{"id":218929,"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00218929","sets":["1164:2735:10865:10970"]},"path":["10970"],"owner":"44499","recid":"218929","title":["GP-HSMMの尤度計算並列化による身体動作分節の高速化"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-07-19"},"_buckets":{"deposit":"ccd789b3-a8a9-4adb-aedb-6d3ffbaee5d9"},"_deposit":{"id":"218929","pid":{"type":"depid","value":"218929","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"GP-HSMMの尤度計算並列化による身体動作分節の高速化","author_link":["570352","570353","570349","570354","570350","570351"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"GP-HSMMの尤度計算並列化による身体動作分節の高速化"},{"subitem_title":"Method of High-Speed Segmentation for Bodily Motion According to Likelihood Calculation Parallelizing Based on GP-HSMM","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2022-07-19","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"三菱電機株式会社"},{"subitem_text_value":"三菱電機株式会社"},{"subitem_text_value":"京都大学"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Mitsubishi Electric Co.","subitem_text_language":"en"},{"subitem_text_value":"Mitsubishi Electric Co.","subitem_text_language":"en"},{"subitem_text_value":"Kyoto University","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/218929/files/IPSJ-MPS22139002.pdf","label":"IPSJ-MPS22139002.pdf"},"date":[{"dateType":"Available","dateValue":"2024-07-19"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-MPS22139002.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":"8653b142-6b0c-4f31-9b34-3a73287b7c24","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2022 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"川村, 美帆"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"佐々木, 雄一"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"中村, 裕一"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Miho, Kawamura","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yuichi, Sasaki","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yuichi, Nakamura","creatorNameLang":"en"}],"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":"本論文では，ガウス過程の隠れマルコフモデル（GP-HSMM）を用いて，尤度計算を並列化し，身体動作に対してより高速に分節化する方法を提案する．従来手法では，ガウス過程で学習した信号パターンと観測データとの全ての組み合わせについて尤度行列を計算する必要があり，学習と推論に時間がかかるという問題があった．このため，マルチコア CPU による並列計算で尤度行列の計算を高速化した．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"4","bibliographic_titles":[{"bibliographic_title":"研究報告数理モデル化と問題解決（MPS）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2022-07-19","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"2","bibliographicVolumeNumber":"2022-MPS-139"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"updated":"2025-01-19T14:59:11.036718+00:00","created":"2025-01-19T01:19:16.332153+00:00","links":{}}