• Nuclear Engineering and Technology
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• v.50 no.6
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• pp.915-922
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• 2018

Metallic fuel has been considered for sodium-cooled fast reactors because it can maximize the uranium resources. It generates rare earth elements as fission products, where it is reported by aggravating the fuel-cladding chemical interaction at the operating temperature. Rare earth elements form a multicomponent alloy (Ce-Nd-Pr-La-Sm-etc.) during reactor operation, where it shows a higher reaction thickness than a single element. Experiments have been carried out by simplifying multicomponent alloys for mono or binary systems because complex alloys have difficulty in the analysis. In previous experiments, xCe-yNd was fabricated with two elements, Ce and Nd, which have a major effect on the fuel-cladding chemical interaction, and the thickness of the reaction layer reached maximum when the rare earth elements ratio was 1:1. The objective of this study is to evaluate the effect and relationship of rare earth elements on such synergistic behavior. Single and binary rare earth model alloys were prepared by selecting five rare earth elements (Ce, Nd, Pr, La, and Sm). In the single system, Nd and Pr behaviors were close to diffusion, and Ce showed a eutectic reaction. In the binary system, Ce and Sm further increased the reaction layer, and La showed a non-synergy effect.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1743-1747
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• 2022

Sentinel-2 can be used as proxy data for the Korean Compact Advanced Satellite 500-4 (CAS500-4), also known as Agriculture and Forestry Satellite, in terms of spectral wavelengths and spatial resolution. This letter examined cloud detection for later use in the CAS500-4 based on deep learning technologies. DeepLabV3+, a traditional Convolutional Neural Network (CNN) model, and Shifted Windows (Swin) Transformer, a state-of-the-art (SOTA) Transformer model, were compared using 22,728 images provided by Radiant Earth Foundation (REF). Swin Transformer showed a better performance with a precision of 0.886 and a recall of 0.875, which is a balanced result, unbiased between over- and under-estimation. Deep learning-based cloud detection is expected to be a future operational module for CAS500-4 through optimization for the Korean Peninsula.

• Journal of the Korean earth science society
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• v.42 no.4
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• pp.445-458
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• 2021

Gravity Recovery and Climate Experiment (GRACE) gravimeter satellites observed the Earth gravity field with unprecedented accuracy since 2002. After the termination of GRACE mission, GRACE Follow-on (GFO) satellites successively observe global gravity field, but there is missing period between GRACE and GFO about one year. Many previous studies estimated terrestrial water storage (TWS) changes using hydrological models, vertical displacements from global navigation satellite system observations, altimetry, and satellite laser ranging for a continuity of GRACE and GFO data. Recently, in order to predict TWS changes, various machine learning methods are developed such as artificial neural network and multi-linear regression. Previous studies used hydrological and climate data simultaneously as input data of the learning process. Further, they excluded linear trends in input data and GRACE/GFO data because the trend components obtained from GRACE/GFO data were assumed to be the same for other periods. However, hydrological models include high uncertainties, and observational period of GRACE/GFO is not long enough to estimate reliable TWS trends. In this study, we used convolutional neural networks (CNN) method incorporating only climate data set (temperature, evaporation, and precipitation) to predict TWS variations in the missing period of GRACE/GFO. We also make CNN model learn the linear trend of GRACE/GFO data. In most river basins considered in this study, our CNN model successfully predicts seasonal and long-term variations of TWS change.

• Journal of electromagnetic engineering and science
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• v.5 no.4
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• pp.183-188
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• 2005

A simple microwave backscattering model for vegetation canopies on earth surfaces is developed in this study. A natural earth surface is modeled as a two-layer structure comprising a vegetation layer and a ground layer. This scattering model includes various scattering mechanisms up to the first-order multiple scattering( double-bounce scattering). Radar backscatter from ground surface has been modeled by the polarimetric semi-empirical model (PSEM), while the backscatter from the vegetation layer modeled by the vector radiative transfer model. The vegetation layer is modeled by random distribution of mixed scattering particles, such as leaves, branches and trunks. The number of input parameters has been minimized to simplify the scattering model. The computation results are compared with the experimental measurements, which were obtained by ground-based scatterometers and NASA/JPL air-borne synthetic aperture radar(SAR) system. It was found that the scattering model agrees well with the experimental data, even though the model used only ten input parameters.

• Atmosphere
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• v.30 no.2
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• pp.115-124
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• 2020

The Northern Hemisphere extratropical prediction skill of the Korea Meteorological Administration (KMA) Global Data Assimilation and Prediction System (GDAPS) is examined for January 2019. The real-time prediction skill, evaluated with mean squared skill score (MSSS) of 30-90°N geopotential height field at 500 hPa (Z500), is ~8 days in the troposphere. The MSSS of Z500 considerably decreases after 3 days mainly due to the increasing eddy errors. The eddy errors are largely explained by the eddy-phased errors with minor contribution of amplitude errors. In particular, planetary-scale eddy errors are considered as a main reason of rapidly increasing errors. It turns out that such errors are associated with the blocking highs over North Pacific (NP) and Euro-Atlantic (EA) regions. The model overestimates the blocking highs over NP and EA regions in time, showing dependence of blocking predictability on blocking initializations. This result suggests that the extratropical prediction skill could be improved by better representing blocking in the model.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1060-1065
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• 2005

An Independent Administrative Corporation Japan Agency for Marine-Earth Science and Technology (JAMSTEC) is developing light-and-small Autonomous Underwater Vehicles (AUV)$^{1)}$, named 'MR-X1' (Marine Robot Experimental 1), which can cruise, investigate and observe by itself without human's help. In this paper, we consider the motion control problem of 'MR-X1' and derive a controller. Since the dynamic property of 'MR-X1' is changed by the influence of the speed, the mathematical model of 'MR-X1' becomes the nonlinear model. In order to design a controller for 'MR-X1', we generally apply nonlinear control theories or linear control theories with some constant speed situation. If we design a controller by applying Linear Quadratic (LQ) optimal control theory, the obtained controller only compensates t e optimality at the designed speed situation, and does not compensate the stability at another speed situations. This paper proposes a controller design method using Linear Matrix Inequalities (LMIs)$^{2),3),4)}$, which can adapt the speed variation of 'MR-X1'. And examples of numerical analysis using our designed controller are shown.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.11 no.2
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• pp.89-100
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• 1993

This paper is a study on the earth volume calculation using CAD and LISP(LIST Processing) in the route alignment. The utility enlargement in the industry field and the considerable progress of computer make the automatic design and manufacture for the development of CAD/CAM/CAE technique possible, and the automatic design of civil engineering works is continuously progressive. In this study we are intend to improve an effect of civil engineering work by the automatic earth volume calculation in route alignment. This paper aims to construct the automatic design system of civil engineering work and the procedures; (1) The programming of the self-scanning program of the land information introducing Digital Terrain Model concept in the map (2) Systematic algorithm construction using LISP and grafting CAD system (3) Automatic design and calculation of the mass curve and earth volume.

• Ocean and Polar Research
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• v.40 no.3
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• pp.99-114
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• 2018

In the present study, we assess the GloSea5 (Global Seasonal Forecasting System version 5) near-surface ocean current forecasts using globally observed surface drifter dataset. Annual mean surface current fields at 0-day forecast lead time are quite consistent with drifter-derived velocity fields, and low values of root mean square (RMS) errors distributes in global oceans, except for regions of high variability, such as the Antarctic Circumpolar Current, Kuroshio, and Gulf Stream. Moreover a comparison with the global high-resolution forecasting system, HYCOM (Hybrid Coordinate Ocean Model), signifies that GloSea5 performs well in terms of short-range surface-current forecasts. Predictions from 0-day to 4-week lead time are also validated for the global ocean and regions covering the main ocean basins. In general, the Indian Ocean and tropical regions yield relatively high RMS errors against all forecast lead times, whilst the Pacific and Atlantic Oceans show low values. RMS errors against forecast lead time ranging from 0-day to 4-week reveal the largest increase rate between 0-day and 1-week lead time in all regions. Correlation against forecast lead time also reveals similar results. In addition, a strong westward bias of about $0.2m\;s^{-1}$ is found along the Equator in the western Pacific on the initial forecast day, and it extends toward the Equator of the eastern Pacific as the lead time increases.

• Journal of The Korean Association For Science Education
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• v.24 no.5
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• pp.886-901
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• 2004

The purpose of this study was to document the changes in astronomical concepts for preservice and inservice elementary school teachers after being presented with the newly devised Earth Motions Centric Solar System Model. The subjects of the study were 31 preservice and 30 inservice elementary schools teachers in the Jeonbuk Province. First, the author investigated the naive theories of the subjects, and then, compared that data to the data obtained after their exposure to the model. The total number of items on the instrument for this study was 10. These items included questions about the motion of interior planets, the phases and sizes of interior planets, and the motion of exterior planets and comets. After analyzing the answers to the items before the experiment, the author was able to confirm the existence of the naive theories regarding astronomical phenomena. Also, after the experiment, the author was able to observe the conceptual change in thought of the preservice and inservice elementary school teachers. Results showed that learning through the new model had positive effects on the preservice and inservice elementary school teachers' conceptualization of the interior planets' motion, phases and sizes, and the exterior planets' motion.

• Journal of the Korean earth science society
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• v.35 no.5
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• pp.342-353
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• 2014

Data for model analysis derived from the finite volume (fv) GCM (Goddard Earth Observing System Ver. 4, GEOS-4) and the Land Data Assimilation System (LDAS) have been utilized in a mesoscale model. These data are tested to provide initial conditions and lateral boundary forcings to the Purdue Mesoscale Model (PMM) for a case study of the Midwestern flood that took place from 21-23 May 1998. The simulated results with fvGCM and LDAS soil moisture and temperature data are compared with that of ECMWF reanalysis. The initial conditions of the land surface provided by fvGCM/LDAS show significant differences in both soil moisture and ground temperature when compared to ECMWF control run, which results in a much different atmospheric state in the Planetary Boundary Layer (PBL). The simulation result shows that significant changes to the forecasted weather system occur due to the surface initial conditions, especially for the precipitation and temperature over the land. In comparing precipitation, moisture budgets, and surface energy, not only do the intensity and the location of precipitation over the Midwestern U.S. coincide better when running fvGCM/LDAS, but also the temperature forecast agrees better when compared to ECMWF reanalysis data. However, the precipitation over the Rocky Mountains is too large due to the cumulus parameterization scheme used in the PMM. The RMS errors and biases of fvGCM/LDAS are smaller than the control run and show statistical significance supporting the conclusion that the use of LDAS improves the precipitation and temperature forecast in the case of the Midwestern flood. The same method can be applied to Korea and simulations will be carried out as more LDAS data becomes available.