• Journal of Radiation Protection and Research
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• v.42 no.2
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• pp.91-97
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• 2017

Background: A gamma energy identifying algorithm using spectral decomposition combined with smoothing method was suggested to confirm the existence of the artificial radio isotopes. The algorithm is composed by original pattern recognition method and smoothing method to enhance the performance to identify gamma energy of radiation sensors that have low energy resolution. Materials and Methods: The gamma energy identifying algorithm for the compact radiation sensor is a three-step of refinement process. Firstly, the magnitude set is calculated by the original spectral decomposition. Secondly, the magnitude of modeling error in the magnitude set is reduced by the smoothing method. Thirdly, the expected gamma energy is finally decided based on the enhanced magnitude set as a result of the spectral decomposition with the smoothing method. The algorithm was optimized for the designed radiation sensor composed of a CsI (Tl) scintillator and a silicon pin diode. Results and Discussion: The two performance parameters used to estimate the algorithm are the accuracy of expected gamma energy and the number of repeated calculations. The original gamma energy was accurately identified with the single energy of gamma radiation by adapting this modeling error reduction method. Also the average error decreased by half with the multi energies of gamma radiation in comparison to the original spectral decomposition. In addition, the number of repeated calculations also decreased by half even in low fluence conditions under $10^4$ ($/0.09cm^2$ of the scintillator surface). Conclusion: Through the development of this algorithm, we have confirmed the possibility of developing a product that can identify artificial radionuclides nearby using inexpensive radiation sensors that are easy to use by the public. Therefore, it can contribute to reduce the anxiety of the public exposure by determining the presence of artificial radionuclides in the vicinity.

• Journal of Radiation Protection and Research
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• v.43 no.3
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• pp.85-96
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• 2018

Background: A variety of inorganic scintillators have been developed and improved for use in radiation detection and measurement, and in situ gamma-ray spectrometry in the environment remains an important area in nuclear safety. In order to verify the feasibility of promising scintillators in an actual environment, a performance test is necessary to identify gamma-ray peaks and calculate the radioactivity from their net count rates in peaks. Materials and Methods: Among commercially available scintillators, $LaBr_3(Ce)$ scintillators have so far shown the highest energy resolution when detecting and identifying gamma-rays. However, the intrinsic background of this scintillator type affects efficient application to the environment with a relatively low count rate. An algorithm to subtract the intrinsic background was consequently developed, and the in situ calibration factor at 1 m above ground level was calculated from Monte Carlo simulation in order to determine the radioactivity from the measured net count rate. Results and Discussion: The radioactivity of six natural radionuclides in the environment was evaluated from in situ gamma-ray spectrometry using an $LaBr_3(Ce)$ detector. The results were then compared with those of a portable high purity Ge (HPGe) detector with in situ object counting system (ISOCS) software at the same sites. In addition, the radioactive cesium in the ground of Jeju Island, South Korea, was determined with the same assumption of the source distribution between measurements using two detectors. Conclusion: Good agreement between both detectors was achieved in the in situ gamma-ray spectrometry of natural as well as artificial radionuclides in the ground. This means that an $LaBr_3(Ce)$ detector can produce reliable and stable results of radioactivity in the ground from the measured energy spectrum of incident gamma-rays at 1 m above the ground.

• Nuclear Engineering and Technology
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• v.50 no.8
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• pp.1324-1329
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• 2018

This study presents a practical application of complementary safety indicators, which can be applied in a safety assessment of a radioactive waste repository by excluding a biosphere simulation and comparing the artificial radiation originating from the repository with the background natural radiation. Complementary safety indicators (radiotoxicity flux from geosphere and radiotoxicity concentration in seawater) were applied in the safety assessment of a rock-cavern type low and intermediate level radioactive waste (LILW) repository in the Republic of Korea. The natural radionuclide ($^{40}K$, $^{226,228}Ra$, $^{232}Th$, and $^{234,235,238}U$) concentrations in the groundwater and seawater at the Gyeongju LILW repository site were measured. Based on the analyzed concentrations of natural radionuclides, the levels of natural radiation were determined to be $8.6{\times}10^{-5}$ - $8.0{\times}10^{-4}Sv/m^2/yr$ and $6.95{\times}10^{-5}Sv/m^3$ for radiotoxicity flux from the geosphere and radiotoxicity concentration in seawater, respectively. From simulation results obtained using a Goldsim-based safety assessment model, it was determined that the radiotoxicity of radionuclides released from the repository is lower than that of the natural radionuclides inherently present in the natural waters. The applicability of the complementary safety indicators to the safety case was discussed with regard to reduction of the uncertainty associated with biosphere simulations, and communication with the public.

• Proceedings of the Korean Radioactive Waste Society Conference
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• 2003.11a
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• pp.534-538
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• 2003

Final disposal of radioactive waste generated from Nuclear Power Plant (NPP) requires the detailed knowledge of the natures and quantities of radionuclides in waste package. Many of these radionuclides are difficult to measure and expensive to assay. Thus it is suggested to the Indirect method by which the concentrations of DTM (Difficult-to-Measure) nuclide is decided using the relation of concentrations (Scaling Factor) between Key (Easy-to-Measure) nuclide and DTM nuclide with measured concentrations of Key nuclide. In general, scaling factor is determined by using of log mean average (LMA) and regression. These methods are adequate to apply most corrosion product nuclides. But in case of fission product nuclides and some corrosion product nuclides, the predicted values aren't well matched with the original values. In this study, the models using artificial neural network (ANN) for C-14 and Sr-90 are compared with those using LMA and regression. The assessment of models is executed in the two parts divided by a training part and a validation part. For all of two nuclides in the training part, the predicted values using ANN are well matched with the measured values compared with those using LMA and regression. In the validation part, the accuracy of the predicted values using ANN is better than that using LMA and is similar to or better than that using regression. It is concluded that the predicted values using ANN model are better than those using conventional model in some nuclides and ANN model can be used as the complement of LMA and regression model.

• 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.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.20 no.3
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• pp.269-278
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• 2022

The buffer is a critical barrier component in an engineered barrier system, and its purpose is to prevent potential radionuclides from leaking out from a damaged canister by filling the void in the repository. No experimental parameters exist that can describe the buffer expansion phenomenon when Kyeongju bentonite, which is a buffer candidate material available in Korea, is exposed to groundwater. As conventional experiments to determine these parameters are time consuming and complicated, simple swelling pressure tests, numerical modeling, and machine learning are used in this study to obtain the parameters required to establish a numerical model that can simulate swelling. Swelling tests conducted using Kyeongju bentonite are emulated using the COMSOL Multiphysics numerical analysis tool. Relationships between the swelling phenomenon and mechanical parameters are determined via an artificial neural network. Subsequently, by inputting the swelling tests results into the network, the values for the mechanical parameters of Kyeongju bentonite are obtained. Sensitivity analysis is performed to identify the influential parameters. Results of the numerical analysis based on the identified mechanical parameters are consistent with the experimental values.

• 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.

• 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.

• 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.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.