Self-attention-based architectures, particularly ViTs, have proven effective in this context by enabling richer feature extraction from complex physiological data than traditional convolutional ...

Global and Local Feature Extraction Using a Convolutional Autoencoder and Neural Networks for Diagnosing Centrifugal Pump Mechanical Faults Centrifugal pumps are important types of electro-mechanical ...

The experimental results show that this method can effectively integrate the channel attention module and the fully convolutional autoencoder. Although it is an unsupervised feature learning model, it ...

Motivated by recent advances in self-supervised learning, we propose Hierarchical Contrastive Masked Autoencoder (HiCMAE), a novel self-supervised framework that leverages large-scale self-supervised ...

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The network architecture is exhibits in Figure 2. The key technical contribution of our method is a convolutional autoencoder-based boundary and mask adversarial learning framework, which uses both ...

We discuss the relationship between these features and the specific prediction task. Lastly, we indicate that CAE might not be effective in feature extraction on one dataset, but domain adaptation ...

As the number of smart meters increases, compression of metering data becomes essential for data transmission, storing and processing perspectives. Specifically, feature extraction can be used for the ...