• Journal of Korean Society of Forest Science
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• v.111 no.3
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• pp.405-417
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• 2022

The purpose of this study was to describe the photosynthetic features of Prunus maximowiczii and Prunus serrulate Lindl. var. pubescens (Makino) Nakai in response to drought stress. Specifically, we studied the effects of drought on photosynthetic ability and photosystem II activity. Drought stress (DS) was induced by cutting the water supply for 30 days. DS decreased the moisture contents in the soil, and between the 10th and 12th days of DS, both species had 10% or less of x., After the 15th day of DS, it was less than 5%, which is a condition for disease to start. We observed a remarkable decrease of maximum photosynthesis rate starting from 10th day of DS; the light compensation point was also remarkable. Dark respiration and net apparent quantum yield decreased significantly on the 15th day of DS, and then increased on the 20th day. In addition, the stomatal transpiration rate of P. maximowiczii decreased significantly on the15th day of DS, and then increased on the 20th day. Water use efficiency increased on the 15th day of DS, and then decreased on the 20th day. The stomatal transpiration rate of P. serrulate decreased significantly on the 20th day of DS, and then increased afterward, while its water use efficiency increased on the 20th day of DS, and then decreased afterward. These results indicate that the closure of stoma prevented water loss, resulting in a temporary increase of water use efficiency. Chlorophyll fluorescence analysis detected remarkable decreases in the functional index (PI_ABS) and energy transfer efficiency in P. maximowiczii after the 15th day of DS. Meanwhile, photosystem II activity decreased in P. serrulate after 20 days of DS. In addition, Ts-Ta, PI_ABS, DI_O/RC, ET_O/RC followed similar trends as those of the soil moisture content and photosynthetic properties, indicating that they can be used as useful variables in predicting DS in trees.

• Journal of Korean Home Economics Education Association
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• v.33 no.4
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• pp.139-157
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• 2021

The purpose of this study is to derive implications for Korean home economics curriculum to emphasize consumer competency of adolescents by analyzing the content components of consumer competency presented in 'consumer life' area of middle school home economics curriculum of 3 states in the U.S. The analysis results and implications are summarized as follows: First, the U.S. home economics curriculum is composed of various contents, including credit management, savings/investment/ insurance, taxes, and financial situation, and financial decision-making, to improve adolescent's understanding of finance. In the next revision of Korean curriculum, for financial stability in prolonged life after retirement, it is would be necessary to include contents on basic financial knowledge and technology for financial information utilization so that students can establish financial plans for different life stages in consideration of various variables such as changes in economic environment, etc. Second, the U.S. home economics curriculum was developed to help students make better purchase decisions by applying economic concepts such as prices and interest rates, economic trends and the impact of demand and supply, purchase methods and contract conditions, etc. However, Korean home economics curriculum only focus on purchase plan and purchase decision-making process. It would be necessary to foster consumer transaction competency by introducing economic concepts suitable middle school level. Third, to emphasize "consumer civic competency", Ohio was focusing on "claim of consumer rights" and Wisconsin was focusing on the "acceptance of consumer responsibility." In order to enhance adolescent's consumer civic competency, it would be necessary for Korean curriculum to balance the claim of right and the acceptance of consumer responsibility in the following term, and to emphasize the contents on consumer policies, laws and consumer advocacy to create a consumer environment where consumer sovereignty is realized.

• Journal of Korean Home Economics Education Association
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• v.35 no.1
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• pp.1-14
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• 2023

The objective of the study was to examine the associations of dietary carbohydrate and fat intake with the prevalence of metabolic syndrome in Korean women. A cross-sectional study was employed based on data from the Korea National Health and Nutrition Examination (2007-2016). A total of 22,850 women aged 19 to 69 years were studied after excluding responses from pregnant or lactating women and those with missing metabolic values. Dietary intake data were collected with a 24-hour recall method. Dietary carbohydrate and fat intakes were divided into quintiles. After controlling for confounding variables, a multivariable logistic regression and general linear model were used. The findings indicated that HDL cholesterol levels were lower (p for trend<0.01), while triglyceride levels (p for trend=0.04), waist circumference (p for trend<0.01), and systolic blood pressure (p for trend<0.01) were higher among participants in the highest quintile of carbohydrate intake compared to those in the lowest quintile. Participants in the highest quintile of fat intake had lower waist circumference (p for trend=0.02), triglyceride level (p for trend<0.01), and systolic blood pressure (p for trend<0.01), while higher HDL cholesterol level (p for trend<0.01) compared to those in the lowest fat intake quintile. Metabolic syndrome was more likely to be present in the highest quintile of carbohydrates intake than in the lowest quintile (5^th quintile vs. 1^st quintile, OR: 1.32; 95% CI: 1.11 to 1.57). However, metabolic syndrome was less likely to be present in the highest quintile of fat intake than in the lowest quintile (5^th quintile vs. 1^st quintile, OR: 0.73; 95% CI: 0.61 to 0.86). This study revealed that high dietary carbohydrate intake and low dietary fat intake were associated with metabolic syndrome in Korean women.

• The Korean Journal of Applied Statistics
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• v.36 no.1
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• pp.49-62
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• 2023

Solar energy, which is rapidly increasing in proportion, is being continuously developed and invested. As the installation of new and renewable energy policy green new deal and home solar panels increases, the supply of solar energy in Korea is gradually expanding, and research on accurate demand prediction of power generation is actively underway. In addition, the importance of solar radiation prediction was identified in that solar radiation prediction is acting as a factor that most influences power generation demand prediction. In addition, this study can confirm the biggest difference in that it attempted to predict solar radiation using medium-term forecast weather data not used in previous studies. In this paper, we combined the multi-linear regression model, KNN, random fores, and SVR model and the clustering technique, K-means, to predict solar radiation by hour, by calculating the probability density function for each cluster. Before using medium-term forecast data, mean absolute error (MAE) and root mean squared error (RMSE) were used as indicators to compare model prediction results. The data were converted into daily data according to the medium-term forecast data format from March 1, 2017 to February 28, 2022. As a result of comparing the predictive performance of the model, the method showed the best performance by predicting daily solar radiation with random forest, classifying dates with similar climate factors, and calculating the probability density function of solar radiation by cluster. In addition, when the prediction results were checked after fitting the model to the medium-term forecast data using this methodology, it was confirmed that the prediction error increased by date. This seems to be due to a prediction error in the mid-term forecast weather data. In future studies, among the weather factors that can be used in the mid-term forecast data, studies that add exogenous variables such as precipitation or apply time series clustering techniques should be conducted.

• Korean Journal of Legislative Studies
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• v.26 no.2
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• pp.113-140
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• 2020

This study is meaningful in that it is the first analysis of Korean elections using the concept of spatial autocorrelation. Spatial autocorrelation means that an event occurring in one location in space has a high correlation with an event occurring in the surrounding area. The voter turnout rate in the 21^st general election of Seoul area was divided into the early-voting turnout and voting-day turnout, and the spatial pattern of the turnout was examined. Most of the previous studies were based on the unit of the precinct and personal data, but this study analyzed on the basis of the lower unit, Eup-myeon-dong, and analyzed using spatial data and aggregate data. Moran I index showed a fairly high spatial autocorrelation of 0.261 in the voting-day turnout, while the index of the early-voting turnout was low at 0.095, indicating that there was little spatial autocorrelation despite statistical significance. The voting-day turnout, which showed strong spatial autocorrelation, was compared and analyzed using the OLS regression model and the spatial statistics model. In the general regression model, the coefficient of determination R² rose from 0.585261 to 0.656631 in the spatial error model, showing an increase in explanatory power of about 7 percentage points. This means that the spatial statistical model has high explanatory power. The most interesting result is the relationship between the early-voting turnout and the voting-day turnout. The higher the early-voting turnout is, the lower the voting-day turnout is. When the early-voing turnout increases by about 2%, the voting-day turnout drops by about 1%. In this study, the variables affecting the early-voting turnout and the voting-day turnout are very different. This finding is different from the previous researches.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.427-435
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• 2023

Changes in flowering time due to weather fluctuations impact plant growth and ecosystem dynamics. Accurate prediction of flowering timing is crucial for effective forest ecosystem management. This study uses a process-based model to predict flowering timing in 2023 for five major tree species in Korean forests. Models are developed based on nine years (2009-2017) of flowering data for Abeliophyllum distichum, Robinia pseudoacacia, Rhododendron schlippenbachii, Rhododendron yedoense f. poukhanense, and Sorbus commixta, distributed across 28 regions in the country, including mountains. Weather data from the Automatic Mountain Meteorology Observation System (AMOS) and the Korea Meteorological Administration (KMA) are utilized as inputs for the models. The Single Triangle Degree Days (STDD) and Growing Degree Days (GDD) models, known for their superior performance, are employed to predict flowering dates. Daily temperature readings at a 1 km spatial resolution are obtained by merging AMOS and KMA data. To improve prediction accuracy nationwide, random forest machine learning is used to generate region-specific correction coefficients. Applying these coefficients results in minimal prediction errors, particularly for Abeliophyllum distichum, Robinia pseudoacacia, and Rhododendron schlippenbachii, with root mean square errors (RMSEs) of 1.2, 0.6, and 1.2 days, respectively. Model performance is evaluated using ten random sampling tests per species, selecting the model with the highest R². The models with applied correction coefficients achieve R² values ranging from 0.07 to 0.7, except for Sorbus commixta, and exhibit a final explanatory power of 0.75-0.9. This study provides valuable insights into seasonal changes in plant phenology, aiding in identifying honey harvesting seasons affected by abnormal weather conditions, such as those of Robinia pseudoacacia. Detailed information on flowering timing for various plant species and regions enhances understanding of the climate-plant phenology relationship.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.481-493
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• 2023

In high-density urban areas, the urban heat island effect increases urban temperatures, leading to negative impacts such as worsened air pollution, increased cooling energy consumption, and increased greenhouse gas emissions. In urban environments where it is difficult to secure additional green spaces, rooftop greening is an efficient greenhouse gas reduction strategy. In this study, we not only analyzed the current status of the urban heat island effect but also utilized high-resolution satellite data and spatial information to estimate the available rooftop greening area within the study area. We evaluated the mitigation effect of the urban heat island phenomenon and carbon sequestration capacity through temperature predictions resulting from rooftop greening. To achieve this, we utilized WorldView-2 satellite data to classify land cover in the urban heat island areas of Busan city. We developed a prediction model for temperature changes before and after rooftop greening using machine learning techniques. To assess the degree of urban heat island mitigation due to changes in rooftop greening areas, we constructed a temperature change prediction model with temperature as the dependent variable using the random forest technique. In this process, we built a multiple regression model to derive high-resolution land surface temperatures for training data using Google Earth Engine, combining Landsat-8 and Sentinel-2 satellite data. Additionally, we evaluated carbon sequestration based on rooftop greening areas using a carbon absorption capacity per plant. The results of this study suggest that the developed satellite-based urban heat island assessment and temperature change prediction technology using Random Forest models can be applied to urban heat island-vulnerable areas with potential for expansion.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1591-1604
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• 2023

In ocean color remote sensing, atmospheric correction is a vital process for ensuring the accuracy and reliability of ocean color products. Furthermore, in recent years, the remote sensing community has intensified its requirements for understanding errors in satellite data. Accordingly, research is currently addressing errors in remote sensing reflectance (R_rs) resulting from inaccuracies in meteorological variables (total ozone, pressure, wind field, and total precipitable water) used as auxiliary data for atmospheric correction. However, there has been no investigation into the error in R_rs caused by the variability of the water vapor profile, despite it being a recognized error source. In this study, we used the Second Simulation of a Satellite Signal Vector version 2.1 simulation to compute errors in water vapor transmittance arising from variations in the water vapor profile within the GOCI-II observation area. Subsequently, we conducted an analysis of the associated errors in ocean color products. The observed water vapor profile not only exhibited a complex shape but also showed significant variations near the surface, leading to differences of up to 0.007 compared to the US standard 62 water vapor profile used in the GOCI-II atmospheric correction. The resulting variation in water vapor transmittance led to a difference in aerosol reflectance estimation, consequently introducing errors in R_rs across all GOCI-II bands. However, the error of R_rs in the 412-555 nm due to the difference in the water vapor profile band was found to be below 2%, which is lower than the required accuracy. Also, similar errors were shown in other ocean color products such as chlorophyll-a concentration, colored dissolved organic matter, and total suspended matter concentration. The results of this study indicate that the variability in water vapor profiles has minimal impact on the accuracy of atmospheric correction and ocean color products. Therefore, improving the accuracy of the input data related to the water vapor column concentration is even more critical for enhancing the accuracy of ocean color products in terms of water vapor absorption correction.

• Clean Technology
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• v.29 no.4
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• pp.255-261
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• 2023

Power semiconductors are semiconductors used for power conversion, transformation, distribution, and control. Recently, the global demand for high-voltage power semiconductors is increasing across various industrial fields, and optimization research on high-voltage IGBT components is urgently needed in these industries. For high-voltage IGBT development, setting the resistance value of the wafer and optimizing key unit processes are major variables in the electrical characteristics of the finished chip. Furthermore, the securing process and optimization of the technology to support high breakdown voltage is also important. Etching is a process of transferring the pattern of the mask circuit in the photolithography process to the wafer and removing unnecessary parts at the bottom of the photoresist film. Ion implantation is a process of injecting impurities along with thermal diffusion technology into the wafer substrate during the semiconductor manufacturing process. This process helps achieve a certain conductivity. In this study, dry etching and wet etching were controlled during field ring etching, which is an important process for forming a ring structure that supports the 3.3 kV breakdown voltage of IGBT, in order to analyze four conditions and form a stable body junction depth to secure the breakdown voltage. The field ring ion implantation process was optimized based on the TEG design by dividing it into four conditions. The wet etching 1-step method was advantageous in terms of process and work efficiency, and the ring pattern ion implantation conditions showed a doping concentration of 9.0E13 and an energy of 120 keV. The p-ion implantation conditions were optimized at a doping concentration of 6.5E13 and an energy of 80 keV, and the p+ ion implantation conditions were optimized at a doping concentration of 3.0E15 and an energy of 160 keV.

• Journal of Korea Water Resources Association
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• v.56 no.12
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• pp.993-1003
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• 2023

Due to the increasing greenhouse gas emissions, the global mean temperature has risen by 1.1℃ compared to pre-industrial levels, and significant changes are expected in functioning of water supply systems. In this study, we assessed impacts of climate change and instreamflow management on water supply reliability in the Geum River basin, Korea. We proposed univariate climate response functions, where mean precipitation and potential evaporation were coupled as an explanatory variable, to assess impacts of climate stress on multiple water supply reliabilities. To this end, natural streamflows were generated in the 19 sub-basins with the conceptual GR6J model. Then, the simulated streamflows were input into the Water Evaluation And Planning (WEAP) model. The dynamic optimization by WEAP allowed us to assess water supply reliability against the 2020 water demand projections. Results showed that when minimizing the water shortage of the entire river basin under the 1991-2020 climate, water supply reliability was lowest in the Bocheongcheon among the sub-basins. In a scenario where the priority of instreamflow maintenance is adjusted to be the same as municipal and industrial water use, water supply reliability in the Bocheongcheon, Chogang, and Nonsancheon sub-basins significantly decreased. The stress tests with 325 sets of climate perturbations showed that water supply reliability in the three sub-basins considerably decreased under all the climate stresses, while the sub-basins connected to large infrastructures did not change significantly. When using the 2021-2050 climate projections with the stress test results, water supply reliability in the Geum River basin was expected to generally improve, but if the priority of instreamflow maintenance is increased, water shortage is expected to worsen in geographically isolated sub-basins. Here, we suggest that the climate response function can be established by a single explanatory variable to assess climate change impacts of many sub-basin's performance simultaneously.