Anomaly Signal Imputation Using Latent Coordination Relations
| dc.contributor.author | Chalongvorachai, Thasorn | |
| dc.contributor.author | Woraratpanya, Kuntpong | |
| dc.date.accessioned | 2026-08-06T10:44:02Z | |
| dc.date.available | 2026-08-06T10:44:02Z | |
| dc.date.issued | 2024-01-01 | |
| dc.description.abstract | Missing data is a critical challenge in industrial data analysis, particularly during anomaly incidents caused by system equipment malfunctions or, more critically, by cyberattacks in industrial systems. It impedes effective imputation and compromises data integrity. Existing statistical and machine learning techniques struggle with heavily missing data, often failing to restore original data characteristics. To address this, we propose Anomaly Signal Imputation Using Latent Coordination Relations, a framework employing a variational autoencoder (VAE) to learn from complete data and establish a robust imputation model based on latent space coordination points. Experimental results from a water treatment testbed show significant improvements in output signal fidelity despite substantial data loss, outperforming conventional techniques. | |
| dc.identifier.citation | IEEE Access, 12, 117072-117089, 2024 | |
| dc.identifier.doi | 10.1109/ACCESS.2024.3448236 | |
| dc.identifier.issn | 21693536 | |
| dc.identifier.other | 2-s2.0-85201758714 | |
| dc.identifier.uri | https://dspace.kmitl.ac.th/handle/123456789/15094 | |
| dc.source | IEEE Access | |
| dc.subject | anomaly detection | |
| dc.subject | Data imputation | |
| dc.subject | latent coordination relations | |
| dc.subject | neural networks | |
| dc.subject | time series analysis | |
| dc.subject | variational autoencoder | |
| dc.title | Anomaly Signal Imputation Using Latent Coordination Relations | |
| dc.type | Article |