{"created":"2023-05-15T14:55:00.791101+00:00","id":74767,"links":{},"metadata":{"_buckets":{"deposit":"76b58384-76d8-4a50-aaff-313dffbc3c68"},"_deposit":{"created_by":1,"id":"74767","owners":[1],"pid":{"revision_id":0,"type":"depid","value":"74767"},"status":"published"},"_oai":{"id":"oai:repo.qst.go.jp:00074767","sets":["11"]},"author_link":["737681","737680","737679","737678"],"item_10004_biblio_info_7":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2019-03","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"3","bibliographicPageEnd":"152","bibliographicPageStart":"147","bibliographicVolumeNumber":"62","bibliographic_titles":[{"bibliographic_title":"表面と真空"}]}]},"item_10004_description_5":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"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.","subitem_description_type":"Abstract"}]},"item_10004_publisher_8":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"公益社団法人日本表面真空学会"}]},"item_10004_relation_17":{"attribute_name":"関連サイト","attribute_value_mlt":[{"subitem_relation_type_id":{"subitem_relation_type_id_text":"https://doi.org/10.1380/vss.62.147","subitem_relation_type_select":"DOI"}}]},"item_10004_source_id_9":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2433-5835","subitem_source_identifier_type":"ISSN"}]},"item_access_right":{"attribute_name":"アクセス権","attribute_value_mlt":[{"subitem_access_right":"metadata only access","subitem_access_right_uri":"http://purl.org/coar/access_right/c_14cb"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"上野, 哲朗"}],"nameIdentifiers":[{"nameIdentifier":"737678","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"日野, 英逸"}],"nameIdentifiers":[{"nameIdentifier":"737679","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"小野, 寛太"}],"nameIdentifiers":[{"nameIdentifier":"737680","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Ueno, Tetsuro","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"737681","nameIdentifierScheme":"WEKO"}]}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"article","resourceuri":"http://purl.org/coar/resource_type/c_6501"}]},"item_title":"機械学習によるX線スペクトル計測の効率化","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"機械学習によるX線スペクトル計測の効率化"}]},"item_type_id":"10004","owner":"1","path":["11"],"pubdate":{"attribute_name":"公開日","attribute_value":"2019-03-15"},"publish_date":"2019-03-15","publish_status":"0","recid":"74767","relation_version_is_last":true,"title":["機械学習によるX線スペクトル計測の効率化"],"weko_creator_id":"1","weko_shared_id":-1},"updated":"2023-05-16T07:44:43.503914+00:00"}