{"updated":"2025-01-19T22:04:42.148144+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00198189","sets":["934:989:9698:9823"]},"path":["9823"],"owner":"44499","recid":"198189","title":["ベイズ的変数選択に基づく分光スペクトル分解"],"pubdate":{"attribute_name":"公開日","attribute_value":"2019-07-17"},"_buckets":{"deposit":"28959edc-3875-427d-87dd-be0dcc301924"},"_deposit":{"id":"198189","pid":{"type":"depid","value":"198189","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"ベイズ的変数選択に基づく分光スペクトル分解","author_link":["476861","476860","476863","476862"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"ベイズ的変数選択に基づく分光スペクトル分解"},{"subitem_title":"Spectral Deconvolution Based on Bayesian Variable Selection","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"[オリジナル論文] スペクトル分析，ベイズ的スペクトル分解，ベイズ的変数選択","subitem_subject_scheme":"Other"}]},"item_type_id":"3","publish_date":"2019-07-17","item_3_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"電気通信大学大学院情報理工学研究科"},{"subitem_text_value":"電気通信大学大学院情報理工学研究科"}]},"item_3_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Informatics and Engineering, University of Electro Communications","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Informatics and Engineering, University of Electro Communications","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/198189/files/IPSJ-TOM1202005.pdf","label":"IPSJ-TOM1202005.pdf"},"date":[{"dateType":"Available","dateValue":"2021-07-17"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-TOM1202005.pdf","filesize":[{"value":"2.8 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"17"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"6a47718c-901d-4702-a659-9a6ed6387120","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2019 by the Information Processing Society of Japan"}]},"item_3_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"川島, 貴大"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"庄野, 逸"}],"nameIdentifiers":[{}]}]},"item_3_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Takahiro, Kawashima","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Hayaru, Shouno","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_3_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11464803","subitem_source_identifier_type":"NCID"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_6501","resourcetype":"journal article"}]},"item_3_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1882-7780","subitem_source_identifier_type":"ISSN"}]},"item_3_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"分光学において，測定したスペクトルデータからピーク数・ピーク形状・各ピークのパラメータを推定することは，試料の物性を知るために重要なタスクである．この分光スペクトルのピーク分離に関してレプリカ交換モンテカルロ法によるベイズ的スペクトル分解が提案されており，人工および実データに対して適切にピークを分離できることが示されている．しかしこの手法ではモデル選択の方法に基づいてピーク数を推定するため，複数のモデルを事前に用意する必要があり，さらに計算時間も大きくなる問題がある．そこで本研究では先行研究のモデルを拡張し，ベイズ的変数選択法の枠組みで階層モデリングを適用することで，人工データに対してより高速なピーク数とピーク形状およびパラメータの同時推定が可能であることを示した．さらに提案手法による実データへの応用としてコランダムのラマンスペクトルの分析を行い，有効なピークを抽出できることを確認した．","subitem_description_type":"Other"}]},"item_3_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"In the field of spectroscopy, estimating the number of peaks and the shape and parameters of each peak from spectral data is a significant task. With respect to this task, Bayesian spectral deconvolution with replica exchange MCMC has been proposed and the effectiveness was shown. However, in Bayesian spectral deconvolution, we have to prepare multiple models and the computational time becomes large. Thus, in this study, we extended the previous model based on Bayesian variable selection, and we found that more efficient spectral deconvolution is possible for synthetic spectral data. In addition, we tried analyzing Raman spectrum of corundum and were able to extract valid peaks.","subitem_description_type":"Other"}]},"item_3_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"43","bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌数理モデル化と応用（TOM）"}],"bibliographicPageStart":"34","bibliographicIssueDates":{"bibliographicIssueDate":"2019-07-17","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"2","bibliographicVolumeNumber":"12"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"created":"2025-01-19T01:02:31.072939+00:00","id":198189,"links":{}}