Title :
Using GAs to estimate confidence intervals for missing spatial data
Author_Institution :
GE Global Res. Center, Niskayuna, NY, USA
Abstract :
A technique for conditional spatial simulation using genetic algorithms is described. This technique can be used to characterize regions of missing data in regularly sampled data. The proposed technique is much faster than simulated annealing, the current state of the art in spatial simulation. An application of this technique for determining confidence intervals for missing data in optical measurements of gas turbines is discussed.
Keywords :
data analysis; data visualisation; edge detection; gas turbines; genetic algorithms; optical variables measurement; simulation; spatial data structures; GA; conditional spatial simulation; confidence interval estimation; data region characterization; gas turbine; genetic algorithm; missing spatial data; optical measurement; sampled data; simulated annealing; Autocorrelation; Blades; Genetic algorithms; Laboratories; Manufacturing; Mechanical variables measurement; Shape measurement; Simulated annealing; Size measurement; Turbines;
Conference_Titel :
Soft Computing in Industrial Applications, 2003. SMCia/03. Proceedings of the 2003 IEEE International Workshop on
Print_ISBN :
0-7803-7855-5
DOI :
10.1109/SMCIA.2003.1231350
