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Related Experiment Video

Updated: Jan 17, 2026

Design and Analysis for Fall Detection System Simplification
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Rapid few-shot tabular machine learning for Φ-OTDR event classification.

Zheyuan Lin, Xinhang Lin, Wanxin Li

    Optics Express
    |September 23, 2025
    PubMed
    Summary
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    This study introduces a novel few-shot learning method for classifying events in phase-sensitive optical time-domain reflectometry (Φ-OTDR) systems. The approach significantly improves accuracy with limited data, enabling faster infrastructure monitoring.

    Area of Science:

    • Sensor Technology
    • Machine Learning
    • Data Science

    Background:

    • Distributed optical fiber sensing using phase-sensitive optical time-domain reflectometry (Φ-OTDR) is vital for infrastructure monitoring.
    • Challenges include limited data, high computational costs, and slow training for event classification.

    Purpose of the Study:

    • To develop an efficient event classification method for Φ-OTDR systems using limited data.
    • To integrate tabular machine learning with few-shot learning (FSL) for improved performance and speed.

    Main Methods:

    • Extracted time-domain and frequency-domain features from Φ-OTDR data, converting them into structured tabular data.
    • Employed a pre-trained tabular prior-data fitted network (TabPFN) model for few-shot learning.
    • Evaluated performance using five-fold cross-validation and independent testing with varying sample sizes.

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    Main Results:

    • Achieved 90.0% cross-validation accuracy and 89.0% test accuracy with only 10 samples per class.
    • Performance improved to 97.78% (CV) and 100% (test) with more samples (50-60 per class).
    • Model fitting completed within seconds, demonstrating low computational overhead.

    Conclusions:

    • The proposed FSL method significantly enhances event classification accuracy in Φ-OTDR systems, especially with limited data.
    • The approach offers superior performance compared to traditional ML and other FSL baselines in few-shot scenarios.
    • The method's efficiency and accuracy support rapid industrial deployment for applications like pipeline monitoring.