{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00234834","sets":["1164:5352:11553:11625"]},"path":["11625"],"owner":"44499","recid":"234834","title":["時系列信号の周波数異常検知に対する統計的信頼性保証"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-06-13"},"_buckets":{"deposit":"0f3cdb2c-3010-4f92-892b-72b9839f5ada"},"_deposit":{"id":"234834","pid":{"type":"depid","value":"234834","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"時系列信号の周波数異常検知に対する統計的信頼性保証","author_link":["640600","640604","640599","640601","640603","640602"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"時系列信号の周波数異常検知に対する統計的信頼性保証"},{"subitem_title":"Anomaly Detection in the Frequency Domain with Statistical Reliability","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"情報論的学習理論と機械学習2","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2024-06-13","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"名古屋大学"},{"subitem_text_value":"名古屋大学"},{"subitem_text_value":"名古屋大学／理化学研究所"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Nagoya University","subitem_text_language":"en"},{"subitem_text_value":"Nagoya University","subitem_text_language":"en"},{"subitem_text_value":"Nagoya University / RIKEN","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/234834/files/IPSJ-BIO24078007.pdf","label":"IPSJ-BIO24078007.pdf"},"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-BIO24078007.pdf","filesize":[{"value":"1.6 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"41"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_login","version_id":"fd617da3-18f0-425e-b88b-2fc7f15a614b","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 by the Institute of Electronics, Information and Communication Engineers This SIG report is only available to those in membership of the SIG."}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"山田, 彬文"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"田地, 宏一"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"竹内, 一郎"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Akifumi, Yamada","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kouichi, Taji","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Ichiro, Takeuchi","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA12055912","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":"2188-8590","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"現在，機械装置から得られる時系列信号 (センサデータ) を対象とした，周波数領域における異常検知の分野において，AI を活用する研究が盛んに行われている．しかし，AI による異常の提示 (データ駆動の仮説生成) とその検証に同じデータを用いる場合，検証時の偽陽性率 (False Positive Rate, FPR) が高くなるという問題が生じる．そこで本研究では，周波数異常検知の結果を定量的に評価するために，選択的推論 (Selective Inference, SI) の枠組みを用いる．これにより，偽陽性率制御の観点から，異常検知結果に対する統計的信頼性を保証する．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"There are many applications of artificial intelligence (AI) in the field of anomaly detection in the frequency domain for time series sensor data from machinery and equipment. However, False Positive Rate (FPR) cannot be controlled when the same data is used for both anomaly detection (data-driven hypothesis generation) and its testing. Therefore, we use Selective Inference (SI) framework to quantitatively evaluate the results of frequency anomaly detection and show that our proposed method guarantees the statistical reliability for the anomaly detection in terms of precisely controlling the False Positive Rate. ","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"8","bibliographic_titles":[{"bibliographic_title":"研究報告バイオ情報学（BIO）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2024-06-13","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"7","bibliographicVolumeNumber":"2024-BIO-78"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":234834,"updated":"2025-01-19T09:41:51.572750+00:00","links":{},"created":"2025-01-19T01:36:41.064609+00:00"}