Paper
16 July 2003 Calibration of a portable cost-effective chemical residue detection system with adaptive neural net control
Author Affiliations +
Abstract
The Sensory Research Institute at the Florida State University has quantitatively characterized a chemical residue detection system with adaptive neural net data processing. Two separate configurations, "Stormy" and "Gaea", were trained by exposure to decreasing amounts of n-amyl acetate from chemical emitters randomly distributed among a collection of non-emitters. The concentration of chemical in the sampled air stream was controlled precisely. The detection threshold for "Stormy" was 1.14 ppt; that for "Gaea" was 1.9 ppt. Cycle time for sampling and chemical analysis of each sample port was on the order of seconds. Possible effects on the sensors of environmental factors such as ambient humidity, temperature, and air velocity were not considered. Besides processing individual air sample data, the neural nets can sense concentration gradients and track to chemical source. The adaptive neural nets are accessed by a voice recognition system and are capable of point testing or free-ranging search. The service life of the detectors, the neural net processors, and auxiliary packaging is approximately 8 years under normal field use. Maintenance requires a good quality kibble and an occasional romp in the park.
© (2003) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Alan C. Tripp and James C. Walker "Calibration of a portable cost-effective chemical residue detection system with adaptive neural net control", Proc. SPIE 5048, Nondestructive Detection and Measurement for Homeland Security, (16 July 2003); https://doi.org/10.1117/12.484789
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KEYWORDS
Neural networks

Sensors

Chemical analysis

Nose

Biological and chemical sensing

Chemical detection

Calibration

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