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  1. 原著論文

Machine-Learning Optimization of Multiple Measurement Parameters Nonlinearly Affecting the Signal Quality

https://repo.qst.go.jp/records/82945
https://repo.qst.go.jp/records/82945
e36b63f6-d4e0-4d5c-96bd-64d7628aa99e
Item type 学術雑誌論文 / Journal Article(1)
公開日 2021-06-08
タイトル
タイトル Machine-Learning Optimization of Multiple Measurement Parameters Nonlinearly Affecting the Signal Quality
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 Takahiro, Fujisaku

× Takahiro, Fujisaku

WEKO 1014917

Takahiro, Fujisaku

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So, Frederick

× So, Frederick

WEKO 1014918

So, Frederick

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Ryuji, Igarashi

× Ryuji, Igarashi

WEKO 1014919

Ryuji, Igarashi

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Masahiro, Shirakawa

× Masahiro, Shirakawa

WEKO 1014920

Masahiro, Shirakawa

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Takahiro, Fujisaku

× Takahiro, Fujisaku

WEKO 1014921

en Takahiro, Fujisaku

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So, Frederick

× So, Frederick

WEKO 1014922

en So, Frederick

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Ryuji, Igarashi

× Ryuji, Igarashi

WEKO 1014923

en Ryuji, Igarashi

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Masahiro, Shirakawa

× Masahiro, Shirakawa

WEKO 1014924

en Masahiro, Shirakawa

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抄録
内容記述タイプ Abstract
内容記述 Determination of optimal measurement parameters is essential for measurement experiments. They can be manually optimized if the linear correlation between them and the corresponding signal quality is known or easily determinable. However, in practice, this correlation is often nonlinear and not known apriori; hence, complicated trial and error procedures are employed for finding optimal parameters while avoiding local optima. In this work, we propose a novel approach based on machine learning for optimizing multiple measurement parameters, which nonlinearly influence the signal quality. Optically detected magnetic resonance measurements of nitrogen-vacancy centers in fluorescent nanodiamonds were used as a proof-of-concept system. We constructed a suitable dataset of optically detected magnetic resonance spectra for predicting the optimal laser and microwave powers that deliver the highest contrast and signal-tonoise ratio values by means of linear regression, neural networks, and random forests. The model developed by the considered neural network turned out to have a significantly higher coefficient of determination than the other methods. The proposed method thus provided a novel approach for the rapid setting of measurement parameters that influence the signal quality in a nonlinear way, opening a gate for fields like nuclear magnetic resonance, electron paramagnetic resonance, and fluorescence microscopy to benefit from it
書誌情報 ACS Measurement Science Au

巻 1, p. 20-26, 発行日 2021-07
出版者
出版者 ACS
ISSN
収録物識別子タイプ ISSN
収録物識別子 2694-250X
DOI
識別子タイプ DOI
関連識別子 10.1021/acsmeasuresciau.1c00009
関連サイト
識別子タイプ URI
関連識別子 https://pubs.acs.org/doi/pdf/10.1021/acsmeasuresciau.1c00009
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