船型データの分布を考慮した深層学習による造波抵抗推定

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2020/03/01

タイトル(英語): Wave-making Resistance Estimation Through Deep Learning Considering the Distribution of Ship Figure

著者名: 李　欣（横浜国立大学　大学院工学府），新井　洋（ジャパン マリンユナイテッド(株)　技術研究所），濱上　知樹（横浜国立大学　大学院工学府）

著者名(英語): Xin Li (Graduate School of Engineering, Yokohama National University), Hiroshi Arai (Japan Marine United Corporation), Tomoki Hamagami (Graduate School of Engineering, Yokohama National University)

キーワード: 深層学習，造波抵抗推定，オートエンコーダー　　deep learning，wave-making resistance，auto-encoder

要約(英語): A method for the estimation of wave-making resistance from the hull form and Froude number through deep learning is proposed. At the same time, this research also gives a solution when the data are skewed, which solves the problem of low generalization performance. The reduction of wave-making resistance is an essential issue in hull form design. However, the estimation of wave-making resistance is a time-consuming task that depends on experimental measurements. To enable direct estimation of the wave resistance from hull form, deep learning, which enables end-to-end learning, is an effective approach. The proposed method has two phases. First, auto-encoders, which reduce the dimension of the offset and the profile data, are generated, while performing to the skewed offset data, use an improved sampling method. Subsequently, after the regularization of these data, a deep neural net for regression estimation of wave-making resistance is generated. The results of evaluation experiments show that the proposed method can estimate wave-making resistance with high precision.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.140 No.3 （2020） 特集Ⅰ：スマートシステムと計測・制御技術―IoT時代の計測・制御技術―　特集Ⅱ：産業応用をにらんだ高周波システム・電子デバイス・回路・材料技術

本誌掲載ページ: 391-397 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/140/3/140_391/_article/-char/ja/