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A Convolutional Variational Autoencoder (CVAE) was developed for this purpose. We demonstrate the efficacy of our approach using the transient data generated from the simulations. The simulation data ...

This paper innovatively proposes a temporal–spatial pyramid variational autoencoder (TS-PVAE) model for the nonlinear temporal–spatial feature pyramid extraction from multirate data. This structure ...

Next, Dear-DIA uses a variational autoencoder to extract the peak features of fragment ions and maps the features into Euclidean space, and then clusters the features, with different classes of ...

In most of the fault detection methods, the time domain signals collected from the mechanical equipment usually need to be transformed into frequency domain or other high-level data, highly relying on ...

2.2 Feature extraction models. We now present the proposed subject-invariant feature extraction approach and the mTBI-identification model. The proposed model aims to achieve discriminative properties ...

In this letter, we propose a multilevel dual-direction modifying variational autoencoder (MD 2 MVAE) for hyperspectral feature extraction. Its architecture is inspired by the spectral–spatial ...

Uninformative features, especially transformational image features, present a problem for feature extraction in single cell images. It can be hard to disentangle these uninformative features from the ...