{"id":215257,"links":{},"created":"2025-01-19T01:16:01.857623+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00215257","sets":["6504:10735:10807"]},"path":["10807"],"owner":"44499","recid":"215257","title":["Generative Adversarial Networkを用いた行動認識における欠損センサデータ補間"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-03-04"},"_buckets":{"deposit":"4d2de202-15e0-4442-9f3a-0035abf3363b"},"_deposit":{"id":"215257","pid":{"type":"depid","value":"215257","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"Generative Adversarial Networkを用いた行動認識における欠損センサデータ補間","author_link":["554155","554156","554157"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Generative Adversarial Networkを用いた行動認識における欠損センサデータ補間"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"ネットワーク","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":"豊橋技科大"},{"subitem_text_value":"豊橋技科大"},{"subitem_text_value":"豊橋技科大"}]},"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/215257/files/IPSJ-Z83-2W-01.pdf","label":"IPSJ-Z83-2W-01.pdf"},"date":[{"dateType":"Available","dateValue":"2021-12-29"}],"format":"application/pdf","filename":"IPSJ-Z83-2W-01.pdf","filesize":[{"value":"852.0 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"b04848fe-2127-4aca-b905-1f4cec51d43f","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":"石田, 義人"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"多田, 剛史"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"大村, 廉"}],"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":"近年，ウェアラブルセンサを用いた人の行動認識を行う機会が多くなった．行動認識では一般的に事前に学習済みの認識器を用いて認識を行うが，通信の切断やセンサの故障などにより認識器に入力されるデータが事前学習時と異なる場合，認識器が正常に動作しない事態が発生してしまう．このような問題に対する既存研究として，ARARアルゴリズムを用いた欠損箇所の補完を行った研究がある．しかし，この手法では補間に直前までのデータを使用しているため，長期的なデータの欠損に対応することが困難である．そこで本研究ではGenerative  Adversarial Networks （GAN）を用いてデータ補間を行う手法を提案する．提案手法では長期的なデータ欠損にも対応可能であることが期待できる．","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"320","bibliographic_titles":[{"bibliographic_title":"第83回全国大会講演論文集"}],"bibliographicPageStart":"319","bibliographicIssueDates":{"bibliographicIssueDate":"2021-03-04","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2021"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"updated":"2025-01-19T16:14:47.874368+00:00"}