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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.

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Developing a data acquisition, analysis and reporting system for an academic research centre

Robertson, Murray and McGlone, Thomas and Johnston, Andrea and Florence, Alastair and Johnston, Blair and Dziewierz, Jerzy and Tachtatzis, Christos and Cleary, Alison and Gachagan, Anthony and Andonovic, Ivan and Sefcik, Jan (2015) Developing a data acquisition, analysis and reporting system for an academic research centre. In: ACM International Conference on Multimedia Retrieval (ICMR), 2015-06-23 - 2015-06-26.

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Abstract

The ICT-CMAC (Intelligent Decision Support and Control Technologies for Continuous Manufacture and Crystallisation) project has built, as part of the overall intelligent decision support platform, a researcher focused laboratory data handling platform. Here we report the approaches taken to capture data directly from instruments, process, store and report this into our ELN (Electronic Laboratory Notebook). This seamless flow of data eliminates errors and greatly reduces the need for manual, routine post-process analysis. Case studies are described to demonstrate the efficiency of this platform and to highlight the advantages of structured data with the potential of future machine learning and intelligent decision support.