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Real-time Satellite Anomaly Data Tagging Based on DAE-LSTM

Caiyuan Xia

Caiyuan Xia

School of Computer Science & Engineering, Xi’an Technological UniversityXi’an, China
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Xia, Caiyuan
 e 
Qianshi Yan

Qianshi Yan

School of Computer Science & Engineering, Xi’an Technological UniversityXi’an, China
Cerca questo autore su
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Yan, Qianshi
  
31 mag 2023
Real-time Satellite Anomaly Data Tagging Based on DAE-LSTM's Cover Image
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Pubblicato online: 31 mag 2023
Pagine: 40 - 49
DOI: https://doi.org/10.2478/ijanmc-2023-0044
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© 2023 Caiyuan Xia et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figer 1.

LSTM unit
LSTM unit

Figer 2.

Self-coding network structure
Self-coding network structure

Figer 3.

Program structure diagram
Program structure diagram

Figer 4.

Effect of the number of neurons on the accuracy of the model
Effect of the number of neurons on the accuracy of the model

Figer 5.

Effect of the number of network layers on the accuracy of the model
Effect of the number of network layers on the accuracy of the model

Figer 6.

Effect of time window length on accuracy rate
Effect of time window length on accuracy rate

Figer 7.

Difference statistics
Difference statistics

Figer 8.

Program execution proces
Program execution proces

Figer 9.

Relationship between training volume and accuracy rate
Relationship between training volume and accuracy rate

Figer 10.

Experimental results
Experimental results
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