Optimized flight safety event detection in the national general aviation flight information database-Reference-Cited by-同舟云学术

Optimized flight safety event detection in the national general aviation flight information database

Published:2022-04-25 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing
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Author:

LaBella Aidan P.¹,Karns Joshua A.¹,Akhbardeh Farhad¹,Desell Travis¹,Walton Andrew J.²,Morgan Zechariah³,Wild Brandon³,Dusenbury Mark³

Affiliation:

1. Rochester Institute of Technology

2. Liberty University

3. University of North Dakota

Funder

Federal Aviation Administration

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3477314.3507115

Reference11 articles.

1. Federal Aviation Administration and United States. Federal Aviation Administration . 2009 . Pilot's Handbook of Aeronautical Knowledge. Skyhorse Publishing Inc . Federal Aviation Administration and United States. Federal Aviation Administration. 2009. Pilot's Handbook of Aeronautical Knowledge. Skyhorse Publishing Inc.

2. Shelby Balogh . 2017. Use Of FOQA Data To Estimate The Probability Of Vehicle Upset Or Loss Of Control In-Flight. Master's thesis . University of North Dakota , Grand Forks, ND, USA . Shelby Balogh. 2017. Use Of FOQA Data To Estimate The Probability Of Vehicle Upset Or Loss Of Control In-Flight. Master's thesis. University of North Dakota, Grand Forks, ND, USA.

3. Cessna Aircraft Company. [n. d.]. Cessna Skyhawk Pilots Operating Handbook. https://www.cpaviation.com/images/downloads/Cessn\a%20172M.pdf Cessna Aircraft Company. [n. d.]. Cessna Skyhawk Pilots Operating Handbook. https://www.cpaviation.com/images/downloads/Cessn\a%20172M.pdf

4. Federal Aviation Administration. [n. d.]. Fact Sheet - General Aviation Safety. https://www.faa.gov/news/fact_sheets/news_story.cfm?newsId=21274 Federal Aviation Administration. [n. d.]. Fact Sheet - General Aviation Safety. https://www.faa.gov/news/fact_sheets/news_story.cfm?newsId=21274

5. Antonio Fernández , D Martinez , Pablo Hernández , Samuel Cristóbal , Florian Schwaiger , and José Mar ıa Nunez. 2019 . Flight Data Monitoring (FDM) Unknown Hazards detection during Approach Phase using Clustering Techniques and AutoEncoders . Proceedings of the SESAR Innovation Days (2019). Antonio Fernández, D Martinez, Pablo Hernández, Samuel Cristóbal, Florian Schwaiger, and José Marıa Nunez. 2019. Flight Data Monitoring (FDM) Unknown Hazards detection during Approach Phase using Clustering Techniques and AutoEncoders. Proceedings of the SESAR Innovation Days (2019).

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