Bayesian Conditional GAN for Unsupervised Anomaly Detection in Structural Health Monitoring Time-Series Dataset
- DOI
- 10.3390/s26134253
- Published
- 2026-07-04
- Container
- Sensors
- Publisher
- MDPI AG
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.3390/s26134253,
title = {Bayesian Conditional GAN for Unsupervised Anomaly Detection in Structural Health Monitoring Time-Series Dataset},
author = {Yohannes L. Alemu and Christian Walther and Manuel Schneider and Norbert Greifzu and Leon Quinten Thiebes and Andreas Wenzel and Uwe Plank-Wiedenbeck and Tom Lahmer},
year = {2026},
journal = {Sensors},
doi = {10.3390/s26134253},
url = {https://doi.org/10.3390/s26134253}
}RIS
TY - JOUR TI - Bayesian Conditional GAN for Unsupervised Anomaly Detection in Structural Health Monitoring Time-Series Dataset AU - Yohannes L. Alemu AU - Christian Walther AU - Manuel Schneider AU - Norbert Greifzu AU - Leon Quinten Thiebes AU - Andreas Wenzel AU - Uwe Plank-Wiedenbeck AU - Tom Lahmer PY - 2026 JO - Sensors DO - 10.3390/s26134253 UR - https://doi.org/10.3390/s26134253 ER -
APA
Alemu, Y. L., Walther, C., Schneider, M., Greifzu, N., Thiebes, L. Q., Wenzel, A., Plank-Wiedenbeck, U., & Lahmer, T. (2026). Bayesian Conditional GAN for Unsupervised Anomaly Detection in Structural Health Monitoring Time-Series Dataset. Sensors. https://doi.org/10.3390/s26134253
Source records
- crossref · retrieved 2026-09-26T18:47:16.292Z