Long Short-Term Memory Autoencoder
No mentions found
This entity hasn't been tracked yet, or Iris is still building its knowledge base.
Related Articles from SNS
Detecting Cyber Attacks in Power System AGC Using a Drifted Ornstein-Uhlenbeck Process
arXiv:2606.02075v1 Announce Type: new Abstract: The Automatic Generation Control (AGC) system, reliant on real-time measurements over communication networks, is susceptible to stealthy false data injection attacks (FDIAs), risking equipment damage and economic losses. We propose a robust FDIA detection method using maximum likelihood estimation (MLE) of a drifted multivariate Ornstein-Uhlenbeck (OU) process.
Anomaly Detection for Electro-Hydrostatic Actuators using LSTM Autoencoder
Announce Type: new Abstract: Electro-Hydrostatic Actuators (EHAs) are widely used in aerospace and industrial systems, where timely detection of sensor anomalies is essential to ensure safe and reliable operation. However, the large volume and high sampling frequency of EHA sensor data pose challenges for accurate and efficient anomaly detection. Conventional statistical and classical machine-learning methods such as Z-score, Interquartile Range (IQR), Median Absolute Deviation (MAD),...
Food industries embrace AI sensors to improve efficiencies
Food industries embrace AI sensors to improve efficiencies Lisa Lock Scientific Editor Andrew Zinin Lead Editor Food waste is a nagging problem that weighs heavily on global food production, distribution and sales industries—but an emerging generation of AI sensors is providing a raft of fresh solutions. The embrace of AI in food industries has been swift, which is why Flinders University researchers have worked with an international research team to build the first comprehensive overview of...