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.


    Zugriff

    Zugriff über TIB

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

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


    Beteiligte:
    Smith, A.D. (Autor:in) / Sinha, A. (Autor:in)


    Erscheinungsdatum :

    1998


    Format / Umfang :

    8 Seiten, 10 Quellen




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch