Optical topological insulators, as an emerging type of photonic material, present substantial benefits for optical communication. The advanced pattern recognition capabilities of deep learning have ...

Figure 1. The structural flowchart of the method proposed in this paper. 3. Stacked Convolutional Autoencoder with Fusion Selection Kernel Attention Mechanism 3.1. Construction of Network Structure In ...

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 ...

3.1 2D convolutional autoencoder An autoencoder (AE) is a neural network model primarily used for unsupervised learning. It achieves dimensionality reduction and feature extraction by learning to ...

However, some PS methods have spectral and spatial distortions that influence subsequent analyses. Thus, this study aimed to develop a PS method based on convolutional autoencoder (CAE) for Landsat 8 ...

Each thin blue arrow represents a neural weight, which is just a number, typically between about -2 and +2. Weights are sometimes called trainable parameters. The small red arrows are special weights ...

Depending on the specific soft sensing problem and data characteristics, suitable deep learning model architectures can be developed, including deep neural networks (DNN), convolutional neural ...

This project is a practice implementation of an autoencoder, The primary use case for this autoencoder is for anomaly detection in sales data, but it can be adapted for other purposes. The autoencoder ...