GeneA-SLAM2: Dynamic SLAM with AutoEncoder-Preprocessed Genetic Keypoints Resampling and Depth Variance-Guided Dynamic Region Removal This paper introduces GeneA-SLAM2, an RGB-D SLAM system for ...

In complex industrial production environments, the efficacy of fault diagnostic techniques has become increasingly important and can enhance the reliability and safety of systems. In recent years, the ...

Advanced integration of 2DCNN-GRU model for accurate identification of shockable life-threatening cardiac arrhythmias: a deep learning approach ...

In this article, we propose a dual-constraint autoencoder to alleviate the problem of feature confusion through the dual constraint of adversarial learning and global memory bank.

As an alternative, we trained modified autoencoder networks to mimic human-like behavior in a binaural detection task. The autoencoder architecture emphasizes interpretability and, hence, we “opened ...

The code and data of this repository are provided to promote reproducible research. They are not intended for clinical care or commercial use. The software is provided "as is", without warranty of any ...

In chemical plants and other industrial facilities, the rapid and accurate detection of the root causes of process faults is essential for the prevention of unknown accidents. This study focused on ...

A variational autoencoder (VAE) is a deep neural system that can be used to generate synthetic data. VAEs share some architectural similarities with regular neural autoencoders (AEs) but an AE is not ...