• Journal of Satellite, Information and Communications
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• v.12 no.3
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• pp.22-34
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• 2017

The K-DRUM(K-water hydrologic & hydraulic Distributed RUnoff Model), a distributed rainfall-runoff model of K-water, calculates predicted runoff and water surface level of a dam using precipitation data. In order to obtain long-term hydrometeorological information, K-DRUM requires long-term weather forecast. In this study, we built a system providing long-term hydrometeorological information using predicted rainfall ensemble of GloSea5(Global Seasonal Forecast System version 5), which is the seasonal meteorological forecasting system of KMA introduced in 2014. This system produces K-DRUM input data by automatic pre-processing and bias-correcting GloSea5 data, then derives long-term inflow predictions via K-DRUM. Web-based UI was developed for users to monitor the hydrometeorological information such as rainfall, runoff, and water surface level of dams. Through this UI, users can also test various dam management scenarios by adjusting discharge amount for decision-making.

• Journal of Wetlands Research
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• v.17 no.1
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• pp.80-90
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• 2015

Variability of precipitation pattern and intensity are increasing due to the urbanization and industrialization which induce increasing impervious area and the climate change. Therefore, more severe urban inundation and flood damage will be occurred by localized heavy precipitation event in the future. In this study, we analyze the future frequency based precipitation under climate change based on the regional frequency analysis. The observed precipitation data from 58 stations provided by Korea Meteorological Administration(KMA) are collected and the data period is more than 30 years. Then the frequency based precipitation for the observed data by regional frequency analysis are estimated. In order to remove the bias from the simulated precipitation by RCP scenarios, the quantile mapping method and outlier test are used. The regional frequency analysis using L-moment method(Hosking and Wallis, 1997) is performed and the future frequency based precipitation for 80, 100, and 200 years of return period are estimated. As a result, future frequency based precipitation in South Korea will be increased by 25 to 27 percent. Especially the result for Jeju Island shows that the increasing rate will be higher than other areas. Severe heavy precipitation could be more and more frequently occurred in the future due to the climate change and the runoff characteristics will be also changed by urbanization, industrialization, and climate change. Therefore, we need prepare flood prevention measures for our flood safety in the future.

• Atmosphere
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• v.27 no.3
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• pp.359-370
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• 2017

In this study, the oceanic Total Precipitable Water (TPW) retrieval algorithm at 16 km altitude of High Altitude Long Endurance Unmanned Aerial Vehicle (HALE UAV) is described. Empirical equation based on Wentz method (1995) that uses the 18.7 and 22.235 GHz channels is developed using the simulated brightness temperature and SeeBor training dataset. To do radiative simulation, Satellite Data Simulator Unit (SDSU) Radiative Transfer Model (RTM) is used. The data of 60% (523) and 40% (349) in the SeeBor training dataset are used to develop and validate the TPW retrieval algorithm, respectively. The range of coefficients for the TPW retrieval at the altitude of 3~18 km with 3 km interval were 153.69~199.87 (${\alpha}$), 54.330~58.468 (${\beta}$), and 84.519~93.484 (${\gamma}$). The bias and RMSE at each altitude were found to be about $-0.81kg\;m^{-2}$ and $2.17kg\;m^{-2}$, respectively. Correlation coefficients were more than 0.9. Radiosonde observation has been generally operated over land. To validate the accuracy of the oceanic TPW retrieval algorithm, observation data from the Korea Meteorological Administration (KMA) Gisang 1 research vessel about six clear sky cases representing spring, autumn, and summer season is used. Difference between retrieved and observed TPW at 16 km altitude were in the range of $0.53{\sim}1.87kg\;m^{-2}$, which is reasonable for most applications. Difference in TPW between retrieval and observation at each altitude (3~15 km) is also presented. Differences of TPW at altitudes more than 6 km were $0.3{\sim}1.9kg\;m^{-2}$. Retrieved TPW at 3 km altitude was smaller than upper level with a difference of $-0.25{\sim}0.75kg\;m^{-2}$ compared to the observed TPW.

• Journal of Microbiology and Biotechnology
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• v.19 no.7
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• pp.718-726
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• 2009

The objective of this study was to develop and validate secondary models that can predict growth parameters of L. monocytogenes Scott A as a function of concentrations (0-3%) of a commercial potassium lactate (PL) and sodium diacetate (SDA) mixture, pH (5.5-7.0), and temperature (4-37DC). A total of 120 growth curves were fitted to the Baranyi primary model that directly estimates lag time (LT) and specific growth rate (SGR). The effects of the variables on L. monocytogenes Scott A growth kinetics were modeled by response surface analysis using quadratic and cubic polynomial models of the natural logarithm transformation of both LT and SGR. Model performance was evaluated with dependent data and independent data using the prediction bias ($B_f$) and accuracy factors ($A_f$) as well as the acceptable prediction zone method [percentage of relative errors (%RE)]. Comparison of predicted versus observed values of SGR indicated that the cubic model fits better than the quadratic model, particularly at 4 and $10^{\circ}C$. The $B_f$and $A_f$for independent SGR were 1.00 and 1.08 for the cubic model and 1.08 and 1.16 for the quadratic model, respectively. For cubic and quadratic models, the %REs for the independent SGR data were 92.6 and 85.7, respectively. Both quadratic and cubic polynomial models for SGR and LT provided acceptable predictions of L. monocytogenes Scott A growth in the matrix of conditions described in the present study. Model performance can be more accurately evaluated with $B_f$and $A_f$and % RE together.

• Journal of Korean Academy of Nursing
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• v.26 no.1
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• pp.73-93
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• 1996

This study aimed to analyse the test contents of the national examination for the registered nurses (NERN) over 3 years from 1991 to 1993 in Korea. In recent years in Korea, the MCQ(multiple choice question) has been showing to be a highly recognized method for assessing the qualification of registered nurses. Unfortunately, nursing faculties have found NERN had some bad MCQs through having evaluation workshop for Some MCQs often provide so many unwriting clues which become a bias of the results, and some items fell into the category of the lower level of educational taxonomy such as isolated recall a fact or data. Frequently the stems of the questions are ambigous, unclear, disputable, esoterical or trivial. Considering those fallacies of the national examination, it is very critical to review the test items to see whether it is of high quality, is more fair, reliable and objective in depth. Therefore, this study was done to provide data for the improvement of the test contents as well as the teachers's assessment skill. For this study, the ad hoc committee was composed of 16 members, including 5 education board members of Korean Academic Nurses Association and 11 nursing faculty members. This committee had one day panel discussion and filled the checklist for this study. The process of analysing data was held over 10 times during 1992-1994. The analysis focussed on educational taxonomy such as cognitive domain(knowledge), psychmotor domain (skill), affective domain(attitude) and the level of learning such as recall, understanding, problems solving, and learning area of theory and practice, and the learning content categorised by nursing process and disease process. The test analysed using difficulty index and the structure of the test items was analysed. The conclusions and suggestion as follows : 1. In learning area, the average ratio of the theory and practice was 1 : 1.1 which was less than 1 : 2 suggested by Korean National Health Institute, and the ratio was different by the 8 leaning subjects of nursing. 2. In category of the educational taxonomy, the knowledge domain was emphasized mostly（79. 7％）, the skill domain was 14.9％, and the attitude domain was 5.4％ only. 3. In the level of learning, generally, the test items of the level of recall（45.5％） and the understanding（46.3％） were covered almost and the problem solving was 8.1％. 4. In the learning contents, generally, the test items related to nursing process was 67.2％ and that of disease process was 32.8％. However, this proportion was different by the 8 leaning subjects. Even though the nursing diagnosis has been emphasized in nursing curricula recently, the test items of this was identified very few. 5. In the structure of the test item, some were not clear, incorrect grammar, unclear description and some have clues to answer. 6. In the item analysis, the non-acceptable level of the difficulty index (means too easy) was 65.7％, and the acceptable level was 33.9％. Considering the results we would like to suggest the followings, 1. Since the test items of knowledge domain was dominant, the test items of the practice domain and attitude domain should be emphasized more. 2. The regular review and analysis of NERN should be arranged in order to improve the quality of the test items which will give influence to the nursing education positively.

• Journal of Digital Convergence
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• v.18 no.11
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• pp.259-266
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• 2020

Many developments have been steadily carried out by researchers with applying knowledge-based expert system or machine learning algorithms to the financial field. In particular, it is now common to perform knowledge based system trading in using stock prices. Recently, deep learning technologies have been applied to real fields of stock trading marketplace as GPU performance and large scaled data have been supported enough. Especially, LSTM has been tried to apply to stock price prediction because of its compatibility for time series data. In this paper, we implement stock price prediction using LSTM. In modeling of LSTM, we propose a fitness combination of model parameters and activation functions for best performance. Specifically, we propose suitable selection methods of initializers of weights and bias, regularizers to avoid over-fitting, activation functions and optimization methods. We also compare model performances according to the different selections of the above important modeling considering factors on the real-world stock price data of global major companies. Finally, our experimental work brings a fitness method of applying LSTM model to stock price prediction.

• Geophysics and Geophysical Exploration
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• v.17 no.1
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• pp.28-33
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• 2014

We investigate the spatial distribution of highly conductive layer using the one-dimensional inversions of the new magnetotelluric (MT) measurements obtained at the mid-mountain (400 ~ 900 m in elevation) western area of Jeju Island and the previous MT data over Jeju Island, Korea. The conductive layer indicates the sedimentary layer comprised of Seoguipo Fomation and U Formation. There is a definite positive correlation between the top of conductive layer and the earth surface in elevation. On the contrary, the bottom of conductive layer has a negative correlation with the surface elevation. In other words, the conductive layer has a shape of convex lens, which is thickest in the central part. The basement beneath the conductive layer could be concave in the central part of Jeju Island. A kriging considering the correlation between the layer boundary and the surface elevation provides a reliable geoelectric structure model of Jeju Island. However, further studies, i.e. three-dimensional modeling and interpretation integrated with other geophysical or logging data, are required to reveal the possible presence of three-dimensional conductive body near the subsurface vent of Mt. Halla and the causes of the bias in the depths of layer estimated from MT and core log data.

• Journal of Korean Society of Transportation
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• v.24 no.2 s.88
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• pp.53-63
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• 2006

The objective of this study is to estimate Korean transportation service index (KTSI), and to explore possible uses of the KTSI. The KTSI was monthly index to represent the level of passenger and freight services by road, railroad, air and maritime modes, which was developed from eight series. Four of these series measure the level of passenger services (passenger-kilometers) by road, railroad. air and maritime modes : monthly data from January 1995 to December 2004. Similarly. the remaining four series measure the level or freight activity (tonnage) by four modes during the same period. Given the weights of modal revenues, component series were aggregated into two indexes (passenger index and freight index) and a composite index using Chained Fisher Ideal index. which was a geometric mean of the Laspeyres index and the Passche index. The Fisher Ideal index is one of the 'superlative' indexes, which diminish 'substitution bias' as current-weighted indexes. As a result, the freight index and the composite index explain economic conditions better than the passenger index. Based on the composite index. the newly estimated KTSI shows an average lag time of one and a half years at peaks and three months at troughs in comparison with domestic business cycles. Nonetheless. the following efforts are needed for more credible and useful estimates; establishment of data collection scheme in time. credibility uplift of used data, development of various indexation methods.

• Environmental and Resource Economics Review
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• v.25 no.1
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• pp.1-25
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• 2016

This study investigated the variability of WTP estimates(i.e. mean or median) with ad hoc assumptions of specific parametric probability distributions(i.e. normal, logistic, lognormal, and exponential distribution) to estimate WTP function using dichotomous choice CV data on mortality risk reduction. From the perspective of policy decision, the variability of these WTP estimates are intolerable in comparison with those of Turnbull nonparametric estimation method which is free from ad hoc distribution assumptions. The Turnbull nonparametric estimation can avoid a kind of misspecification bias due to ad hoc assumption of specific parametric distributions. Furthermore, the WTP estimates by Turnbull nonparametric estimation are robust because the similar estimates are elicited from a dichotomous choice or double dichotomous choice CV data, and the statistically significant WTP estimates can be obtained even though it is not possible by parametric estimation methods. If there are considerable variability among those WTP estimates by parametric estimation methods in condition with no criteria of model adequacy, the mean WTPs from Turnbull nonparametric estimation can be the robust estimates without ad hoc assumptions, which can avoid controversial issues in the perspective of policy decisions.

• Journal of Korea Water Resources Association
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• v.46 no.6
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• pp.569-583
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• 2013

This study is to evaluate the future climate change impact on hydrological components in the Seolmacheon ($8.54km^2$) mixed forest catchment located in the northwest of South Korea using SWAT (Soil and Water Assessment Tool) model. To reduce the uncertainty, the model was spatially calibrated (2007~2008) and validated (2009~2010) using daily observed streamflow, evapotranspiration, and soil moisture data. Hydrological predicted values matched well with the observed values by showing coefficient of determination ($R^2$) from 0.74 to 0.91 for streamflow, from 0.56 to 0.71 for evapotranspiration, and from 0.45 to 0.71 for soil moisture. The HadGEM3-RA future weather data of Representative Concentration pathway (RCP) 4.5 and 8.5 scenarios of the IPCC (Intergovernmental Panel on Climate Change) AR5 (Assessment Report 5) were adopted for future assessment after bias correction of ground measured data. The future changes in annual temperature and precipitation showed an upward tendency from $0.9^{\circ}C$ to $4.2^{\circ}C$ and from 7.9% to 20.4% respectively. The future streamflow showed an increase from 0.6% to 15.7%, but runoff ratio showed a decrease from 3.8% to 5.4%. The future predicted evapotranspiration about precipitation increased from 4.1% to 6.8%, and the future soil moisture decreased from 4.3% to 5.5%.