Paper Info
Title
Application of support vector machines to vapor detection and classification for environmental monitoring of spacecraft
Abstract
Electronic noses (E-nose) have gained popularity in various applications such as food inspection, cosmetics quality control [1], toxic vapor detection to counter terrorism, detection of Improvised Explosive Devices (IED), narcotics detection, etc. In the paper, we summarized our results on the application of Support Vector Machines (SVM) to gas detection and classification using E-nose. First, based on experimental data from Jet Propulsion Lab. (JPL), we created three different data sets based on different pre-processing techniques. Second, we used SVM to detect gas sample data from non-gas background data, and used three sensor selection methods to improve the detection rate. We were able to achieve 85% correct detection of gases. Third, SVM gas classifier was developed to classify 15 different single gases and mixtures. Different sensor selection methods were applied and FSS & BSS feature selection method yielded the best performance.
Year
DOI
Venue
2006
10.1007/11760191_177
ISNN (2)
Keywords
Field
DocType
correct detection,support vector machine,environmental monitoring,toxic vapor detection,different data,gas detection,different single gas,different pre-processing technique,detection rate,narcotics detection,different sensor selection method,experimental data,quality control,electronic nose,feature selection
Electronic nose,Data set,Pattern recognition,Feature selection,Computer science,Support vector machine,Explosive material,Jet propulsion,Artificial intelligence,Artificial neural network,Classifier (linguistics)
Conference
Volume
ISSN
ISBN
3973
0302-9743
3-540-34482-9
Citations 
PageRank 
References 
2
0.44
6
Authors
6
Name
Order
Citations
PageRank
Tao Qian151.66
Xiaokun Li240.92
Bulent Ayhan311918.06
Roger Xu411114.71
Chiman Kwan544071.64
Tim Griffin620.44