Title
Toxic Vapor Classification and Concentration Estimation for Space Shuttle and International Space Station
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.
Year
DOI
Venue
2004
10.1007/978-3-540-28647-9_90
ADVANCES IN NEURAL NETWORKS - ISNN 2004, PT 1
Keywords
Field
DocType
support vector machine,learning vector quantization,back propagation,attitude control,comparative study,electronic nose,neural network,international space station
Electronic nose,Pattern recognition,Computer science,Learning vector quantization,Support vector machine,Space Shuttle,Attitude control,Artificial intelligence,Linear discriminant analysis,Artificial neural network,Backpropagation
Conference
Volume
ISSN
Citations 
3173
0302-9743
2
PageRank 
References 
Authors
0.47
4
5
Name
Order
Citations
PageRank
Tao Qian151.66
Roger Xu211114.71
Chiman Kwan344071.64
Bruce R. Linnell441.24
Rebecca Young520.47