{"created":"2025-01-19T01:43:07.041105+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00239458","sets":["1164:6390:11456:11717"]},"path":["11717"],"owner":"44499","recid":"239458","title":["Preliminary Investigation of Packing Process Recognition Using Cross Knowledge Distillation"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-09-19"},"_buckets":{"deposit":"6f1e04c3-bd1e-47aa-929b-38bfabc33e32"},"_deposit":{"id":"239458","pid":{"type":"depid","value":"239458","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"Preliminary Investigation of Packing Process Recognition Using Cross Knowledge Distillation","author_link":["656406","656409","656411","656408","656407","656410"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Preliminary Investigation of Packing Process Recognition Using Cross Knowledge Distillation"},{"subitem_title":"Preliminary Investigation of Packing Process Recognition Using Cross Knowledge Distillation","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"機械学習・知識蒸留手法","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2024-09-19","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Information Science and Technology, Osaka University"},{"subitem_text_value":"Graduate School of Information Science and Technology, Osaka University"},{"subitem_text_value":"Graduate School of Information Science and Technology, Osaka University"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Information Science and Technology, Osaka University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Information Science and Technology, Osaka University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Information Science and Technology, Osaka University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"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/239458/files/IPSJ-CDS24041003.pdf","label":"IPSJ-CDS24041003.pdf"},"date":[{"dateType":"Available","dateValue":"2026-09-19"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-CDS24041003.pdf","filesize":[{"value":"1.7 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":"47"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"8f9a797c-b49a-4c1e-8877-0a2b4b734087","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Hongyin, Qiao"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Hamada, Rizk"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Takuya, Maekawa"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Hongyin, Qiao","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Hamada, Rizk","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Takuya, Maekawa","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA12628327","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":"2188-8604","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"Human activity recognition (HAR) often utilizes inertial measurement unit (IMU) data due to its effectiveness in various environments. However, IMU data lacks spatial position information and environmental context, limiting its robustness against changes in motion patterns, such as speed variations. Skeleton data, while providing detailed spatial information and better capturing human motion dynamics, can lead to serious occlusion problems in industrial environments with complex surroundings. To address these challenges, we propose a novel cross-knowledge distillation method, a machine-learning technique that transfers knowledge from a larger teacher model to a smaller student model. This approach enhances a student model based on IMU data through cross-modal knowledge distillation from a skeleton-based model. By leveraging the skeleton-based model to extract insights on capturing spatial and temporal information on the body in time-series data and superior prediction capabilities, the student model is significantly improved in its ability to capture intricate and rapidly evolving motion patterns. This method enables the student model to assimilate salient features from the teacher model, thereby enhancing overall performance in HAR tasks.","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Human activity recognition (HAR) often utilizes inertial measurement unit (IMU) data due to its effectiveness in various environments. However, IMU data lacks spatial position information and environmental context, limiting its robustness against changes in motion patterns, such as speed variations. Skeleton data, while providing detailed spatial information and better capturing human motion dynamics, can lead to serious occlusion problems in industrial environments with complex surroundings. To address these challenges, we propose a novel cross-knowledge distillation method, a machine-learning technique that transfers knowledge from a larger teacher model to a smaller student model. This approach enhances a student model based on IMU data through cross-modal knowledge distillation from a skeleton-based model. By leveraging the skeleton-based model to extract insights on capturing spatial and temporal information on the body in time-series data and superior prediction capabilities, the student model is significantly improved in its ability to capture intricate and rapidly evolving motion patterns. This method enables the student model to assimilate salient features from the teacher model, thereby enhancing overall performance in HAR tasks.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"8","bibliographic_titles":[{"bibliographic_title":"研究報告コンシューマ・デバイス＆システム（CDS）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2024-09-19","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"3","bibliographicVolumeNumber":"2024-CDS-41"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":239458,"updated":"2025-01-19T08:16:23.849141+00:00","links":{}}