{"created":"2023-05-15T14:56:44.006084+00:00","id":76994,"links":{},"metadata":{"_buckets":{"deposit":"55eefa5a-aa0c-410e-90a4-d30632c85fc1"},"_deposit":{"created_by":1,"id":"76994","owners":[1],"pid":{"revision_id":0,"type":"depid","value":"76994"},"status":"published"},"_oai":{"id":"oai:repo.qst.go.jp:00076994","sets":["10:29"]},"author_link":["801384","801383"],"item_10005_date_7":{"attribute_name":"発表年月日","attribute_value_mlt":[{"subitem_date_issued_datetime":"2019-09-26","subitem_date_issued_type":"Issued"}]},"item_10005_description_5":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"放射線グラフト電解質膜の含水率およびプロトン導電率の支配因子を明らかにするため、重回帰分析法や人工ニューラルネットワーク(ANN)法を用いて予測モデルを構築した。はじめに8種類の異なる基材高分子から作製された電解質膜の含水率・導電率のデータを既報から抽出して用いた。説明変数となる基材高分子の物性値は、文献引用、実測定、量子化学計算、によって収集した。含水率と導電率の予測において、ANNモデルは重回帰分析よりも予測精度が高かった。ANNモデルを解析することで、基材高分子の誘電率や分極モーメントといった物性値が含水率や導電率に大きく影響を及ぼすことがわかった。","subitem_description_type":"Abstract"}]},"item_10005_description_6":{"attribute_name":"会議概要（会議名, 開催地, 会期, 主催者等）","attribute_value_mlt":[{"subitem_description":"第68回高分子討論会","subitem_description_type":"Other"}]},"item_access_right":{"attribute_name":"アクセス権","attribute_value_mlt":[{"subitem_access_right":"metadata only access","subitem_access_right_uri":"http://purl.org/coar/access_right/c_14cb"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"澤田, 真一"}],"nameIdentifiers":[{"nameIdentifier":"801383","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Sawada, Shinichi","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"801384","nameIdentifierScheme":"WEKO"}]}]},"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":"conference object","resourceuri":"http://purl.org/coar/resource_type/c_c94f"}]},"item_title":"機械学習に基づくグラフト型電解質膜の電気化学特性評価","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"機械学習に基づくグラフト型電解質膜の電気化学特性評価"}]},"item_type_id":"10005","owner":"1","path":["29"],"pubdate":{"attribute_name":"公開日","attribute_value":"2019-09-30"},"publish_date":"2019-09-30","publish_status":"0","recid":"76994","relation_version_is_last":true,"title":["機械学習に基づくグラフト型電解質膜の電気化学特性評価"],"weko_creator_id":"1","weko_shared_id":-1},"updated":"2023-05-15T23:46:49.053153+00:00"}