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    Learning models from avionics data streams

    Author
    Gurny, Sinclair
    View/Open
    180039_Gurny_rpi_0185N_11670.pdf (839.6Kb)
    Other Contributors
    Varela, Carlos A.; Cutler, Barbara M.; Patterson, Stacy;
    Date Issued
    2020-05
    Subject
    Computer science
    Degree
    MS;
    Terms of Use
    This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.;
    Metadata
    Show full item record
    URI
    https://hdl.handle.net/20.500.13015/2521
    Abstract
    In aviation, there are many values that are useful for pilots to know that cannot be measured from sensors and require calculation using various charts or other means to accurately estimate. Aircraft take-off distance requires knowing wind speed, pressure altitude, and temperature. However, it is possible for the inputs of these calculations to change during flight, or be calculated incorrectly by mistake, and it would be useful for pilots to know these values in real-time. PILOTS is a programming language for spatio-temporal data stream processing. We have added improved integration for machine learning algorithms as well as a linguistic abstraction for training these models. In data-driven systems, it can be useful to use distributed processes for computation. We have designed a declarative framework for federated learning and the aggregation of results from multiple related models within PILOTS. Furthermore, we built a model using PILOTS that is able to estimate weight in real-time during take-off of a fixed-wing aircraft using data available from the avionics. We evaluated the results of several models on accuracy and timeliness. Data was collected from the flight simulator X-Plane. Accidents such as the fatal crash of Cessna 172R N4207P could have been prevented using the weight estimation methods illustrated.;
    Description
    May 2020; School of Science
    Department
    Dept. of Computer Science;
    Publisher
    Rensselaer Polytechnic Institute, Troy, NY
    Relationships
    Rensselaer Theses and Dissertations Online Collection;
    Access
    Restricted to current Rensselaer faculty, staff and students. Access inquiries may be directed to the Rensselaer Libraries.;
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