Reconstruction of Missing Data Completely at Random for Trains Based on Improved GAN

Reconstruction of missing data for heavy-haul trains is critical to ensuring safe train operation. However, existing generative model training methods require a complete dataset, making it difficult for them to address the issue of missing data completely at random. To address this issue, this study...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	Journal of advanced computational intelligence and intelligent informatics Jg. 29; H. 5; S. 1068 - 1076
Hauptverfasser:	He, Jing, Chen, Xin, Zhang, Changfan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Tokyo Fuji Technology Press Co. Ltd 20.09.2025
Schlagworte:	Feature extraction Generative adversarial networks Missing data Reconstruction
ISSN:	1343-0130, 1883-8014
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!