{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00213264","sets":["1164:2735:10526:10725"]},"path":["10725"],"owner":"44499","recid":"213264","title":["学習データが限定された環境下における汎用予測制御実現のためのアプローチ-汚泥乾燥機自動制御-"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-10-11"},"_buckets":{"deposit":"34597e4b-33ee-4417-a2fd-0f9f94188ef0"},"_deposit":{"id":"213264","pid":{"type":"depid","value":"213264","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"学習データが限定された環境下における汎用予測制御実現のためのアプローチ-汚泥乾燥機自動制御-","author_link":["545403","545402"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"学習データが限定された環境下における汎用予測制御実現のためのアプローチ-汚泥乾燥機自動制御-"},{"subitem_title":"Approach for realizing general-purpose control in an environment where learning data is limited -control of sludge dryer machine-","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2021-10-11","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"室蘭工業大学大学院"},{"subitem_text_value":"室蘭工業大学しくみ解明系領域"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"The Graduate School of Engineering, Muroran Institute of Technology","subitem_text_language":"en"},{"subitem_text_value":"Presently with College of Information and Systems, Muroran Institute of Technology","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/213264/files/IPSJ-MPS21135005.pdf","label":"IPSJ-MPS21135005.pdf"},"date":[{"dateType":"Available","dateValue":"2023-10-11"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-MPS21135005.pdf","filesize":[{"value":"1.2 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":"17"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"fa842a1d-aa26-4f97-a475-6809fcaecdc0","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2021 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"松山, 蓮"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"渡邉, 真也"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10505667","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":"2188-8833","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"一般的に機械学習を実現する上で高品質で豊富な学習データは必要不可欠なものである．しかし，実問題においてそのようなデータを準備することは，導入・運用コストの面から難しい場合が少なくない．本研究ではこの問題に対応するため，過去のデータから操作量を予測する LSTM と，異常検知手法として有名なオートエンコーダ，そして専門知識に基づいて操作量を決定するエキスパートシステムを組み合わせた汎用制御手法を提案する．提案手法の有効性を検証するため，汚泥乾燥機システムに提案手法を適用し，検証実験を行った．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"High-quality and rich learning data is indispensable for machine learning. However, it is very difficult to prepare such data in actual problems, due to the high installation and operational costs. Focusing on this problem, In this paper, we propose a general-purpose control method that combines an LSTM that predicts the operation from past data, an autoencoder that is famous as an anomaly detection method, and an expert system that determines the operation amount based on specialized knowledge. In this paper, we verify the effectiveness of this method by applying it to a sludge dryer system.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告数理モデル化と問題解決（MPS）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2021-10-11","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"5","bibliographicVolumeNumber":"2021-MPS-135"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":213264,"updated":"2025-01-19T17:12:55.338421+00:00","links":{},"created":"2025-01-19T01:14:09.002832+00:00"}