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Eng. Proc., 2025, ICSEE 2024

The 2024 International Conference on Science and Engineering of Electronics

Wuhan, China | 22–26 November 2024

Volume Editors:
Ying Tan, Peking University, China
Adrian Ioinovici, Shanghai University of Electrical Power, China

Number of Papers: 2

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Cover Story (view full-size image): The 2024 International Conference on Science and Engineering of Electronics (ICSEE 2024) was held in Wuhan, China, on 22–26 November 2024. This conference serves as a platform for experts, [...] Read more.

8 pages, 1173 KiB

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

Viewed by 243

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 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 KiB

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

Viewed by 542

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 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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