{"created":"2023-06-19T10:30:18.507384+00:00","id":10595,"links":{},"metadata":{"_buckets":{"deposit":"cd9ae6b7-644e-4559-9f27-1f6c0fecdaa7"},"_deposit":{"created_by":18,"id":"10595","owners":[18],"pid":{"revision_id":0,"type":"depid","value":"10595"},"status":"published"},"_oai":{"id":"oai:muroran-it.repo.nii.ac.jp:00010595","sets":["216:322","216:487","46"]},"author_link":["59041","923","58685","58753"],"item_79_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2021","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"5","bibliographicPageEnd":"145","bibliographicPageStart":"137","bibliographicVolumeNumber":"104","bibliographic_titles":[{"bibliographic_title":"電子情報通信学会論文誌C","bibliographic_titleLang":"ja"}]}]},"item_79_description_23":{"attribute_name":"フォーマット","attribute_value_mlt":[{"subitem_description":"application/pdf","subitem_description_type":"Other"}]},"item_79_description_7":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"近年，シミュレーション技術の発展により，計算機を用いた光導波路デバイスの自動最適設計が盛んに検討されている．しかしながら，3 次元光導波路デバイスでは，1回の解析の計算コストが高く，最適化過程では更に莫大な計算コストを要する．本研究では，機械学習を用いた光導波路デバイスの最適設計の効率化についての検討を行っている．機械学習に分類されるニューラルネットワークに伝搬解析において計算が必要なパラメータをあらかじめ学習させることで，デバイスの特性評価を効率化し，より効率的な最適設計を行うことができる．本論文では，方向性結合型光デバイスの3次元設計を考え，モード結合理論(CMT)を用いた解析を行う．CMT解析で伝搬ステップごとに計算が必要となる位相不整合量とモード結合係数をニューラルネットワークに学習させることで効率的なデバイス評価を可能にし，進化的手法を用いた効率的な光導波路デバイスの最適設計についての検討を行っている","subitem_description_language":"ja","subitem_description_type":"Abstract"}]},"item_79_link_17":{"attribute_name":"出版者版へのリンク","attribute_value_mlt":[{"subitem_link_text":"10.14923/transelej.2020STP0003","subitem_link_url":"https://doi.org/10.14923/transelej.2020STP0003"}]},"item_79_link_5":{"attribute_name":"室蘭工業大学研究者データベースへのリンク","attribute_value_mlt":[{"subitem_link_text":"井口 亜希人（IGUCHI Akito）","subitem_link_url":"http://rdsoran.muroran-it.ac.jp/html/200000200_ja.html"},{"subitem_link_text":"辻 寧英（TSUJI 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