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.

서 론
재료 및 방법
    딥러닝의 간단한 소개
    학습데이터 선정
    텐서플로 프로그램을 이용한 딥러닝
    Matlab 프로그램을 이용한 딥러닝
    데이터 특성 분석을 통한 DNN 모델 개선
결과 및 고찰
결과분석
결 론
사 사
References

• 장민성(부경대학교 조선해양시스템공학과 대학원생) | Min Sung JANG (Student, Department of Naval Architecture and Marine Systems Engineering, Pukyong National University, Busan 48513, Korea)
• 김동준(부경대학교 조선해양시스템공학과 교수) | Dong-Joon KIM (Professor, Department of Naval Architecture and Marine Systems Engineering, Pukyong National University, Busan 48513, Korea) Corresponding author
• 자오양(부경대학교 마린융합디자인공학과 대학원생) | Yang ZHAO (Student, Department of Marine design Convergence engineering, Pukyong National University, Busan 48513, Korea)