• Journal of Astronomy and Space Sciences
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• v.37 no.4
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• pp.229-236
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• 2020

There has been increasing necessity of more precise prediction and measurements of aviation radiation in Korea. For our air crew and passengers' radiation safety, we develop our own radiation prediction model of KREAM. In this paper, we validate the KREAM model based on comparison with Liulin observations. During early three months of this year, we perform total 25 experiments to measure aviation radiation exposure using Liulin-6K in commercial flights. We found that KREAM's result is very well consistent with Liulin observation in general. NAIRAS shows mostly higher results than Liulin observation, while CARI-6M shows generally lower results than the observations. The percent error of KREAM compared with Liulin observation is 10.95%. In contrast, the error for NAIRAS is 43.38% and 22.03% for CARI-6M. We found that the increase of the altitude might cause sudden increase in radiation exposure, especially for the polar route. As more comprehensive and complete analysis is required to validate KREAM's reliability to use for the public service, we plan to expand these radiation measurements with Liulin and Tissue Equivalent Proportional Counter (TEPC) in the near future.

• Journal of Radiation Industry
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• v.17 no.4
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• pp.551-557
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• 2023

In recent years, radiation has become a socially important issue, increasing the need for accurate prediction of radiation levels. In this study, machine learning-based models such as Multiple Linear Regression (MLR), Random Forest (RF), XGBoost, and LightGBM, which predict the dose rate by time(nSv h^-1) by selecting only important variables, were used, and the correlation between temperature, humidity, cumulative precipitation, wind direction, wind speed, local air pressure, sea pressure, solar radiation, and radiation dose rate (nSv h^-1) was analyzed by collecting weather data and radiation dose rate for about 6 months in Jangseong, Jeollanam-do. As a result of the evaluation based on the RMSE (Root Mean Squared Error) and R-Squared (R-Squared coefficient of determination) scores, the RMSE of the XGBoost model was 22.92 and the R-Squared was 0.73, showing the best performance among the models used. As a result of optimizing hyperparameters of all models using the GridSearch method and comparing them by adding variables inside the measuring instrument, it was confirmed that the performance improved to 2.39 for RMSE and 0.99 for R-Squared in both XGBoost and LightGBM.

• Journal of the Korean Society of Radiology
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• v.17 no.2
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• pp.277-284
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• 2023

Since the radiological risk is different depending on the working environment, protection measures and policies must be developed through analysis of the field area environment. Evaluating the characteristics of the field area that uses radiation should be conducted through comparative analysis with other industries, not just the numerical value of the field area. In this study, evaluation factors were derived from exposure records by the department to compare radiation occupational exposure records by sector. And then, we developed a polygonal model for comparative analysis and applied them to eight work fields through ten evaluation factors. Based on the occupational exposure record in 2020, a polygonal model was applied to compare and evaluate the characteristics of the radiation work area. Through this, the usefulness of the polygonal model was confirmed, and protection policy measures for the industry were proposed.

• Journal of the Korean Society of Radiology
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• v.12 no.4
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• pp.459-466
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• 2018

Risk of radiation worker and radiation workplace are being mainly assessed by exposure dose. But, the radiation used in radiation workplace and the work environment are different. Because the nondestructive work environment varies depending on the work subject, the existence and nonexistence of shielding board, and so on. So, we need to consider the various factors in effective radiation protection aspect. We conducted a survey of radiation workers with over two years' experience in NDT workplace and heared the thoughts of experts. As a result, radiation source, exposure dose, current status of workplace management, workers with personel dosimetry problem and status of periodic regulatory inspection were chosen as main factors of radiation risk index model. Also, we primarily set weighting factors in order of importance based on questionnaires. Finally, we determined weighting factor for details of main factors through the professional advice. Therefore, we will be able to develop the radiation risk index model for assessing the risk of nondestructive test workplace based on main factors that are selected through this study.

• Journal of Korea Water Resources Association
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• v.49 no.2
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• pp.165-175
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• 2016

In order to examine the applicability, the evaporation estimation approaches based on solar radiation are classified into 3 different model groups (Model groups A, B, and C) in this study. Each group is tested in the 6 study stations (Seoul, Daejeon, Jeonju, Busan, Mokpo, and Jeju). The model parameters of each model group are estimated and verified with measured pan evaporation data. The applicability of verified model groups are compared with results of Penman (1948) combination approach. Nash-Sutcliffe (N-S) efficiency coefficients greater than 0.663 in all study stations indicate satisfactory estimates of evaporation. On the other hand, in the model verification process, N-S efficiency coefficients greater than 0.526 in all study stations indicate also satisfactory estimates of evaporation. However, N-S efficiency coefficients in all study cases except Model groups B and C in Busan are less than those of Penman (1948) combination approach. Therefore, it is concluded in this study that the evaporation estimation approaches based on solar radiation have capability to replace Penman (1948) combination approach for the estimation of evaporation in case that some meteorological data (wind speed, relative humidity) are missing or not measured.

• Journal of Advanced Navigation Technology
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• v.25 no.6
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• pp.569-575
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• 2021

Deep learning shows differences in prediction performance depending on data quality and model. This study uses various input data and multiple deep learning models to build an optimal deep learning model for predicting solar radiation, which has the most influence on power generation prediction. did. As the input data, the weather data of the Korea Meteorological Administration and the clairvoyant meteorological image were used by segmenting the image of the Korea Meteorological Agency. , comparative evaluation, and predicting solar radiation by constructing multiple deep learning models connecting the models with the best error rate in each model. As an experimental result, the RMSE of model A, which is a multiple deep learning model, was 0.0637, the RMSE of model B was 0.07062, and the RMSE of model C was 0.06052, so the error rate of model A and model C was better than that of a single model. In this study, the model that connected two or more models through experiments showed improved prediction rates and stable learning results.

• Atmosphere
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• v.24 no.4
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• pp.471-479
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• 2014

A road surface temperature prediction model (UM-Road) using input data of the Unified Model (UM) output and road physical properties is developed and verified with the use of the observed data at road weather information system. The UM outputs of air temperature, relative humidity, wind speed, downward shortwave radiation, net longwave radiation, precipitation and the road properties such as slope angles, albedo, thermal conductivity, heat capacity at maximum 7 depth are used. The net radiation is computed by a surface radiation energy balance, the ground heat flux at surface is estimated by a surface energy balance based on the Monin-Obukhov similarity, the ground heat transfer process is applied to predict the road surface temperature. If the observed road surface temperature exists, the simulated road surface temperature is corrected by mean bias during the last 24 hours. The developed UM-Road is verified using the observed data at road side for the period from 21 to 31 March 2013. It is found that the UM-Road simulates the diurnal trend and peak values of road surface temperature very well and the 50% (90%) of temperature difference lies within ${\pm}1.5^{\circ}C$ (${\pm}2.5^{\circ}C$) except for precipitation case.

• Atmosphere
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• v.14 no.2
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• pp.11-22
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• 2004

A technique on atmospheric correction algorithm to the multi-band reflectance of Landsat TM imagery has been developed using an atmospheric radiation transfer model for eliminating the atmospheric and surface diffusion effects. Despite the fact that the technique of satellite image processing has been continually developed, there is still a difference between the radiance value registered by satellite borne detector and the true value registered at the ground surface. Such difference is caused by atmospheric attenuations of radiance energy transfer process which is mostly associated with the presence of aerosol particles in atmospheric suspension and surface irradiance characteristics. The atmospheric reflectance depend on atmospheric optical depth and aerosol concentration, and closely related to geographical and environmental surface characteristics. Therefore, when the effects of surface diffuse and aerosol reflectance are eliminated from the satellite image, it is actually corrected from atmospheric optical conditions. The objective of this study is to develop an algorithm for making atmospheric correction in satellite image. The study is processed with the correction function which is developed for eliminating the effects of atmospheric path scattering and surface adjacent pixel spectral reflectance within an atmospheric radiation model. The diffused radiance of adjacent pixel in the image obtained from accounting the average reflectance in the $7{\times}7$ neighbourhood pixels and using the land cover classification. The atmospheric correction functions are provided by a radiation transfer model of LOWTRAN 7 based on the actual atmospheric soundings over the Korean atmospheric complexity. The model produce the upward radiances of satellite spectral image for a given surface reflectance and aerosol optical thickness.

• Journal of Biosystems Engineering
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• v.43 no.4
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• pp.352-361
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• 2018

Purpose: The objective of this study was to develop a multilayer perceptron (MLP) model to estimate solar radiation using a solar module. Methods: Data for the short-circuit current of a solar module and other environmental parameters were collected for a year. For MLP learning, 14,400 combinations of input variables, learning rates, activation functions, numbers of layers, and numbers of neurons were trained. The best MLP model employed the batch backpropagation algorithm with all input variables and two hidden layers. Results: The root-mean-squared error (RMSE) of each learning cycle and its average over three repetitions were calculated. The average RMSE of the best artificial neural network model was $48.13W{\cdot}m^{-2}$. This result was better than that obtained for the regression model, for which the RMSE was $66.67W{\cdot}m^{-2}$. Conclusions: It is possible to utilize a solar module as a power source and a sensor to measure solar radiation for an agricultural sensor node.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 1998.10a
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• pp.189-199
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• 1998

Wave-induced current model is developed in our study and this model is composed with wave transform model and current model. Two types of wave model are used in our study, one is Copeland(1985) type which is applied in the offshore region and the other is Watanabe and Maruyama(1984) type which is applied in the surf zone. The depth-integrated and time-averaged governing equation of an unsteady nonlinear form is used in the wave induced current model. Lateral mising, radiation stresses, surface and bottom stresses are considered in our current model. Copeland's(1985) relult is used to calculate radiation stress and Berkmeir & Darlymple's(1976) is used as a surface friction formula. Numerical solutions are obtained by Leendertse scheme and compared with Noda's(1974) experimental results for the uniform slope coastal region test and Nishimura & Maruyama's(1985) experimental relults and numerical simulation results for the detached breakwater test. The results from our wave model show good agreement with the others and also show nonlinear effects around the detached breakwater. Wave induced current model is developed in this study and this model shows nonlinear effects around the detached breakwater and can be applied in the surf zone and also consider the friction stresses.