Scaling Analysis of Thermographic Using Neural Networks

摘要

Sequences of thermographic images of burning residue produced by M198 155(unicharge) test rounds fired at Yuma Proving Ground (YPG) were collected for analysis to elucidate the evolution of conditions in the breech after firing and to provide guidance in determining safe loading protocols for future autoloaders. In order to better understand the thermal enviromnent in the breech, we are developing advanced analytical tools that can be used to quantitatively characterize sequences of thermographic images. However, for this study the calibration data required to extract the temperature profiles the YPG thermographic images for these analyses was unavailable. No analytic solution could be determined to perform the highly nonlinear reverse transformation from RGB space to intensities; therefore, a neural network was employed. Furthermore, the experimental data provided by YPG were only measurable over a restricted range of temperatures extending from approximately 80 deg C to 11O deg C. Since the highest temperatures measured in the thermographic data did not correspond to a hazardous condition, more complex measures than simple statistical averages of the temperature had to be used. A new numerical technique represented by sparse data sets was introduced for measuring the scaling properties of single-valued surfaces in 3-space.