A deep sequence‐to‐sequence method for accurate long landing prediction based on flight data-Reference-Cited by-同舟云学术

A deep sequence‐to‐sequence method for accurate long landing prediction based on flight data

Published:2021-05-13 Issue: Volume: Page:
ISSN:1751-956X
Container-title:IET Intelligent Transport Systems
language:en
Short-container-title:IET intell. transp. syst

Author:

Kang Zongwei¹²,Shang Jiaxing¹²^ORCID,Feng Yong¹²,Zheng Linjiang¹²,Wang Qixing¹²,Sun Hong³,Qiang Baohua⁴⁵,Liu Zhen⁶

Affiliation:

1. College of Computer Science Chongqing University Chongqing China

2. Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing University Chongqing China

3. Flight Technology and Flight Safety Research Base Civil Aviation Flight University of China Guanghan China

4. Guangxi Key Laboratory of Optoelectronic Information Processing Guilin University of Electronic Technology Guilin China

5. Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China

6. Chongqing Adtech Science and Technology Co., Ltd

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

Institution of Engineering and Technology (IET)

Subject

Law,Mechanical Engineering,General Environmental Science,Transportation

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/itr2.12078

Reference37 articles.

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3. Van Es G.W.H. Tritschler K. Tauss M.:Development of a landing overrun risk index 21st annual European Aviation Safety Seminar (EASS) Nicosia Cyprus NLR Air Transport Safety Institute Report NLR‐TP‐280. (2009)

4. Bayesian Network for Managing Runway Overruns in Aviation Safety

5. Sheridan K. et al.:An application of dbscan clustering for flight anomaly detection during the approach phase. In:AIAA Scitech 2020 Forum. p.1851(2020)

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