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Density estimation is a widely used method to perform unsupervised anomaly detection. By learning the density function, data points with relatively low densities are classified as anomalies. Unfortunately, the presence of anomalies in training data may significantly impact the density estimation process, thereby imposing significant challenges to the use of more sophisticated density estimation methods such as those based on deep neural networks. In this work, we propose RobustRealNVP, a deep density estimation framework that enhances the robustness of flow-based density estimation methods, enabling their application to unsupervised anomaly detection. RobustRealNVP differs from existing flow-based models from two perspectives. First, RobustRealNVP discards data points with low estimated densities during optimization to prevent them from corrupting the density estimation process. Furthermore, it imposes Lipschitz regularization to the flow-based model to enforce smoothness in the estimated density function. We demonstrate the robustness of our algorithm against anomalies in training data from both theoretical and empirical perspectives. The results show that our algorithm achieves competitive results as compared to state-of-the-art unsupervised anomaly detection methods.
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This content will become publicly available on May 29, 2026
Oscillation and amplitude-based unsupervised flight maneuver scoring: A data-driven pilot expertise measure
Objective and scalable pilot assessment is crucial due to rising training demands and subjective evaluation limitations. Existing data-driven methods often require extensive data labelling and focus on maneuver classification over execution quality. Addressing the need for interpretable, label-independent proficiency metrics, this paper introduces an unsupervised pipeline to quantify roll control smoothness during turns from X-Plane logs. The methodology features adaptive trend slicing for segmentation, zero-phase Butterworth filtering (0.6-4.5 Hz) to isolate roll oscillation during turns, and extraction of time domain descriptors (RMS, Peak to Peak, and crest factor). After aggregation and outlier removal, via isolation forest, K-means clustering identified distinct “smooth” vs “oscillatory” performance groups with high statistical validity. Principal Component Analysis (PCA) further derived a continuous “smoothness index,” capturing 79.6% of the variance. This study confirms unsupervised oscillation metrics can differentiate control finesse, offering a quantitative foundation for augmenting training assessment and enabling future calibration against expert evaluations.
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- Award ID(s):
- 2505815
- PAR ID:
- 10613726
- Publisher / Repository:
- International Symposium on Aviation Psychology
- Date Published:
- Format(s):
- Medium: X
- Location:
- Corvalis, OR
- Sponsoring Org:
- National Science Foundation
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