{"created":"2025-01-19T01:19:14.020084+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00218889","sets":["1:10446:10687"]},"path":["10687"],"owner":"44499","recid":"218889","title":["AI判断の根拠を説明するXAIを使いこなす：4．Shapelets学習によるインフラ・製造分野向け時系列波形の異常診断技術 -異常の検知や診断に有効な波形パターンを発見するAI-"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-07-15"},"_buckets":{"deposit":"a1196939-0cb6-4b63-b315-4dfb4b0a215c"},"_deposit":{"id":"218889","pid":{"type":"depid","value":"218889","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"AI判断の根拠を説明するXAIを使いこなす：4．Shapelets学習によるインフラ・製造分野向け時系列波形の異常診断技術 -異常の検知や診断に有効な波形パターンを発見するAI-","author_link":["570259","570258"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"AI判断の根拠を説明するXAIを使いこなす：4．Shapelets学習によるインフラ・製造分野向け時系列波形の異常診断技術 -異常の検知や診断に有効な波形パターンを発見するAI-"},{"subitem_title":"Mastering XAI Which Explains the Evidence of AI Judgment：Time-Series Waveform Anomaly Diagnostic Methods Utilizing Learning Shapelets for Infrastructure and Manufacturing Fields","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"特集","subitem_subject_scheme":"Other"}]},"item_type_id":"30","publish_date":"2022-07-15","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_30_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"（株）東芝 研究開発センター"}]},"item_30_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Toshiba Corp.","subitem_text_language":"en"}]},"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/218889/files/IPSJ-O-MGN630804.pdf","label":"IPSJ-O-MGN630804.pdf"},"date":[{"dateType":"Available","dateValue":"2024-07-15"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-O-MGN630804.pdf","filesize":[{"value":"2.1 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"0","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"1579e750-fb3c-4914-aca4-2c7e3f85e753","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2022 by the Information Processing Society of Japan"}]},"item_30_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"山口, 晃広"}],"nameIdentifiers":[{}]}]},"item_30_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"YAMAGUCHI, Akihiro","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_6501","resourcetype":"article"}]},"item_30_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00116625","subitem_source_identifier_type":"NCID"}]},"item_30_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"インフラ設備や製造装置のセンサから時系列波形データを収集しAIにより異常や正常などの状態を自動判別する時系列波形異常診断技術に注目が集まっている．本稿では，インフラ・製造分野における技術課題として，AIの説明性，異常データの収集，誤判断のリスク管理，データの信頼性を述べる．次に，説明性のある波形診断技術としてshapelets学習法を説明し，残りの課題も解決するよう拡張した技術を産業分野への適用事例とともに紹介する．","subitem_description_type":"Other"}]},"item_30_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"e24","bibliographic_titles":[{"bibliographic_title":"情報処理"}],"bibliographicPageStart":"e21","bibliographicIssueDates":{"bibliographicIssueDate":"2022-07-15","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"8","bibliographicVolumeNumber":"63"}]},"relation_version_is_last":true,"item_30_identifier_registration":{"attribute_name":"ID登録","attribute_value_mlt":[{"subitem_identifier_reg_text":"10.20729/00218781","subitem_identifier_reg_type":"JaLC"}]},"weko_creator_id":"44499"},"id":218889,"updated":"2025-01-19T14:59:21.786225+00:00","links":{}}