2023
DOI: 10.1016/j.applthermaleng.2023.120676
|View full text |Cite
|
Sign up to set email alerts
|

Reduction and reconstruction strategy of active thermal control system based on unsupervised learning and thermo-optics for spaceborne high-resolution remote sensor

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2024
2024
2024
2024

Publication Types

Select...
3

Relationship

0
3

Authors

Journals

citations
Cited by 3 publications
(1 citation statement)
references
References 38 publications
0
1
0
Order By: Relevance
“…Moreover, Wang et al [19] reported a significant reduction in the ATCS for spaceborne high-resolution remote sensors (HRSs), achieving a 78.6% reduction rate in the ATCS's resource occupancy. The authors employed an unsupervised learning framework integrating kernel-based principal component analysis with Gaussian mixture model clustering.…”
Section: Atcssmentioning
confidence: 99%
“…Moreover, Wang et al [19] reported a significant reduction in the ATCS for spaceborne high-resolution remote sensors (HRSs), achieving a 78.6% reduction rate in the ATCS's resource occupancy. The authors employed an unsupervised learning framework integrating kernel-based principal component analysis with Gaussian mixture model clustering.…”
Section: Atcssmentioning
confidence: 99%