SDTAN: Scalable Deep Time-Aware Attention Network for Interpretable Hard Landing Prediction-Reference-Cited by-同舟云学术

SDTAN: Scalable Deep Time-Aware Attention Network for Interpretable Hard Landing Prediction

Author:

Chen Haodong¹,Shang Jiaxing¹^ORCID,Zheng Linjiang¹^ORCID,Li Xu¹^ORCID,Liu Xiaodong²,Sun Hong³,Zhao Xinbin⁴,Yu Liling⁴

Affiliation:

1. College of Computer Science, Chongqing University, Chongqing, China

2. School of Computer Science, Civil Aviation Flight University of China, Guanghan, China

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

4. Aviation Safety Institute, China Academy of Civil Aviation Science and Technology, Beijing, China

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Natural Science Foundation of Chongqing, China

Key Laboratory of Flight Techniques and Flight Safety, the Civil Aviation Administration Of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Link

Reference48 articles.

2. Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting;li;Proc Adv Neural Inf Process Syst,2019

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