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機械学習によるX線スペクトル計測の効率化
https://repo.qst.go.jp/records/74767
https://repo.qst.go.jp/records/74767a06e25da-1b65-4033-ab72-c48176988633
Item type | 一般雑誌記事 / Article(1) | |||||
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公開日 | 2019-03-15 | |||||
タイトル | ||||||
タイトル | 機械学習によるX線スペクトル計測の効率化 | |||||
言語 | ||||||
言語 | jpn | |||||
資源タイプ | ||||||
資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||
資源タイプ | article | |||||
アクセス権 | ||||||
アクセス権 | metadata only access | |||||
アクセス権URI | http://purl.org/coar/access_right/c_14cb | |||||
著者 |
上野, 哲朗
× 上野, 哲朗× 日野, 英逸× 小野, 寛太× Ueno, Tetsuro |
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抄録 | ||||||
内容記述タイプ | Abstract | |||||
内容記述 | We present an adaptive design of experiment (DoE) by machine learning for X-ray spectroscopy to improve its efficiency. One of the machine learning techniques, Gaussian process regression predicts a spectrum from the experimental data and determines the optimal energy points to measure. Adaptive DoE successfully reduces total energy points to measure as compared to an X-ray magnetic circular dichroism spectroscopy experiment by a conventional DoE. This method has potential applicability to various measurements and reduces the time and cost of experiments. | |||||
書誌情報 |
表面と真空 巻 62, 号 3, p. 147-152, 発行日 2019-03 |
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出版者 | ||||||
出版者 | 公益社団法人日本表面真空学会 | |||||
ISSN | ||||||
収録物識別子タイプ | ISSN | |||||
収録物識別子 | 2433-5835 | |||||
関連サイト | ||||||
識別子タイプ | DOI | |||||
関連識別子 | https://doi.org/10.1380/vss.62.147 |