• Nuclear Engineering and Technology
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• v.51 no.8
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• pp.1871-1885
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• 2019

The deterministic MOC code STREAM of the Computational Reactor Physics and Experiment (CORE) laboratory of Ulsan National Institute of Science and Technology (UNIST), was initially designed for the calculation of pressurized water reactor two- and three-dimensional assemblies and cores. Since fast reactors play an important role in the generation-IV concept, it was decided that the code should be upgraded for the analysis of fast neutron spectrum reactors. This paper presents a coupled code - TULIP/STREAM, developed for the fast reactor assembly and core calculations. The TULIP code produces self-shielded multi-group cross-sections using a one-dimensional cylindrical model. The generated cross-section library is used in the STREAM code which solves eigenvalue problems for a two-dimensional assembly and a multi-assembly whole reactor core. Multiplication factors and steady-state power distributions were compared with the reference solutions obtained by the continuous energy Monte-Carlo code MCS. With the developed code, a sensitivity study of the number of energy groups, the order of anisotropic P_N scattering, and the multi-group cross-section generation model was performed on the k_eff and power distribution. The 2D core simulation calculations show that the TULIP/STREAM code gives a k_eff error smaller than 200 pcm and the root mean square errors of the pin-wise power distributions within 2%.

• Geophysics and Geophysical Exploration
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• v.17 no.4
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• pp.242-246
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• 2014

Spectral induced polarization (SIP) is a useful technique, which uses electrochemical properties, for exploration of metallic sulfide minerals. Equivalent circuit analysis is commonly conducted to calculate IP parameters from SIP data. An equivalent circuit model, which indicates the SIP response of rock, has a non-uniqueness problem. For this reason, it is very important to select the proper model for accurate analysis. Thus, this study focused on suggesting a new model, which suitable for the analysis of an anomalous SIP response, such as ore. A suitability of the new model was verified by comparing it with the existing Dias model and Cole-Cole models. Analysis errors were represented as a normalized root mean square error (NRMSE). The analysis result using the Dias model was the NRMSE of 10.50% and was the NRMSE using the Cole-Cole model of 17.03%. Howerver, because the NRMSE of the new model is 0.87%, it is considered that the new model is more useful for analyzing the anomalous SIP data than other models.

• Survey Research
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• v.11 no.1
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• pp.123-144
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• 2010

In new sample design for Korea Housing Survey to research about housing policy, total strata are forty five because individual results of sixteen regions are estimated. The sample size is determined by sample errors of several variables which are the living area, family income, householder income, and living expenses. The sample size of each region is determined by relative standard error of existing result, and the strata sample size is to use the square root proportion allocation. Enumeration districts are sampled by the probability proportion to size systematic sampling in proportion to the enumeration district size, and the systemic sampling to use assortment characteristics. We considered a new apartment complex because of variation reflections which are rebuilder and redevelopment of houses. To get estimators of mean and variance, we used the design weighting, non-response adjusting, and post-stratification. In order to consider estimation efficiency, we calculate the design effect using estimators of variance.

• Journal of The Korean Society of Agricultural Engineers
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• v.52 no.6
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• pp.85-94
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• 2010

The objective of this study is to estimate the flood runoff for three guaged stations within Namgang-Dam watershed which are operated by KWATER. For a flood runoff simulation, HEC-HMS was applied and the simulated runoff was compared with observed from 2004 to 2008. The watershed area of Sancheong, Shinan, and Changchon were 693.6 $km^2$, 413.4 $km^2$, and 346.48 $km^2$, respectively. The average runoff ratio of observed runoff for three watersheds were 0.725, 0.418, and 0.586, respectively. The dominant land cover of three watersheds are forest with the value of 71.6 %, 73.1 %, and 82.0 %. Three different cases according to the potential maximum retention of forest areas for calculating the curve number were applied to decrease the error between the simulated and observed. The simulated peak runoff of case 3 which applied the 90 % of potential maximum retention of curve number which is equivalent to AMCI for all the AMCI, AMCII, and AMCIII conditions showed least root mean square error (RMSE). The case 1, which was suggested by previous study, showed high discrepancy between the simulated and observed. Since the forest area consists of more than 70 % for all three watersheds, the application of curve number for forest is critical to improve the estimation errors. Further research is required to estimate the more accurate curve number for forest area.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.8
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• pp.783-789
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• 2017

In terms of 3D orientation estimation based on nine-axis IMMU(inertial and magnetic measurement unit), there are two disturbance components decreasing estimation accuracy: one is external acceleration disturbing accelerometer's signals and the other is magnetic disturbance related to magnetometer's signals. In order to minimize effects by these two disturbances, two approaches including switching approach and model-based approach have been suggested and further research comparing these two has also been conducted. Nevertheless, effect of disturbance modeling differences on orientation estimation accuracy in model-based approach has not been studied before. This paper compares the recently reported two orientation estimation algorithms that have difference in disturbance models, in order to investigate the effect of disturbance models on accuracy of IMMU-based orientation estimation under various operating conditions. This research shows that the difference in disturbance models leads to difference in process noise covariance matrix. Consequently, this affected the orientation estimation, i.e., the estimation differences between the algorithms were root mean square errors of $1.35^{\circ}$ in average and $3.63^{\circ}$ in yaw estimation.

• Ocean and Polar Research
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• v.43 no.3
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• pp.179-192
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• 2021

In this study, an integrated wave model from global to coastal scales was developed to improve the operational wave prediction performance of the Korean Meteorological Administration (KMA). In this system, the wave model was upgraded to the WaveWatch III version 6.07 with the improved parameterization of the source term. Considering the increased resolution of the wind input field and the introduction of the high-performance KMA 5^th Supercomputer, the spatial resolution of global and regional wave models has been doubled compared to the operational model. The physical processes and coefficients of the wave model were optimized for the current KMA global atmospheric forecasting system, the Korean Integrated Model (KIM), which is being operated since April 2020. Based on the sensitivity experiment results, the wind-wave growth parameter (β_max) for the global wave model was determined to be 1.33 with the lowest root mean square errors (RMSE). The value of β_max showed the lowest error when applied to regional/coastal wave models for the period of the typhoon season when strong winds occur. Applying the new system to the case of August 2020, the RMSE for the 48-hour significant wave height prediction was reduced by 13.4 to 17.7% compared to the existing KMA operating model. The new integrated wave prediction system plans to replace the KMA operating model after long-term verification.

• Journal of the Korean earth science society
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• v.40 no.4
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• pp.406-413
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• 2019

Red-Green-Blue (RGB) imagery techniques are useful for both forecasters and public users because they are intuitively understood, have advantageous visualization, and do not lose observational information. This study presents a novel RGB convective cloud product and its application to tropical cyclone analysis using Communication, Oceanography, and Meteorology (COMS) satellite observations. The RGB convective cloud product was developed using the brightness temperature differences between WV ($6.75{\mu}m$) and IR1 ($10.8{\mu}m$), and IR2 ($12.0{\mu}m$) and IR1 ($10.8{\mu}m$) as well as the brightness temperature in the IR1 bands of the COMS, with the threshold values estimated from the Korea Meteorological Administration (KMA) radar observations and the EUMETSAT RGB recipe. To verify the accuracy of the convective cloud RGB product, the product was applied to the center positions analysis of two typhoons in 2013. Thus, the convective cloud RGB product threshold values were estimated for WV-IR1 (-20 K to 15 K), IR1 (210 K to 300 K), and IR1-IR2 (-4 K to 2 K). The product application in typhoon analysis shows relatively low bias and root mean square errors (RMSE)s of 23 and 28 km for DANAS in 2013, and 17 and 22 km for FRANCISCO in 2013, as compared to the best tracks data from the Regional Specialized Meteorological Center (RSMC) in Tokyo. Consequently, our proposed RGB convective cloud product has the advantages of high accuracy and excellent visualization for a variety of meteorological applications.

• Journal of Advanced Navigation Technology
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• v.23 no.3
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• pp.221-227
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• 2019

In this paper, interacting multiple model (IMM) filter was designed that guarantees a stable navigation performance even in the unstable satellite navigation position. In order to design IMM filter in INS / GPS integrated navigation system, sub filter of the IMM filter is defined as Kalman filter. In the IMM filter configuration, two subfilters are determined. Each Kalman filter defines the six-teenth state composed of position, velocity, attitude, and sensor error from the INS error equation and the states additionally derived in case of the coloured measurement noise. In order to verify the performance of the proposed filter, we compared the performance how the filter works in the presence of arbitrary error in GPS navigation solution. The Monte Carlo simulation was performed 100 times and the results were compared with the root mean square(RMS). The results show that the proposed method is stable against errors and show fast convergence.

• Journal of Advanced Navigation Technology
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• v.25 no.5
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• pp.409-414
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• 2021

The growing concerns on the emission of particulate matter has prompted a demand for highly reliable particulate matter forecasting. Currently, several studies on particulate matter prediction use various deep learning algorithms. In this study, we compared the predictive performances of typical neural networks used for particulate matter prediction. We used deep neural network(DNN), recurrent neural network, and long short-term memory algorithms to design an optimal predictive model on the basis of a hyperparameter search. The results of a comparative analysis of the predictive performances of the models indicate that the variation trend of the actual and predicted values generally showed a good performance. In the analysis based on the root mean square error and accuracy, the DNN-based prediction model showed a higher reliability for prediction errors compared with the other prediction models.

• Korean Journal of Agricultural Science
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• v.49 no.3
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• pp.483-493
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• 2022

It is important to improve the efficiency of plant breeding and crop yield to fulfill increasing food demands. In plant phenotyping studies, the capability to correlate morphological traits such as plant height, stem diameter, leaf length, leaf width, leaf angle and size of panicle of the plants has an important role. However, manual phenotyping of plants is prone to human errors and is labor intensive and time-consuming. Hence, it is important to develop techniques that measure plant phenotypic traits accurately and rapidly. The aim of this study was to determine the feasibility of point cloud data based on a 3D light detection and ranging (LiDAR) system for plant phenotyping. The obtained results were then verified through manually acquired data from the sorghum samples. This study measured the plant height, plant crown diameter and the panicle height and diameter. The R² of each trait was 0.83, 0.94, 0.90, and 0.90, and the root mean square error (RMSE) was 6.8 cm, 1.82 cm, 5.7 mm, and 7.8 mm, respectively. The results showed good correlation between the point cloud data and manually acquired data for plant phenotyping. The results indicate that the 3D LiDAR system has potential to measure the phenotypes of sorghum in a rapid and accurate way.