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2024 IEEE Pacific Rim Conference on Communications, computers and Signal Processing, PACRIM 2024

作者： Li, Xin Hu, Baojun Dong, Xiaodai University of Victoria Dept. Electrical and Computer Engineering Canada

ISBN: (纸本)9798350362312

Precise indoor positioning has become increasingly important with the rise of Internet of things (IoT), robotics, and autonomous vehicles, etc. In this paper, we focus on time of arrival (TOA) based ranging through channel impulse response (CIR) by machine learning algorithms. The CIR data is obtained in extensive experiments using ultra-wideband (UWB) transceivers as the anchor and tag. We collect a dataset of 412, 172 traces of CIR data across 12 indoor line-of-sight (LOS) scenarios. This dataset is used to train and test three machine learning models, including long short-term memory (LSTM), gated recurrent units (GRU), and multi-layer perception (MLP), to predict the range between the anchor and tag. The results demonstrate that LSTM and GRU models outperform traditional methods and the UWB device's built-in ranging algorithm in terms of mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). © 2024 IEEE.

关键词： Ultra-wideband (UWB)

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Learning-Based Ultra-Wideband Indoor NLOS Identification

Learning-Based Ultra-Wideband Indoor NLOS Identification

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2024 IEEE Pacific Rim Conference on Communications, computers and Signal Processing, PACRIM 2024

作者： Hu, Baojun Li, Xin Dong, Xiaodai University of Victoria Dept. Electrical and Computer Engineering Canada

ISBN: (纸本)9798350362312

This paper focuses on improving the identification accuracy of the line-of-sight (LOS) and non-line-of-sight (NLOS) conditions by channel impulse response (CIR) using machine learning methods. We propose a new definition for soft and hard NLOS based on ranging error percentages. Our research shows that accurately differentiating these two NLOS scenarios significantly enhances the LOS/NLOS identification accuracy. Experiment results demonstrate notable improvements in LOS/NLOS classification accuracy, with the accuracy of LOS detection improving from 72.33% to 93.84% using a convolutional neural network (CNN). In differentiating between hard NLOS and LOS, our accuracy reaches up to 99.82%. © 2024 IEEE.

关键词： Ultra-wideband (UWB)

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Design and Analysis of an SPM Machine for EPS Applications to Reduce Torque Ripple

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Transactions of the Korean Institute of electrical Engineers 2024年第7期73卷 1155-1161页

作者： Jang, Gwan-Hui Choi, Gilsu Kim, Sehwan Dept. of Electrical and Computer Engineering Inha University Korea Republic of Korea Institute of Machinery and Materials Korea Republic of

Minimizing the torque ripple of the electric machines used in electric power steering (EPS) systems is essential to improve the drivability of vehicles. This study introduces mathematical expressions to reduce torque ripple. It also describes an efficient design process to meet multiple design objectives and constraints. A surrogate model-based optimization algorithm with adaptive sampling is applied to perform the optimal design of a fractional-slot surface-mounted permanent magnet synchronous machine (SPMSM). The results show that the optimized design achieves significant improvements in torque density, efficiency, and cost-effectiveness while meeting both torque ripple and cogging torque constraints. Copyright The Korean Institute of electrical Engineers.

关键词： Cost effectiveness

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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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Empirical Validation of Fuzzy Logic Based Predictive Load Scheduling in Mimic Home Energy Management System

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Journal of Internet technology 2023年第7期24卷 1429-1436页

作者： Jegadeesan, Nirmala Balasubramanian, G. Hemavathi, N. Chen, Yu-Chi School of Electrical and Electronics Engineering SASTRA Deemed University India Dept. Computer Science and Information Engineering National Taipei University of Technology Taiwan

Load scheduling plays a vital role in the home energy management systems. The main objective of this load scheduling is to balance the power demand and supply power without degrading the performance of the loads and consumers tolerance. Though many research works concentrate on load scheduling, very few works concentrated on real time scenario. On the other hand, research work concentrated on optimal load scheduling through fuzzy logic requires incorporation of fuzzy based system in either simulated or real-time home energy management system. Hence, the proposal aims to schedule the loads in a simulated home energy management system through fuzzy logic controller using MATLAB Simscape with required subsystems. The proposed simulated environment considers four different resistive loads. Intelligent scheduling is aimed to achieve efficient load scheduling. A fuzzy controller has three inputs namely integrated source, state of charge of battery, and power demand whereas probability of scheduling is considered as output. The efficiency of the proposed fuzzy-based load scheduling scheme is evaluated under various load conditions for different sub-systems with varying input power. The results exhibit the efficacy of the proposed scheme. © 2023 Taiwan Academic Network Management Committee. All rights reserved.

关键词： Fuzzy logic

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Innovative Solutions For Oil Well Monitoring: Data-Driven Multiphase Virtual Flow Metering Using Ensemble Machine Learning And Historical Field Data

Journal of Applied Science and Engineering

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Journal of Applied Science and engineering 2025年第12期28卷 2427-2438页

作者： Farag, Wael A. Ismail, Hussein H. Nadeem, Muhammad Electrical Power Engineering Dept. Cairo University Giza Egypt College of Engineering and Technology American University of the Middle East Kuwait

By combining data-driven ensemble machine-learning algorithms and historical oil field portable test reports, this paper proposes a Data-Drive Multiphase Virtual Flow Meter (DD-MVFM) that estimates oil, gas, and water flow rates and provides real-time monitoring, and predicts future production with appropriate accuracy. The proposed DD − MVFM utilizes the existing hardware used for measurements of basic variables such as temperature, and pressure at different locations at the well-head structure. The DD − MVFM can be employed in three ways. The first way is to be used as a verification tool for multiphase physical flow meters (MPFMs), making sure they are working properly and increasing confidence in the collected readings. The second way is to use the DD − MVFM as a redundant tool when the MPFMs are not available or going through maintenance. The third way, which is the main objective of our research, is to employ the proposed DD MVFM as a standalone for the complete replacement of current and future MPFM installments. This, significantly lowers the operating cost, reducing the required portable field tests, and saving the need to build a major infrastructure for the set-up of MPFMs for new oil wells. Consequently, this contributes to the ambitious goal of reducing CO2 emissions. The development of the DD − MVFM encompasses the fusion of multiple data wrangling and machine learning algorithms to achieve the required performance. The testing results of the proposed DD − MVFM and the prediction experiments show that it successfully achieved a performance of 85%. These results will be significantly improved in the future after incorporating more data from the previous and coming field test reports. © The Author(’s).

关键词： Oil well testing

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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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Exploring emerging spreaders through GCN-based link prediction and a novel centrality method 17

Exploring emerging spreaders through GCN-based link predicti...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Nandi, Suman Maji, Giridhar Dutta, Animesh National Institute of Technology Dept. of Computer Science and Engineering West Bengal Durgapur India Asansol Polytechnic Dept. of Electrical Engineering West Bengal India

ISBN: (纸本)9798331531195

Exploring influential spreaders and predicting missing links in complex networks is essential for understanding and effectively controlling network dynamics. This paper presents a Graph Convolutional Network (GCN)-based link prediction method to estimate the probability of future link formation. We incorporate node features that capture local and global topological connectivity structures and feed these into the GCN model, where convolutional layers aggregate neighboring information and transform node features. This approach enables the model to capture structural patterns by integrating local and global information from neighboring nodes. In the final layer, the GCN model computes a prediction score representing the likelihood of an edge's existence, using insights gained during training. Finally, considering the predicted links, we update the network structure and introduce a novel centrality method called Emerging Spreader Centrality (ESC) to identify emerging spreaders within this augmented network. We conduct two separate experiments to evaluate the performance of the GCN-based link prediction and the ESC method, comparing their effectiveness with various state-of-the-art methods. Results demonstrate that our approach not only effectively predicts future links but also identifies emerging spreaders in the augmented networks. © 2025 IEEE.

关键词： Prediction models

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Cross-Domain Adaptation Through Adversarial Teaching for Unmanned Ariel Vehicles in Smart Cities 22

Cross-Domain Adaptation Through Adversarial Teaching for Unm...

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22nd International Learning and technology Conference, L and T 2025

作者： Mazhar Ul Haq, Qazi Yao, Leethar Lin, Bo-Yu Yuan Ze University Dept. of IBPI and Computer Science and Engineering Zhongli Taiwan National Taipei University of Technology Dept. of Electrical Engineering Taipei Taiwan

ISBN: (纸本)9798331532215

Person re-identification has been an important issue of surveillance systems in smart cities. However, this requires huge datasets to supervise deep learning models for accurately identifying and tracking people in smart cities. In this paper, we propose adversarial learning in integration with a mutual mean teaching model known as adversarial teaching to take the issue of domain adaptation. Initially, they learn a source dataset in a supervised way. This model information is then transferred to another model through unsupervised learning and pseudo-labels are generated by generative adversarial learning. The multi-class invariance is handled by adversarial learning. The performance of the proposed model is evaluated on two different datasets source and target datasets. Multiple experiments on Duke and Market datasets indicate higher accuracy of F-1 score on both datasets compared to previous methods, demonstrating the superior performance of the proposed method. © 2025 IEEE.

关键词： Adversarial machine learning

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Subthreshold Swing Variation in p-type LTPS TFT Under Short-Term Voltage and Temperature Stress

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Transactions of the Korean Institute of electrical Engineers 2024年第5期73卷 807-810页

作者： Lee, Ji-Ho Kang, Kyeong-Soo Park, Ji-Hwan Park, Chanjin Lee, Soo-Yeon Dept. of Electrical and Computer Engineering SOFT Foundry Institute Seoul National University Seoul Korea Republic of

In this paper, we analyze the short-term degradation characteristics of low-temperature polycrystalline silicon (LTPS) thin-film transistor (TFT) to analyze the origin of short-term image sticking (STIS) in the mobile display. The STIS effect is highly relevant to the change in electrical characteristics of driving transistor of the pixel circuit. We mainly focus on the subthreshold swing (SS) variation, as the pixel circuits can internally compensate for the variation in threshold voltage. To examine the short-term degradation, each voltage and temperature stress corresponding to white and black grayscales are applied to the LTPS TFT for 10 minutes. We found that these stress conditions induce SS degradation of the LTPS TFT. This degradation can cause current variation of driving transistor, resulting in non-uniform luminance. Thus, the short-term SS degradation is strongly related to the origin of STIS effect. Copyright © The Korean Institute of electrical Engineers.

关键词： Thin film transistors

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