{"id":235992,"updated":"2025-01-19T09:26:15.783733+00:00","links":{},"created":"2025-01-19T01:37:48.175229+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00235992","sets":["6504:11678:11689"]},"path":["11689"],"owner":"44499","recid":"235992","title":["超音波非破壊検査における拡散モデルを用いた欠陥位置推定"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-03-01"},"_buckets":{"deposit":"eababaa8-6d0f-4829-b129-30fe3255efa8"},"_deposit":{"id":"235992","pid":{"type":"depid","value":"235992","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"超音波非破壊検査における拡散モデルを用いた欠陥位置推定","author_link":["644934","644937","644936","644935"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"超音波非破壊検査における拡散モデルを用いた欠陥位置推定"}]},"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":"2024-03-01","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":"群馬大"},{"subitem_text_value":"群馬大"},{"subitem_text_value":"群馬大"},{"subitem_text_value":"群馬大"}]},"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/235992/files/IPSJ-Z86-4Q-06.pdf","label":"IPSJ-Z86-4Q-06.pdf"},"date":[{"dateType":"Available","dateValue":"2024-07-03"}],"format":"application/pdf","filename":"IPSJ-Z86-4Q-06.pdf","filesize":[{"value":"1.2 MB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"7a7e1c66-b489-4ba7-b701-689366bb5382","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"安藤, 佑咲"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"中島, 未椰"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"斎藤, 隆泰"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"加藤, 毅"}],"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":"構造物を検査する方法としてレーザー超音波可視化試験(LUVT)が注目されている．LUVT では構造物表面をレーザーで走査し，超音波の伝搬を可視化することで，構造物の欠陥有無を検査できる．検査員不足等の問題からLUVTで得られた画像を深層学習を用いて検査を自動化する試みが検討されている．しかし，深層学習による非破壊検査の多くは教師あり学習を採用しており，教師あり学習に必要な正例画像が入手困難であることが課題となっている．そこで，本研究では欠陥のない画像のみを用いた教師なし学習による欠陥位置推定法を提案する．数値実験により，提案法が欠陥分類性能だけでなく，位置推定性能も向上させたことを確認した．","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"298","bibliographic_titles":[{"bibliographic_title":"第86回全国大会講演論文集"}],"bibliographicPageStart":"297","bibliographicIssueDates":{"bibliographicIssueDate":"2024-03-01","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2024"}]},"relation_version_is_last":true,"weko_creator_id":"44499"}}