Toxic Vapor Classification and Concentration Estimation for Space Shuttle and International Space Station

Abstract

Abstract. During space walks, the space suits of astronauts may be contaminated by toxic vapors such as hydrazine, which are used for attitude control. Here we present some initial results on vapor classification and concentration estimation by using Support Vector Machine (SVM). The vapor was collected by electronic nose. By collaborating closely with NASA KCS, we achieved great results. For example, for Kam15f (90-second) data set, the classification success rate was 97.5% using SVM as compared to 87% using the linear discriminant method in [1]. Comparative studies were conducted between the SVM classifier and other classifiers such as Back Propagation (BP) Neural Network, 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 by using SVM, we achieved more than 99% correct estimation of concentration by using the 90^th second data samples.