In this study, the positions of Cs-137 gamma ray source are estimated from the plastic scintillating fiber bundle sensor with length of 5 m, using machine learning data analysis. Seven strands of plastic scintillating fibers are bundled by black shrink tube and two photomultiplier tubes are used as a gamma ray sensing and light measuring devices, respectively. The dose rate of Cs-137 used in this study is 6 μSv·h−1. For the machine learning modeling, Keras framework in a Python environment is used. The algorithm chosen to construct machine learning model is regression with 15,000 number of nodes in each hidden layer. The pulse-shaped signals measured by photomultiplier tubes are saved as discrete digits and each pulse data consists of 1,024 number of them. Measurements are conducted separately to create machine learning data used in training and test processes. Measurement times were different for obtaining training and test data which were 1 minute and 5 seconds, respectively. It is because sufficient number of data are needed in case of training data, while the measurement time of test data implies the actual measuring time. The machine learning model is designated to estimate the source positions using the information about time difference of the pulses which are created simultaneously by the interaction of gamma ray and plastic scintillating fiber sensor. To evaluate whether the double-trained machine learning model shows enhancement in accuracy of source position estimation, the reference model is constructed using training data with one-time learning process. The double-trained machine learning model is designed to construct first model and create a second training data using the training error and predetermined coefficient. The second training data are used to construct a final model. Both reference model and double-trained models constructed with different coefficients are evaluated with test data. The evaluation result shows that the average values calculated for all measured position in each model are different from 7.21 to 1.44 cm. As a result, by constructing the double-trained machine learning model, the final accuracy shows 80% of improvement ratio. Further study will be conducted to evaluate whether the double-trained machine learning model is applicable to other data obtained from measurement of gamma ray sources with different energy and set a methodology to find optimal coefficient.

[ ]의 토양침적으로 인한 농작물 오염 평가를 위한 동적격실모델이 제시되었다 토양침투(percolation), 쟁기질에 의한 토양혼합(soil mixing), 뿌리흡수(plant uptake), 용출(leaching to a deep soil), 토양고착(fixation to a clay mineral)이 모델에서 고려된 의 주요 이동경로이며 의 토양이동에 대한 토양특성(pH, 점토함량, 유기물함량, 이온교환성 K 농도)의 영향을 반영하기 위하여 Absalom 모델을 적용하였다. 모델의 검증을 위해 다른 토양특성을 가진 17종류의 논토양에서 2년 연속 벼를 재배하면서 수행한 모의침적실험으로부터 구한 벼에 대한 전이계수를 모델에 의한 예측치와 비교하였다. 측정된 벼의 전이계수는 pH와 점토함량 변화에는 뚜렷한 경향을 보여주지 않았으나, 유기물함량의 증가 또는 이온교환성 K 농도의 감소에 따라 다소 증가하는 경향을 보여주었다. 측정된 전이계수는 모델에 의한 예측치와 대체적으로 유사한 값을 가졌다.

Isotopes of alkali and alkaline earth metals (AM and AEM) are the main contributors to the heat load and the radiotoxicity of spent fuel (SF) . These components are separated from the SF and dissolved in a molten LiCl in an electrolytic reduction process. A mass transfer model is developed to describe the diffusion behavior of Cs, Sr, and Ba in the SF into the molten salt. The model is an analytical solution of Fick's second law of diffusion for a cylinder which is the shape of a cathode in the electrolytic reduction process. And the model is also applied to depict the concentration profile of the oxygen ion which is produced by the electrolysis of LiO. The regressed diffusion coefficients of the model correlating the experimentally measured data are evaluated to be greater in the order of Ba, Cs, and Sr for the metal ions and the diffusion of the oxygen ion is slower than the metal ions which implies that different mechanisms govern the diffusion of the metal ions and the oxygen ions in a molten LiCl.