Web application with data centric approach to ship powering prediction using deep learning

Web application with data centric approach to ship powering prediction using deep learning

This work describes an AI-based web application to predict passenger ship powering requirements. The data centric approach is developed based on the actual passenger ship design data as a design tool to assist naval architects to quickly estimate the ship brake power. It emphasised on the preliminar...

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Bibliographic Details
Main Authors: Khairuddin, Jauhari, Abdul Malik, Adi Maimun, Hiekata, Kazuo, Siow, Chee Loon, Ali, Arifah
Format: Article
Language:English
Published: Elsevier B.V. 2022
Subjects:
Q Science (General)
QA Mathematics
TJ Mechanical engineering and machinery
TK Electrical engineering. Electronics Nuclear engineering
TS Manufactures
Online Access:http://eprints.utm.my/104129/1/AdiMaimunAbdul2022_WebApplicationwithDataCentricApproach.pdf
http://eprints.utm.my/104129/
http://dx.doi.org/10.1016/j.simpa.2022.100226
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Institution: Universiti Teknologi Malaysia
Language: English
Description
Summary:This work describes an AI-based web application to predict passenger ship powering requirements. The data centric approach is developed based on the actual passenger ship design data as a design tool to assist naval architects to quickly estimate the ship brake power. It emphasised on the preliminary design stage to minimise design tasks and laborious calculations. Based on the study, it is observed that the model shows good agreement to the existing empirical method results with 10% mean absolute errors. Significantly, this presents the approach ability to facilitate faster and effective preliminary design, and scalability for large and complex systems.

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