{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00233945","sets":["1164:3865:11476:11605"]},"path":["11605"],"owner":"44499","recid":"233945","title":["The Earth Speaks: Advanced Vehicle Classification with Seismic Data"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-05-08"},"_buckets":{"deposit":"cc0edf1a-d085-4226-852b-78640cf9c112"},"_deposit":{"id":"233945","pid":{"type":"depid","value":"233945","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"The Earth Speaks: Advanced Vehicle Classification with Seismic Data","author_link":["636601","636599","636598","636600"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"The Earth Speaks: Advanced Vehicle Classification with Seismic Data"},{"subitem_title":"The Earth Speaks: Advanced Vehicle Classification with Seismic Data","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"[MBL/ITS]自動運転と測位","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2024-05-08","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"Computational Learning Theory Team, RIKEN-AIP"},{"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":"Computational Learning Theory Team, RIKEN-AIP","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/233945/files/IPSJ-MBL24111024.pdf","label":"IPSJ-MBL24111024.pdf"},"date":[{"dateType":"Available","dateValue":"2026-05-08"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-MBL24111024.pdf","filesize":[{"value":"480.0 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"35"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"4c34934e-8cbb-4457-85d0-393bb06f0084","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":"Sherief, Hashima"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Hamada, Rizk"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Sherief, Hashima","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Hamada, Rizk","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11851388","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":"2188-8817","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"Recently, privacy-preserving vehicle classification (VC) methods have gained great focus, especially with the growing intelligent sensor technologies. Amongst these, seismic-aided VC is a promising intelligent solution but a challenging issue due to the interference of signals sourced from various noises. Seismic-aided VC overcomes the challenges of image/video classification as it can easily/low cost detect the traffic volume without private user information and in different weather conditions. Therefore, this paper proposes an efficient deep learning-based vehicle classification approach within the fractional wavelet domain to identify vehicle categories. The system achieves a high classification accuracy of 98% with real-time processing of 0.05 seconds.","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Recently, privacy-preserving vehicle classification (VC) methods have gained great focus, especially with the growing intelligent sensor technologies. Amongst these, seismic-aided VC is a promising intelligent solution but a challenging issue due to the interference of signals sourced from various noises. Seismic-aided VC overcomes the challenges of image/video classification as it can easily/low cost detect the traffic volume without private user information and in different weather conditions. Therefore, this paper proposes an efficient deep learning-based vehicle classification approach within the fractional wavelet domain to identify vehicle categories. The system achieves a high classification accuracy of 98% with real-time processing of 0.05 seconds.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"7","bibliographic_titles":[{"bibliographic_title":"研究報告モバイルコンピューティングと新社会システム（MBL）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2024-05-08","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"24","bibliographicVolumeNumber":"2024-MBL-111"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":233945,"updated":"2025-01-19T09:55:46.623025+00:00","links":{},"created":"2025-01-19T01:35:37.044275+00:00"}