{"created":"2023-05-15T13:27:14.950937+00:00","id":883,"links":{},"metadata":{"_buckets":{"deposit":"5a2bb450-8d9f-4694-9684-994c289098a0"},"_deposit":{"created_by":15,"id":"883","owners":[15],"pid":{"revision_id":0,"type":"depid","value":"883"},"status":"published"},"_oai":{"id":"oai:tfulib.repo.nii.ac.jp:00000883","sets":["99:123"]},"author_link":[],"item_10001_biblio_info_7":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2022-03-31","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"23","bibliographicPageEnd":"109","bibliographicPageStart":"95","bibliographic_titles":[{"bibliographic_title":"感性福祉研究所年報","bibliographic_titleLang":"ja"},{"bibliographic_title":"Report of Kansei Fukushi Research Institute","bibliographic_titleLang":"en"}]}]},"item_10001_description_5":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"　本研究では小型呼気センサの実現を目指し、10個の反応特性が異なる酸化チタンナノチューブ薄膜ガスセンサを用いて、その応答から多成分混合ガス（一酸化炭素、ヘリウム、酸素、窒素）内の一酸化炭素と酸素の濃度予測を、機械学習アルゴリズムの一つであるニューラルネットワークを用いて行い、隠れ層数の最適数を検討した。学習の結果、一酸化炭素濃度に対して0.001%p、酸素濃度に対して0.01%p の精度で予測可能なニューラルネットワークモデルを得た。また、その予測精度は5 層以上ではほぼ変わらないことが見出されたことから、隠れ層数が5 層程度のニューラルネットワークが最適であると考えられる。","subitem_description_language":"ja","subitem_description_type":"Abstract"}]},"item_10001_identifier_registration":{"attribute_name":"ID登録","attribute_value_mlt":[{"subitem_identifier_reg_text":"10.57314/00000830","subitem_identifier_reg_type":"JaLC"}]},"item_10001_publisher_8":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"東北福祉大学感性福祉研究所","subitem_publisher_language":"ja"}]},"item_10001_source_id_9":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1344-9966","subitem_source_identifier_type":"PISSN"}]},"item_10001_version_type_20":{"attribute_name":"著者版フラグ","attribute_value_mlt":[{"subitem_version_resource":"http://purl.org/coar/version/c_ab4af688f83e57aa","subitem_version_type":"AM"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"岩田, 一樹","creatorNameLang":"ja"},{"creatorName":"イワタ, カズキ","creatorNameLang":"ja-Kana"},{"creatorName":"Iwata, Kazuki","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"阿部, 宏之","creatorNameLang":"ja"},{"creatorName":"アベ, ヒロユキ","creatorNameLang":"ja-Kana"},{"creatorName":"Abe, Hiroyuki","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"庭野, 道夫","creatorNameLang":"ja"},{"creatorName":"ニワノ, ミチオ","creatorNameLang":"ja-Kana"},{"creatorName":"Niwano, Michio","creatorNameLang":"en"}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2022-05-31"}],"displaytype":"detail","filename":"pp.95-109 岩田一樹, 阿部宏之, 庭野道夫.pdf","filesize":[{"value":"1.7 MB"}],"format":"application/pdf","licensetype":"license_11","mimetype":"application/pdf","url":{"label":"pp.95-109 岩田一樹, 阿部宏之, 庭野道夫","url":"https://tfulib.repo.nii.ac.jp/record/883/files/pp.95-109 岩田一樹, 阿部宏之, 庭野道夫.pdf"},"version_id":"d1f7df69-5872-4d33-8a7c-b73045e20880"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"ニューラルネットワーク","subitem_subject_language":"ja","subitem_subject_scheme":"Other"},{"subitem_subject":"呼気センサ","subitem_subject_language":"ja","subitem_subject_scheme":"Other"},{"subitem_subject":"ガスセンサ","subitem_subject_language":"ja","subitem_subject_scheme":"Other"},{"subitem_subject":"多成分予測","subitem_subject_language":"ja","subitem_subject_scheme":"Other"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"journal article","resourceuri":"http://purl.org/coar/resource_type/c_6501"}]},"item_title":"ニューラルネットワークを用いた呼気センサの 高精度成分濃度予測 －予測精度の隠れ層数依存性－","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"ニューラルネットワークを用いた呼気センサの 高精度成分濃度予測 －予測精度の隠れ層数依存性－","subitem_title_language":"ja"}]},"item_type_id":"10001","owner":"15","path":["123"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2022-05-31"},"publish_date":"2022-05-31","publish_status":"0","recid":"883","relation_version_is_last":true,"title":["ニューラルネットワークを用いた呼気センサの 高精度成分濃度予測 －予測精度の隠れ層数依存性－"],"weko_creator_id":"15","weko_shared_id":-1},"updated":"2023-10-26T03:11:42.281408+00:00"}