• Nuclear Engineering and Technology
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• v.54 no.12
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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 the Korean Society of Marine Environment & Safety
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• v.27 no.5
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• pp.618-629
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• 2021

The Fukushima nuclear power plant (NPP) accident caused by the East Japan Earthquake in 2011 and the recent increase in the frequency of earthquakes in Korea have caused safety concerns regarding radionuclide exposure. In addition, the Tokyo Electric Power Company (TEPCO) in Japan recently decided to release radionuclide-contaminated water from Fukushima's NPP into the Pacific Ocean, raising public concerns that the possibility of radionuclide contamination through both domestic- and foreign fishery products is increasing. Although many studies have been conducted on the input of artificial radionuclides into the Pacific after the Fukushima NPP accident, studies on the distribution and accumulation of artificial radionuclides in marine products from East Asia are lacking. Therefore, in this study, we attempted to explore recent research on the distribution of artificial radionuclides (e.g., ¹³⁷Cs, ^239+240Pu, ⁹⁰Sr, and etc.) in marine products from Korean seas after the Fukushima NPP accident. In addition, we also discuss future research directions as it is necessary to prepare for likely radiation accidents in the future around Korea associated with the new nuclear facilities planned by 2030 in China and owing to the discharge of radionuclide-contaminated water from the Fukushima NPP.

• Nuclear Engineering and Technology
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• v.54 no.1
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• pp.269-274
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• 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
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• v.30 no.4
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• pp.167-174
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• 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
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• v.50 no.8
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• pp.1355-1363
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• 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.

• Journal of Korean Society of Environmental Engineers
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• v.36 no.11
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• pp.747-752
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• 2014

Iodine-131, an artificial radionuclide, mostly exists as iodide ion ($^{131}I^-$) and iodate ion ($^{131}IO_3{^-}$) in the water, and When a short time contacted, it could not be removed by poly aluminum chloride (PACl) and powdered activated carbon (PAC). Although the removal rate of iodine-131 was not related with turbidity of raw water, it showed linear relationship with contact time with PAC. With the mixture of PACl (24 mg/L or more) and PAC (40 mg/L or more), about 40% of iodine-131 could be removed. Iodine-131 could be removed little by sand filtration, but approximately 100% by granular activated carbon (GAC), both virgin-GAC and spent-GAC. Microfiltration process could remove little iodine-131 while reverse osmosis process could remove about 92% of iodine-131.

• Journal of Korean Society for Atmospheric Environment
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• v.11 no.4
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• pp.351-359
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• 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.

• Journal of Korean Society of Environmental Engineers
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• v.37 no.8
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• pp.465-471
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• 2015

Adsorption experiments for radionuclides such as $^3H$, $^{90}Sr$ and $^{99}Tc$ were conducted using fractured rock collected in unsaturated zone. The released radionuclide through artificial barrier from the near surface repository can be transported by the flow of rainfall or pore water through fractures in unsaturated zone and reach to groundwater flow. Therefore, it is important to investigate transport behavior (retardation) of radionuclides through fractured rock for the safety assessment and long-term performance of repository. Fractured rock samples were collected and characterized by X-ray microtomography (XMT) analysis, which can be used to develop a more robust unsaturated fracture transport model. When fracture-filling materials are exist, distribution coefficient of $^{90}Sr$ is higher than without fracture-filling materials. In this study, batch sorption distribution coefficient ($K_d$) of radionuclide was determined and used to increase our understanding of radionuclide retardtion through fracture-filling materials.

• Nuclear Engineering and Technology
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• v.54 no.8
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• pp.3034-3042
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• 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
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• v.48 no.4
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• pp.175-183
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• 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.