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Application of Support Vector Machines to Vapor Detection and Classification for Environmental Monitoring of Spacecraft

  • Tao Qian
  • Xiaokun Li
  • Bulent Ayhan
  • Roger Xu
  • Chiman Kwan
  • Tim Griffin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)

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.

Keywords

Support Vector Machine Support Vector Machine Classifier Electronic Nose Sensor Output Sensor Selection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Tao Qian
    • 1
  • Xiaokun Li
    • 1
  • Bulent Ayhan
    • 1
  • Roger Xu
    • 1
  • Chiman Kwan
    • 1
  • Tim Griffin
    • 2
  1. 1.Intelligent Automation, Inc.RockvilleUSA
  2. 2.NASA Kennedy Space Center (KSC)USA

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