Invention Grant
- Patent Title: Analyzing flight data using predictive models
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Application No.: US14651784Application Date: 2013-12-12
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Publication No.: US10248742B2Publication Date: 2019-04-02
- Inventor: Travis Desell , James Higgins , Sophine Clachar
- Applicant: University of North Dakota
- Applicant Address: US ND Grand Forks
- Assignee: University of North Dakota
- Current Assignee: University of North Dakota
- Current Assignee Address: US ND Grand Forks
- Agency: Schwegman Lundberg & Woessner, P.A.
- International Application: PCT/US2013/074755 WO 20131212
- International Announcement: WO2014/093670 WO 20140619
- Main IPC: G06F7/60
- IPC: G06F7/60 ; G06F17/50 ; G06F17/10 ; G06N99/00 ; G06N20/00 ; G01C23/00 ; G06Q10/04 ; G06Q10/10 ; G06Q50/30 ; G08G5/00

Abstract:
Various embodiments for analyzing flight data using predictive models are described herein. In various embodiments, a quadratic least squares model is applied to a matrix of time-series flight parameter data for a flight, thereby deriving a mathematical signature for each flight parameter of each flight in a set of data including a plurality of sensor readings corresponding to time-series flight parameters of a plurality of flights. The derived mathematical signatures are aggregated into a dataset. A similarity between each pair of flights within the plurality of flights is measured by calculating a distance metric between the mathematical signatures of each pair of flights within the dataset, and the measured similarities are combined with the dataset. A machine-learning algorithm is applied to the dataset, thereby identifying, without predefined thresholds, clusters of outliers within the dataset by using a unified distance matrix.
Public/Granted literature
- US20150324501A1 ANALYZING FLIGHT DATA USING PREDICTIVE MODELS Public/Granted day:2015-11-12
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