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International Open Access Week! Discover open research knowledge at Strathprints...

25-31 October is International Open Access Week 2021, the theme of which is 'Building Structural Equity' into open knowledge, aligning closely with UNESCO's recently released Recommendation on Open Science.

The Strathprints repository provides a digital archive of University of Strathclyde research outputs. Up to 95% of University of Strathclyde research content published since 2015, such as papers and articles, is available from this repository as Open Access thereby supporting equity in open knowledge - and the team supporting open initiatives at Strathclyde is working tirelessly to make even more content open! Explore recent Open Access research content by world leading researchers across science, engineering, business, social sciences and humanities disciplines.

Or explore all Open Access research content...

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Group by: Publication Date | Item type | No Grouping
Jump to: 2021 | 2020 | 2019 | 2018 | 2017 | 2016
Number of items: 12.

2021

Bolbot, Victor and Gkerekos, Christos and Theotokatos, Gerasimos; Castanier, Bruno and Cepin, Marco and Bigaud, David and Berenguer, Christophe, eds. (2021) Supplementing fault trees calculations with neural networks. In: Proceedings of the 31st European Safety and Reliability Conference. Research Publishing, Singapore. ISBN 9819730000000

Bolbot, Victor and Gkerekos, Christos and Theotokatos, Gerasimos (2021) Ships traffic encounter scenarios generation using sampling and clustering techniques. In: 1st International Conference on the Stability and Safety of Ships and Ocean Vehicles, 2021-06-07 - 2021-06-11, Online.

Gkerekos, Christos and Theotokatos, Gerasimos and Ancic, Ivica and Rye Torben, Sverre and Rejani Miyazaki, Michel (2021) Hybrid, real time engine modelling for the prediction and monitoring of marine power plant emissions and performance. In: 3rd International Conference on Modelling and Optimisation of Ship Energy Systems, 2021-05-19 - 2021-05-20, Online.

2020

Gkerekos, Christos and Lazakis, Iraklis (2020) A novel, data-driven heuristic framework for vessel weather routing. Ocean Engineering, 197. 106887. ISSN 0029-8018

2019

Gkerekos, Christos and Lazakis, Iraklis and Theotokatos, Gerasimos (2019) Machine learning models for predicting ship main engine Fuel Oil Consumption : a comparative study. Ocean Engineering, 188. 106282. ISSN 0029-8018

Cheliotis, Michail and Gkerekos, Christos and Lazakis, Iraklis and Theotokatos, Gerasimos (2019) A novel data condition and performance hybrid imputation method for energy effcient operations of marine systems. Ocean Engineering. pp. 1-48. ISSN 0029-8018 (In Press)

Lazakis, Iraklis and Gkerekos, Christos and Theotokatos, Gerasimos (2019) Investigating an SVM-driven, one-class approach to estimating ship systems condition. Ships and Offshore Structures, 14 (5). pp. 432-441. ISSN 1754-212X

2018

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, GBR.

2017

Gkerekos, Christos and Lazakis, Iraklis and Theotokatos, Gerasimos; Soares, C. Guedes and Teixeira, Angelo P., eds. (2017) Implementation of a self-learning algorithm for main engine condition monitoring. In: Maritime Transportation and Harvesting of Sea Resources. CRC Press, PRT, 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, GBR, pp. 105-111. ISBN 9781909024632

2016

Gkerekos, Christos and Lazakis, Iraklis and Theotokatos, Gerasimos; Lazakis, Iraklis and Theotokatos, Gerasimos, eds. (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, GBR, pp. 103-110. ISBN 9781909522169

This list was generated on Tue Oct 19 05:53:08 2021 BST.