DocumentCode
2064295
Title
Using GAs to estimate confidence intervals for missing spatial data
Author
Eklund, Neil H.
Author_Institution
GE Global Res. Center, Niskayuna, NY, USA
fYear
2003
fDate
23-25 June 2003
Firstpage
91
Lastpage
95
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Soft Computing in Industrial Applications, 2003. SMCia/03. Proceedings of the 2003 IEEE International Workshop on
Print_ISBN
0-7803-7855-5
Type
conf
DOI
10.1109/SMCIA.2003.1231350
Filename
1231350
Link To Document