• Nuclear Engineering and Technology
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• 제54권12호
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• pp.4671-4678
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• 2022

The purpose of this comprehensive research is to observe the impact of scintillator crystal type on entire detection process. For this aim, MCNPX (version 2.6.0) is used for designing of a physical plastic scintillation detector available in our laboratory. The modelled detector structure is validated using previous studies in the literature. Next, different types of plastic scintillation crystals were assessed in the same geometry. Several fundamental detector properties are determined for six different plastic scintillation crystals. Additionally, the deposited energy quantities were computed using the MCNPX code. Although six scintillation crystals have comparable compositions, the findings clearly indicate that the crystal composed of PVT 80% + PPO 20% has superior counting and detecting characteristics when compared to the other crystals investigated. Moreover, it is observed that the highest deposited energy amount, which is a result of the highest collision number in the crystal volume, corresponds to a PVT 80% + PPO 20% crystal. Despite the fact that plastic detector crystals have similar chemical structures, this study found that performing advanced Monte Carlo simulations on the detection discrepancies within the structures can aid in the development of the most effective spectroscopy procedures by ensuring maximum efficiency prior to and during use.

• Nuclear Engineering and Technology
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• 제54권9호
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• pp.3398-3402
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• 2022

We fabricated a 15 m long position-sensitive plastic scintillation optical fiber (PSOF) detector consisting of a PSOF, two photomultiplier tubes, four fast amplifiers, and a digitizer. A single PSOF was used as a sensing part to estimate the gamma-ray source position, and ¹³⁷Cs, an uncollimated solid-disk-type radioactive isotope, was used as a gamma-ray emitter. To improve the sensitivity, accuracy, and measurement time of a PSOF detector compared to those of previous studies, the performance of the amplifier was optimized, and the digital signal processing (DSP) was newly designed in this study. Moreover, we could measure very low dose rates of gamma-rays with high sensitivity and accuracy in a very short time using our proposed PSOF detector. The results of this study indicate that it is possible to accurately and quickly locate the position of a very low dose rate gamma-ray source in a wide range of contaminated areas using the proposed position-sensitive PSOF detector.

• Journal of Radiation Protection and Research
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• 제46권4호
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• pp.204-212
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• 2021

Background: Identification of radioisotopes for plastic scintillation detectors is challenging because their spectra have poor energy resolutions and lack photo peaks. To overcome this weakness, many researchers have conducted radioisotope identification studies using machine learning algorithms; however, the effect of data normalization on radioisotope identification has not been addressed yet. Furthermore, studies on machine learning-based radioisotope identifiers for plastic scintillation detectors are limited. Materials and Methods: In this study, machine learning-based radioisotope identifiers were implemented, and their performances according to data normalization methods were compared. Eight classes of radioisotopes consisting of combinations of ²²Na, ⁶⁰Co, and ¹³⁷Cs, and the background, were defined. The training set was generated by the random sampling technique based on probabilistic density functions acquired by experiments and simulations, and test set was acquired by experiments. Support vector machine (SVM), artificial neural network (ANN), and convolutional neural network (CNN) were implemented as radioisotope identifiers with six data normalization methods, and trained using the generated training set. Results and Discussion: The implemented identifiers were evaluated by test sets acquired by experiments with and without gain shifts to confirm the robustness of the identifiers against the gain shift effect. Among the three machine learning-based radioisotope identifiers, prediction accuracy followed the order SVM > ANN > CNN, while the training time followed the order SVM > ANN > CNN. Conclusion: The prediction accuracy for the combined test sets was highest with the SVM. The CNN exhibited a minimum variation in prediction accuracy for each class, even though it had the lowest prediction accuracy for the combined test sets among three identifiers. The SVM exhibited the highest prediction accuracy for the combined test sets, and its training time was the shortest among three identifiers.

• 방사선산업학회지
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• 제11권3호
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• pp.157-165
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• 2017

In this paper, we calculate the light photons collection efficiency of large-volume plastic scintillation detector mainly used for radiation portal monitor (RPM). A Monte Carlo light photon transport code, DETECT2000, were used to quantitatively evaluate light collection efficiency of plastic scintillation detector. DETECT2000 calculated the placement of light collection efficiency based on the energy spectrum. We calculated the light collection efficiency relative to the position of the energy spectrum that proportional to the placement of the source. The $850{\times}285{\times}65mm^3$ size of polyvinyl toluene (PVT) scintillator was used for measurements. Through DETECT2000 simulation, the light collection efficiency of $5{\times}5$ arrays were calculated and verification was performed by comparing with experimentally measured. And then, the corrected MCNP simulation by applying the light collection efficiency in $21{\times}13$ arrays was compared and analyzed. Comparing the Monte Carlo simulation with measured results, it shows an average difference of 10.1% in $5{\times}5$ arrays. Particularly, about twice of the difference was found in the edge of first column, which coupled with PMT. In whole $5{\times}5$ array, the overall ratio was the same except for the first column. And then comparing the energy spectra of the $21{\times}13$ array with and without the light collection efficiency, it shows a difference of 6.69% in Compton edge area. The DETECT2000 based light collection efficiency simulation showed well agreement with the point source experiment. And comparing with measured energy spectra, we could compare the differences according to whether or not the light collection efficiency was applied. As a results, it is possible to increase the accuracy and reliability of Monte Carlo simulation results by pre-calculating the light collection efficiency according to the PVT geometry by using the DETECT2000.

• Nuclear Engineering and Technology
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• 제54권3호
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• pp.1037-1048
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• 2022

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 insitu monitoring system.

• 천문학회보
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• 제34권1호
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• pp.188.2-188.2
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• 2009

• 방사성폐기물학회지
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• 제7권2호
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• pp.87-92
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• 2009

방사선량 측정을 위하여 섬광체와 광섬유를 이용한 원격 측정용 센서를 개발하였다. 유리 광섬유와 상용화된 플라스틱섬광체로 원거리 측정 가능성을 시험하였고, 에폭시 수지로 자체 개발한 섬광검출소재로 방사선 측정센서로써의 성능을 평가하였다. 에폭시 수지와 유기섬광물질의 배합별 물질 특성을 측정하여 최적의 조건을 도출하였다. 광섬유와 섬광체를 연결할 때, 불완전한 접속으로 인한 광 손실을 줄이기 위하여 섬광검출소재 제조 과정 중 소재내로 광섬유를 삽입하여 일체형으로 센서를 제조하였다. 일체형 센서는 유리광섬유의 단점을 보완하여 플라스틱 광섬유를 적용하였으며, 방사선 반응 체적별 검출효율을 평가하기 위하여 검출소재 밑단으로부터 일정 거리의 광섬유를 배치하여 측정하였다. 개발한 방사선 검출용 센서는 오염도 원거리 측정뿐만 아니라 측정센서로써의 적용도 가능할 것으로 예상된다.

• 분석과학
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• 제18권6호
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• pp.495-499
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• 2005

방사선에 대한 거부감에도 불구하고 RT(radiation technology)의 발전과 더불어 이용량뿐만 아니라 적용분야도 계속적으로 증가하고 있다. 이러한 방사선을 안전하게 관리하기 위해서는 방사선 측정 소재의 개발이 요구된다. 본 연구에서는 표면오염 측정용 장비의 소재로 주로 이용되고 있는 얇은 필름 형태의 플라스틱 검출기를 제조하였다. 플라스틱 유기섬광체는 다양한 형상으로 쉽게 제조가 가능하고 제조 방법이 간단한 용매법을 이용하였다. 이러한 용매법을 이용하기 위해서 높은 투명도를 지니면서 용매에 쉽게 녹을 수 있는 고분자 소재인 폴리설폰을 선정하여 제조하였으며, 방사선 측정용 섬광체로서 광학적 특성 및 방사선 검출 등을 평가하였다.