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

Microgravity Sci Technol. 1999;12(3-4):91-100.

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-A (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.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Acceleration*
  • Algorithms
  • Data Interpretation, Statistical
  • Databases, Factual
  • Electronic Data Processing
  • Ergometry / statistics & numerical data
  • Exercise Therapy / statistics & numerical data*
  • Humans
  • Neural Networks, Computer*
  • Reproducibility of Results
  • Space Flight / instrumentation*
  • Spacecraft / instrumentation*
  • Weightlessness*