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  [in Japanese] Wave-making Resistance Estimation Through Deep Learning Considering the Distribution of Ship Figure  [in Japanese]

Access this Article

Search this Article

Author(s)

    • 李 欣 Li Xin
    • 横浜国立大学 大学院工学府 Graduate School of Engineering, Yokohama National University

Abstract

<p>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.</p>

Journal

  • IEEJ Transactions on Electronics, Information and Systems

    IEEJ Transactions on Electronics, Information and Systems 140(3), 391-397, 2020

    The Institute of Electrical Engineers of Japan

Codes

  • NII Article ID (NAID)
    130007804400
  • NII NACSIS-CAT ID (NCID)
    AN10065950
  • Text Lang
    JPN
  • ISSN
    0385-4221
  • NDL Article ID
    030294241
  • NDL Call No.
    Z16-795
  • Data Source
    NDL  J-STAGE 
Page Top