Spectral analysis method trial by machine learning

Oba, Masaki ; Miyabe, Masabumi ; Akaoka, Katsuaki ; Wakaida, Ikuo

Emission spectrum analysis was attempted by neural network machine learning. As a result, it was shown that the composition ratio can be analyzed with an error of several percent.

Language	:	Japanese
Journal	:
Volume	:
Number	:
Pages	:
Publication Year/Month	:
Meeting title	:	2020年第67回応用物理学会春季学術講演会
Held date	:	2020/03
Location (city)	:	東京
Location (country)	:	日本
Patent information	:
PDF	:
Paper URL	:
DOI for research data	:
Keywords	:	neural network; machine learning; Spectra; Elemental Analysis; Fukushima Daiichi Nuclear Power Station Accident; 福島第一原子力発電所事故
Research Facility	:	ウラン濃縮研究棟
Press Release	:
Article of JAEA R&D Review	:
Cooperating Institute	:

Accesses	:	- Accesses
Web of Science® Times Cited Count	:	Times Cited Count： If you would like to get the latest times cited, please access the Web of Science®. http://www.webofknowledge.com/wos
InCites™	:
Altmetrics	:

Registration No. : BB20200017
JAEA Abstracts No. :
Paper Submission No. :

[CLARIVATE ANALYTICS], [WEB OF SCIENCE], [HIGHLY CITED PAPER & CUP LOGO] and [HOT PAPER & FIRE LOGO] are trademarks of Clarivate Analytics, and/or its affiliated company or companies, and used herein by permission and/or license.