Now showing items 3-6 of 6

  • Failure prognostic schemes and database design of a software tool for efficient management of wind turbine maintenance. 

    Sinha, Yashwant; Steel, John A. (Multi-Science / Sage http://dx.doi.org/10.1260/0309-524X.39.4.453, 2015)
    SINHA, Y. and STEEL, J., 2015. Failure prognostic schemes and database design of a software tool for efficient management of wind turbine maintenance. Wind Engineering, Vol 39 (4), pp. 453-478.
    Wind Turbines require numerous and varied types of maintenance activities throughout their lifespan, the frequency of which increases with years in operation. At present the proportion of maintenance cost to the total cost ...
  • My cost runneth over: data mining to reduce construction cost overruns. 

    Ahiaga-Dagbui, Dominic D.; Smith, Simon D. (Association of Researchers in Construction Management (ARCOM) http://www.arcom.ac.uk/-docs/proceedings/ar2013-0559-0568_Ahiaga-Dagbui_Smith.pdf, 2013-09)
    AHIAGA-DAGBUI, D. D. and SMITH, S. D., 2013. My cost runneth over: data mining to reduce construction cost overruns. In S. D. SMITH and D. D. AHIAGA-DAGBUI, eds. Proceedings 29th Annual ARCOM Conference. 2-4 September 2013. Nottingham: Association of Researchers in Construction Management (ARCOM). Pp. 559-568.
    Most construction projects overrun their budgets. Among the myriad of explanations giving for construction cost overruns is the lack of required information upon which to base accurate estimation. Much of the financial ...
  • Real time evolutionary algorithms in robotic neural control systems. 

    Jagadeesan, Ananda Prasanna (The Robert Gordon University School of Engineering, 2006)
    This thesis describes the use of a Real-Time Evolutionary Algorithm (RTEA) to optimise an Artificial Neural Network (ANN) on-line (in this context “on-line” means while it is in use). Traditionally, Evolutionary Algorithms ...
  • Selective dropout for deep neural networks. 

    Barrow, Erik; Eastwood, Mark; Jayne, Chrisina (Springer https://doi.org/10.1007/978-3-319-46675-0_57, 2016-09-29)
    BARROW, E., EASTWOOD, M. and JAYNE, C. 2016. Selective dropout for deep neural networks. Lecture notes in computer science, 9949, Neural information processing: Proceedings of 23rd International conference on neural information processing (ICONIP 2016), 16-21 October 2016, Kyoto, Japan. Cham: Springer [online], pages 519-528. Available from: https://doi.org/10.1007/978-3-319-46675-0_57.
    Dropout has been proven to be an effective method for reducing overfitting in deep artificial neural networks. We present 3 new alternative methods for performing dropout on a deep neural network which improves the ...