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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Park, BoJeong Kim, JongWoo Mun, SeoYeong Choi, Younglim Kim, Hyunseok Dong-A University Dept. of Electric Engineering Busan Korea Republic of Dong-A University Dept. of Software Innovation Center Busan Korea Republic of Dong-A University Dept. of Computer Engineering Busan Korea Republic of

ISBN: (纸本)9798331510756

This paper aims to develop a robotic golf trainer using a wheeled robot and create a model that accurately recognizes the user's golf motion. Since existing 3D pose models show limitations in golf motion recognition [1], [2], we propose an optimized recognition model by combining various methods and preprocessing golf datasets to overcome these problems. We used HRNet to estimate 2D pose from video frames and fed these data into a transformer-based model to generate three hypotheses through residual linking and embedding layers. These were integrated to finally design a structure to predict 3D pose, and batch normalization, dropout, and LeakyReLU were used to improve the stability and nonlinearity handling of the model. Experimental results show that the proposed model effectively learns temporal information from multi-view 2D pose data without 3D annotation and performs well even when 3D data is scarce. The proposed method can be used in various applications such as human-robot interaction and sports motion analysis including golf, and further research on multiple datasets and real-world environments is expected to improve further and generalize the model's performance. © 2025 IEEE.

关键词： Human robot interaction

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AROhI: An Interactive Toolkit for Estimating ROI of Data Analytics 15

AROhI: An Interactive Toolkit for Estimating ROI of Data Ana...

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15th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2025

作者： Zambare, Noopur Idoko, Jacob Acharya, Jagrit Ginde, Gouri Indian Institute of Technology Dept. of Mechanical Engineering Jodhpur India University of Calgary Dept. of Electrical and Software Engineering Canada

ISBN: (纸本)9798331507695

The cost of adopting new technology is rarely analyzed and discussed;however, it is vital for any technological investment because of the cost and effort associated with it. Thus, it is crucial to consider Return On Investment (ROI) when performing data analytics. Decisions on 'How much analytics is needed?' are hard to answer. ROI could guide decision support on the What?, How?, and How Much? Analytics for a given problem. This work details a comprehensive tool that provides conventional and advanced ML approaches for demonstration using software requirements dependency extraction and their ROI analysis as a use case. Utilizing advanced ML techniques such as Active Learning, Transfer Learning and primitive Large language model: BERT (Bidirectional Encoder Representations from Transformers) as its various components for automating dependency extraction, the tool outcomes demonstrate a mechanism to compute the ROI of ML algorithms to present trade-offs between the cost and benefits of a technology investment. © 2025 IEEE.

关键词： Adversarial machine learning

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IEEE Vehicular Technology Society Bylaws Changes [Society News]

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IEEE VEHICULAR TECHNOLOGY MAGAZINE 2025年第1期20卷 111-112页

作者： Rubinstein, Tom Dept. of Electrical & Computer Engineering Auburn University Auburn AL USA

Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.

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A Study on a Personalized Robot Golf Trainer System with Multi-Persona and Ethical Guidelines for Robots

A Study on a Personalized Robot Golf Trainer System with Mul...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Choi, Younglim Park, BoJeong Kim, JongWoo Mun, SeoYeong Kim, Hyunseok Dong-A University Dept. of Software Innovation Center Busan Korea Republic of Dong-A University Dept. of Electronic Engineering Busan Korea Republic of Dong-A University Dept. of Mechanical Engineering Busan Korea Republic of Dong-A University Dept. of Computer Engineering Busan Korea Republic of

ISBN: (纸本)9798331510756

This paper proposes a multi-persona and robot ethics system for a personalized robot golf trainer. The system combines a wheeled mobile robot without arms and GolfPoseNet for analyzing golf postures and integrates a MediaPipe Pose-based user command recognition system to enhance the golf learning experience. To improve the learning process for users of all ages, the system incorporates a multipersona approach. It features custom training designed according to the Robot Service Personality (RSP) model developed by the author, with interaction styles tailored to each service personality type. Additionally, the system optimizes feedback style and training speed based on each user's preferences and learning style, resulting in a highly personalized experience. In an outdoor golf setting, the system aims to support efficient learning by utilizing both an external camera and the robot's camera for accurate posture analysis, offering real-time feedback. Furthermore, by integrating robot ethics, the system ensures user privacy and safety while fostering trust in AI robots. This study provides foundational insights into the potential for personalized AI service robot development in golf posture analysis and human-robot interaction, serving as groundwork for future system advancements. © 2025 IEEE.

关键词： Human robot interaction

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A Study of Emotional Expression Interface for Wheeled Mobile Robot to Enhance Emotional Bonding

A Study of Emotional Expression Interface for Wheeled Mobile...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Mun, SeoYeong Park, BoJeong Kim, JongWoo Choi, Younglim Kim, Hyunseok Dong-A University Dept. of Mechanical Engineering Busan Korea Republic of Dong-A University Dept. of Electronic Engineering Busan Korea Republic of Dong-A University Dept. of Software Innovation Center Busan Korea Republic of Dong-A University Dept. of Computer Engineering Busan Korea Republic of

ISBN: (纸本)9798331510756

This study aims to investigate how the emotional expression of robots and their physical design significantly influence the establishment of emotional bonds with users in Human-Robot Interaction (HRI). Utilizing the commercially available Wheeled Mobile Robot(WMR) designed for service tasks as our experimental platform, we've developed an intuitive interface that enhances emotional and physical interactions. By enriching the robot's emotional expression capabilities and incorporating thoughtful design features-such as LED color variations and ear-like elements-we pave the way for more authentic user experiences. Our upcoming user studies will rigorously evaluate the emotional connections, intimacy, and satisfaction levels that users feel during their interactions with the WMR, employing quantitative and qualitative analyses to measure the effectiveness of each design element. Ultimately, this research underscores the potential for robots to form meaningful relationships with users, making technology more relatable and engaging. © 2025 IEEE.

关键词： Human robot interaction

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A Serendipity Recommendation Method for Book Categories Using BERT to Strengthen the Web Service of the Book

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Journal of Web engineering 2025年第2期24卷 199-216页

作者： Kim, Youngmo Kim, Seok-Yoon Park, Byeongchan Dept. of Computer Science and Engineering Soongsil University South Korea

In the field of book search, research on a web service-based user-customized book recommendation system is being conducted to respond to increasingly diverse user requirements. The collaborative filtering algorithm, which is mainly used for book recommendation, has a problem in that it is difficult to reflect the user’s recent interest without considering the changes in preference over time, and the user’s satisfaction decreases because it repeatedly recommends only similar items. In this paper, we propose a book recommendation method using category similarity based on deep learning. The proposed method is to predict books to be used next time by inputting users’ past and current book usage history through BERT, a natural language processing model, and to recommend popular books in other categories with high similarity to the predicted book category in the BERT model to reflect serendipity. This method reflects serendipity, which can lead to users’ recent interests and practical preferences, so that recommendation accuracy and user satisfaction can be satisfied at the same time. © 2025 River Publishers.

关键词： BERT book category recommendation serendipity Web service

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Study on the Analysis and Reduction Method for AC Copper Loss in Electric Vehicle Traction Motor

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Transactions of the Korean Institute of Electrical Engineers 2025年第2期74卷 295-301页

作者： Wang, Chang-Hyeon Kim, Nam-Ho Choi, Ho-Yong Jung, Sang-Yong Dept. of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of Special Motor Engineering 1 Team Hyosung Heavy Industies Korea Republic of

The advancements in electrified drive systems have enabled the design of high-power density electric vehicle (EV) motors. However, the increased operating frequency necessary for achieving high-power density amplifies AC copper losses, primarily due to skin and proximity effects. This study focuses on reducing AC copper losses in an 8-pole, 48-slot Interior Permanent Magnet Synchronous Motor (IPMSM) using 2D finite element analysis (FEA). The analysis evaluates the contributions of skin and proximity effects to total copper losses and investigates the influence of slot leakage flux. Additionally, the impact of slot and conductor geometries on leakage flux, its variation over time, and the resulting AC copper losses were systematically analyzed. By balancing manufacturability and performance, optimal design parameters were derived to minimize AC copper losses. The results demonstrate that adjustments in slot width, slot dept., and conductor placement significantly affect leakage flux and copper losses. The proposed design strategies improve motor efficiency and thermal performance under high-speed operating conditions, contributing to the development of high-performance EV motors. Copyright © The Korean Institute of Electrical Engineers.

关键词： Permanent magnets

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Classifying white blood cells using combining different convolutional neural networks

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Multimedia Tools and Applications 2025年 1-24页

作者： Toptaş, Murat Toptaş, Buket Hanbay, Davut Software Eng. Dept. Engineering and Natural Science Faculty Bandırma Onyedi Eylül University Balıkesir Turkey Computer Eng. Dept. Engineering Faculty Inonu University Malatya44280 Turkey

White blood cells are warrior cells that protect the human body against external factors. Each of these warrior cells performs a distinct task, making every piece of information about them highly valuable in the medical field. In this article, a classification framework for the four known types of white blood cells is proposed. It is hoped that the classification of these types will contribute to the prediction of diseases such as AIDS, malaria, leukemia, and many others. In the proposed method, images of white blood cells from the Blood Cell Classification and Detection dataset were used as input to Convolutional Neural Networks. The feature vectors extracted using these Convolutional Neural Network architectures were combined into a single vector. A Minimum Redundancy Maximum Relevance algorithm was then employed to identify the most effective features within the feature vector. Experiments were conducted using these selected features, and the analysis of each experiment was reported in detail. The Support Vector Machines classifier achieved an accuracy of 98.63% in classifying white blood cell types by combining features from multiple deep learning architectures. The experimental results demonstrated that the features obtained from different layers of the Convolutional Neural Networks had varying impacts on the classification performance. © The Author(s) 2025.

关键词： Convolutional neural networks

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Design and Optimization of an IR-Based Automation System with Telegram Bot Integration for Remote Device Control 4

Design and Optimization of an IR-Based Automation System wit...

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4th International Conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2025

作者： Adnan Sharar Oyon, Md. Shihab Himel, Abid Hasan Mredul, Khaliduzzaman Ahmed, Abir Ali, M Tanseer Hossainl, Chowdhury Akram Rahman, Md. Abdur Dept. of Electrical and Electronic Engineering Dhaka Bangladesh Dhaka Bangladesh Dept. of Computer Engineering Dhaka Bangladesh

ISBN: (纸本)9798331530938

Automation is advancing rapidly, with infrared (IR) communication being a key method for controlling electronic devices. Despite this, challenges remain in optimizing IR controllers for consistent performance across varying angles and task loads. A 3D design and a robust algorithm have been developed to enhance the system's accuracy, while security is ensured by matching a unique TOKEN with the Telegram bot for each session. Error rates were analyzed across different angles, with specific zones determined based on performance. Minimal error rates defined the working zone, while higher rates indicated critical and invalid zones. Error rates increased as the transmitter moved further from the optimal angles, such as a 0-6 % error rate when operating within a 0° - 5° range. Reaction time was calculated across multiple tasks, showing an average of 5 seconds per command. Mismatch rates, observed during higher task loads, rose sharply when handling many tasks. For instance, the mismatch rate was 0 % for 12 tasks but 20 % when executing 20 tasks. Performance evaluations demonstrated optimal IR signal strength for reliable control within a distance of 10 to 15 inches, with diminishing effectiveness beyond 20 inches. These findings provide a foundation for developing more reliable and efficient IR control systems for practical applications. © 2025 IEEE.

关键词： Bot (Internet)

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Integrated Word2Vec-based Speech Annotations for Enhanced EEG Decoding of Speech Intentions 13

Integrated Word2Vec-based Speech Annotations for Enhanced EE...

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13th International Winter Conference on Brain-computer Interface, BCI 2025

作者： Jang, Se-Na Park, Hyeong-Yeong Kim, Seong-Wook Yu, Seong-Hyun Lee, Minji Jeong, Ji-Hoon Dept. Computer Science Chungbuk National University Cheongju Korea Republic of Dept. Biomedical Software Engineering The Catholic University of Korea Bucheon Korea Republic of

ISBN: (纸本)9798331521929

Brain-computer interface (BCI) technology has promising applications as an intuitive communication tool and in fields such as language rehabilitation. This study aims to decode human speech intentions by analyzing EEG signals recorded during actual and imagined speech. EEG data were collected using a 64 channels system, with preprocessing to remove artifacts. Automatic speech recognition (ASR) was used to extract precise speech onset times, generating time-specific speech annotations corresponding with EEG data. Pretrained Word2Vec embeddings were integrated to provide semantic context, combining neural signals with high-level linguistic features. Support vector machine (SVM), linear discriminant analysis (LDA) were employed for decoding. The results demonstrate that integrating speech annotations improves decoding accuracy, even for imagined speech, highlighting the potential of BCI technology for advanced applications in communication and rehabilitation. © 2025 IEEE.

关键词： Semantics

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