{"created":"2025-01-19T01:15:36.565548+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00214813","sets":["6504:10735:10808"]},"path":["10808"],"owner":"44499","recid":"214813","title":["合金種を考慮した深層学習による金属材料の破面分類"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-03-04"},"_buckets":{"deposit":"c8246abe-f02e-4b3d-81dc-4548f48f94b8"},"_deposit":{"id":"214813","pid":{"type":"depid","value":"214813","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"合金種を考慮した深層学習による金属材料の破面分類","author_link":["552782","552783","552781","552785","552784"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"合金種を考慮した深層学習による金属材料の破面分類"}]},"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":"2021-03-04","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":"阪府大"},{"subitem_text_value":"阪府大"},{"subitem_text_value":"大阪産業技術研究所"},{"subitem_text_value":"大阪産業技術研究所"},{"subitem_text_value":"大阪産業技術研究所"}]},"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/214813/files/IPSJ-Z83-2C-03.pdf","label":"IPSJ-Z83-2C-03.pdf"},"date":[{"dateType":"Available","dateValue":"2021-12-28"}],"format":"application/pdf","filename":"IPSJ-Z83-2C-03.pdf","filesize":[{"value":"579.4 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"eba7f4a3-2370-4099-bf11-4a20d787c32e","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2021 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"上杉, 徳照"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"近藤, 充晟"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"濱田, 真行"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"喜多, 俊輔"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"平田, 智丈"}],"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":"破面解析（フラクトグラフィ）とは、破面に残された痕跡を調査することにより得られた情報を元に、破壊の機構や原因を解析する手法である。しかし、破面解析には多くの経験・知識が必要であり、リソース不足が叫ばれる昨今、破面解析専門の技術者を育成することは、多大な労力が必要である。そのため、人工知能システムにより破面を分類することができれば、破面解析の省力化とともに、技術者育成に役立つものと考えられる。本研究は畳み込みニューラルネットワークを用いて、SEM(走査型透過電子顕微鏡)観察によるミクロ破面の破面形態を自動分類するシステムの構築と、合金種別を考慮することによる分類精度向上を目的とする。","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"14","bibliographic_titles":[{"bibliographic_title":"第83回全国大会講演論文集"}],"bibliographicPageStart":"13","bibliographicIssueDates":{"bibliographicIssueDate":"2021-03-04","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2021"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":214813,"updated":"2025-01-19T16:27:23.841608+00:00","links":{}}