{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00215013","sets":["6504:10735:10808"]},"path":["10808"],"owner":"44499","recid":"215013","title":["弱教師あり学習による人工衛星テレメトリデータの状態監視手法"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-03-04"},"_buckets":{"deposit":"317b0c79-0d50-4362-8e5e-53766f20e54c"},"_deposit":{"id":"215013","pid":{"type":"depid","value":"215013","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"弱教師あり学習による人工衛星テレメトリデータの状態監視手法","author_link":["553378","553377","553376","553379"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"弱教師あり学習による人工衛星テレメトリデータの状態監視手法"}]},"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":"東大"},{"subitem_text_value":"東大"},{"subitem_text_value":"東大"},{"subitem_text_value":"東大"}]},"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/215013/files/IPSJ-Z83-7Q-02.pdf","label":"IPSJ-Z83-7Q-02.pdf"},"date":[{"dateType":"Available","dateValue":"2021-12-28"}],"format":"application/pdf","filename":"IPSJ-Z83-7Q-02.pdf","filesize":[{"value":"954.5 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"9705609a-1e33-4fbe-8739-da2f877245bc","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":"二木, 浩司"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Chun, Fui Liew"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"矢入, 健久"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"福島, 裕介"}],"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":"人工衛星テレメトリデータは極めて情報量が多く，運用者が衛星の状態を監視するためには人工知能による支援が有用である。このためのデータ駆動型の機械学習アルゴリズムの研究が進められているが，純粋な教師なし学習による状態監視法は解釈性が低いことがある。ゆえに，専門家の知見による修正を加えながら学習を進めるという弱教師あり学習の考え方が重要となる。本報告では，実際のテレメトリを弱教師あり学習の代表的手法である制約付きk-means法によって解析した結果を示す。純粋なk-means法に比べて可読性が高まっていることを確認し，このクラスタリングをマルコフモデル化することにより，人工衛星の状態異常を検知することができることを示した。","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"428","bibliographic_titles":[{"bibliographic_title":"第83回全国大会講演論文集"}],"bibliographicPageStart":"427","bibliographicIssueDates":{"bibliographicIssueDate":"2021-03-04","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2021"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":215013,"updated":"2025-01-19T16:21:41.523739+00:00","links":{},"created":"2025-01-19T01:15:47.993098+00:00"}