Application of Recent Developments in Deep Learning to ANN-based Automatic Berthing Systems

International Journal of Engineering and Technology Innovation

View Publication Info
 
 
Field Value
 
Title Application of Recent Developments in Deep Learning to ANN-based Automatic Berthing Systems
 
Creator Lee, Daesoo
Lee, Seung-Jae
Yu-Jeong Seo
 
Subject automatic ship berthing
artificial neural network
deep neural network
batch normalization
 
Description Previous studies on Artificial Neural Network (ANN)-based automatic berthing showed considerable increases in performance by training ANNs with a set of berthing datasets. However, the berthing performance deteriorated when an extrapolated initial position was given. To overcome the extrapolation problem and improve the training performance, recent developments in Deep Learning (DL) are adopted in this paper. Recent activation functions, weight initialization methods, input data-scaling methods, a higher number of hidden layers, and Batch Normalization (BN) are considered, and their effectiveness has been analyzed based on loss functions, berthing performance histories, and berthing trajectories. Finally, it is shown that the use of recent activation and weight initialization method results in faster training convergence and a higher number of hidden layers. This leads to a better berthing performance over the training dataset. It is found that application of the BN can overcome the extrapolated initial position problem.
 
Publisher Taiwan Association of Engineering and Technology Innovation
 
Date 2020-01-01
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
 
Format application/pdf
 
Identifier http://ojs.imeti.org/index.php/IJETI/article/view/4354
10.46604/ijeti.2020.4354
 
Source International Journal of Engineering and Technology Innovation; Vol 10 No 1 (2020); 75-90
2226-809X
2223-5329
 
Language eng
 
Relation http://ojs.imeti.org/index.php/IJETI/article/view/4354/924
http://ojs.imeti.org/index.php/IJETI/article/view/4354/930
 
Rights Copyright (c) 2019 Daesoo Lee, Seung-Jae Lee, Yu-Jeong Seo
http://creativecommons.org/licenses/by-nc/4.0
 

Contact Us

The PKP Index is an initiative of the Public Knowledge Project.

For PKP Publishing Services please use the PKP|PS contact form.

For support with PKP software we encourage users to consult our wiki for documentation and search our support forums.

For any other correspondence feel free to contact us using the PKP contact form.

Find Us

Twitter

Copyright © 2015-2018 Simon Fraser University Library