{"created":"2025-01-19T01:11:59.486677+00:00","updated":"2025-01-19T18:01:30.756524+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00210788","sets":["6164:6165:6640:10580"]},"path":["10580"],"owner":"44499","recid":"210788","title":["機械学習を用いたAndroid端末上での時系列データ予測に向けて"],"pubdate":{"attribute_name":"公開日","attribute_value":"2020-06-17"},"_buckets":{"deposit":"ac9e488f-fcf4-4ae8-a642-da3179618225"},"_deposit":{"id":"210788","pid":{"type":"depid","value":"210788","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"機械学習を用いたAndroid端末上での時系列データ予測に向けて","author_link":["534388","534389","534391","534390"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"機械学習を用いたAndroid端末上での時系列データ予測に向けて"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"コンシューマデバイス","subitem_subject_scheme":"Other"}]},"item_type_id":"18","publish_date":"2020-06-17","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_18_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"お茶の水女子大学"},{"subitem_text_value":"工学院大学"},{"subitem_text_value":"九州大学"},{"subitem_text_value":"お茶の水女子大学"}]},"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/210788/files/IPSJ-DICOMO2020075.pdf","label":"IPSJ-DICOMO2020075.pdf"},"date":[{"dateType":"Available","dateValue":"2022-06-17"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-DICOMO2020075.pdf","filesize":[{"value":"2.0 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":"44"}],"accessrole":"open_date","version_id":"6f19d47b-5058-4363-9910-34552de4e2b2","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2020 by the Information Processing Society of Japan"}]},"item_18_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"佐藤, 里香"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"山口, 実靖"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"神山, 剛"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"小口, 正人"}],"nameIdentifiers":[{}]}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_5794","resourcetype":"conference paper"}]},"item_18_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"近年，スマートフォンの普及が急速に進み，大容量のデータ通信が行われるようになった．それに伴って無線 LAN への接続需要が高まってきているが，無線環境下でのトラフィックの輻輳やパケットロスといった問題が生じている．突発的に生じる輻輳は一度起こると制御が難しい上，コントロールしようとしてさらに輻輳が悪化してしまうことがあるため，輻輳が起こる前にそれを予測していくことが望ましい．また輻輳の予知に関して，データを端末外に出すセキュリティ上の問題やデータ転送に要する時間等の課題から，端末内での処理が好ましいと言える．そこで本研究では，Android 端末上でトラフィックの輻輳を事前予測，制御を行って輻輳を回避することを最終目標とする．そこでまずサーバ機などの性能の高いマシン上でトラフィックの輻輳を深層学習により予測し，そのモデルを Android 端末に導入してサーバ機と同等の精度や処理速度で予測できるようにすることを目指す．本稿ではまず端末におけるトラフィックをサーバ機上で深層学習により予測した結果を示し，またその学習モデルをスマートフォン端末に組み込める形式に変換できることを確認する．","subitem_description_type":"Other"}]},"item_18_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"518","bibliographic_titles":[{"bibliographic_title":"マルチメディア，分散協調とモバイルシンポジウム2094論文集"}],"bibliographicPageStart":"512","bibliographicIssueDates":{"bibliographicIssueDate":"2020-06-17","bibliographicIssueDateType":"Issued"},"bibliographicVolumeNumber":"2020"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":210788,"links":{}}