By Topic

Using genetic algorithms to estimate confidence intervals for missing spatial data

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
Eklund, N.H.W. ; GE Global Res. Center, Ind. Artificial Intelligence Lab., Niskayuna, NY

Gas turbine blades, which come in many shapes and sizes, must meet strict engineering specifications. The current manual blade measurement system is slow and labor intensive. As part of the development of an optical measurement system, an approach for characterizing missing data was required. A novel technique for conditional spatial simulation using genetic algorithms (GAs) was developed. The problem is encoded using the "random key genetic algorithm" (RKGA) approach. The RKGA allows the use of a sampling distribution for missing measurements that can accommodate values uncharacteristic of the area surrounding the missing data, while still allowing realizations of the missing data with reasonable directional semivariance characteristics to be developed. A unique optimization approach was used, consisting of a crossover-only GA, followed by a hill-climbing phase. Each phase addresses different parts of the problem (the low and high special frequencies, respectively). This spatial simulation technique can be used to characterize regions of missing data in regularly sampled measurements. The proposed technique is much faster than simulated annealing, the current state of the art in spatial simulation. An application of this technique to determining confidence intervals for missing data in optical measurements of gas turbines is described

Published in:

Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on  (Volume:36 ,  Issue: 4 )