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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
conference contribution
posted on 2023-05-23, 14:49 authored by Babak Shabani, Jason Ali-LavroffJason Ali-Lavroff, Damien HollowayDamien Holloway, Penev, S, Dessi, D, Thomas, GWave 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.
Funding
Australian Research Council
Incat Tasmania Pty Ltd
History
Publication title
Proceedings from the Smart Ship Technology Online Conference 2020Pagination
1-11ISBN
9781911649106Department/School
School of EngineeringPublisher
The Royal Institution of Naval ArchitectsPlace of publication
United KingdomEvent title
Smart Ship Technology Online Conference 2020Event Venue
onlineDate of Event (Start Date)
2020-10-14Date of Event (End Date)
2020-10-15Rights statement
Copyright 2020 The Royal Institution of Naval ArchitectsRepository Status
- Restricted