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Transforming automated inspection & non-destructive testing: world-leading Open Access research on robotics, sensors & ultrasonics

Strathprints makes available scholarly Open Access content by researchers within the Centre for Ultrasonic Engineering, based within Electronic & Electrical Engineering.

Research at CUE brings together next generation robotics with sensing technology to enable inspection of high value components, such as aerospace components, at the point of manufacture. Non-destructive testing techniques using ultrasound and other sensors can then be deployed to assess components for structural faults or damage and thereby ensure they are built correctly and more efficiently. Research at CUE is to be stimulated by the construction of a new £2.5 million state-of-the-art Robotically-Enabled Sensing (RES) hub within the Department of Electronic & Electrical Engineering (EEE).

Explore the Open Access research by EEE, or explore all of Strathclyde's Open Access research...

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Group by: Publication Date | Item type | No Grouping
Jump to: 2020 | 2019 | 2018 | 2017 | 2016
Number of items: 9.

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 Universität 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 Mon Apr 6 19:56:34 2020 BST.