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Open Access research that is improving renewable energy technology...

Strathprints makes available scholarly Open Access content by researchers across the departments of Mechanical & Aerospace Engineering (MAE), Electronic & Electrical Engineering (EEE), and Naval Architecture, Ocean & Marine Engineering (NAOME), all of which are leading research into aspects of wind energy, the control of wind turbines and wind farms.

Researchers at EEE are examining the dynamic analysis of turbines, their modelling and simulation, control system design and their optimisation, along with resource assessment and condition monitoring issues. The Energy Systems Research Unit (ESRU) within MAE is producing research to achieve significant levels of energy efficiency using new and renewable energy systems. Meanwhile, researchers at NAOME are supporting the development of offshore wind, wave and tidal-current energy to assist in the provision of diverse energy sources and economic growth in the renewable energy sector.

Explore Open Access research by EEE, MAE and NAOME on renewable energy technologies. Or explore all of Strathclyde's Open Access research...

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Group by: Publication Date | Item type | No Grouping
Jump to: 2018 | 2017 | 2016
Number of items: 6.

2018

Lazakis, Iraklis and Gkerekos, Christos and Theotokatos, Gerasimos (2018) Investigating an SVM-driven, one-class approach to estimating ship systems condition. Ships and Offshore Structures. ISSN 1754-212X (In Press)

Gkerekos, Christos and Lazakis, Iraklis and Papageorgiou, Stylianos (2018) Leveraging big data for fuel oil consumption modelling. In: 17th Conference on Computer and IT Applications in the Maritime Industries. Technische Universita╠łt Hamburg-Harburg, Hamburg, pp. 144-152. ISBN 9783892207078

Gkerekos, Christos and Lazakis, Iraklis and Theotokatos, Gerasimos (2018) Exploiting machine learning for ship systems anomaly detection and healthiness forecasting. In: Proceedings of the 2018 Smart Ship Technology Conference. Royal Institution of Naval Architects, London.

2017

Gkerekos, Christos and Lazakis, Iraklis and Theotokatos, Gerasimos (2017) Implementation of a self-learning algorithm for main engine condition monitoring. In: Maritime Transportation and Harvesting of Sea Resources. CRC Press, pp. 981-989. ISBN 978-0-8153-7993-5

Gkerekos, C and Lazakis, I and Theotokatos, G (2017) Ship machinery condition monitoring using performance data through supervised learning. In: Proceedings of the 2017 Smart Ship Technology Conference. Royal Institution of Naval Architects, London, pp. 105-111. ISBN 9781909024632

2016

Gkerekos, Christos and Lazakis, Iraklis and Theotokatos, Gerasimos (2016) Ship machinery condition monitoring using vibration data through supervised learning. In: Proceedings of MSO 2016, International Conference on Maritime Safety and Operations. University of Strathclyde Publishing, pp. 103-110. ISBN 9781909522169

This list was generated on Sun Sep 23 21:10:00 2018 BST.