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DT4X+, an enhanced version of DT4X that modifies the construction of training sets and the symbolic-regression loss so that expressions separate the target classes while preserving the coherence of non-target classes, becomes fully consistent with ARR properties.
An explainable GNN-based anomaly detection framework is proposed that shifts the perspective from sensor-level anomalies to component-level diagnosis, hypothesizing that anomalous measurements are symptoms of altered inter-sensor influences.
Experimental results show that CANARI outperforms the compared baselines that generally use a dual-threshold mechanism (one for anomalies and one for near-anomalies) and provides a proactive solution for anticipating anomalies before they occur, offering a promising approach for resilience, predictive maintenance, and...
CLOE, a new method that combines an autoencoder for dimensionality reduction with a Christoffel Function--based detector applied in the latent space, is introduced and a novel loss function that leverages the Christoffel Function to guide the autoencoder toward representations that better capture the support of the nor...
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