Unsupervised classification of Space Acceleration Measurement System (SAMS) data using ART2-A

A. D. Smith, Alok Sinha

    Research output: Contribution to journalArticle

    1 Scopus citations

    Abstract

    The Space Acceleration Measurement System (SAMS) has been developed by NASA to monitor the microgravity acceleration environment aboard the space shuttle. The amount of data collected by a SAMS unit during a shuttle mission is in the several gigabytes range. Adaptive Resonance Theory 2-4 (ART2-A), an unsupervised neural network, has been used to cluster these data and to develop cause and effect relationships among disturbances and the acceleration environment. Using input patterns formed on the basis of power spectral densities (psd), data collected from two missions, STS-050 and STS-057, have been clustered.

    Original languageEnglish (US)
    Pages (from-to)91-100
    Number of pages10
    JournalMicrogravity Science and Technology
    Volume12
    Issue number3-4
    StatePublished - Dec 1 1999

    All Science Journal Classification (ASJC) codes

    • Modeling and Simulation
    • Engineering(all)
    • Physics and Astronomy(all)
    • Applied Mathematics

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