An unsupervised autoencoder approach achieves moderate success for anomaly detection (accuracy = 0.881) but struggles with recall (0.070). These findings highlight the trade-off between detection ...

Anomalies are ubiquitous in all scientific fields and can express an unexpected event due to incomplete knowledge about the data distribution or an unknown process that suddenly comes into play and ...

Each small blue arrow represents a neural weight, which is just a number, typically between about -2 and +2. Weights are sometimes called trainable parameters. The small red arrows are special weights ...

Given the problem that the existing series arc fault identification methods use existing features such as the time-frequency domain of the current signal as the basis for identification, resulting in ...

Reconstruction-based methods play an important role in unsupervised anomaly detection in images. Ideally, we expect a perfect reconstruction for normal samples and poor reconstruction for abnormal ...