• Journal of Radiation Protection and Research
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• 제35권2호
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• pp.85-90
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• 2010

본 연구에서는, 에너지에 따른 peak의 상대효율을 구하였으며, 검출기의 응답함수를 작성하였다. 이를 위해 고효율, 고분해능을 가진 HPGe 검출기(지름 78.7 mm, 길이 86.5 mm)를 이용하였으며 콤프턴 억제용으로 NaI 검출기를 사용하였다. 감마선 스펙트럼은 $^{23}Na$(p, $\gamma$)$^{24}Mg$ 및 $^{27}Al$(p, $\gamma$)$^{28}Si$ 반응을 이용하여 얻었으며, 이 때 입사 입자의 에너지는 각각 $E_p$ = 1424 keV 및 $E_p$ = 992 keV 이었다. 한편 스펙트럼 측정은 입사 빔 방향에 대해 $55^{\circ}$에서 하였으며, 사용한 가속기는 일본 동경공업대학의 3 MeV Pelletron 가속기를 이용하였다. 검출기의 응답함수는 1.2 MeV에서 9.4 MeV까지 0.75 MeV 간격으로 작성하였다.

• Nuclear Engineering and Technology
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• 제43권1호
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• pp.13-18
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• 2011

In this study, simulations were done of a 661.6 keV line from a point source of $^{137}Cs$ housed in a lead shield. When increasing the scattering angle from 60 to 120 degrees with a 6061 aluminum alloy target placed at angles of 30 and 45 degrees to the incident beam, the spectra showed that the single scattering component increases and that the multiple scattering component decreases. The investigation of the single and multiple scattering components was carried out using a MCNP5 simulation code. The component of the single Compton scattering photons is proportional to the target electron density at the point where the scattering occurs. The single scattering peak increases according to the thickness of the target and saturates at a certain thickness. The signal-to-noise ratio was found to decrease according to the target thickness. The simulation was experimentally validated by measurements. These results will be used to determine the best conditions under which this method can be applied to testing electron densities or to assess the thickness of samples to locate defects in them.

• Nuclear Engineering and Technology
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• 제53권1호
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• pp.199-207
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• 2021

This paper deals with accurate image reconstruction of gamma camera using a coded-aperture mask based on pixel-type CsI(Tl) scintillator coupled with silicon photomultipliers (SiPMs) array. Coded-aperture imaging (CAI) system typically has a smaller effective viewing angle than Compton camera. Thus, if the position of the gamma source to be searched is out of the fully-coded field-of-view (FCFOV) region of the CAI system, artifacts can be generated when the image is reconstructed by using the conventional cross-correlation (CC) method. In this work, we propose an effective method for more accurate reconstruction in CAI considering the source distribution of partially-coded field-of-view (PCFOV) in the reconstruction in attempt to overcome this drawback. We employed an iterative algorithm based on compressed-sensing (CS) and compared the reconstruction quality with that of the CC algorithm. Both algorithms were implemented and performed a systematic Monte Carlo simulation to demonstrate the possiblilty of the proposed method. The reconstructed image qualities were quantitatively evaluated in sense of the root mean square error (RMSE) and the peak signal-to-noise ratio (PSNR). Our simulation results indicate that the proposed method provides more accurate location information of the simulated gamma source than the CC-based method.

• Nuclear Engineering and Technology
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• 제54권12호
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• pp.4684-4692
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• 2022

Radionuclide identification is an important part of the nuclear material identification system. The development of artificial intelligence and machine learning has made nuclide identification rapid and automatic. However, many methods directly use existing deep learning models to analyze the gamma-ray spectrum, which lacks interpretability for researchers. This study proposes an explainable radionuclide identification algorithm based on the convolutional neural network and class activation mapping. This method shows the area of interest of the neural network on the gamma-ray spectrum by generating a class activation map. We analyzed the class activation map of the gamma-ray spectrum of different types, different gross counts, and different signal-to-noise ratios. The results show that the convolutional neural network attempted to learn the relationship between the input gamma-ray spectrum and the nuclide type, and could identify the nuclide based on the photoelectric peak and Compton edge. Furthermore, the results explain why the neural network could identify gamma-ray spectra with low counts and low signal-to-noise ratios. Thus, the findings improve researchers' confidence in the ability of neural networks to identify nuclides and promote the application of artificial intelligence methods in the field of nuclide identification.

• Journal of Radiation Protection and Research
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• 제45권3호
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• pp.117-129
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• 2020

Background: Radiation portal monitors (RPMs) involving plastic scintillators installed at the border inspection sites can detect illicit trafficking of radioactive sources in cargo containers within seconds. However, RPMs may generate false alarms because of the naturally occurring radioactive materials. To manage these false alarms, we previously suggested an energy-weighted algorithm that emphasizes the Compton-edge area as an outstanding peak. This study intends to evaluate the identification of radioactive sources using an improved energy-weighted algorithm. Materials and Methods: The algorithm was modified by increasing the energy weighting factor, and different peak combinations of the energy-weighted spectra were tested for source identification. A commercialized RPM system was used to measure the energy-weighted spectra. The RPM comprised two large plastic scintillators with dimensions of 174 × 29 × 7 ㎤ facing each other at a distance of 4.6 m. In addition, the in-house-fabricated signal processing boards were connected to collect the signal converted into a spectrum. Further, the spectra from eight radioactive sources, including special nuclear materials (SNMs), which were set in motion using a linear motion system (LMS) and a cargo truck, were estimated to identify the source identification rate. Results and Discussion: Each energy-weighted spectrum exhibited a specific peak location, although high statistical fluctuation errors could be observed in the spectrum with the increasing source speed. In particular, ¹³⁷Cs and ⁶⁰Co in motion were identified completely (100%) at speeds of 5 and 10 km/hr. Further, SNMs, which trigger the RPM alarm, were identified approximately 80% of the time at both the aforementioned speeds. Conclusion: Using the modified energy-weighted algorithm, several characteristics of the energy weighted spectra could be observed when the used sources were in motion and when the geometric efficiency was low. In particular, the discrimination between ⁶⁰Co and ⁴⁰K, which triggers false alarms at the primary inspection sites, can be improved using the proposed algorithm.

• Nuclear Engineering and Technology
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• 제55권10호
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• pp.3907-3912
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• 2023

Radiation portal monitors (RPMs) installed at airports and harbors to prevent illicit trafficking of radioactive materials generally use large plastic scintillators. However, their energy resolution is poor and radionuclide identification is nearly unfeasible. In this study, to improve isotope identification, a RPM system based on a multi-array plastic scintillator and convolutional neural network (CNN) was evaluated by measuring the spectra of radioactive sources. A multi-array plastic scintillator comprising an assembly of 14 hexagonal scintillators was fabricated within an area of 50 × 100 cm². The energy spectra of ¹³⁷Cs, ⁶⁰Co, ²²⁶Ra, and ⁴K (KCl) were measured at speeds of 10-30 km/h, respectively, and an energy-weighted algorithm was applied. For the CNN, 700 and 300 spectral images were used as training and testing images, respectively. Compared to the conventional plastic scintillator, the multi-arrayed detector showed a high collection probability of the optical photons generated inside. A Compton maximum peak was observed for four moving radiation sources, and the CNN-based classification results showed that at least 70% was discriminated. Under the speed condition, the spectral fluctuations were higher than those under dwelling condition. However, the machine learning results demonstrated that a considerably high level of nuclide discrimination was possible under source movement conditions.