Autoencoders have become a hot researched topic in unsupervised learning due to their
ability to learn data features and act as a dimensionality reduction method. With rapid …

Deep learning (DL) has attained remarkable achievements in diagnosing faults for rotary
machineries. Capitalizing on the formidable learning capacity of DL, it has the potential to …

UMAP is a nonparametric graph-based dimensionality reduction algorithm using applied
Riemannian geometry and algebraic topology to find low-dimensional embeddings of …

Many of the existing machine learning algorithms, both supervised and unsupervised,
depend on the quality of the input characteristics to generate a good model. The amount of …

Due to the existence of complex disturbances and frequent switching of operational
conditions characteristics in the real industrial processes, the process data under different …

Semi-supervised learning methods using Generative adversarial networks (GANs) have
shown promising empirical success recently. Most of these methods use a shared …

Generative adversarial networks (GANs) are famous for generating samples by training a
generator and a discriminator via an adversarial procedure. For hyperspectral image …

Classifying hyperspectral images (HSIs) with limited samples is a challenging issue. The
generative adversarial network (GAN) is a promising technique to mitigate the small sample …

Generally, the measured health condition data from mechanical system often exhibits
imbalanced distribution in real-world cases. To enhance fault diagnostic accuracy of the …

Convolutional neural networks (CNNs) have demonstrated outstanding performance on
image classification. To classify the hyperspectral images (HSIs), existing CNN-based …