Radioisotope identification using sparse representation with dictionary learning approach for an environmental radiation monitoring system

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 70
  • Download : 0
A radioactive isotope identification algorithm is a prerequisite for a low-resolution scintillation detector applied to an unmanned radiation monitoring system. In this paper, a sparse representation with dictionary learning approach is proposed and applied to plastic gamma-ray spectra. Label-consistent K-SVD was used to learn a discriminative dictionary for the spectra corresponding to a mixture of four isotopes (133Ba, 22Na, 137Cs, and 60Co). A Monte Carlo simulation was employed to produce the simulated data as learning samples. Experimental measurement was conducted to obtain practical spectra. After determining the hyper parameters, two dictionaries tailored to the learning samples were tested by varying with the source position and the measurement time. They achieved average accuracies of 97.6% and 98.0% for all testing spectra. The average accuracy of each dictionary was above 96% for spectra measured over 2 s. They also showed acceptable performance when the spectra were artificially shifted. Thus, the proposed method could be useful for identifying radioisotopes in gamma-ray spectra from a plastic scintillation detector even when a dictionary is adapted to only simulated data. Furthermore, owing to the outstanding properties of sparse representation, the proposed approach can easily be built into an in-situ monitoring system.
Publisher
KOREAN NUCLEAR SOC
Issue Date
2022-03
Language
English
Citation

NUCLEAR ENGINEERING AND TECHNOLOGY, v.54, no.3, pp.1037 - 1048

ISSN
1738-5733
DOI
10.1016/j.net.2021.09.032
URI
http://hdl.handle.net/10203/292810
Appears in Collection
NE-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0