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Youngdoo Son

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NeurIPS Conference 2025 Conference Paper

Adaptive Time Encoding for Irregular Multivariate Time-Series Classification

  • Sangho Lee
  • Kyeongseo Min
  • Youngdoo Son
  • Hyungrok Do

Time series are often irregularly sampled with uneven time intervals. In multivariate cases, such irregularities may lead to misaligned observations across variables and varying observation counts, making it difficult to extract intrinsic patterns and degrading the classification performance of deep learning models. In this study, we propose an adaptive time encoding approach to address the challenge of irregular sampling in multivariate time-series classification. Our approach generates latent representations at learnable reference points that capture missingness patterns in irregular sequences, enhancing classification performance. We also introduce consistency regularization techniques to incorporate intricate temporal and intervariable information into the learned representations. Extensive experiments demonstrate that our method achieves state-of-the-art performance with high computational efficiency in irregular multivariate time-series classification tasks.

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EAAI Journal 2024 Journal Article

Evaluating practical adversarial robustness of fault diagnosis systems via spectrogram-aware ensemble method

  • Hoki Kim
  • Sangho Lee
  • Jaewook Lee
  • Woojin Lee
  • Youngdoo Son

While machine learning models have shown superior performance in fault diagnosis systems, researchers have revealed their vulnerability to subtle noises generated by adversarial attacks. Given that this vulnerability can lead to misdiagnosis or unnecessary maintenance, the assessment of the practical robustness of fault diagnosis models is crucial for their deployment and use in real-world scenarios. However, research on the practical adversarial robustness of fault diagnosis models remains limited. In this work, we present a comprehensive analysis on rotating machinery diagnostics and discover that existing attacks often over-estimate the robustness of these models in practical settings. In order to precisely estimate the practical robustness of models, we propose a novel method that unveils the hidden risks of fault diagnosis models by manipulating the spectrum of signal frequencies—an area that has been rarely explored in the domain of adversarial attacks. Our proposed attack, Spectrogram-Aware Ensemble Method (SAEM), the hidden vulnerability of fault diagnosis systems through achieving a higher attack performance in practical black-box settings. Through experiments, we reveal the potential dangers of employing non-robust fault diagnosis models in real-world applications and suggest directions for future research in industrial applications.

Details DOI
v2026.09.13