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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. (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 |
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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: | 2020-12-08 |
Downloads: | 0 |
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