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

• 해양환경안전학회지
• /
• 제27권5호
• /
• pp.618-629
• /
• 2021

2011년 동일본대지진에 의해 발생한 후쿠시마 원자력 발전소 사고와 최근 국내 지진 발생 빈도의 증가는 원자력 발전소의 지진 안전성에 대한 불안감을 야기하였다. 더불어 최근(2021년) 일본 동경전력은 후쿠시마 원전 오염수의 태평양 방류를 결정하였으며, 이로 인해 국내외 수산물을 통한 방사능 오염 가능성이 높아지면서 국민들의 우려가 급증하고 있다. 후쿠시마 원전사고 이후 해양으로의 인공방사능 유입에 관한 연구는 국제적으로 많이 이루어졌으나, 한국인의 주요 식재료인 동아시아 연근해의 수산물에서 인공방사능의 분포 현황 및 축적에 대한 연구는 상대적으로 부족한 실정이다. 따라서 이 논문에서는 후쿠시마 원전사고 이후, 국내산 수산물에서의 원전 기원 인공방사능(예, ¹³⁷Cs, ^239+240Pu, ⁹⁰Sr 등)의 분포 특성과 관련한 최근 연구 사례들을 소개하고자 한다. 또한, 후쿠시마 원전오염수의 방류와 더불어 2030년까지 계획된 중국의 신규 원전 시설로 인한 향후 한반도 주변해역의 방사능 유출 영향에 대한 대비 및 사전 연구가 필요한 시점이기에 향후 연구 방향들을 제안하고자 한다.

• Nuclear Engineering and Technology
• /
• 제54권1호
• /
• pp.269-274
• /
• 2022

An artificial neural network (ANN) that identifies radionuclides from low-count gamma spectra of a NaI scintillator is proposed. The ANN was trained and tested using simulated spectra. 14 target nuclides were considered corresponding to the requisite radionuclide library of a radionuclide identification device mentioned in IEC 62327-2017. The network shows an average identification accuracy of 98.63% on the validation dataset, with the gross counts in each spectrum N_c = 100~10000 and the signal to noise ratio SNR = 0.05-1. Most of the false predictions come from nuclides with low branching ratio and/or similar decay energies. If the N_c>1000 and SNR>0.3, which is defined as the minimum identifiable condition, the averaged identification accuracy is 99.87%. Even when the source and the detector are covered with lead bricks and the response function of the detector thus varies, the ANN which was trained using non-shielding spectra still shows high accuracy as long as the minimum identifiable condition is satisfied. Among all the considered nuclides, only the identification accuracy of ²³⁵U is seriously affected by the shielding. Identification of other nuclides shows high accuracy even the shielding condition is changed, which indicates that the ANN has good generalization performance.

• Journal of Radiation Protection and Research
• /
• 제30권4호
• /
• pp.167-174
• /
• 2005

The concentrations of natural and artificial radionuclides in soil and gamma ray dose rate in air at 233 locations in Korea have been determined. The national mean concentrations of $^{232}Th,\;^{226}Ra,\;^{40}K,\;^{137}Cs\;and\;^{90}Sr$ in soil were $60{\pm}31,\;33{\pm}14,\;673{\pm}238,\;35{\pm}9.3\;and\;5.0{\pm}3.4\;Bq\;kg^{-1}$, respectively. The mean gamma-ray dose rate at 1 m above the ground was $7918\;nGy\;h^{-1}$. $^{137}Cs$ concentration had highly significant correlation with organic matter content and cation exchange capacity. $^{90}Sr$ concentration had slightly coherent with pH. The results have been compared with other global radioactivity and radiation measurements.

• Nuclear Engineering and Technology
• /
• 제50권8호
• /
• pp.1355-1363
• /
• 2018

Many radionuclides exist in normal environment and artificial radionuclides also can be detected. The radionuclides ($^{131}I$) are widely used for labeling compounds and radiation therapy. In Korea, the radionuclide ($^{131}I$) is produced at the Radioisotope Production Facility (RIPF) at the Korea Atomic Energy Research Institute in Daejeon. The residents around the RIPF assume that $^{131}I$ detected in environmental samples is produced from RIPF. To ensure the safety of the residents, the radioactive concentration of $^{131}I$ near the RIPF was investigated by monitoring environmental samples along the Gap River. The selected geographical places are near the nuclear installation, another possible location for $^{131}I$ detection, and downstream of the Gap River. The first selected places are the "front gate of KAERI", and the "Donghwa bridge". The second selected place is the sewage treatment plant. Therefore, the Wonchon bridge is selected for the upstream of the plant and the sewage treatment plant is selected for the downstream of the plant. The last selected places are the downstream where the two paths converged, which is Yongshin bridge (in front of the cogeneration plant). In these places, environmental samples, including sediment, fish, surface water, and aquatic plants, were collected. In this study, the radioactive iodine ($^{131}I$) detection along the Gap River will be investigated.

• 대한환경공학회지
• /
• 제36권11호
• /
• pp.747-752
• /
• 2014

인공방사성핵종 I-131은 물속에서 요오드 이온($^{131}I^-$), 요오드산염 이온($^{131}IO_3{^-}$) 등 대부분 이온상태로 존재했으며 접촉시간이 짧을 경우, 정수약품(PACl)과 분말활성탄(PAC)에 의해 제거되지 않는 것으로 나타났다. 원수 탁도 증가에 따른 I-131의 제거 효과는 없었으나 분말활성탄과 I-131과의 접촉시간 증가에 따른 I-131의 제거율은 증가하는 것을 확인하였다. 혼합 주입(PACl + PAC)에 의한 I-131을 40% 이상 제거하기 위해서는 PACl 24 mg/L 이상, PAC 40 mg/L 이상을 주입해야 하는 것을 알 수 있었다. 모래여과에 의한 I-131은 제거되지 않은 것으로 나타났으며, 입상활성탄(GAC)에 의한 제거율은 신탄, 구탄 모두에서 최소검출한계 0.10 Bq/L 미만으로 거의 100% 제거됨을 알 수 있었다. 막에 의한 I-131의 제거율은 정밀여과막에 의해서는 제거되지 않았으며 역삼투막에서는 92%가 제거되었다.

• 한국대기환경학회지
• /
• 제11권4호
• /
• pp.351-359
• /
• 1995

In order to investigate geochemical behaviors of artificial radionuclide($^{137}$ Cs), the fallout deposition of arificial radioisotope($^{137}$ Cs) was measured from May to October in 1994 at the Korea Ocean Research & Development Institute(KORDI), Ansan, Kyunggido, Korea. And to study radioisotopic behavior and cumulative action in soil, soil samples were collected from Kwang-Leung Forest, Kyunggidom and artificial radioisotope ($^{137}$ Cs) and natural radioisotope($^{210}$ Pb) were identified. The amount of $^{137}$ Cs in atmosphere collected by wet deposition process in May was found to be 4.95 to 11.96mBq m$^{-2}$ whereas the amounts of $^{137}$ Cs by dry deposition process in May and October were found to be 4.0mBq g$^{-1}$ and 3.0mBq g$^{-1}$ , respectively. The amount of $^{137}$ Cs accumulated in soil was measured to be 311mBq cm$^{-2}$ , which contained 83% of the total inputs from atmospheric fallout (374 mBq cm$^{-2}$ ) since 1960s. In addition, the accumulation rate and the annual flux of $^{210}$ Pb into soils were 0.32cm yr$^{-1}$ and 34 mBq cm$^{-2}$ yr$^{-1}$ , respectively. Conclusively, it was found that arificial radioisotopes were mainly from the stratosphere and soil resupension of continental China through the troposphere.

• 대한환경공학회지
• /
• 제37권8호
• /
• pp.465-471
• /
• 2015

불포화대 암반 단열에서 방사성 핵종인 $^3H$, $^{90}Sr$ and $^{99}Tc$의 흡착실험이 진행되었다. 천층처분시설의 인공방벽을 통과해 누출된 방사성 핵종은 빗물이나 공극수에 의해 불포화대 암반 단열을 통하여 지하수로 도달하게 된다. 그러므로 처분장의 장기간 안전점검을 위해, 불포화대 암반 단열을 통한 방사성 핵종의 거동을 연구하는 것이 중요하다. 천층처분주변에서 채취된 불포화대 암반 단열 샘플을 이용하여 X-ray microtomography 분석을 수행하였고, 회분식 흡착실험을 이용하여 방사성 핵종인 $^3H$, $^{90}Sr$ and $^{99}Tc$의 흡착실험이 진행되었다. 암반 단열의 충전물질로 불석광물 및 점토광물 존재 시 중흡착성 핵종인 $^{90}Sr$의 흡착 분배계수 값이 충전물질이 존재하지 않을 때 보다 높게 나타내었다. 본 연구를 통해, 암반 단열 특성화 및 방사성 핵종의 흡착분배계수를 구했으며, 불포화대 암반 단열을 통한 핵종의 거동이 지연됨을 이해할 수 있었다.

• Nuclear Engineering and Technology
• /
• 제54권8호
• /
• pp.3034-3042
• /
• 2022

The results of radioecological monitoring of the cooling pond Beloyarsk NPP (Russia) have been presented. The influence of waste technological waters of thermal and fast NPP reactors on the content of artificial radionuclides in surface waters and bottom sediments of the Beloyarsk reservoir has been studied. The long-term dynamics of the specific activity of ⁶⁰Co, ⁹⁰Sr, ¹³⁷Cs and ³H in the main components of the freshwater ecosystem at different distances from the source of radionuclide discharge has been estimated. Critical radionuclides (⁶⁰Co and ¹³⁷Cs), routes of their entry and periods of maximum discharge of radioisotopes into the cooling pond have been determined. It is shown that the technology of electricity generation at Beloyarsk NPP, based on fast reactors, has a much smaller effect on the flow of artificial radionuclides into the freshwater ecosystem of the reservoir. During the entire period of monitoring studies, the decrease in the specific activity of radionuclides from NPP origin in surface waters was 4.3-74.5 times, in bottom sediments 10-505 times. The maximum discharge of artificial radionuclides into the reservoir was noted during the period of restoration and decontamination work aimed at eliminating emergencies at the AMB thermal reactors of the first stage of the Beloyarsk NPP.

• Journal of Radiation Protection and Research
• /
• 제48권4호
• /
• pp.175-183
• /
• 2023

Background: Recently, biological adsorbents have been developed for removing radionuclides from radioactive liquid waste due to their high selectivity, eco-friendliness, and renewability. However, since they can be damaged by radiation in radioactive waste, a method for estimating the bio-adsorbent performance as a time should consider the radiation damages in terms of their renewability. This paper aims to develop a simulation method that applies a deep learning technique to rapidly and accurately estimate the adsorption performance of bio-adsorbents when inserted into liquid radioactive waste. Materials and Methods: A model that describes various interactions between a bio-adsorbent and liquid has been constructed using numerical methods to estimate the adsorption capacity of the bio-adsorbent. To generate datasets for machine learning, Monte Carlo N-Particle (MCNP) simulations were conducted while considering radioactive concentrations in the adsorbent column. Results and Discussion: Compared with the result of the conventional method, the proposed method indicates that the accuracy is in good agreement, within 0.99% and 0.06% for the R² score and mean absolute percentage error, respectively. Furthermore, the estimation speed is improved by over 30 times. Conclusion: Note that an artificial neural network can rapidly and accurately estimate the survival rate of a bio-adsorbent from radiation ionization compared with the MCNP simulation and can determine if the bio-adsorbents are reusable.