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  1. 原著論文

Extraction of Physical Parameters from X-ray Spectromicroscopy Data Using Machine Learning

https://repo.qst.go.jp/records/49424
https://repo.qst.go.jp/records/49424
d2744fd3-af08-418c-af66-3afb61c7f3a7
Item type 学術雑誌論文 / Journal Article(1)
公開日 2019-02-28
タイトル
タイトル Extraction of Physical Parameters from X-ray Spectromicroscopy Data Using Machine Learning
言語
言語 eng
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 journal 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 Suzuki, Yuta

× Suzuki, Yuta

WEKO 727268

Suzuki, Yuta

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Hino, Hideitsu

× Hino, Hideitsu

WEKO 727269

Hino, Hideitsu

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Ueno, Tetsuro

× Ueno, Tetsuro

WEKO 727270

Ueno, Tetsuro

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Takeichi, Yasuo

× Takeichi, Yasuo

WEKO 727271

Takeichi, Yasuo

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Kotsugi, Masato

× Kotsugi, Masato

WEKO 727272

Kotsugi, Masato

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Ono, Kanta

× Ono, Kanta

WEKO 727273

Ono, Kanta

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上野 哲朗

× 上野 哲朗

WEKO 727274

en 上野 哲朗

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抄録
内容記述タイプ Abstract
内容記述 Materials informatics has significantly accelerated the discovery and analysis of materials in the last decade. Spectroscopic data provide essential information about materials and hence are widely used for materials analysis. However, data analysis, that is, the extraction of physical parameters, of spectra is often conducted by manually comparing spectra and on-the-fly data analysis has not been realized yet. Considering that more than 100,000 X-ray absorption spectra (XAS) can be measured per day using the scanning transmission X-ray microscopy system at the Photon Factory, an automated analysis methodology is urgently required. If physical parameters are to be estimated from the spectra, the on-the-fly analysis can be realized by space mapping of these parameters using a high-throughput spectromicroscopy experiment capable of adaptive measurements. XAS often shows complex spectral features with a few hundred or more “high-dimensional” data points, and the physical parameters, such as element, charge, and symmetry can be extracted from the XAS. Feature extraction, a popular machine learning approach to treat high-dimensional datasets, involves the projection of data onto a few features (parameters), thus retaining the relevant information. In this study, we propose a methodology to estimate the physical parameters from XAS using feature extraction with dimensionality reduction.
書誌情報 Microscopy and Microanalysis

巻 24, 号 S2, p. 478-479, 発行日 2018-08
出版者
出版者 Cambridge University Press
ISSN
収録物識別子タイプ ISSN
収録物識別子 1431-9276
DOI
識別子タイプ DOI
関連識別子 10.1017/S1431927618014629
関連サイト
識別子タイプ DOI
関連識別子 https://doi.org/10.1017/S1431927618014629
関連名称 https://doi.org/10.1017/S1431927618014629
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