{"created":"2025-01-19T01:00:34.292631+00:00","updated":"2025-01-19T22:58:34.934175+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00195657","sets":["1164:2836:9672:9770"]},"path":["9770"],"owner":"44499","recid":"195657","title":["LSTMを用いた大規模イベント向けBLE屋内位置推定手法の検討"],"pubdate":{"attribute_name":"公開日","attribute_value":"2019-05-16"},"_buckets":{"deposit":"44947ea9-fa6b-48a8-8d89-5d0ba107c457"},"_deposit":{"id":"195657","pid":{"type":"depid","value":"195657","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"LSTMを用いた大規模イベント向けBLE屋内位置推定手法の検討","author_link":["466756","466754","466757","466755"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"LSTMを用いた大規模イベント向けBLE屋内位置推定手法の検討"},{"subitem_title":"Basic Study of BLE Indoor Location Estimation using LSTM for Large-scale Events","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"位置推定・センシング","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2019-05-16","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"名古屋大学大学院工学研究科"},{"subitem_text_value":"名古屋大学大学院工学研究科"},{"subitem_text_value":"名古屋大学大学院工学研究科"},{"subitem_text_value":"名古屋大学大学院工学研究科/名古屋大学未来社会創造機構"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Engineering, Nagoya University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Engineering, Nagoya University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Engineering, Nagoya University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Engineering, Nagoya University / Institutes of Innovation for Future Society, Nagoya 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/195657/files/IPSJ-DPS19179029.pdf","label":"IPSJ-DPS19179029.pdf"},"date":[{"dateType":"Available","dateValue":"2021-05-16"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-DPS19179029.pdf","filesize":[{"value":"2.1 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":"34"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"5281125d-3560-4dbd-b997-5e6aa90bef9d","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2019 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"浦野, 健太"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"廣井, 慧"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"米澤, 拓郎"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"河口, 信夫"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10116224","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":"2188-8906","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"本稿では,移動する BLE タグからのパケットを環境内の複数のスキャナで受信し,受信信号強度をもとに行う BLE 屋内位置推定において,LSTM ベースのニューラルネットワークの利用を試みる.BLE による位置推定では,受信信号強度の不安定さが精度に影響を及ぼす.そこで,Fingerprint や三点測位に代わり,ニューラルネットワークの利用が試みられている.オートエンコーダを利用した受信信号強度のデノイジングや,対応デバイスや周波数が類似する Wi-Fi では CSI を学習させるなどが行われ,精度の改善が報告されている一方で,多数の人が介在する実環境への対応可能性は十分検証されていない.提案手法のニューラルネットワークは全結合層と LSTM 層からなり,受信信号強度の時系列を用いて BLE タグの位置を推定する.受信信号強度に生じるノイズやパケットロスへの対処を行えるよう,時系列を入力してその変化からの位置推定を試みる.ネットワークのトレーニング時は,単純なシミュレーションを用いて生成したデータでの学習の後,実環境で収集したデータで追加の学習を行う.精度評価では,ニューラルネットワークの層の構成を複数用意し,良いと思われる構成を探す.被験者以外に多くの参加者が場内を歩行していた大規模展示会での実験で収集したデータを用い,最も精度の良かった構成では 75 パーセンタイルで誤差 2.44m となった.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"7","bibliographic_titles":[{"bibliographic_title":"研究報告マルチメディア通信と分散処理(DPS)"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2019-05-16","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"29","bibliographicVolumeNumber":"2019-DPS-179"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":195657,"links":{}}