Events aboard the space shuttle such as crew movement, crew exercise, thruster firings, etc., disrupt the microgravity environment required for many on-board experiments. Automatic detection of these events would allow astronauts to minimize their impact on experiments. Hence, using space acceleration measurement system (SAMS) data collected on the USMP-3 mission, a knowledge base is generated to aid in the detection of disruptive events aboard the USMP-4 mission. Input patterns containing power spectral density information of SAMS data are used to train an adaptive resonance theory 2-A (ART2-A) neural network. The weight vectors of the ART2-A are used as the knowledge base; each weight vector is assigned a label or name representing a set of events. The labeled knowledge base is then tested by presenting input patterns created from the data collected during an exercise event.


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    Title :

    Generation of knowledge base for space acceleration measurement system (SAMS) data using an adaptive resonance theory 2-A (ART2-A) neural network


    Contributors:
    Smith, A.D. (author) / Sinha, A. (author)


    Publication date :

    1998


    Size :

    8 Seiten, 10 Quellen




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English