Convolutional variational autoencoder for real-time monitoring of high-dimensional partially observed ordinal categorical data streams
Zhiwen Fang et al.
What the paper says
In the current rapidly developing technological environment, the integration of artificial intelligence (AI) into quality technology marks a transformative shift, presenting significant opportunities and challenges for online process monitoring and diagnosis. This study introduces a novel convolutional variational autoencoder (CVAE)-based adaptive sampling strategy tailored for high-dimensional partially observed ordinal categorical data streams (HPODS) under resource constraints. The proposed method compresses HPODS into a lower-dimensional latent space and reconstructs outputs from sampled latent vector, enabling effective data reconstruction and real-time monitoring. An adaptive sampling procedure is further developed to dynamically allocate data collection resources, balancing exploration and exploitation. Extensive simulations and a case study on leather defect detection validate the effectiveness of the approach.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.