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Open AccessEditorial

Statement of Peer Review

by Ying Tan

Eng. Proc. 2025, 86(1), 6; https://doi.org/10.3390/engproc2025086006 - 25 Sep 2025

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Abstract

In submitting conference proceedings to Engineering Proceedings, the volume editors of the proceedings certify to the publisher that all papers published in this volume have been subjected to peer review administered by the volume editors [...] Full article

(This article belongs to the Proceedings of The 2024 International Conference on Science and Engineering of Electronics)

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8 pages, 1173 KB

Open AccessProceeding Paper

Innovative Physics Pedagogy Using Ant Colony Optimization for Wind Power System Methodologies

by Natchanun Prainetr and Supachai Prainetr

Eng. Proc. 2025, 86(1), 1; https://doi.org/10.3390/engproc2025086001 - 14 Feb 2025

Cited by 1 | Viewed by 975

Abstract

This paper introduces an innovative approach to physics education by integrating the Ant Colony Optimization (ACO) algorithm within a simulation-based learning environment to optimize wind turbine blade angles. Using a simulated wind farm model, students analyze the impact of blade angle adjustments on [...] Read more.

This paper introduces an innovative approach to physics education by integrating the Ant Colony Optimization (ACO) algorithm within a simulation-based learning environment to optimize wind turbine blade angles. Using a simulated wind farm model, students analyze the impact of blade angle adjustments on energy output. The results demonstrate that the ACO algorithm effectively determines optimal blade angles, maximizing energy production. Compared to traditional instructional methods, this approach enhances students’ understanding of wind energy principles and the practical application of metaheuristic optimization techniques in physics and engineering. Furthermore, the study highlights the potential of combining ACO with active learning strategies in STEM education to cultivate advanced problem-solving skills and foster deeper engagement with complex energy systems. Full article

(This article belongs to the Proceedings of The 2024 International Conference on Science and Engineering of Electronics)

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9 pages, 2935 KB

Open AccessProceeding Paper

Applying Existing Large Language Models for Print Circuit Board Routing

by Kangkang Zhang, Huailong Zhang, Aobo Li, Zhiping Yang and Xiuqin Chu

Eng. Proc. 2025, 86(1), 2; https://doi.org/10.3390/engproc2025086002 - 18 Feb 2025

Cited by 1 | Viewed by 2430

Abstract

Large language models (LLMs), such as GPT-4.0 and Gemini, have achieved excellent performance on natural-language tasks, and they also show high expectations for logical reasoning. In the realm of print circuit board (PCB) routing, complex routing scenarios still rely on manual routing performed [...] Read more.

Large language models (LLMs), such as GPT-4.0 and Gemini, have achieved excellent performance on natural-language tasks, and they also show high expectations for logical reasoning. In the realm of print circuit board (PCB) routing, complex routing scenarios still rely on manual routing performed by seasoned engineers, which consumes significant human resources and time. This paper proposes an approach using few-shot and chain-of-thought training LLMs to tackle this issue, enabling LLMs to assist engineers in design tasks with a small number of samples. We tested the performance of LLMs in different routing scenarios with a few examples, validating the applicability of this method. Furthermore, we explored fine-tuning techniques to enhance the effectiveness of the few-shot learning approach, to overcome the limitation of scarce real-world PCB cases, and we employed code synthetic cases to fine-tune the model in place of actual PCB scenarios, ultimately improving the LLMs’ capability to manage intricate routing tasks. The results validate the feasibility and effectiveness of this method, offering a promising avenue for reducing the manual burden in PCB design. Full article

(This article belongs to the Proceedings of The 2024 International Conference on Science and Engineering of Electronics)

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8 pages, 1917 KB

Open AccessProceeding Paper

Optimizing Fault Detection Algorithms in Synchronous Generator Using Wavelet Transform and Fuzzy Logic for Enhanced Fault Analysis

by Supus Kotpay, Suracha Panunchai, Natchanun Prainetr and Supachai Prainetr

Eng. Proc. 2025, 86(1), 3; https://doi.org/10.3390/engproc2025086003 - 4 Jul 2025

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Abstract

This paper proposes a robust fault detection and analysis model for 126 MVA synchronous generators connected to 16 kV and 230 kV transmission lines, developed in MATLAB Simulink R2025a. The model simulates various fault scenarios, including short-circuit conditions, to enhance the fault detection [...] Read more.

This paper proposes a robust fault detection and analysis model for 126 MVA synchronous generators connected to 16 kV and 230 kV transmission lines, developed in MATLAB Simulink R2025a. The model simulates various fault scenarios, including short-circuit conditions, to enhance the fault detection accuracy. The proposed approach combines wavelet transform for precise signal decomposition with fuzzy logic for reliable decision-making, enabling real-time fault detection and classification. The enhanced signal processing framework facilitates faster fault identification and localization, while the fuzzy logic system ensures accurate and consistent fault categorization. The simulation results demonstrate significant improvements in the protection and operational control of synchronous generators, achieving both high reliability and precision. These findings underscore the algorithm’s suitability for deployment in modern power systems, offering a scalable and effective solution for fault management. Full article

(This article belongs to the Proceedings of The 2024 International Conference on Science and Engineering of Electronics)

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8 pages, 1925 KB

Open AccessProceeding Paper

A Novel Real-Time Monitoring and Fault Detection Platform for Enhanced Reliability in Brushless Direct-Current Motor Drive System

by Sittadach Morkmechai, Natchanun Prainetr and Supachai Prainetr

Eng. Proc. 2025, 86(1), 4; https://doi.org/10.3390/engproc2025086004 - 4 Jul 2025

Viewed by 1153

Abstract

Electric vehicle applications frequently use brushless direct-current (BLDC) motors due to their high torque and efficiency. However, coil damage may result from their use at high rotating speeds and extremely high temperatures, requiring preventative maintenance. This study describes the creation of a better [...] Read more.

Electric vehicle applications frequently use brushless direct-current (BLDC) motors due to their high torque and efficiency. However, coil damage may result from their use at high rotating speeds and extremely high temperatures, requiring preventative maintenance. This study describes the creation of a better online monitoring platform that is coupled with an improved fault detection and protection system for small electric vehicles. Designing a fault detection system with real-time analysis to identify open-circuit problems is part of the process. The results indicate that the reliability and operating efficiency of electric vehicle applications have been greatly enhanced by the development of a potential fault-monitoring and protection solution. Full article

(This article belongs to the Proceedings of The 2024 International Conference on Science and Engineering of Electronics)

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7 pages, 626 KB

Open AccessProceeding Paper

Optimized CO₂ Emission Forecasting for Thailand’s Electricity Sector Using a Multivariate Gray Model

by Kamrai Janprom, Tungngern Phetkamhang, Sittadach Morkmechai and Supachai Prainetr

Eng. Proc. 2025, 86(1), 5; https://doi.org/10.3390/engproc2025086005 - 4 Jul 2025

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Abstract

This paper proposes an advanced forecasting model for predicting carbon dioxide (CO₂) emissions in Thailand’s electricity generation sector. The model integrates a multivariate gray model with the fminsearch optimization algorithm in MATLAB (R2025a) to address the critical challenge of accurate emission [...] Read more.

This paper proposes an advanced forecasting model for predicting carbon dioxide (CO₂) emissions in Thailand’s electricity generation sector. The model integrates a multivariate gray model with the fminsearch optimization algorithm in MATLAB (R2025a) to address the critical challenge of accurate emission forecasting, a key driver of climate change. Historical data on CO₂ emissions, gross domestic product (GDP), peak electricity demand, and electricity user numbers are utilized to enhance predictive accuracy. Comparative analysis demonstrates that the optimized model significantly outperforms the conventional multivariate gray model, achieving mean absolute percentage error (MAPE) values of 7.74% for the training set and 1.75% for the testing set. The results highlight the effectiveness of the proposed approach as a robust tool for policymakers and stakeholders in Thailand’s energy sector, offering actionable insights to support informed decision-making in managing and reducing CO₂ emissions. Full article

(This article belongs to the Proceedings of The 2024 International Conference on Science and Engineering of Electronics)

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