{"created":"2023-05-15T14:55:06.607483+00:00","id":74898,"links":{},"metadata":{"_buckets":{"deposit":"6fff6c47-f184-487b-96ff-e9cc3cfa2100"},"_deposit":{"created_by":1,"id":"74898","owners":[1],"pid":{"revision_id":0,"type":"depid","value":"74898"},"status":"published"},"_oai":{"id":"oai:repo.qst.go.jp:00074898","sets":["10:28"]},"author_link":["740329","740330","740333","740331","740332","740328"],"item_10005_date_7":{"attribute_name":"発表年月日","attribute_value_mlt":[{"subitem_date_issued_datetime":"2019-03-20","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では、イオン交換容量(IEC)を上げるとプロトン導電率は上昇するものの、機械的強度や化学的安定性は低下してしまう。そのため本研究では、低IECかつ高導電性の電解質膜の創出 を目標とし、機械学習を活用してプロトン導電率を予測する手法を開発した。はじめに、さまざまな基材から作製された放射線グラフト電解質膜の導電率のデータを収集した。また文献、実測定、および量子化学計算により、導電率の説明変数となり得る基材高分子の物性データを収集した。導電率の全データ(208点)を訓練データ(147点)とテストデータ(61点)に分割した。訓練データを用いて説明変数から導電率を予測するANNモデルを作成した後、モデルの予測精度をテストデータで検証した。さまざまな説明変数データセットを用いたときのANNモデルの予測精度、および各説明変数が予測に及ぼす影響を検討した。","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":"Future Trend in Polymer Science 2018","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":"740328","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"田中, 健一"}],"nameIdentifiers":[{"nameIdentifier":"740329","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"船津, 公人"}],"nameIdentifiers":[{"nameIdentifier":"740330","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"前川, 康成"}],"nameIdentifiers":[{"nameIdentifier":"740331","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Sawada, Shinichi","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"740332","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Maekawa, Yasunari","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"740333","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":"機械学習による放射線グラフト電解質膜のプロトン導電率予測","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"機械学習による放射線グラフト電解質膜のプロトン導電率予測"}]},"item_type_id":"10005","owner":"1","path":["28"],"pubdate":{"attribute_name":"公開日","attribute_value":"2019-03-22"},"publish_date":"2019-03-22","publish_status":"0","recid":"74898","relation_version_is_last":true,"title":["機械学習による放射線グラフト電解質膜のプロトン導電率予測"],"weko_creator_id":"1","weko_shared_id":-1},"updated":"2023-05-16T07:32:32.400842+00:00"}