{"created":"2025-01-19T01:33:24.455357+00:00","updated":"2025-01-19T10:25:36.283532+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00232502","sets":["1164:5159:11541:11549"]},"path":["11549"],"owner":"44499","recid":"232502","title":["グラフ転移学習と最適輸送に基づく時変ネットワークのための非同期カルマンフィルタ"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-02-22"},"_buckets":{"deposit":"cde2ce57-bccb-41b8-b4c6-520847a14309"},"_deposit":{"id":"232502","pid":{"type":"depid","value":"232502","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"グラフ転移学習と最適輸送に基づく時変ネットワークのための非同期カルマンフィルタ","author_link":["629429","629431","629427","629433","629432","629430","629434","629428"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"グラフ転移学習と最適輸送に基づく時変ネットワークのための非同期カルマンフィルタ"},{"subitem_title":"Asynchronous Kalman Filtering for Time-varying Networks Based on Graph Transfer Learning and Optimal Transport","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"SIP1","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2024-02-22","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"大阪大学工学部"},{"subitem_text_value":"大阪大学工学研究科"},{"subitem_text_value":"大阪大学工学研究科"},{"subitem_text_value":"大阪大学工学研究科"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Engineering, Osaka University","subitem_text_language":"en"},{"subitem_text_value":"Division of Electrical and Information Engineering, School of Engineering, Osaka University","subitem_text_language":"en"},{"subitem_text_value":"Division of Electrical and Information Engineering, School of Engineering, Osaka University","subitem_text_language":"en"},{"subitem_text_value":"Division of Electrical and Information Engineering, School of Engineering, Osaka 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/232502/files/IPSJ-SLP24151032.pdf","label":"IPSJ-SLP24151032.pdf"},"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-SLP24151032.pdf","filesize":[{"value":"1.2 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"22"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_login","version_id":"219f2847-3ba0-4e9e-a72d-4d6a694d6ab2","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 by the Institute of Electronics, Information and Communication Engineers This SIG report is only available to those in membership of the SIG."}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"福原, 伝博"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"原, 惇也"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"東, 広志"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"田中, 雄一"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Tsutahiro, Fukuhara","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Junya, Hara","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Hiroshi, Higashi","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yuichi, Tanaka","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10442647","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":"2188-8663","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"本報告では，異なる 2 種の時変ネットワーク間で，交互に最新のネットワーク状態を推定するためのカルマンフィルタを提案する．具体的には，グラフノード間の関係性の強度，すなわち辺の重みとしてネットワーク状態を定義し，これを推定することを考える．様々な制約からセンサ数や計算資源は限られているため，特に大規模ネットワークにおいて全ての状態を監視したり，管理したりすることは現実的に困難である．この問題を回避するため，ネットワークから統計的性質が類似したノードの集合（コミュニティ）を取り出し，コミュニティに対して統計的解析を行った後に，その解析結果を他のコミュニティに利用することを考える．この場合，コミュニティ間のノード数が異なることから，適切にカルマンフィルタのパラメータ群を転移する技術が必要となる．そこで，本報告では，2 種の非同期なネットワーク間の協調的カルマンフィルタを提案する．提案手法では，2 種のコミュニティ間で交互にカルマンフィルタの状態推定を行う．コミュニティ間のパラメータ群転移には最適輸送を用いる．まず，状態空間モデルを原ネットワーク上で定式化し，最適輸送に基づいて被転移ネットワークから転移ネットワーク上へパラメータ群を移す．次に，ベイズ推定に基づいたグラフフィルタ転移手法を用いて，転移ネットワーク上でカルマンフィルタを導出する．合成データを用いた状態推定実験において，提案手法が既存手法に比べて優れた性能を示したので報告する．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"This report presents a filter transfer method for tracking and estimating dynamic states, i.e., Kalman filtering, between two different time-varying networks. A network state represents the inter-node significance, which is formulated as an edge weight of the graph. However, in practice, monitoring all states of a large network over time is infeasible due to the limited sensing and storage burden. To avoid the problem, one may extract one community from the network and perform an intra-community analysis based on the statistics in each community. Then, the statistics are utilized for analysis of another community. This leads to the requirement to transfer a set of parameters in Kalman filter from one community to the others. In this report, we propose a cooperative Kalman filter between two asynchronous networks. The proposed Kalman filter performs its estimation alternately in time between two communities. We formulate a state-space model in the source domain and transfer it into the target domain based on optimal transport. To this aim, we apply a graph filter transfer method based on Bayesian inference. The experiments on synthetic data demonstrate that the proposed method effectively estimates the current state in the two time-varying networks.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"2","bibliographic_titles":[{"bibliographic_title":"研究報告音声言語情報処理（SLP）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2024-02-22","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"32","bibliographicVolumeNumber":"2024-SLP-151"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":232502,"links":{}}