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Application of Machine Learning Methods to Neutron Transmission Spectroscopic Imaging for Solid–Liquid Phase Fraction Analysis
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 ofdoi:10.3390/app11135988 fatcat:byy54abd2bgc7dkfrj2j3nsfje