Convolutional Neural Networks (CNNs): Including C3D and ... a novel hybrid approach for anomaly detection in crowded scenes by synergistically integrating Variational Autoencoders (VAEs) and ...

To detect anomalies in OLTCs and analyze the generated vibration signals, a convolutional variational autoencoder (CVAE) is utilized, trained individually for each transformer family. This approach ...

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 ...

In the field of dimensionality reduction, deep learning offers many outstanding methods, including representative ones such as Stacked Autoencoders (SAE), Variational Autoencoders (VAE), and the ...

This GitHub repository contains two directories : (1) variational autoencoder (VAE) and (2) denoising convolutional VAE (DCVAE). This contains programs for VAE and DCVAE models used in our work. For ...

Next, Dear-DIA uses a variational autoencoder to extract the peak features ... Dear-DIA uses a convolutional neural network to recalculate the peak shape similarity of fragments in the same ...