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computer Vision-Based Object Detection and dept. Estimation to Generate Point Clouds and Estimate Distances

Transactions of the Korean Institute of Electrical Engineers 2025年第1期74卷 164-169页

作者： Eom, Tae-Hyun Paik, Woojin Dept. of Computer Engineering Konkuk University Glocal Campus Korea Republic of Dept. of Computer Engineering Konkuk University Glocal Campus Korea Republic of

This research proposes a distance estimation method using Mono Camera-based object detection and dept. estimation to generate Point Cloud data. The study aims to enhance the applicability of Mono Cameras in autonomous vehicles and robots, reducing costs compared to Stereo Camera systems. The method utilizes YOLOv8 for object detection and dept. Anything V2 for dept. estimation. Results indicate that while the proposed method offers potential, it exhibits higher error rates in distance estimation compared to Stereo Camera-based approaches, primarily due to the limitations of current dept. estimation technologies. The study highlights the need for further improvements in dept. estimation models, particularly to address environmental factors such as lighting. Additionally, the research demonstrates that combining dept. estimation with bounding box methods helps reduce estimation errors, showing promise for more stable performance. Future work will focus on improving dept. estimation accuracy and making the proposed method more efficient for real-time applications, with the potential to integrate into Visual SLAM, a key technology in autonomous driving systems. © 2025 Korean Institute of Electrical Engineers. All rights reserved.

关键词： Stereo image processing

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Predicting Customer Satisfaction based on Neutrosophic sets: Applied on mobile food ordering application

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Neutrosophic Sets and systems 2025年 80卷 214-228页

作者： Elsanabary, Walaa Radwan, Nouran M. Computer and Information Systems Dept. Sadat Academy for Management Sciences Egypt

Customers’ satisfaction prediction is a vital process for all business organizations to draw new customers and maintain existing customers. One of the most efficient methods to predict customers' satisfaction is classifying customers' feedback. In the real world, customer’s feedback is ambiguous, confusing and inconsistent. This option can be stated in neutrosophic logic as indeterminacy membership, associated with truth and falsity membership. In this study, a classification model based on neutrosophic sets to handle the inconsistency of customer responses is presented. Also, significant factors that impact customers' satisfaction are defined. In order to show the procedures and the application of the proposed method, a case study to determine the customers' satisfaction while using food order application (Talabat) in Egypt is presented. A comparison between the classical classification models and the proposed model based on neutrosophic sets is presented. The experimental results indicate that the proposed classifying model achieved accuracy results around 95.36% to 99.95%, higher than the classical one that achieve around 90.3% to 93.99%. Next, a sensitivity analysis is performed for reliability validation as to determine the most factors that affect customers’ satisfaction. © 2025, University of New Mexico. All rights reserved.

关键词： Sales

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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 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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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 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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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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Air Hockey Player Motion Learning Using PPO Model Reinforcement Learning

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Transactions of the Korean Institute of Electrical Engineers 2024年第12期73卷 2341-2349页

作者： Moon, Seonguk Cho, Youngwan Dept. of Computer Engineering SeoKyeong University Korea Republic of

This paper proposes a two-joint arm model using reinforcement learning in an air hockey simulation environment. Reinforcement learning, a method where an agent interacts with the environment to learn optimal behavior strategies, is applied to a physics-based air hockey simulation in this study. Air hockey is a game with simple rules where the puck must be directed into the opponent's goal area. Unlike conventional simple control algorithms, this study aims to implement diverse actions and varied racket trajectories through reinforcement learning. The focus of this paper is on the implementation and performance evaluation of a two-joint arm model that primarily learns a defensive play style. The two-joint arm model is assessed for its ability to respond to various situations and its basic control performance. Through this evaluation, the study aims to verify the fundamental potential of reinforcement learning-based agents and suggest future research directions. Copyright © The Korean Institute of Electrical Engineers.

关键词： Reinforcement learning

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A review on recent driver safety systems and its emerging solutions

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International Journal of computers and Applications 2024年第3期46卷 137-151页

作者： Nair, Akhil Patil, Varad Nair, Rohan Shetty, Adithi Cherian, Mimi Dept. of Computer Engineering Pillai College of Engineering Maharashtra Panvel India

Road safety and accident prevention are critical concerns in modern transportation. This paper presents a comprehensive survey of driver safety systems, focusing on the latest advancements in this field. We analyze the existing literature to identify key research trends in driver safety systems, encompassing various categories of solutions. Our survey delves into the reasons behind road accidents and assesses the effectiveness of emerging technologies and solutions in accident prevention. By categorizing and evaluating these solutions based on the Internet of Things and Machine Learning, we provide valuable insights into the landscape of road accident detection and prevention systems. This survey not only highlights the current state of the art but also serves as a reference for future research and innovation in the domain of driver safety. Abbreviations IoT: Internet of things;CNN: Convolutional Neural Network;SVM: Support vector machine;HRV: Heart rate variability;RRI: R-R Interval;MSPC: Multivariate Statistical process control;EAR: Eye aspect ratio;HUD: Head-up display;GPS: Global positioning system;CAN: Controller area network;GPU: Graphics processing unit;IR: Infrared;GSM: Global system for mobile communication;EEG: Electroencephalogram;PCA: Principal component analysis;SVC: Support vector classifier;SdsAEs: Stacked denoising sparse autoencoders;ECG: Electrocardiogram;LED: Light emitting diode;NFC: Near field communication;PSO: Personal security officer;PPG: Photoplethysmography;EDA: Electrodermal activity;EMG: Electromyography;LCD: Liquid crystal display;RF SoCs: Radiofrequency system on chip;PLR: Piecewise linear representation;BAC: Blood alcohol content;BPNN: Backpropagation Neural Network;ADSD: Automated driver sleepiness detection;EOG: Electroocoulogram;KNN: K nearest neighbor;CBR: Case-based reasoning;RF: Random forest;NIR: Near-infrared;LBP: Local binary pattern;PERCLOS: Percentage of Eye Closure;SVD: Singular value decomposition;FFT: Fast Fourier transf

关键词： Electrocardiograms

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Optimal Design of a Variable Reluctance Resolver to Minimize Position Error

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Transactions of the Korean Institute of Electrical Engineers 2024年第1期73卷 44-49页

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

The precision of position estimation in VR resolvers significantly relies on the harmonic components present in the induced voltage across the secondary winding. The degraded accuracy can be compensated by carefully designing the stator windings and core geometry. This paper presents an optimized design process aimed at minimizing the position error of VR resolvers. The alterations in the resolver output signal's position information, resulting from the phase shift of the stator windings, third harmonic injection-based rotor geometry modifications, and changes to the stator core shape, are thoroughly analyzed using finite element analysis (FEA). Subsequently, a genetic algorithm-based optimization technique is applied to construct an optimal design process that effectively minimizes the position error in VR resolvers. Case study results demonstrate an approximately 80% enhancement in position estimation accuracy for the optimal model compared to the initial model. © 2024 Korean Institute of Electrical Engineers. All rights reserved.

关键词： Genetic algorithms

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