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Federal
Analysis of Virtual Sensors for Predicting Aircraft Fuel Consumption
National Aeronautics and Space Administration —
Previous research described the use of machine learning algorithms to predict aircraft fuel consumption. This technique, known as Virtual Sensors, models fuel... -
Federal
Comparative Analyses of Operational Flights
National Aeronautics and Space Administration —
This report describes a cooperative experiment conducted by ONERA and NASA, with the support of Airbus S.A.S. and easyJet Airline Company, Ltd. The study evaluated... -
Federal
Distributed Anomaly Detection using 1-class SVM for Vertically Partitioned Data
National Aeronautics and Space Administration —
There has been a tremendous increase in the volume of sensor data collected over the last decade for different monitoring tasks. For example, petabytes of earth... -
Federal
Privacy Preserving Distributed Data Mining
National Aeronautics and Space Administration —
Distributed data mining from privacy-sensitive multi-party data is likely to play an important role in the next generation of integrated vehicle health monitoring... -
Federal
Comparison of Algorithms for Anomaly Detection in Flight Recorder Data of Airline Operations
National Aeronautics and Space Administration —
Published at 12th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference and 14th AIAA/ISSM 17 - 19 September 2012, Indianapolis, Indiana -
Federal
Classification of Aeronautics System Health and Safety Documents
National Aeronautics and Space Administration —
Most complex aerospace systems have many text reports on safety, maintenance, and associated issues. The Aviation Safety Reporting System (ASRS) spans several decades... -
Federal
Anomaly Detection and Diagnosis Algorithms for Discrete Symbols
National Aeronautics and Space Administration —
We present a set of novel algorithms which we call sequenceMiner that detect and characterize anomalies in large sets of high-dimensional symbol sequences that arise...