• Journal of Korea Water Resources Association
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• v.46 no.12
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• pp.1235-1247
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• 2013

This study is to evaluate the climate change impact on future storage behavior of Chungju dam($2,750{\times}10^6m^3$) and the regulation dam($30{\times}10^6m^3$) using SWAT(Soil Water Assessment Tool) model. Using 9 years data (2002~2010), the SWAT was calibrated and validated for streamflow at three locations with 0.73 average Nash-Sutcliffe model Efficiency (NSE) and for two reservoir water levels with 0.86 NSE respectively. For future evaluation, the HadCM3 of GCMs (General Circulation Models) data by scenarios of SRES (Special Report on Emission Scenarios) A2 and B1 of the IPCC (Intergovernmental Panel on Climate Change) were adopted. The monthly temperature and precipitation data (2007~2099) were spatially corrected using 30 years (1977~2006, baseline period) of ground measured data through bias-correction, and temporally downscaled by Change Factor (CF) statistical method. For two periods; 2040s (2031~2050), 2080s (2071~2099), the future annual temperature were predicted to change $+0.9^{\circ}C$ in 2040s and $+4.0^{\circ}C$ in 2080s, and annual precipitation increased 9.6% in 2040s and 20.7% in 2080s respectively. The future watershed evapotranspiration increased up to 15.3% and the soil moisture decreased maximum 2.8% compared to baseline (2002~2010) condition. Under the future dam release condition of 9 years average (2002~2010) for each dam, the yearly dam inflow increased maximum 21.1% for most period except autumn. By the decrease of dam inflow in future autumn, the future dam storage could not recover to the full water level at the end of the year by the present dam release pattern. For the future flood and drought years, the temporal variation of dam storage became more unstable as it needs careful downward and upward management of dam storage respectively. Thus it is necessary to adjust the dam release pattern for climate change adaptation.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.3
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• pp.21-36
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• 2019

Dead fuel moisture content is a key variable in fire danger rating as it affects fire ignition and behavior. This study evaluates simple regression models estimating the moisture content of standardized 10-h fuel stick (10-h FMC) at three sites with different characteristics(urban and outside/inside the forest). Equilibrium moisture content (EMC) was used as an independent variable, and in-situ measured 10-h FMC was used as a dependent variable and validation data. 10-h FMC spatial distribution maps were created for dates with the most frequent fire occurrence during 2013-2018. Also, 10-h FMC values of the dates were analyzed to investigate under which 10-h FMC condition forest fire is likely to occur. As the results, fitted equations could explain considerable part of the variance in 10-h FMC (62~78%). Compared to the validation data, the models performed well with R2 ranged from 0.53 to 0.68, root mean squared error (RMSE) ranged from 2.52% to 3.43%, and bias ranged from -0.41% to 1.10%. When the 10-h FMC model fitted for one site was applied to the other sites, $R^2$ was maintained as the same while RMSE and bias increased up to 5.13% and 3.68%, respectively. The major deficiency of the 10-h FMC model was that it poorly caught the difference in the drying process after rainfall between 10-h FMC and EMC. From the analysis of 10-h FMC during the dates fire occurred, more than 70% of the fires occurred under a 10-h FMC condition of less than 10.5%. Overall, the present study suggested a simple model estimating 10-h FMC with acceptable performance. Applying the 10-h FMC model to the automatic mountain weather observation system was successfully tested to produce a national-scale 10-h FMC spatial distribution map. This data will be fundamental information for forest fire research, and will support the policy maker.

• Journal of Veterinary Clinics
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• v.33 no.2
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• pp.107-112
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• 2016

The objective of this study was to determine potential contributing factors associated with biosecurity level of farrow-to-finish pig farms and to develop a classification tree model to explore how these factors related to each other based on prediction model. To this end, the author analyzed data (n = 193) extracted from a cross-sectional study of 344 farrow-to-finish farms which was conducted between March and September 2014 aimed to explore swine disease status at farm level. Standardized questionnaires with information about basic demographical data and management practices were collected in each farm by on-site visit of trained veterinarians. For the classification of the data sets regarding biosecurity level as a dependent variable and predictor variables, Chi-squared Automatic Interaction Detection (CHAID) algorithm was applied for modeling classification tree. The statistics of misclassification risk was used to evaluate the fitness of the model in terms of prediction results. Categorical multivariate input data (40 variables) was used to construct a classification tree, and the target variable was biosecurity level dichotomized into low versus high. In general, the level of biosecurity was lower in the majority of farms studied, mainly due to the limited implementation of on-farm basic biosecurity measures aimed at controlling the potential introduction and transmission of swine diseases. The CHAID model illustrated the relative importance of significant predictors in explaining the level of biosecurity; maintenance of medical records of treatment and vaccination, use of dedicated clothing to enter the farm, installing fence surrounding the farm perimeter, and periodic monitoring of the herd using written biosecurity plan in place. The misclassification risk estimate of the prediction model was 0.145 with the standard error of 0.025, indicating that 85.5% of the cases could be classified correctly by using the decision rule based on the current tree. Although CHAID approach could provide detailed information and insight about interactions among factors associated with biosecurity level, further evaluation of potential bias intervened in the course of data collection should be included in future studies. In addition, there is still need to validate findings through the external dataset with larger sample size to improve the external validity of the current model.

• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.1
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• pp.156-169
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• 2015

This study was to evaluate the RCP climate change impact on hydrological components in the Imha-Dam watershed using SWAT(Soil and Water Assessment Tool) Model. The model was calibrated for six year(2002~2007) and validated for six year(2008~2013) using daily observed streamflow data at three watershed stations. The overall simulation results for the total released volume at this point appear reasonable by showing that coefficient of determination($R^2$) were 0.70~0.85 and Nash-Sutcliffe model efficiency(NSE) were 0.67-0.82 for streamflow, respectively. For future hydrologic evaluation, the HadGEM3-RA climate data by scenarios of Representative Concentration Pathway(RCP) 4.5 and 8.5 of the Korea Meteorological Administration were adopted. The biased future data were corrected using 34 years(1980~2013, baseline period) of weather data. Precipitation and temperature showed increase of 10.8% and 4.9%, respectively based on the baseline data. The impacts of future climate change on the evapotranspiration, soil moisture, surface runoff, lateral flow, return flow and streamflow showed changes of +11.2%, +1.9%, +10.0%, +12.1%, +18.2%, and +11.2%, respectively.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.9
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• pp.1349-1354
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• 2019

Objective: The objective of this study was to determine the best approach for handling missing records of first to successful insemination (FS) in Japanese Black heifers. Methods: Of a total of 2,367 records of heifers born between 2003 and 2015 used, 206 (8.7%) of open heifers were missing. Four penalty methods based on the number of inseminations were set as follows: C1, FS average according to the number of inseminations; C2, constant number of days, 359; C3, maximum number of FS days to each insemination; and C4, average of FS at the last insemination and FS of C2. C5 was generated by adding a constant number (21 d) to the highest number of FS days in each contemporary group. The bootstrap method was used to compare among the 5 methods in terms of bias, mean squared error (MSE) and coefficient of correlation between estimated breeding value (EBV) of non-censored data and censored data. Three percentages (5%, 10%, and 15%) were investigated using the random censoring scheme. The univariate animal model was used to conduct genetic analysis. Results: Heritability of FS in non-censored data was $0.012{\pm}0.016$, slightly lower than the average estimate from the five penalty methods. C1, C2, and C3 showed lower standard errors of estimated heritability but demonstrated inconsistent results for different percentages of missing records. C4 showed moderate standard errors but more stable ones for all percentages of the missing records, whereas C5 showed the highest standard errors compared with noncensored data. The MSE in C4 heritability was $0.633{\times}10^{-4}$, $0.879{\times}10^{-4}$, $0.876{\times}10^{-4}$ and $0.866{\times}10^{-4}$ for 5%, 8.7%, 10%, and 15%, respectively, of the missing records. Thus, C4 showed the lowest and the most stable MSE of heritability; the coefficient of correlation for EBV was 0.88; 0.93 and 0.90 for heifer, sire and dam, respectively. Conclusion: C4 demonstrated the highest positive correlation with the non-censored data set and was consistent within different percentages of the missing records. We concluded that C4 was the best penalty method for missing records due to the stable value of estimated parameters and the highest coefficient of correlation.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.5
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• pp.189-196
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• 2022

Skyline query is a scheme for exploring objects that are suitable for user preferences based on multiple attributes of objects. Existing skyline queries return search results as batch processing, but the need for real-time search results has increased with the advent of interactive apps or mobile environments. Online algorithm for Skyline improves the return speed of objects to explore preferred objects in real time. However, the object navigation process requires unnecessary navigation time due to repeated comparative operations. This paper proposes a Pre-processing Online Algorithm for Skyline Query (POA) to eliminate unnecessary search time in Online Algorithm exploration techniques and provide the results of skyline queries in real time. Proposed techniques use the concept of range-limiting to existing Online Algorithm to perform pretreatment and then eliminate repetitive rediscovering regions first. POAs showed improvement in standard distributions, bias distributions, positive correlations, and negative correlations of discrete data sets compared to Online Algorithm. The POAs used in this paper improve navigation performance by minimizing comparison targets for Online Algorithm, which will be a new criterion for rapid service to users in the face of increasing use of mobile devices.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.119-137
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• 2022

Recent recommendation system studies apply various deep learning models to represent user and item interactions better. One of the noteworthy studies is ONCF(Outer product-based Neural Collaborative Filtering) which builds a two-dimensional interaction map via outer product and employs CNN (Convolutional Neural Networks) to learn high-order correlations from the map. However, ONCF has limitations in recommendation performance due to the problems with CNN and the absence of side information. ONCF using CNN has an inductive bias problem that causes poor performances for data with a distribution that does not appear in the training data. This paper proposes to employ a Vision Transformer (ViT) instead of the vanilla CNN used in ONCF. The reason is that ViT showed better results than state-of-the-art CNN in many image classification cases. In addition, we propose a new architecture to reflect side information that ONCF did not consider. Unlike previous studies that reflect side information in a neural network using simple input combination methods, this study uses an independent auxiliary classifier to reflect side information more effectively in the recommender system. ONCF used a single latent vector for user and item, but in this study, a channel is constructed using multiple vectors to enable the model to learn more diverse expressions and to obtain an ensemble effect. The experiments showed our deep learning model improved performance in recommendation compared to ONCF.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.467-479
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• 2023

Satellite altimeters have continuously observed sea surface height (SSH) in the global ocean for the past 30 years, providing clear evidence of the rise in global mean sea level based on observational data. Accurate altimeter-observed SSH is essential to study the spatial and temporal variability of SSH in regional seas. In this study, we used measurements from the Ieodo Ocean Research Station (IORS) and validate SSHs observed by satellite altimeters (Envisat, Jason-1, Jason-2, SARAL, Jason-3, and Sentinel-3A/B). Bias and root mean square error of SSH for each satellite ranged from 1.58 to 4.69 cm and 6.33 to 9.67 cm, respectively. As the matchup distance between satellite ground tracks and the IORS increased, the error of satellite SSHs significantly amplified. In order to validate the correction of the tide and atmospheric effect of the satellite data, the tide was estimated using harmonic analysis, and inverse barometer effect was calculated using atmospheric pressure data at the IORS. To achieve accurate tidal corrections for satellite SSH data in the seas around the Korean Peninsula, it was confirmed that improving the accuracy of tide data used in satellites is necessary.

• Journal of Preventive Medicine and Public Health
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• v.32 no.4
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• pp.452-458
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• 1999

Objectives: The authors conducted the study to evaluate bias when potentially diseased subjects were included in cohort members while analyzing risk factors of chronic liver diseases. Methods: Total of 14,529 subjects were followed up for the incidence of liver diseases from January 1993 to June 1997. We have used databases of insurance company with medical records, cancer registry, and death certificate data to identify 102 incident cases. The cohort members were classified into potentially diseased group(n=2,217) when they were HBsAg positive, serum GPT levels higher than 40 units, or had or has liver diseases in baseline surveys. Cox's model were used for potentially diseased group, other members, and total subjects, respectively. Results: The risk factors profiles were similar for total and potentially diseased subjects: HBsAg positivity, history of acute liver disease, and recent quittance of smoking or drinking increased the risk. while intake of pork and coffee decreased it. For the potentially diseased, obesity showed marginally significant protective effect. Analysis of subjects excluding the potentially diseased showed distinct profiles: obesity increased the risk, while quitting smoking or drinking had no association. For these intake of raw liver or processed fish or soybean paste stew increased risk; HBsAg positivity, higher levels of liver enzymes and history of acute liver diseases increased the risk. Conclusions: The results suggested the potential bias in risk ratio estimates when potentially diseased subjects were included in cohort study on chronic liver diseases, especially for lifestyles possibly modified after disease onset. The analytic strategy excluding potentially diseased subjects was considered appropriate for identifying risk factors for chronic liver diseases.

• Korean Journal of Agricultural and Forest Meteorology
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• v.12 no.1
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• pp.1-10
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• 2010

A regional climate model (RCM) can be a powerful tool to enhance spatial resolution of climate and weather information (IPCC, 2001). In this study we conducted dynamical downscaling using Weather Research and Forecasting Model (WRF) as a RCM in order to obtain high resolution regional agroclimate indices over the Korean Peninsula. For the purpose of obtaining detailed high resolution agroclimate indices, we first reproduced regional weather for the period of March to June, 2002-2008 with dynamic downscaling method under given lateral boundary conditions from NCEP/NCAR (National Centers for Environmental Prediction/National Center for Atmospheric Research) reanalysis data. Normally, numerical model results have shown biases against observational results due to the uncertainties in the modelis initial conditions, physical parameterizations and our physical understanding on nature. Hence in this study, by employing a statistical method, the systematic bias in the modelis results was estimated and corrected for better reproduction of climate on high resolution. As a result of the correction, the systematic bias of the model was properly corrected and the overall spatial patterns in the simulation were well reproduced, resulting in more fine-resolution climatic structures. Based on these results, the fine-resolution agro-climate indices were estimated and presented. Compared with the indices derived from observation, the simulated indices reproduced the major and detailed spatial distributions. Our research shows a possibility to simulate regional climate on high resolution and agro-climate indices by using a proper downscaling method with a dynamical weather forecast model and a statistical correction method to minimize the model bias.