A sparse stacked denoise autoencoder(SSDAE) based anomaly detection model is proposed, which uses the autoencoder model to capture the nonlinear feature ...

In this paper, a novel nonlinear process monitoring method based on stacked denoising autoencoder (SDAE) and k-nearest neighbor(kNN) rule is proposed.

This paper proposes a novel feature learning method, stacked convolutional sparse denoising auto-encoder (SCSDAE) for wafer map pattern recognition (WMPR) in ...

This study proposes a deep learning method based on wavelet decomposition and stacked denoising autoencoder for detecting anomalies in noisy UAV data. The ...

This paper proposes a new stacked pruning sparse denoising autoencoder (sPSDAE) model for intelligent fault diagnosis of rolling bearings.

Automated feature learning for nonlinear process monitoring–An approach using stacked denoising autoencoder and k-nearest neighbor rule. J. Process Control.

Tech Science Press is a publisher of peer-reviewed, open access journals since its establishment in 1997.

Mar 27, 2024 · Autoencoders have been extensively used in the development of recent anomaly de- tection techniques. The premise of their application is ...

Feb 1, 2023 · An AI-enabled anomaly detection method is proposed to monitor industrial processes. •. The feature fusion strategy is designed to learn ...

Anomaly Detection: The process of detectingdata instances that significantly deviate from the majority of the whole dataset. Contributed by Chunyang Zhang.