Hierarchical encoder-decoder architecture for carrier airwake prediction using attention in frequency domain

Yuhao Yang; Zewei Zheng

doi:10.1109/CFASTA57821.2023.10243277

Hierarchical encoder-decoder architecture for carrier airwake prediction using attention in frequency domain

Yuhao Yang^*
, Zewei Zheng

^*Corresponding author for this work

School of Automation Science and Electrical Engineering

Beihang University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The carrier airwake is the result of ocean air flowing across the stern of a moving aircraft carrier. This airwake has a huge impact on how accurately a carrier-based aircraft can land. If it is possible to make accurate predictions for the future carrier airwake, the predicted results will be used to aid the landing process and ensure a more precise landing. In this study, the properties of the carrier airwake are first analyzed to provide a basis for the subsequent design of the neural network. Then we propose a hierarchical encoder-decoder neural network with an attention mechanism in the frequency domain to simultaneously predict the carrier airwake components in all directions. Finally, the full comprehensive experiments show that our model for predicting the carrier airwake is accurate.

Original language	English
Title of host publication	Proceedings of the 2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	630-635
Number of pages	6
ISBN (Electronic)	9798350332162
DOIs	https://doi.org/10.1109/CFASTA57821.2023.10243277
State	Published - 2023
Event	2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023 - Qingdao, China Duration: 14 Jul 2023 → 16 Jul 2023

Publication series

Name	Proceedings of the 2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023

Conference

Conference	2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023
Country/Territory	China
City	Qingdao
Period	14/07/23 → 16/07/23

Keywords

Attention Mechanism in Frequency Domain
Carrier Airwake Prediction
Hierarchical Encoder-Decoder Architecture
Neural Network

Access to Document

10.1109/CFASTA57821.2023.10243277

Cite this

Yang, Y., & Zheng, Z. (2023). Hierarchical encoder-decoder architecture for carrier airwake prediction using attention in frequency domain. In Proceedings of the 2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023 (pp. 630-635). (Proceedings of the 2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CFASTA57821.2023.10243277

Yang, Yuhao ; Zheng, Zewei. / Hierarchical encoder-decoder architecture for carrier airwake prediction using attention in frequency domain. Proceedings of the 2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 630-635 (Proceedings of the 2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023).

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title = "Hierarchical encoder-decoder architecture for carrier airwake prediction using attention in frequency domain",

abstract = "The carrier airwake is the result of ocean air flowing across the stern of a moving aircraft carrier. This airwake has a huge impact on how accurately a carrier-based aircraft can land. If it is possible to make accurate predictions for the future carrier airwake, the predicted results will be used to aid the landing process and ensure a more precise landing. In this study, the properties of the carrier airwake are first analyzed to provide a basis for the subsequent design of the neural network. Then we propose a hierarchical encoder-decoder neural network with an attention mechanism in the frequency domain to simultaneously predict the carrier airwake components in all directions. Finally, the full comprehensive experiments show that our model for predicting the carrier airwake is accurate.",

keywords = "Attention Mechanism in Frequency Domain, Carrier Airwake Prediction, Hierarchical Encoder-Decoder Architecture, Neural Network",

author = "Yuhao Yang and Zewei Zheng",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023 ; Conference date: 14-07-2023 Through 16-07-2023",

year = "2023",

doi = "10.1109/CFASTA57821.2023.10243277",

language = "英语",

series = "Proceedings of the 2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "630--635",

booktitle = "Proceedings of the 2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023",

address = "美国",

}

Yang, Y & Zheng, Z 2023, Hierarchical encoder-decoder architecture for carrier airwake prediction using attention in frequency domain. in Proceedings of the 2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023. Proceedings of the 2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023, Institute of Electrical and Electronics Engineers Inc., pp. 630-635, 2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023, Qingdao, China, 14/07/23. https://doi.org/10.1109/CFASTA57821.2023.10243277

Hierarchical encoder-decoder architecture for carrier airwake prediction using attention in frequency domain. / Yang, Yuhao; Zheng, Zewei.
Proceedings of the 2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 630-635 (Proceedings of the 2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Yang, Yuhao

AU - Zheng, Zewei

PY - 2023

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N2 - The carrier airwake is the result of ocean air flowing across the stern of a moving aircraft carrier. This airwake has a huge impact on how accurately a carrier-based aircraft can land. If it is possible to make accurate predictions for the future carrier airwake, the predicted results will be used to aid the landing process and ensure a more precise landing. In this study, the properties of the carrier airwake are first analyzed to provide a basis for the subsequent design of the neural network. Then we propose a hierarchical encoder-decoder neural network with an attention mechanism in the frequency domain to simultaneously predict the carrier airwake components in all directions. Finally, the full comprehensive experiments show that our model for predicting the carrier airwake is accurate.

AB - The carrier airwake is the result of ocean air flowing across the stern of a moving aircraft carrier. This airwake has a huge impact on how accurately a carrier-based aircraft can land. If it is possible to make accurate predictions for the future carrier airwake, the predicted results will be used to aid the landing process and ensure a more precise landing. In this study, the properties of the carrier airwake are first analyzed to provide a basis for the subsequent design of the neural network. Then we propose a hierarchical encoder-decoder neural network with an attention mechanism in the frequency domain to simultaneously predict the carrier airwake components in all directions. Finally, the full comprehensive experiments show that our model for predicting the carrier airwake is accurate.

KW - Attention Mechanism in Frequency Domain

KW - Carrier Airwake Prediction

KW - Hierarchical Encoder-Decoder Architecture

KW - Neural Network

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DO - 10.1109/CFASTA57821.2023.10243277

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T3 - Proceedings of the 2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023

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BT - Proceedings of the 2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023

PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 14 July 2023 through 16 July 2023

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Yang Y, Zheng Z. Hierarchical encoder-decoder architecture for carrier airwake prediction using attention in frequency domain. In Proceedings of the 2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 630-635. (Proceedings of the 2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023). doi: 10.1109/CFASTA57821.2023.10243277

Hierarchical encoder-decoder architecture for carrier airwake prediction using attention in frequency domain

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