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...

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Bibliographic Details

Published in:	Journal of advanced computational intelligence and intelligent informatics Vol. 29; no. 5; pp. 1068 - 1076
Main Authors:	He, Jing, Chen, Xin, Zhang, Changfan
Format:	Journal Article
Language:	English
Published:	Tokyo Fuji Technology Press Co. Ltd 20.09.2025
Subjects:	Feature extraction Generative adversarial networks Missing data Reconstruction
ISSN:	1343-0130, 1883-8014
Online Access:	Get full text
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