Share Email Print
cover

Proceedings Paper

Modified genetic algorithm for extracting thermal profiles from infrared image data
Author(s): Lloyd G. Allred; Gary E. Kelly
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Analysis of the thermal profile of an electronic circuit card during warm-up can be useful in detecting malfunctioning components on the card. Extracting the thermal profile can require the processing of 10 to 15 images of 600,000 bytes each. By extracting the heat transient associated with the heat sources on the circuit card, this problem of characterizing the thermal transient can be reduced to one of modeling the peak temperatures associated with a handful of components. The thermal profile of each component can be modeled as a function of four parameters: three are functions of the heat dissipation characteristics of the circuit card, and the fourth is proportional to the power consumption of the component generating the heat. Extraction of the parameters was achieved through a modified genetic algorithm. The genetic algorithm was employed when traditional techniques of Newton, non-linear regression, gradient search, and binary search proved to be slow, unstable, and unreliable. In traditional genetic algorithm implementations, improvement in performance ceases when improvement requires a simultaneous mutation of two or more variables. We seem to have circumvented the difficulty by expressing the problem in the differential domain, and coupling the genetic algorithm with a cooperative `follow the leader' approach to optimization. The extracted power consumption parameters are then employed to distinguish between `good' and `bad' cards.

Paper Details

Date Published: 16 December 1992
PDF: 5 pages
Proc. SPIE 1766, Neural and Stochastic Methods in Image and Signal Processing, (16 December 1992); doi: 10.1117/12.130819
Show Author Affiliations
Lloyd G. Allred, Air Force Ogden Air Logistics Ctr. (United States)
Gary E. Kelly, Air Force Ogden Air Logistics Ctr. (United States)


Published in SPIE Proceedings Vol. 1766:
Neural and Stochastic Methods in Image and Signal Processing
Su-Shing Chen, Editor(s)

© SPIE. Terms of Use
Back to Top