| Title: | GS1-1 Deep Learning Based Prediction of Heat Transfer Coefficient Using Spectrogram Images from Boiling Sound |
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| Publication: | ICAROB2024 |
| Volume: | 29 |
| Pages: | 914-917 |
| ISSN: | 2188-7829 |
| DOI: | 10.5954/ICAROB.2024.GS1-1 |
| Author(s): | Fuga Mitsuyama, Ren Umeno, Tomohide Yabuki, Tohru Kamiya |
| Publication Date: | February 22, 2024 |
| Keywords: | Boiling Sound, Heat Transfer Coefficient (HTC), Convolutional Neural Network (CNN), CBAM |
| Abstract: | Cooling methods based on boiling have attracted attention as a thermal solution for electronic equipment. In this situation, it is necessary to measure the heat transfer coefficient (HTC) to design more efficient cooling systems. In this paper, we propose a method to predict of the HTC from boiling sound data using deep learning techniques. The accuracy improved by 1.12% compared to the conventional method through the development of Convolutional Neural Network (CNN) incorporate Convolutional Block Attention Module (CBAM). |
| PDF File: | https://alife-robotics.co.jp/members2024/icarob/data/html/data/GS/GS1-1.pdf |
| Copyright: | © The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/ |
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