An RMSprop-Incorporated Latent Factorization of Tensor Model for Random Missing Data Imputation in Structural Health Monitoring
In structural health monitoring (SHM), ensuring data completeness is critical for enhancing the accuracy and reliability of structural condition assessments. SHM data are prone to random missing values due to signal interference or connectivity issues, making precise data imputation essential. A lat...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
|
| Series: | Algorithms |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4893/18/6/351 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|