• 한국방사성폐기물학회:학술대회논문집
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• 한국방사성폐기물학회 2003년도 가을 학술논문집
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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.

• 한국방사성폐기물학회:학술대회논문집
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• 한국방사성폐기물학회 2004년도 학술논문집
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• pp.254-254
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• 2004

Final disposal of radioactive waste generated from Nuclear Power Plant (NPP) requires the detailed information about the characteristics and the quantities of radionuclides in waste package. Most radionuclides are difficult to measure and expensive to assay. Thus it is suggested to the indirect method by which the concentration of the Difficult-to-Measure (DTM) nuclide is estimated using the correlations of concentration-it is called the scaling factor-between Easy-to-Measure (Key) nuclides and DTM nuclides with the measured concentration of the Key nuclide.(omitted)

• 한국방사선학회논문지
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• 제5권1호
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• pp.5-10
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• 2011

검출한계에 대한 기본개념을 기초로 백그라운드 측정시간과 시료측정시간을 고려하였고, 환경시료중에서 육상시료(하천토, 표층토양, 식수, 지하수, 지표수, 솔잎, 쑥) 분석에서 백그라운드 계측시간과 시료 측정시간의 변화에 따른 MDA 값들을 비교하였다. 물시료 분석결과를 살펴보면 대부분 시료에서 불검출로 나타났으며, 육상시료 분석결과 대부분의 시료에서 "과학기술부고시 제 2008-28호"의 검출하한치 미만으로 측정되었으나, 일부 시료에서는 인공방사성핵종인 $^{137}Cs$이 검출되었다. 이는 과거 50.60년대 행해졌던 대기권 핵실험에 의한 낙진 및 소련의 체르노빌 원전사고 등에 의한 영향으로 우리나라뿐만 아니라 전 세계적으로 검출되고 있는 수준이다. 또한 $^{137}Cs$의 동위원소이며, 상대적으로 반감기가 짧은 $^{134}Cs$가 모든 시료에 대해서 검출되지 않는 것으로 보아 원전운영에 의한 영향이 아님을 알 수 있다.

• 방사성폐기물학회지
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• 제2권1호
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• pp.35-40
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• 2004

Final disposal of radioactive waste generated from Nuclear Power Plant (NPP) requires the detailed information about the characteristics and the quantities of radionuclides in waste package. Most of these radionuclides are difficult to measure and expensive to assay. Thus it is suggested to the indirect method by which the concentration of the Difficult-to-Measure (DTM) nuclide is estimated using the correlations of concentration - it is called the scaling factor - between Easy-to-Measure (Key) nuclides and DTM nuclides with the measured concentration of the Key nuclide. In general, the scaling factor is determined by the log mean average (LMA) method and the regression method. However, these methods are inadequate to apply to fission product nuclides and some activation product nuclides such as 14$^{C}$ and 90$^{Sr}$ . In this study, the artificial neural network (ANN) method is suggested to improve the conventional SF determination methods - the LMA method and the regression method. The root mean squared errors (RMSE) of the ANN models are compared with those of the conventional SF determination models for 14$^{C}$ and 90$^{Sr}$ in two parts divided by a training part and a validation part. The SF determination models are arranged in the order of RMSEs as the following order: ANN model

• 방사선산업학회지
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• 제17권1호
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• pp.19-32
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• 2023

The amount of radioactive waste is expected to dramatically increase with decommissioning of nuclear power plants such as Kori-1, the first nuclear power plant in South Korea. Accurate nuclide analysis is necessary to manage the radioactive wastes safely, but research on verification of radionuclide analysis has yet to be well established. This study aimed to develop the technology that can verify the results of radionuclide analysis based on artificial intelligence. In this study, we propose an anomaly detection algorithm for inspecting the analysis error of radionuclide. We used the data from 'Updated Scaling Factors in Low-Level Radwaste' (NP-5077) published by EPRI (Electric Power Research Institute), and resampling was performed using SMOTE (Synthetic Minority Oversampling Technique) algorithm to augment data. 149,676 augmented data with SMOTE algorithm was used to train the artificial neural networks (classification and anomaly detection networks). 324 NP-5077 report data verified the performance of networks. The anomaly detection algorithm of radionuclide analysis was divided into two modules that detect a case where radioactive waste was incorrectly classified or discriminate an abnormal data such as loss of data or incorrectly written data. The classification network was constructed using the fully connected layer, and the anomaly detection network was composed of the encoder and decoder. The latter was operated by loading the latent vector from the end layer of the classification network. This study conducted exploratory data analysis (i.e., statistics, histogram, correlation, covariance, PCA, k-mean clustering, DBSCAN). As a result of analyzing the data, it is complicated to distinguish the type of radioactive waste because data distribution overlapped each other. In spite of these complexities, our algorithm based on deep learning can distinguish abnormal data from normal data. Radionuclide analysis was verified using our anomaly detection algorithm, and meaningful results were obtained.

• 방사성폐기물학회지
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• 제20권1호
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• pp.99-110
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• 2022

In decommissioning a nuclear power plant, numerous concrete structures need to be demolished and decontaminated. Although concrete decontamination technologies have been developed globally, concrete cutting remains problematic due to the secondary waste production and dispersion risk from concrete scabbling. To minimize workers' radiation exposure and secondary waste in dismantling and decontaminating concrete structures, the following conceptual designs were developed. A micro-blast type scabbling technology using explosive materials and a multi-dimensional contamination measurement and artificial intelligence (AI) mapping technology capable of identifying the contamination status of concrete surfaces. Trials revealed that this technology has several merits, including nuclide identification of more than 5 nuclides, radioactivity measurement capability of 0.1-10⁷ Bq·g^-1, 1.5 kg robot weight for easy handling, 10 cm robot self-running capability, 100% detonator performance, decontamination factor (DF) of 100 and 8,000 cm²·hr^-1 decontamination speed, better than that of TWI (7,500 cm²·hr^-1). Hence, the micro-blast type scabbling technology is a suitable method for concrete decontamination. As the Korean explosives industry is well developed and robot and mapping systems are supported by government research and development, this scabbling technology can efficiently aid the Korean decommissioning industry.