Volume 30 Issue 8
Aug.  2018
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Article Navigation >  High Power Laser and Particle Beams  > 2018  >  30(8): 086002
Ma Tianjun, Sun Jianhai, Hao Baoliang, et al. Microfabrication of folded waveguide using UV-LIGA for 220 GHz traveling wave tube[J]. High Power Laser and Particle Beams, 2015, 27: 024101. doi: 10.11884/HPLPB201527.024101
Citation: Zheng Yifan, Zeng Zhi, Zeng Ming, et al. Discrimination of drugs and explosives in cargo inspections byapplyingmachine learningin muon tomography[J]. High Power Laser and Particle Beams, 2018, 30: 086002. doi: 10.11884/HPLPB201830.180062
shu

Discrimination of drugs and explosives in cargo inspections byapplyingmachine learningin muon tomography

doi: 10.11884/HPLPB201830.180062
  • 1.

    National Engineering Laboratory, Beijing 100084, China

  • 2.

    Key Laboratory of Particle & Radiation lmaging of Ministry of Education (Tsinghua University), Beijing 100084, China

  • 3.

    Department of Engineering Physics, Tsinghua University, Beijing 100084, China

  • 4.

    Department of Physics, Royal Institute of Technology, Albanova University Center, Stockholm 10691, Sweden

Funds:

National Natural Science Foundation of China 11035002

More Information
  • Author Bio:

    Zheng Yifan(1993—), female, master candidate, engaged in muon tomography; yfzheng11@126.com

  • Corresponding author: Zeng Zhi(1978—), male, associate professor; zengzhi@tsinghua.edu.cn
  • Received Date: 2018-03-05
  • Rev Recd Date: 2018-04-25
  • Publish Date: 2018-08-15
    Abstract
  • A previously under-explored difficulty in cargo inspections is how to efficiently detect drugs and explosives concealed in large dense metals.Cosmic ray muon tomography is a promising non-destructive imaging technique to solve the problem because muons are naturally generated in the atmosphere and have sufficient energy to completely penetrate large dense containers.In this work it is investigated that to what extent drugs and explosives of a certain size could be discriminated from air background and metals by muon tomography within acceptable measuring time.A Geant4 Monte Carlo simulation is built based on the Tsinghua University MUon Tomography facility (TUMUTY) and a support vector machine (SVM) classifier based on machine learning is trained to differentiate drugs and explosives from air background and metals automatically.For various 20 cm×20 cm×20 cm objects, with 10 min to 30 min measuring time, drugs and explosives could be discriminated from background and metals by muon tomography with an error rate of about 1%.With 1 min, the error rate deteriorates to 12.9%.
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    • References(11)
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