Domain Adversarial Graph Convolutional Network for Fault Diagnosis Under Variable Working Conditions
Unsupervised domain adaptation (UDA)-based methods have made great progress in mechanical fault diagnosis under variable working conditions. In UDA, three types of information, including class label, ...
Abstract: Graph convolutional neural networks can effectively process geometric data and thus have been successfully used in point cloud data representation. However, existing graph-based methods ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results
Feedback