Semi-supervised prototype network based on compact-uniform-sparse representation for rotating machinery few-shot class incremental fault diagnosis-Reference-Cited by-同舟云学术

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Semi-supervised prototype network based on compact-uniform-sparse representation for rotating machinery few-shot class incremental fault diagnosis

Published:2024-12 Issue: Volume:255 Page:124660
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Zhang Yu,Han Dongying^ORCID,Shi Peiming

Publisher

Elsevier BV

Reference54 articles.

1. Towards solving NLP tasks with optimal transport loss;Bhardwaj;Journal of King Saud University - Computer and Information Sciences,2022

2. Improvement of K-means cluster quality by post processing resulted clusters;Borlea;Procedia Computer Science,2022

3. A unified form of fuzzy C-means and K-means algorithms and its partitional implementation;Borlea;Knowledge-Based Systems,2021

4. A multi-gradient hierarchical domain adaptation network for transfer diagnosis of bearing faults;Chen;Expert Systems with Applications,2023

5. An automatic speed adaption neural network model for planetary gearbox fault diagnosis;Chen;Measurement,2021

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