A fusion of a time-series autoencoder and an image EfficientNet, with a frozen encoder and an ontology-based penalty, reaches 93% weighted F1 for next-step anomaly prediction in a rocket assembly dataset.
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NSF-MAP: Neurosymbolic Multimodal Fusion for Robust and Interpretable Anomaly Prediction in Assembly Pipelines
A fusion of a time-series autoencoder and an image EfficientNet, with a frozen encoder and an ontology-based penalty, reaches 93% weighted F1 for next-step anomaly prediction in a rocket assembly dataset.