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Identification and classification of explosives using semi-supervised learning and laser-induced breakdown

Qianqian Wang1, Geer Teng1, Chenyu Li1

  • 1School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China.

Journal of Hazardous Materials
|February 21, 2019
PubMed
Summary

Semi-supervised learning enhances Laser-Induced Breakdown Spectroscopy (LIBS) for explosive detection. This method accurately identifies explosives, even with similar substances like plastics, saving time on data labeling.

Keywords:
Explosives detectionKNNLIBSSemi-supervised learning

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Area of Science:

  • Analytical Chemistry
  • Spectroscopy
  • Machine Learning

Background:

  • Explosion events in public places pose significant security risks.
  • Accurate detection of explosive materials is crucial for antiterrorism and public safety.
  • Laser-Induced Breakdown Spectroscopy (LIBS) shows promise for explosive identification but has limitations.

Purpose of the Study:

  • To improve the accuracy and efficiency of explosive identification using LIBS.
  • To explore the application of semi-supervised learning in conjunction with LIBS for distinguishing explosive materials.
  • To reduce the reliance on extensive labeled datasets for explosive detection models.

Main Methods:

  • Development of a semi-supervised learning model utilizing labeled data.
  • Integration of the semi-supervised model with LIBS for explosive analysis.
  • Application of the K-nearest neighbor algorithm enhanced by the semi-supervised approach.
  • Discrimination of explosives from interfering substances, such as plastics with similar elemental compositions.

Main Results:

  • The combined semi-supervised learning and LIBS approach demonstrated effective discrimination of explosives.
  • The method successfully distinguished explosives from interfering plastic materials.
  • The algorithm showed good robustness and practicality in real-world scenarios.
  • Significant time savings were achieved by reducing the need for large amounts of labeled data.

Conclusions:

  • Semi-supervised learning offers a viable and efficient enhancement to LIBS for explosive detection.
  • The developed method provides a robust solution for identifying explosive threats in security applications.
  • This approach minimizes prior information requirements and speeds up the data acquisition process for model training.