{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00205140","sets":["6504:10247:10254"]},"path":["10254"],"owner":"6748","recid":"205140","title":["機械学習による浄水プロセスにおける凝集後濁度予測手法"],"pubdate":{"attribute_name":"公開日","attribute_value":"2020-02-20"},"_buckets":{"deposit":"904eaa64-c0bf-4e5c-bf23-3be9d08c04a0"},"_deposit":{"id":"205140","pid":{"type":"depid","value":"205140","revision_id":0},"owners":[6748],"status":"published","created_by":6748},"item_title":"機械学習による浄水プロセスにおける凝集後濁度予測手法","author_link":["508875","508876","508874","508872","508871","508873"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"機械学習による浄水プロセスにおける凝集後濁度予測手法"}]},"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":"2020-02-20","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":"北海道科学大"},{"subitem_text_value":"北海道科学大"},{"subitem_text_value":"中大"},{"subitem_text_value":"中大"},{"subitem_text_value":"前澤工業"},{"subitem_text_value":"北海道科学大"}]},"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/205140/files/IPSJ-Z82-7C-06.pdf","label":"IPSJ-Z82-7C-06.pdf"},"date":[{"dateType":"Available","dateValue":"2020-06-19"}],"format":"application/pdf","filename":"IPSJ-Z82-7C-06.pdf","filesize":[{"value":"659.0 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"c672a19a-dc90-4871-9448-a145287189cc","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2020 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"鈴木, 昭弘"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"川上, 敬"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"山村, 寛"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Eryanti, Utami Putri"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"根本, 雄一"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"大江, 亮介"}],"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":"本研究の目的は浄水場の凝集プロセスにおける凝集剤の注入量を最適化するために、凝集剤を注入した処理水の初期の画像から最終的な凝集後濁度を予測することである。凝集プロセスでは凝集剤を注入し攪拌することによって時間を追うごとにフロックと呼ばれる多量の集塊が形成される。このフロックの形成初期段階の処理水の画像から機械学習を用いて凝集後濁度を予測する。深層畳み込みネットワークを利用し、河川水を用いた実験を行った結果、(1)特徴が出やすいと思われる凝集の時間帯を明らかにし、(2)河川水の凝集中の画像からおよそ90%前後の精度で予測可能であることを明らかにした。","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"38","bibliographic_titles":[{"bibliographic_title":"第82回全国大会講演論文集"}],"bibliographicPageStart":"37","bibliographicIssueDates":{"bibliographicIssueDate":"2020-02-20","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2020"}]},"relation_version_is_last":true,"weko_creator_id":"6748"},"updated":"2025-01-19T19:50:13.473745+00:00","created":"2025-01-19T01:07:18.842488+00:00","links":{},"id":205140}