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

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

International Journal of Advanced Network, Monitoring and Controls
Volume 8 (2023): Issue 1 (January 2023)
By: Caiyuan Xia and  Qianshi Yan  
Open Access
|May 2023

Figures & Tables

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
DOI: https://doi.org/10.2478/ijanmc-2023-0044 | Journal eISSN: 2470-8038
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Language: English
Page range: 40 - 49
Published on: May 31, 2023
Published by: Xi’an Technological University
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
LSTM Network,
Self Coding Technology,
Satellite Data,
Deep Learning
Related subjects:
Computer sciences,
Computer sciences, other

© 2023 Caiyuan Xia, Qianshi Yan, published by Xi’an Technological University
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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