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Proceedings Paper

A new approach to chemical agent detection, classification, and estimation
Author(s): Tao Qian; Genshe Chen; Erik Blasch; Robert Lynch; Yongwei Qi
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Paper Abstract

Chemical and biological agent detection has gained a great deal of interest in various applications. We present a new approach to vapor classification and concentration estimation in spacecraft environment. The approach consists of two steps. First, a classifier based on a Support Vector Machine (SVM) is used to identify the presence of toxic vapors. Second, once the vapors are classified, a cubic spline fitting and linear additive model for mixtures based concentration estimation algorithm is used to estimate the concentration of vapor. Once trained, the estimation algorithm can accurately estimate vapor concentrations for both single and mixture vapors under different humidity conditions. Extensive performance evaluations were performed by using e-nose data collected at NASA KCS. We achieved more than 99% accuracy for single vapors and 98% for binary mixture vapors. The classification success rate was 87% using the linear discriminant method. Comparative studies were conducted between the SVM classifier and other classifiers such as Probability Neural Network (PNN) and Learning Vector Quantization (LVQ). In all cases, the SVM classifier showed superior performance over other classifiers. In the concentration estimation part, we achieved less than 3% error in single vapor cases and less than 10% error in mixture cases.

Paper Details

Date Published: 17 March 2008
PDF: 12 pages
Proc. SPIE 6973, Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security 2008, 69730K (17 March 2008); doi: 10.1117/12.778501
Show Author Affiliations
Tao Qian, Intelligent Automation, Inc. (United States)
Genshe Chen, Intelligent Automation, Inc. (United States)
Erik Blasch, AFRL/RYAA (United States)
Robert Lynch, Naval Undersea Warfare Ctr. (United States)
Yongwei Qi, Goal Prospect Foreign Language School (China)

Published in SPIE Proceedings Vol. 6973:
Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security 2008
William J. Tolone; William Ribarsky, Editor(s)

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