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Machine learning and cloud computing for remote monitoring of wave piercing catamarans: a case study using MATLAB on Amazon web services

Citation

Shabani, B and Ali-Lavroff, J and Holloway, DS and Penev, S and Dessi, D and Thomas, G, Machine learning and cloud computing for remote monitoring of wave piercing catamarans: a case study using MATLAB on Amazon web services, Proceedings from the Smart Ship Technology Online Conference 2020, 14-15 October 2020, online, pp. 1-11. ISBN 9781911649106 (2020) [Refereed Conference Paper]


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Copyright Statement

Copyright 2020 The Royal Institution of Naval Architects

Official URL: https://www.rina.org.uk/Smart_Ship_Technology_2020...

Abstract

Wave load cycles, wet-deck slamming events, accelerations and motion comfort are important considerations for high-speed catamarans operating in moderate to large waves. This paper provides an overview of data analytics methods and cloud computing resources for remotely monitoring motions and structural responses of a 111 m high-speed catamaran. To satisfy the data processing requirements, MATLAB Reference Architectures on Amazon Web Services (AWS) were used. Such combination enabled fast parallel computing and advanced feature engineering in a time-efficient manner. A MATLAB Production Server on AWS has been set up for near real-time analytics and execution of functions developed according to the class guidelines. A case study using Long Short-Term Memory (LSTM) networks for ship speed and Motion Sickness Incidence (MSI) is provided and discussed. Such data architecture provides a flexible and scalable solution, leading to deeper insights through big data processing and machine learning, which supports hull monitoring functions as a service.

Item Details

Item Type:Refereed Conference Paper
Keywords:machine learning, cloud computing, remote monitoring, catamaran
Research Division:Information and Computing Sciences
Research Group:Artificial intelligence
Research Field:Artificial intelligence not elsewhere classified
Objective Division:Transport
Objective Group:Water transport
Objective Field:Domestic passenger water transport (e.g. ferries)
UTAS Author:Shabani, B (Dr Babak Shabani)
UTAS Author:Ali-Lavroff, J (Dr Jason Ali-Lavroff)
UTAS Author:Holloway, DS (Associate Professor Damien Holloway)
ID Code:141663
Year Published:2020
Funding Support:Australian Research Council (LP170100555)
Deposited By:Engineering
Deposited On:2020-11-08
Last Modified:2021-03-22
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