[HTML][HTML] Reviewing federated learning aggregation algorithms; strategies, contributions, limitations and future perspectives
The success of machine learning (ML) techniques in the formerly difficult areas of data
analysis and pattern extraction has led to their widespread incorporation into various …
analysis and pattern extraction has led to their widespread incorporation into various …
[HTML][HTML] Battery safety: Machine learning-based prognostics
Lithium-ion batteries play a pivotal role in a wide range of applications, from electronic
devices to large-scale electrified transportation systems and grid-scale energy storage …
devices to large-scale electrified transportation systems and grid-scale energy storage …
Few shot cross equipment fault diagnosis method based on parameter optimization and feature mertic
H Tao, L Cheng, J Qiu… - Measurement Science and …, 2022 - iopscience.iop.org
With the rapid development of industrial informatization and deep learning technology,
modern data-driven fault diagnosis (MIFD) methods based on deep learning have been …
modern data-driven fault diagnosis (MIFD) methods based on deep learning have been …
Generalized MAML for few-shot cross-domain fault diagnosis of bearing driven by heterogeneous signals
Despite a few recent meta-learning studies have facilitated few-shot cross-domain fault
diagnosis of bearing, they are limited to homogenous signal analysis and have challenges …
diagnosis of bearing, they are limited to homogenous signal analysis and have challenges …
Attention-based deep meta-transfer learning for few-shot fine-grained fault diagnosis
Deep learning-based fault diagnosis methods have made tremendous progress in recent
years; however, most of these methods are coarse grained and data demanding that cannot …
years; however, most of these methods are coarse grained and data demanding that cannot …
Meta-learning approaches for learning-to-learn in deep learning: A survey
Y Tian, X Zhao, W Huang - Neurocomputing, 2022 - Elsevier
Compared to traditional machine learning, deep learning can learn deeper abstract data
representation and understand scattered data properties. It has gained considerable …
representation and understand scattered data properties. It has gained considerable …
Cross-domain fault diagnosis of bearing using improved semi-supervised meta-learning towards interference of out-of-distribution samples
The study of cross-domain semi-supervised fault diagnosis of bearings using meta-learning
technique has important practical significance. However, existing methods fail to consider …
technique has important practical significance. However, existing methods fail to consider …
Domain adaptation meta-learning network with discard-supplement module for few-shot cross-domain rotating machinery fault diagnosis
Y Zhang, D Han, J Tian, P Shi - Knowledge-Based Systems, 2023 - Elsevier
Intelligent diagnostic methods based on deep learning have proven to be effective in
equipment management and maintenance. However, in practical industrial applications in …
equipment management and maintenance. However, in practical industrial applications in …
Diagnosis of arrhythmias with few abnormal ECG samples using metric-based meta learning
Z Liu, Y Chen, Y Zhang, S Ran, C Cheng… - Computers in Biology and …, 2023 - Elsevier
A major challenge in artificial intelligence based ECG diagnosis lies that it is difficult to
obtain sufficient annotated training samples for each rhythm type, especially for rare …
obtain sufficient annotated training samples for each rhythm type, especially for rare …
Few-shot fault diagnosis of turnout switch machine based on semi-supervised weighted prototypical network
The turnout switch machine is a critical equipment of the signal system, which has a
significant influence on the safety of train. However, it is difficult to obtain a mass of labeled …
significant influence on the safety of train. However, it is difficult to obtain a mass of labeled …