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http://dx.doi.org/10.3796/KSFOT.2022.58.3.272

A study on estimating the main dimensions of a small fishing boat using deep learning  

JANG, Min Sung (Department of Naval Architecture and Marine Systems Engineering, Pukyong National University)
KIM, Dong-Joon (Department of Naval Architecture and Marine Systems Engineering, Pukyong National University)
ZHAO, Yang (Department of Marine design Convergence engineering, Pukyong National University)
Publication Information
Journal of the Korean Society of Fisheries and Ocean Technology / v.58, no.3, 2022 , pp. 272-280 More about this Journal
Abstract
The first step is to determine the principal dimensions of the design ship, such as length between perpendiculars, beam, draft and depth when accomplishing the design of a new vessel. To make this process easier, a database with a large amount of existing ship data and a regression analysis technique are needed. Recently, deep learning, a branch of artificial intelligence (AI) has been used in regression analysis. In this paper, deep learning neural networks are used for regression analysis to find the regression function between the input and output data. To find the neural network structure with the highest accuracy, the errors of neural network structures with varying the number of the layers and the nodes are compared. In this paper, Python TensorFlow Keras API and MATLAB Deep Learning Toolbox are used to build deep learning neural networks. Constructed DNN (deep neural networks) makes helpful in determining the principal dimension of the ship and saves much time in the ship design process.
Keywords
Fishing boat design; Determination of main dimension; DNN; Regression analysis;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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