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

机译：使用半监督学习和激光诱导击穿光谱法对炸药进行鉴定和分类

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摘要

Public places are often under threat from explosion events, which pose health and safety risks to the public. Therefore, the detection of explosive materials has become an important concern in the fields of antiterrorism and security. Laser-induced breakdown spectroscopy (LIBS) has been demonstrated to be useful in identifying explosives but has limitations. This study focuses on using semi-supervised learning combined with LIBS for explosive identification. Labeled data were utilized for the construction of a semi-supervised model for distinguishing explosive clusters and improving the accuracy of the IC-nearest neighbor algorithm. The method requires only minimal prior information, and the time for obtaining a large amount of labeled data can be saved. The results of our investigation demonstrated that semi-supervised learning with LIBS can be used to discriminate explosives from interfering substances (plastics) containing similar components. The algorithm exhibits good robustness and practicability.
机译：公共场所经常受到爆炸事件的威胁，爆炸事件对公众构成健康和安全风险。因此，爆炸物的检测已成为反恐与安全领域的重要关注。已证明激光诱导击穿光谱法（LIBS）可用于识别爆炸物，但有局限性。这项研究的重点是结合LIBS使用半监督学习进行爆炸物识别。标记的数据被用于构造一个半监督模型，以区分爆炸性团簇并提高IC近邻算法的准确性。该方法仅需要最少的先验信息，并且可以节省获得大量标记数据的时间。我们的调查结果表明，LIBS的半监督学习可用于区分爆炸物与包含相似成分的干扰物质（塑料）。该算法具有良好的鲁棒性和实用性。

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  • 来源
    《Journal of Hazardous Materials》 |2019年第5期|423-429|共7页
  • 作者

    Wang Qianqian; Teng Geer; Li Chenyu; Zhao Yu; Peng Zhong;

  • 作者单位

    Beijing Inst Technol, Sch Opt & Photon, Beijing 100081, Peoples R China;

    Beijing Inst Technol, Sch Opt & Photon, Beijing 100081, Peoples R China;

    Beijing Inst Technol, Sch Opt & Photon, Beijing 100081, Peoples R China;

    Beijing Inst Technol, Sch Opt & Photon, Beijing 100081, Peoples R China|North Univ China, Sch Informat & Commun Engn, Taiyuan 030051, Shanxi, Peoples R China;

    Beijing Inst Technol, Sch Opt & Photon, Beijing 100081, Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Semi-supervised learning; Explosives detection; LIBS; KNN;

    机译：半监督学习;爆炸物检测;LIBS;KNN;

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