines. © The Korean Institute of electrical Engineers.

关键词： Permanent magnets

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Study on a Regression Model for the Electromagnetic Characteristics of SPMSM Based on Convolutional Neural Network with Attention Mechanism

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Transactions of the Korean Institute of electrical Engineers 2025年第2期74卷 302-309页

作者： Ji, Tae-Hyuk Song, In Seok Kim, Hyung-Woo Jung, Sang-Yong Dept. of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

Surface-mounted permanent magnet synchronous motors (SPMSMs) are widely used in high-performance system. However, they suffer from torque ripple and cogging torque, which degrade output quality and cause noise and vibration, necessitating optimized design. While finite element analysis offers high accuracy for optimization, it comes with significant computational cost. To address this limitation, this paper proposes an attention mechanism-based convolutional neural network (CNN) regression model to predict SPMSM electromagnetic performance. CNNs capture spatial structures of motor designs, while attention mechanisms highlight key design features, boosting prediction accuracy and efficiency. This study analyzes the effects of attention mechanisms and proposes a CNN-based regression model incorporating them, confirming the effectiveness of the attention mechanism through comparisons with conventional CNN models. © The Korean Institute of electrical Engineers.

关键词： Convolutional neural networks

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A Study on Rotor Shape for Reducing Low-Order Radial Force in Interior Permanent Magnet Synchronous Motors for Air Compressor

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Transactions of the Korean Institute of electrical Engineers 2025年第1期74卷 56-63页

作者： Cheo, Daeseon Kim, Nam-Ho Cho, Jae-Hoon Kwon, Yong-Jun Jung, Sang-Yong Dept. of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

In this paper, a rotor design to reduce low order raidal force and electromagnetic vibration in interier permanent magnet synchronous motor (IPMSM) for an air compressor is proposed. First, according to the analysis results of radial force and no-load air-gap flux density of V-type 1-layer and 2-layer models, 2-layer model is selected as the initial model since it exhibits lower low radial force than 1-layer model. Additionally, the no-load air-gap flux density of the 2-layer model is further analyzed, confirming that the low order radial forces affected by 20th order harmonic and the fundamental component of no-load air-gap flux density have a 180° phase difference, canceling each other out. Moreover, the sensitivity anlysis of rotor notches reveals that the 3-Notch model effectively increases magnitude of 20th order harmonic. Based on the aformentionted analysis, as a result, by applying notches to the 2-layer model rotor, the low order radial force and electromagnetic vibration are reduced. Copyright © The Korean Institute of electrical Engineers.

关键词： Permanent magnets

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Multi-Objective Optimization of Torque and Torque Ripple in Double V-Type IPMSM Using a Random Forest-Based Modified PSO-GA Hybrid Method

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Transactions of the Korean Institute of electrical Engineers 2025年第2期74卷 266-272页

作者： Kwon, Yong-Jun Choi, Dae-Sun Wang, Chang-Hyeon Oh, Ho-Jin Yoon, Han-Joon Jung, Sang-Yong Dept. of Electrical and Computer Engineering SungkyunKwan University Korea Republic of

Interior Permanent Magnet Synchronous Machines (IPMSMs) are widely used not only as drive motors for Electric Vehicles (EVs) but also in various industrial fields due to their high efficiency and high power output characteristics. However, because of the embedded magnets, IPMSMs exhibit significant torque ripple during operation, necessitating the use of optimization algorithms to address this issue. For IPMSMs, which have a large number of design variables, the feasible design space is defined by multiple constraints, increasing the complexity of the design optimization process. Therefore, this paper proposes a Random Forest-based Modified PSO-GA hybrid method to perform optimization in the presence of multiple constraints and applies it to the torque and torque ripple improvement design of a Double V-Type IPMSM. ©The Korean Institute of electrical Engineers.

关键词： Permanent magnets

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Analysis of Magnetization Characteristic Based B-H Curve Fitting According to the Operation Conditions for Traction Motor

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Transactions of the Korean Institute of electrical Engineers 2025年第2期74卷 310-315页

作者： Kim, Young-Hyun Lee, Jung-Ho Dept. of Electrical Engineering Hanbat University Korea Republic of Dept. of Electrical Engineering Daejeon Institute of Science and Technology Korea Republic of

This paper examines the demagnetization characteristics of rare earth permanent magnets (PMs) used in electric machinery. An analytical model was designed considering a controllable shape, temperature, and the external magnetic field in PMs used in the rotor of an interior permanent magnet synchronous motor (IPMSM) for driving an electric railway vehicle (ERV). The demagnetization process was analysed based on the magnetization and demagnetization theory for PM materials in computer simulation. The result of the analysis can be used to calculate the magnetization and demagnetization phenomenon according to the input B-H curves. So, this paper presents the conditions of demagnetization by the external magnetic field in the running and stopped states and proposes a simulation method that can analyse demagnetization phenomena according to each condition and be used to design the IPMSM that maximizes efficiency and torque characteristics. This was experimentally verified. Copyright © The Korean Institute of electrical Engineers.

关键词： Permanent magnets

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Development of a Reinforcement Learning-Based Control Education Platform Using Python and LW-RCP

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Transactions of the Korean Institute of electrical Engineers 2025年第1期74卷 118-126页

作者： Lee, Jongbeom Lee, Taegun Ju, Doyoon Sam Lee, Young Dept. of Electrical and Computer Engineering Inha University Incheon Korea Republic of

This paper proposes a reinforcement learning-based control education platform utilizing Python and light-weight rapid control prototyping (LW-RCP). The platform employs the Sim-to-Real technique, in which neural networks are trained in a Python-based simulation environment and applied to real systems. The trained networks are converted into a format compatible with Matlab/Simulink. The lab-built LW-RCP is used to implement a real-time controller under the Simulink environment by incorporating the converted networks. The proposed platform allows students to easily apply reinforcement learning theory to real systems, contributing to the integration of reinforcement learning control into control curriculum. The effectiveness of the proposed platform is demonstrated by implementing a reinforcement learning controller for the pendubot system. The implemented controller performs the swing-up and transition control and exhibits strong disturbance rejection and recovery properties. © 2025 Korean Institute of electrical Engineers. All rights reserved.

关键词： Python

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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 High Power Density Active-Clamp Forward-Flyback Converter with Three Planar Transformer for xEV Low Voltage DC-DC Converter

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Transactions of the Korean Institute of electrical Engineers 2025年第3期74卷 468-476页

作者： Kwon, Gyeonghyun Lee, Dongin Jeong, Seongwook Jeong, Yeonho Youn, Hanshin Dept. of Electrical Engineering Incheon National University Korea Republic of Dept. of Electrical Computer and Biomedical Engineering University of Rhode Island KingstonRI United States

This paper proposes an Active-Clamp Forward-Flyback (ACFF) converter with three planar transformers to achieve 4 kW output power. As autonomous driving technology rapidly advances, the demand for higher output power from low-voltage DC-DC converters (LDC) in electric vehicles has significantly increased. Conventionally, two 2 kW ACFF converters with four magnetic cores connected in series-parallel have been used to meet 4 kW power requirement, but this configuration has proven inefficient in terms of size, power density, and cost. The proposed converter improves power density by integrating the four magnetic cores of the conventional 4 kW ACFF converter into three, reducing the overall magnetic volume and allowing for a more compact design. Furthermore, the use of planar magnetic components enables the winding to be embedded in a 4-layer PCB. Copyright © The Korean Institute of electrical Engineers.

关键词： High frequency transformers

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