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Discrimination of drugs and explosives in cargo inspections by applying machine learning in muon tomography
KTH, School of Engineering Sciences (SCI), Physics. KTH, School of Biotechnology (BIO), Centres, Albanova VinnExcellence Center for Protein Technology, ProNova. National Engineering Laboratory, Beijing 100084, China; Key Laboratory of Particle & Radiation Imaging of Ministry of Education (Tsinghua University), Beijing 100084, China; Department of Engineering Physics, Tsinghua University, Beijing 100084, China.
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2018 (English)In: Qiangjiguang Yu Lizishu/High Power Laser and Particle Beams, ISSN 1001-4322, Vol. 30, no 8, article id 086002Article in journal (Refereed) Published
Abstract [en]

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%.

Place, publisher, year, edition, pages
Editorial Office of High Power Laser and Particle Beams , 2018. Vol. 30, no 8, article id 086002
Keywords [en]
Drugs and explosives, Machine learning, Muon tomography, Scattering density
National Category
Physical Sciences
Identifiers
URN: urn:nbn:se:kth:diva-247201DOI: 10.11884/HPLPB201830.180062Scopus ID: 2-s2.0-85062015316OAI: oai:DiVA.org:kth-247201DiVA, id: diva2:1306039
Note

QC 20190423

Available from: 2019-04-23 Created: 2019-04-23 Last updated: 2019-04-23Bibliographically approved

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CiteExportLink to record
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