Unsupervised domain adaptation with GSDE achieves ~80% accuracy in cross-process TIG-laser weld penetration prediction, improving supervised baselines by over 43%.
Time-frequency supervised contrastive learning via pseudo -labeling: An unsupervised domain adaptation network for rolling bearing fault diagnosis under time- varying speeds
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A cross-process welding penetration status prediction algorithm based on unsupervised domain adaptation in laser and TIG welding
Unsupervised domain adaptation with GSDE achieves ~80% accuracy in cross-process TIG-laser weld penetration prediction, improving supervised baselines by over 43%.