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Literary linguistics: Open Access research in English language

Strathprints makes available Open Access scholarly outputs by English Studies at Strathclyde. Particular research specialisms include literary linguistics, the study of literary texts using techniques drawn from linguistics and cognitive science.

The team also demonstrates research expertise in Renaissance studies, researching Renaissance literature, the history of ideas and language and cultural history.

Explore some of this Open Access research from English. Or explore all Strathclyde Open Access research...

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Jump to: 2022 | 2021 | 2020 | 2019 | 2018 | 2017 | 2016
Number of items: 13.

2022

Bolbot, Victor and Gkerekos, Christos and Theotokatos, Gerasimos and Boulougouris, Evangelos (2022) Automatic traffic scenarios generation for autonomous ships collision avoidance system testing. Ocean Engineering, 254. 111309. ISSN 0029-8018

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 Sun Jun 26 00:02:49 2022 BST.