{"links":{},"id":232932,"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00232932","sets":["1164:1384:11463:11515"]},"path":["11515"],"owner":"44499","recid":"232932","title":["機械学習システムのための不確実性を考慮した安全性解析およびリスク定量化手法"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-02-26"},"_buckets":{"deposit":"5af5d928-6bed-44f7-b52d-b997b03276ba"},"_deposit":{"id":"232932","pid":{"type":"depid","value":"232932","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"機械学習システムのための不確実性を考慮した安全性解析およびリスク定量化手法","author_link":["631597","631596","631593","631595","631594"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"機械学習システムのための不確実性を考慮した安全性解析およびリスク定量化手法"}]},"item_type_id":"4","publish_date":"2024-02-26","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"早稲田大学"},{"subitem_text_value":"早稲田大学"},{"subitem_text_value":"早稲田大学"},{"subitem_text_value":"早稲田大学"},{"subitem_text_value":"早稲田大学"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Waseda University","subitem_text_language":"en"},{"subitem_text_value":"Waseda University","subitem_text_language":"en"},{"subitem_text_value":"Waseda University","subitem_text_language":"en"},{"subitem_text_value":"Waseda University","subitem_text_language":"en"},{"subitem_text_value":"Waseda University","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/232932/files/IPSJ-SE24216010.pdf","label":"IPSJ-SE24216010.pdf"},"date":[{"dateType":"Available","dateValue":"2026-02-26"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-SE24216010.pdf","filesize":[{"value":"1.8 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":"12"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"4ec6ab0e-5535-455a-835b-d8bd79be9811","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"大久保, 利哉"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Jati, Hiliamsyah Husen"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"鷲崎, 弘宜"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"吉岡, 信和"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"深澤, 良彰"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10112981","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8825","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"近年，自動運転システムなどの，機械学習を用いたセーフティクリティカルなシステムが増加している．また，現代の複雑化したシステムの安全性を解析するための手法として，STAMP (Systems-Theoretic Accident Model and Processes：システム理論に基づくアクシデントモデル）に基づいた安全性解析手法である STPA（STAMP-based Process Analysis）が広く用いられている．しかし，STPA は，機械学習に固有な特性が生む問題を詳細に解析するための機能を有さない．そこで本研究では，STPA と，自動運転システムにおける予測の不確実性に起因するリスクを分析する手法であるCFMEA（Classification Failure Mode and Effect Analysis）を参考に，機械学習システムの安全性を解析しリスクを定量評価するための新しい手法 Classification Uncertainty Risk Analysis（CURA）を提案する．そして，自動運転システムを題材とした，交通標識の分類とシステムの振る舞いについてのケーススタディを実践し，手法の有効性を検証した．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"8","bibliographic_titles":[{"bibliographic_title":"研究報告ソフトウェア工学（SE）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2024-02-26","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"10","bibliographicVolumeNumber":"2024-SE-216"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"created":"2025-01-19T01:34:05.129310+00:00","updated":"2025-01-19T10:16:23.339883+00:00"}