Using Deep Learning to Predict Unstable Approaches for General Aviation Aircraft-Reference-Cited by-同舟云学术

Using Deep Learning to Predict Unstable Approaches for General Aviation Aircraft

Published:2022-12 Issue:12 Volume:19 Page:811-817
ISSN:1940-3151
Container-title:Journal of Aerospace Information Systems
language:en
Short-container-title:Journal of Aerospace Information Systems

Author:

Jain Tanish¹^ORCID,Misra Shlok²^ORCID,Truong Dothang²^ORCID

Affiliation:

1. Stanford University, Stanford, California 94305

2. Embry-Riddle Aeronautical University, Daytona Beach, Florida 32114

Publisher

American Institute of Aeronautics and Astronautics (AIAA)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Aerospace Engineering

Link

https://arc.aiaa.org/doi/pdf/10.2514/1.I011132

Reference12 articles.

1. Implementation Plan 2018-19, N., Aircraft Owners and Pilots Association 30th Joseph T. Nall Report, Frederick, MD, 2018, https://www.aopa.org/training-and-safety/air-safety-institute/accident-analysis/joseph-t-nall-report/nall-report-figure-view?category=all&year=2020&condition=all&report=true.

2. A comparison of general aviation accidents involving airline pilots and instrument-rated private pilots

3. Occupant Injury Severity in General Aviation Accidents Involving Excessive Landing Airspeed

4. Applying Machine Learning to Enhance Runway Safety Through Runway Excursion Risk Mitigation

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