no code implementations • 13 Jun 2022 • Mustafa Abdallah, Byung-Gun Joung, Wo Jae Lee, Charilaos Mousoulis, John W. Sutherland, Saurabh Bagchi
In this paper, we analyze four datasets from sensors deployed from manufacturing testbeds.
no code implementations • 3 Feb 2022 • Wo Jae Lee, Karim Helwani, Arvindh Krishnaswamy, Srikanth Tenneti
The presented approach doesn't assume the presence of labeled anomalies in the training dataset and uses a novel deep neural network architecture to learn the temporal dynamics of the multivariate time series at multiple resolutions while being robust to contaminations in the training dataset.
no code implementations • 11 Feb 2021 • Mustafa Abdallah, Wo Jae Lee, Nithin Raghunathan, Charilaos Mousoulis, John W. Sutherland, Saurabh Bagchi
While there is a rich literature on anomaly detection in many IoT-based systems, there is no existing work that documents the use of ML models for anomaly detection in digital agriculture and in smart manufacturing systems.