ODDS webpage is here. Note that the datasets contains not only time series, but also other data types (videos, texts, and graphs). Request access to this dataset here. Contains 4 folders, A1, A2, A3, ...

Abstract: Anomaly detection modeled as a one-class classification is an essential task for tool condition monitoring (TCM) when only the normal data are available. To confront with the real-world ...

Abstract: This paper proposes a nonlinear version of an anomaly detector with a robust regression detection strategy for hyperspectral imagery. In the traditional Mahalanobis distance-based ...

In this thesis we propose a new form of Variational Autoencoder called the Conditional Latent Space Variational Autoencoder or CL-VAE. By conditioning on a known label in a dataset we can decide what ...

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In the LSTM-AE model, anomaly detection is performed using reconstruction error, defined as the difference between the input sequence and its reconstruction. For the GNN model, anomalies are ...

This enables the synchronous execution of weapon detection and face recognition using one efficient framework. The experiments using existing benchmark datasets validate the performance, achieving ...

AI plays a role in improving defect capture rate and distinguishing between yield-killing and nuisance defects. New developments in wafer edge inspection are proving essential to bonded wafer yields.