• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.2
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• pp.63-77
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• 2022

Our aim was to reduce estimation errors of a wind velocity model used as an early warning system for weather risk management in the agricultural sector. The Rural Development Administration (RDA) agricultural weather observation network's wind velocity data and its corresponding estimated data from January to December 2020 were used to calculate linear regression equations (Y = aX + b). In each linear regression, the wind estimation error at 87 points and eight time slots per day (00:00, 03:00, 06:00, 09.00, 12.00, 15.00, 18.00, and 21:00) is the dependent variable (Y), while the estimated wind velocity is the independent variable (X). When the correlation coefficient exceeded 0.5, the regression equation was used as the wind velocity correction equation. In contrast, when the correlation coefficient was less than 0.5, the mean error (ME) at the corresponding points and time slots was substituted as the correction value instead of the regression equation. To enable the use of wind velocity model at a national scale, a distribution map with a grid resolution of 250 m was created. This objective was achieved b y performing a spatial interpolation with an inverse distance weighted (IDW) technique using the regression coefficients (a and b), the correlation coefficient (R), and the ME values for the 87 points and eight time slots. Interpolated grid values for 13 weather observation points in rural areas were then extracted. The wind velocity estimation errors for 13 points from January to December 2019 were corrected and compared with the system's values. After correction, the mean ME of the wind velocities reduced from 0.68 m/s to 0.45 m/s, while the mean RMSE reduced from 1.30 m/s to 1.05 m/s. In conclusion, the system's wind velocities were overestimated across all time slots; however, after the correction model was applied, the overestimation reduced in all time slots, except for 15:00. The ME and RMSE improved b y 33% and 19.2%, respectively. In our system, the warning for wind damage risk to crops is driven by the daily maximum wind speed derived from the daily mean wind speed obtained eight times per day. This approach is expected to reduce false alarms within the context of strong wind risk, by reducing the overestimation of wind velocities.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.41 no.2
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• pp.19-25
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• 2023

This study aimed to analyze the current status of planted trees in Seoul Forest and propose improvement plans to improve the naturalness in the park. A comprehensive survey of the trees in the park was conducted, and the data gathered was used to build a list of planting trees suitable for an urban park. The analysis of the characteristics of landscape trees in Seoul Forest by type was about the presence or absence of leaves, and they were classified into deciduous trees, evergreen trees, deciduous shrubs, and evergreen shrubs, and herbaceous plants such as groundcover plants separately classified. The study found that Seoul Forest had 57 species of native and naturalized trees, with 27 deciduous trees, 35 deciduous shrubs, 15 evergreen trees, and 98 evergreen shrubs. The park also had 472 species of herbaceous plants, totaling 320,000. The majority of planted trees in Seoul Forest were native species, comprising 59% of the total planted trees, while naturalized species made up 41%. Furthermore, the ratio of deciduous trees to evergreen trees was 81% to 19%, with deciduous trees being the dominant species. The evergreen trees showed a similar trend, with a total of 23 species, including 15 native and 8 foreign species, accounting for 65% of native species. In addition, the study identified six common deciduous shrubs, including Forsythia koreana, orbaria sorbifolia var. stellipila, Deutzia parviflora, Rhododendron lateritium, and Spiraea prunifolia var. simpliciflora, which are frequently planted in areas with abundant water. The study also revealed that among the 10 evergreen shrub species, 9 were native and 1 was foreign. The study aimed to classify the species planted in Seoul Forest into native and foreign species and to provide a data-driven plan to encourage the planting of native species. This study offers valuable insights into planting planning and design for urban parks, which is essential for enhancing naturalness, as most studies have primarily focused on usage patterns and satisfaction in urban parks. By promoting the planting of native species, the naturalness of Seoul Forest can be improved.

• Journal of the Korea Institute of Building Construction
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• v.24 no.2
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• pp.273-284
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• 2024

This study investigates the impact of international oil price fluctuations on overseas construction orders secured by domestic and foreign companies. The analysis employs statistical data spanning the past 20 years, encompassing international oil prices, overseas construction orders from domestic firms, and new overseas construction orders from the top 250 global construction companies. The correlation between these variables is assessed using correlation coefficients(R), determination coefficients(R²), and p-values. The results indicate a strong positive correlation between international oil prices and overseas construction orders. The correlation coefficient between domestic overseas construction orders and oil prices is found to be 0.8 or higher, signifying a significant influence. Similarly, a high correlation coefficient of 0.76 is observed between oil prices and new orders from leading global construction companies. Further analysis reveals a particularly strong correlation between oil prices and overseas construction orders in Asia and the Middle East, potentially due to the prevalence of oil-related projects in these regions. Additionally, a high correlation is observed between oil prices and orders for industrial facilities compared to architectural projects. This suggests an increase in plant construction volumes driven by fluctuations in oil prices. Based on these findings, the study proposes an entry strategy for navigating oil price volatility and maintaining competitiveness in the overseas construction market. Key recommendations include diversifying project locations and supplier bases; utilizing hedging techniques for exchange rate risk management, adapting to local infrastructure and market conditions, establishing local partnerships and securing skilled local labor, implementing technological innovations and digitization at construction sites to enhance productivity and cost reduction The insights gained from this study, coupled with the proposed overseas expansion strategies, offer valuable guidance for mitigating risks in the global construction market and fostering resilience in response to international oil price fluctuations. This approach is expected to strengthen the competitiveness of domestic and foreign construction firms seeking success in the international arena.

• Information Systems Review
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• v.16 no.3
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• pp.161-177
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• 2014

Corporate credit rating assessment consists of complicated processes in which various factors describing a company are taken into consideration. Such assessment is known to be very expensive since domain experts should be employed to assess the ratings. As a result, the data-driven corporate credit rating prediction using statistical and artificial intelligence (AI) techniques has received considerable attention from researchers and practitioners. In particular, statistical methods such as multiple discriminant analysis (MDA) and multinomial logistic regression analysis (MLOGIT), and AI methods including case-based reasoning (CBR), artificial neural network (ANN), and multiclass support vector machine (MSVM) have been applied to corporate credit rating.2) Among them, MSVM has recently become popular because of its robustness and high prediction accuracy. In this study, we propose a novel optimized MSVM model, and appy it to corporate credit rating prediction in order to enhance the accuracy. Our model, named 'GAMSVM (Genetic Algorithm-optimized Multiclass Support Vector Machine),' is designed to simultaneously optimize the kernel parameters and the feature subset selection. Prior studies like Lorena and de Carvalho (2008), and Chatterjee (2013) show that proper kernel parameters may improve the performance of MSVMs. Also, the results from the studies such as Shieh and Yang (2008) and Chatterjee (2013) imply that appropriate feature selection may lead to higher prediction accuracy. Based on these prior studies, we propose to apply GAMSVM to corporate credit rating prediction. As a tool for optimizing the kernel parameters and the feature subset selection, we suggest genetic algorithm (GA). GA is known as an efficient and effective search method that attempts to simulate the biological evolution phenomenon. By applying genetic operations such as selection, crossover, and mutation, it is designed to gradually improve the search results. Especially, mutation operator prevents GA from falling into the local optima, thus we can find the globally optimal or near-optimal solution using it. GA has popularly been applied to search optimal parameters or feature subset selections of AI techniques including MSVM. With these reasons, we also adopt GA as an optimization tool. To empirically validate the usefulness of GAMSVM, we applied it to a real-world case of credit rating in Korea. Our application is in bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. The experimental dataset was collected from a large credit rating company in South Korea. It contained 39 financial ratios of 1,295 companies in the manufacturing industry, and their credit ratings. Using various statistical methods including the one-way ANOVA and the stepwise MDA, we selected 14 financial ratios as the candidate independent variables. The dependent variable, i.e. credit rating, was labeled as four classes: 1(A1); 2(A2); 3(A3); 4(B and C). 80 percent of total data for each class was used for training, and remaining 20 percent was used for validation. And, to overcome small sample size, we applied five-fold cross validation to our dataset. In order to examine the competitiveness of the proposed model, we also experimented several comparative models including MDA, MLOGIT, CBR, ANN and MSVM. In case of MSVM, we adopted One-Against-One (OAO) and DAGSVM (Directed Acyclic Graph SVM) approaches because they are known to be the most accurate approaches among various MSVM approaches. GAMSVM was implemented using LIBSVM-an open-source software, and Evolver 5.5-a commercial software enables GA. Other comparative models were experimented using various statistical and AI packages such as SPSS for Windows, Neuroshell, and Microsoft Excel VBA (Visual Basic for Applications). Experimental results showed that the proposed model-GAMSVM-outperformed all the competitive models. In addition, the model was found to use less independent variables, but to show higher accuracy. In our experiments, five variables such as X7 (total debt), X9 (sales per employee), X13 (years after founded), X15 (accumulated earning to total asset), and X39 (the index related to the cash flows from operating activity) were found to be the most important factors in predicting the corporate credit ratings. However, the values of the finally selected kernel parameters were found to be almost same among the data subsets. To examine whether the predictive performance of GAMSVM was significantly greater than those of other models, we used the McNemar test. As a result, we found that GAMSVM was better than MDA, MLOGIT, CBR, and ANN at the 1% significance level, and better than OAO and DAGSVM at the 5% significance level.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.9 no.4
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• pp.77-85
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• 2014

A goal of Bizcool entrepreneurship education targeting on the youth falls on letting understand the process of starts-up, enhance entrepreneurship will and their business creativities rather than training trivial starts-up skills such as writing business plan for successful starts-up. The effects of education enable Bizcoo students to recognize rightly the concept of starts-up training and lead to spread out demand for entrepreneurship education. The feedback check-up for how entrepreneurship education affects students getting through of it is necessary and possible to bring its' improvement alternatives. Despite of such highlight, not many measuring tools and indexes of evaluating an effectiveness of entrepreneurship education are developed and studied up until. This research suggests for the optimal indexes for them. In specific, this research 49 the first question sets of evaluating an effectiveness of entrepreneurship education classified 3 large categories and 11 following sub categories each of them such as entrepreneurship orientation, creativity, entrepreneurship preparing activities etc,. representing embedding education effects though entrepreneurship education. This research carry out the empirical survey research utilizing driven question sets against 5 different Bizcools sampling 287 students. The survey research delivers the final 3 large categories and 8 following sub categories(Innovativeness, risk-taking, problem-solving potent, cooperative decision-making potent, efficient behavior capacity, data collecting potent, career search, starts-up search and preparation), and 38 measuring indexes by search and confirming factor analysis. This research never drop the confidence test over each indexes and obtain the proper figures. Last but not least, this research confirm the gap between starts-up club members and non members as to an effectiveness of entrepreneurship education and 9 different indexes.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.27 no.4
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• pp.413-433
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• 1994

Jinhae Bay once was a productive area of fisheries. It is, however, now notorious for its red tides; and oxygen deficient water-masses extensively develop at present in summer. Therefore the shellfish production of the bay has been decreasing and mass mortality often occurs. Under these circumstances, the three-dimensional numerical hydrodynamic and the material cycle models, which were developed by the Institute for Resources and Environment of Japan, were applied to analyze the processes affecting the oxygen depletion and also to evaluate the environment capacity for the reception of pollutant loads without dissolved oxygen depletion. In field surveys, oxygen deficient water-masses were formed with concentrations of below 2.0mg/l at the bottom layer in Masan Bay and the western part of Jinhae Bay during the summer. Current directions, computed by the $M_2$ constituent, were mainly toward the western part of Jinhae Bay during flood flows and in opposite directions during ebb flows. Tidal currents velocities during the ebb tide were stronger than that of the flood tide. The comparision between the simulated and observed tidal ellipses showed fairly good agreement. The residual currents, which were obtained by averaging the simulated tidal currents over 1 tidal cycle, showed the presence of counterclockwise eddies in the central part of Jinhae Bay. Density driven currents were generated southward at surface and northward at the bottom in Masan Bay and Jindong Bay, where the fresh water of rivers entered. The material cycle model was calibrated with the data surveyed in the field of the study area from June to July, 1992. The calibrated results are in fairly good agreement with measured values within relative error of $28\%$. The simulated dissolved oxygen distributions of bottom layer were relatively high with the concentration of $6.0{\sim}8.0mg/l$ at the boundaries, but an oxygen deficient water-masses were formed within the concentration of 2.0mg/l at the inner part of Masan Bay and the western part of Jinhae Bay. The results of sensitivity analyses showed that sediment oxygen demand(SOD) was one of the most important influence on the formation of oxygen depletion. Therefore, to control the oxygen deficient water-masses and to conserve the coastal environment, it is an effective method to reduce the SOD by improving the polluted sediment. As the results of simulations, in Masan Bay, oxygen deficient water-masses recovered to 5.0mg/l when the $50\%$ reduction in input COD loads from Masan basin and $70\%$ reduction in SOD was conducted. In the western part of Jinhae Bay, oxygen deficient water-masses recovered to 5.0mg/l when the $95\%$ reduction in SOD and $90\%$ reduction in culturing ground fecal loads was conducted.

• The Journal of Korean Academy of Prosthodontics
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• v.49 no.1
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• pp.1-7
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• 2011

Purpose: The purpose of this study was to investigate the cutting efficiency of coarse grit diamond burs with air-turbine handpiece on natural tooth. Materials and methods: Four groups of coarse grit diamond bur were selected: Komet (A), Shofu (B), Premier (C), and Mani (D). The extracted maxillary central incisors were used, and ten cuts were made on each specimen, using the rotary diamond burs. The surface of each bur was measured at the upper, middle, and bottom of the bur with confocal laser scanning microscope and imaged with SEM. The data were analyzed with one-way ANOVA and t-test at the significance level of 0.05. Results: The surface roughness was measured. At the A diamond bur, the Sa values were $52.93\;{\mu}m$, $48.32\;{\mu}m$, $46.79\;{\mu}m$, $45.06\;{\mu}m$, and $43.43\;{\mu}m$ for control, test 1, 2, 3, and 4 respectively. The Sa values were $50.68\;{\mu}m$, $45.62\;{\mu}m$, $44.41\;{\mu}m$, $44.10\;{\mu}m$, and $42.46\;{\mu}m$ for B diamond bur, $58.02\;{\mu}m$, $55.53\;{\mu}m$, $52.22\;{\mu}m$, $48.26\;{\mu}m$, and $45.36\;{\mu}m$ for C diamond bur, and $50.11\;{\mu}m$, $46.73\;{\mu}m$, $45.46\;{\mu}m$, $42.58\;{\mu}m$, and $41.80\;{\mu}m$ for D diamond bur. Surface roughness after each bur use showed significant changes, but no significant difference was found in surface roughness change between bur systems. Conclusions: Surface roughness in the same bur system showed significant differences after each tooth preparation. However no statistically significant differences were found in surface roughness between bur systems. The SEM images between control and test 4 showed the abraded particles.

• Neonatal Medicine
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• v.17 no.1
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• pp.102-108
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• 2010

Purpose : The goal of this study was to measure bilirubin levels over 6 hours using a transcutaneous bilirubinometer. The change in the bilirubin levels were recorded in anomogram. The natural progress of jaundice in neonates was monitored using the nomogram and cases were identified that needed further follow-up observation and treatment. Methods : The subjects of this study were 986 healthy term or near-term infants at the age of 35 weeks or older who were born at Sung-Ae General Hospital during the period from October 1, 2007 to April 30, 2009 and whose parents were both Koreans. Transcutaneous bilirubin measurements were obtained using a transcutaneous bilirubinometer (Minolta, JM-103) from 6 hours of life to discharge at intervals of 6 hours. A nomogram was derived from the obtained data and compared to the delivery method, gestational age, and feeding method. Results : Percentile graphs were drawn according to time. Based on the graphs, phototherapy was necessary in more than 90 percent of the infants between 35 and 37.6 weeks of age and in 95 percent of the infants 38 weeks and older. The mean bilirubin level at 24, 48, 72 and 96 hours after birth were compared according to the delivery method, gestational age, and feeding method. The bilirubin level in 48 hours was significantly higher in neonates born via cesarean section delivery compared to the neonates born via vaginal delivery, however the levels were not statistically different at the other hours. Conclusion : The results of this study show the nomogram derived from hour-specific transcutaneous bilirubin levels. This information can be used to predict the risk for subsequent significant hyperbilirubinemia.

• Journal of Korea Water Resources Association
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• v.46 no.8
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• pp.807-819
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• 2013

This study proposes a new methodology for future water balance projection considering climate change by assigning a weight to each scenario instead of inputting future streamflows based on GCMs into a water balance model directly. K-nearest neighbor algorithm was employed to assign weights and streamflows in non-flood period (October to the following June) was selected as the criterion for assigning weights. GCM-driven precipitation was input to TANK model to simulate future streamflow scenarios and Quantile Mapping was applied to correct bias between GCM hindcast and historical data. Based on these bias-corrected streamflows, different weights were assigned to each streamflow scenarios to calculate water shortage for the projection periods; 2020s (2010~2039), 2050s (2040~2069), and 2080s (2070~2099). As a result by applying the proposed methodology to project water shortage over the Korean Peninsula, average water shortage for 2020s is projected to increase to 10~32% comparing to the basis (1967~2003). In addition, according to getting decreased in streamflows in non-flood period gradually by 2080s, average water shortage for 2080s is projected to increase up to 97% (516.5 million $m^3/yr$) as maximum comparing to the basis. While the existing research on climate change gives radical increase in future water shortage, the results projected by the weighting method shows conservative change. This study has significance in the applicability of water balance projection regarding climate change, keeping the existing framework of national water resources planning and this lessens the confusion for decision-makers in water sectors.

• Korean Journal of Agricultural and Forest Meteorology
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• v.12 no.2
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• pp.95-106
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• 2010

The performance of Community Land Model version 3.5 - Dynamic Global Vegetation Model (CLM-DGVM) was evaluated through a comparison with the observation over temperate deciduous forest in Gwangneung, Korea. Influence of plant phenology, composition of plant functional type, and climate variability on carbon exchanges was also examined through sensitivity test. To get equilibrium carbon storage, the model was run for 400 years driven by the observed atmospheric data at the deciduous forest of the year 2006. We run the model for 2006 with the equilibrium carbon storage at Gwangneung forest and compared the model output with the observation. A comparison of leaf area index (LAI) between the model and observation indicated that the simulated phenology poorly represented the timing of budburst, leaf-fall, and evolution of LAI. Senescence of the phenology was delayed about four weeks and the simulated maximum LAI (of 5.8 $m^2$ $m^{-2}$) was greater than the observed value (of 4.5 $m^2$ $m^{-2}$). The overestimated LAI contributed to overestimation of both gross primary productivity (GPP) and ecosystem respiration $(R_e)$ through increased photosynthesis and foliar autotropic respiration $(R_a)$, respectively. Despite the discrepancy between the simulated and observed LAI, the simulated tree carbon storage amounts were comparable with the reported values at the site. Change in plant phenology from the simulated to the observed reduced more than six weeks of the plant growth period, resulting in the decreased amount of GPP and $R_e$. These values, however, were still higher (~10% of GPP and 40% of $R_e$) than the observed values. The effect of change in plant functional type composition (from dominant temperate deciduous forest to the coexistence of temperate deciduous and needle leaf forests) on the estimated amount of GPP and $R_e$ was marginal. The influence of climate variability on carbon storage amounts was not significant. The simulated inter-annual variation of GPP and $R_e$ from 1994 to 2003 depended on annual mean air temperature and total radiation but not on precipitation. Other deficiencies of CLM3.5-DGVM have been discussed.