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Application of Machine Learning Methods to Neutron Transmission Spectroscopic Imaging for Solid-Liquid Phase Fraction Analysis
| Title: | Application of Machine Learning Methods to Neutron Transmission Spectroscopic Imaging for Solid-Liquid Phase Fraction Analysis |
| Authors: | Kamiyama, Takashi Browse this author →KAKEN DB | | Hirano, Kazuma Browse this author | | Sato, Hirotaka Browse this author | | Ono, Kanta Browse this author | | Suzuki, Yuta Browse this author | | Ito, Daisuke Browse this author | | Saito, Yasushi Browse this author |
| Keywords: | neutron transmission spectroscopic imaging | | machine learning analysis | | solid-liquid phase fraction | | solidification process | | lead-bismuth eutectic alloy |
| Issue Date: | Jul-2021 |
| Publisher: | MDPI |
| Journal Title: | Applied sciences |
| Volume: | 11 |
| Issue: | 13 |
| Start Page: | 5988 |
| Publisher DOI: | 10.3390/app11135988 |
| Abstract: | In neutron transmission spectroscopic imaging, the transmission spectrum of each pixel on a two-dimensional detector is analyzed and the real-space distribution of microscopic information in an object is visualized with a wide field of view by mapping the obtained parameters. In the analysis of the transmission spectrum, since the spectrum can be classified with certain characteristics, it is possible for machine learning methods to be applied. In this study, we selected the subject of solid-liquid phase fraction imaging as the simplest application of the machine learning method. Firstly, liquid and solid transmission spectra have characteristic shapes, so spectrum classification according to their fraction can be carried out. Unsupervised and supervised machine learning analysis methods were tested and evaluated with simulated datasets of solid-liquid spectrum combinations. Then, the established methods were used to perform an analysis with actual measured spectrum datasets. As a result, the solid-liquid interface zone was specified from the solid-liquid phase fraction imaging using machine learning analysis. |
| Rights: | © 2021 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/). |
| Type: | article |
| URI: | http://hdl.handle.net/2115/82360 |
| Appears in Collections: | 工学院・工学研究院 (Graduate School of Engineering / Faculty of Engineering) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
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