{"id":180800,"updated":"2025-01-20T04:38:19.519436+00:00","links":{},"created":"2025-01-19T00:48:36.744706+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00180800","sets":["6504:9168:9182"]},"path":["9182"],"owner":"6748","recid":"180800","title":["スパース・モデリングを応用したマハラノビス・タグチ法による異常検知"],"pubdate":{"attribute_name":"公開日","attribute_value":"2017-03-16"},"_buckets":{"deposit":"cb145086-51d0-4900-8585-91446d628c08"},"_deposit":{"id":"180800","pid":{"type":"depid","value":"180800","revision_id":0},"owners":[6748],"status":"published","created_by":6748},"item_title":"スパース・モデリングを応用したマハラノビス・タグチ法による異常検知","author_link":["390917","390918"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"スパース・モデリングを応用したマハラノビス・タグチ法による異常検知"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"人工知能と認知科学","subitem_subject_scheme":"Other"}]},"item_type_id":"22","publish_date":"2017-03-16","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_22_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"早大"},{"subitem_text_value":"早大"}]},"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/180800/files/IPSJ-Z79-4C-02.pdf","label":"IPSJ-Z79-4C-02.pdf"},"date":[{"dateType":"Available","dateValue":"2017-05-22"}],"format":"application/pdf","filename":"IPSJ-Z79-4C-02.pdf","filesize":[{"value":"347.2 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"ab539796-24a9-4c29-ae47-260c6ab01f3a","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2017 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"大久保, 豪人"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"永田, 靖"}],"nameIdentifiers":[{}]}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_5794","resourcetype":"conference paper"}]},"item_22_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00349328","subitem_source_identifier_type":"NCID"}]},"item_22_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"マハラノビス・タグチ（MT）法は品質工学（タグチメソッド）における代表的な手法であり，実用的な統計的異常検知手法として，我が国の製造業を中心に広く普及している．特に近年では，センサー等から取得されたデータをもとに設備機器の運転状況監視へ適用された事例も報告されている．本報告では，このMT法にスパース・モデリングに基づく精度行列の学習プロセスを導入した新たな異常検知手法を提案する．そして，提案手法は従来手法に比べ，手法の解釈容易性が高いだけでなく，異常原因の特定や学習データの過剰適合対策としても有用であることを示す．","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"52","bibliographic_titles":[{"bibliographic_title":"第79回全国大会講演論文集"}],"bibliographicPageStart":"51","bibliographicIssueDates":{"bibliographicIssueDate":"2017-03-16","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2017"}]},"relation_version_is_last":true,"weko_creator_id":"6748"}}