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Application of Machine Learning Methods to Neutron Transmission Spectroscopic Imaging for Solid-Liquid Phase Fraction Analysis

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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 (
Type: article
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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