• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.1
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• pp.155-165
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• 2014

In this study, using a complex of physical, chemical, and biological evaluation factors, the ecological vulnerability to climate change were evaluated at each river in the Nakdong river basin. First, runoff, sediment rate, and low flow discharge changes according to AIB climate change scenario using the SWAT model were simulated. Also, for the assessment of chemical and biological factors, 48 points that water quality monitoring sites and ecological health measurement points are matched with each other was selected. The water quality data of BOD and T-P and the biological data of IBI and KSI in each point were reflected in the assessment. Also, the future rise in water temperature of the rivers in Nakdong river basin was predicted, and the impact of water temperature rise on the fish habitat was evaluated. The top 10 most vulnerable points was presented through a summary of each evaluation factor. This study has a contribution to river restoration or management plan according to the characteristics of each river.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.2 s.173
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• pp.303-310
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• 2000

Fracture toughness of five different reactor pressure vessel steels was characterized in the transition temperature region by the ASTM E1921-97 standard method using Charpy-sized small specimens. T he predominant fracture mode of the tested steels was transgranular cleavage in the test conditions. A statistical analysis based on the Weibull distribution was applied to the interpretation of the scattered fracture toughness data. The size-dependence of the measured fracture toughness values was also well predicted by means of the Weibull probabilistic analysis. The measured fracture toughness transition curves followed the temperature-dependence of the ASTM master curve within the expected scatter bands. Therefore, the fracture toughness characteristics in the transition region could be described by a single parameter, so-called the reference temperature (T。), for a given steel. The determined reference temperatures of the tested materials could not be correlated with the conventional index temperatures from Charpy impact tests.

• Environmental Engineering Research
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• v.15 no.2
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• pp.63-70
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• 2010

In this study, the response surface method and experimental design were applied as an alternative to conventional methods for the optimization of coagulation tests. A central composite design, with 4 axial points, 4 factorial points and 5 replicates at the center point were used to build a model for predicting and optimizing the coagulation process. Mathematical model equations were derived by computer simulation programming with a least squares method using the Minitab 15 software. In these equations, the removal efficiencies of turbidity and total organic carbon (TOC) were expressed as second-order functions of two factors, such as alum dose and coagulation pH. Statistical checks (ANOVA table, $R^2$ and $R^2_{adj}$ value, model lack of fit test, and p value) indicated that the model was adequate for representing the experimental data. The p values showed that the quadratic effects of alum dose and coagulation pH were highly significant. In other words, these two factors had an important impact on the turbidity and TOC of treated water. To gain a better understanding of the two variables for optimal coagulation performance, the model was presented as both 3-D response surface and 2-D contour graphs. As a compromise for the simultaneously removal of maximum amounts of 92.5% turbidity and 39.5% TOC, the optimum conditions were found with 44 mg/L alum at pH 7.6. The predicted response from the model showed close agreement with the experimental data ($R^2$ values of 90.63% and 91.43% for turbidity removal and TOC removal, respectively), which demonstrates the effectiveness of this approach in achieving good predictions, while minimizing the number of experiments required.

• Korean Journal of Remote Sensing
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• v.33 no.1
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• pp.25-35
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• 2017

Spatial downscaling with fine resolution auxiliary variables has been widely applied to predict precipitation at fine resolution from coarse resolution satellite-based precipitation products. The spatial downscaling framework is usually based on the decomposition of precipitation values into trend and residual components. The fine resolution auxiliary variables contribute to the estimation of the trend components. The main focus of this study is on quantitative analysis of impacts of trend component estimates on predictive performance in spatial downscaling. Two regression models were considered to estimate the trend components: multiple linear regression (MLR) and geographically weighted regression (GWR). After estimating the trend components using the two models,residual components were predicted at fine resolution grids using area-to-point kriging. Finally, the sum of the trend and residual components were considered as downscaling results. From the downscaling experiments with time-series Tropical Rainfall Measuring Mission (TRMM) 3B43 precipitation data, MLR-based downscaling showed the similar or even better predictive performance, compared with GWR-based downscaling with very high explanatory power. Despite very high explanatory power of GWR, the relationships quantified from TRMM precipitation data with errors and the auxiliary variables at coarse resolution may exaggerate the errors in the trend components at fine resolution. As a result, the errors attached to the trend estimates greatly affected the predictive performance. These results indicate that any regression model with high explanatory power does not always improve predictive performance due to intrinsic errors of the input coarse resolution data. Thus, it is suggested that the explanatory power of trend estimation models alone cannot be always used for the selection of an optimal model in spatial downscaling with fine resolution auxiliary variables.

• Journal of the Korean Operations Research and Management Science Society
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• v.37 no.4
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• pp.211-223
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• 2012

This study purposed to evaluate the influences of rival hospitals on the number of patients who visited the a study territory hospital. Spatial analysis technique was used to measure the impact of rival hospitals in study region. Selected hospitals were all medical school affiliated hospitals which were located in Daejeon metropolitan city and Chungchungnamdo. Patient data was collected from the claims data of the study hospital, and the number of inpatient and outpatients who visited the study hospital between January and June in 2008 were calculated on the smallest administrative district, Eup, Myeon, and Dong, in study region. To control the differences of regional characteristics among Eup, Myeon, Dong, socio-economic variables (total population, number of people aged over 65, number of basic livelihood security recipients, distance from the study hospital to the centroid point of each Eup, Myeon, Dong, number of business, and number of employees) were included in analysis model. These variables were collected from the annual year book of city as well as county located in study region. Cluster analysis classified the study region into three groups by using the difference of between th actual number of inpatient/outpatient and the predicted number of inpatient/outpatient in Eup, Myeon, and Dong. Most areas around the rivalry hospitals were categorized into same group. Multiple regression analysis indicated that areas around rivalry hospitals had statistically significantly negative relationship with the number of inpatients and outpatients who visited the study hospital. As the buffer size was increased from 5Km to 10Km, the standardized regression coefficients were decreased. These study results confirmed that rivalry hospitals in region had negative impacts on the performance of hospitals. It suggests that hospitals will require not only to select their location to minimize the effects of rivalry hospitals, but also to establish their strategy to cope with the rivalry's threats in their region.

• 한국균학회소식:학술대회논문집
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• 2018.05a
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• pp.41-41
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• 2018

There has been an impetus in the development of biocontrol agents (BCAs) with the removal of a number of chemical compounds in the market, especially in the European Union. This has been a major driver in the development of Integrated Pest Management systems (IPM) for both pest and disease control. For control of mycotoxigenic fungi, there is interest in both control of colonization and more importantly toxin contamination of staple food commodities. Thus the relative inoculum potential of biocontrol agent vs the toxigenic specie sis important. The major bottlenecks in the production and development of formulations of biocontrol agents are the resilience of the strains, inoculum quality and formulation with effective field efficacy. It was recently been shown for mycotoxigenic fungi such as Aspergillus flavus, under extreme climate change conditions, growth is not affected although there may be a stimulation of aflatoxin production. Thus, the development of resilient biocontrol strains which can may have conserved control efficacy but have the necessary resilience becomes critical form a food security point of view. Indeed, under predicted climate change scenarios the diversity of pests and fungal diseases are expected to have profound impacts on food security. Thus, when examining the identification of potential biocontrol strains, production and formulation it is critical that the resilience to CC environmental factors are included and quantified. The problems in relation to the physiological competence and the relative humidity range over which efficacy can occur, especially pre-harvest may be increase under climate change conditions. We have examined the efficacy of atoxigenic strains of A. flavus and Clanostachys rosea and other candidates for control of A. flavus and aflatoxin contamination of maize, and for Fusarium verticillioides and fumonisin toxin control. We have also examined the potential use of fluidized-bed drying, nanoparticles/nanospheres and encapsulation approaches to enhance the potential for the production of resilient biocontrol formulations. The objective being the delivery of biocontrol efficacy under extreme interacting climatic conditions. The potential impact of climate change factors on the efficacy of biocontrol of fungal diseases and mycotoxins are discussed.

• The Journal of Industrial Distribution & Business
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• v.11 no.8
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• pp.31-38
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• 2020

Purpose: This study uses 'Autoregressive Integrated Moving Average Model' to predict the impact of a sharp drop in the base rate due to COVID-19 at the present time when government policies for stabilizing house prices are in progress. The purpose of this study is to predict implications for the direction of the government's house policy by predicting changes in house transaction prices and house rental prices after a sharp cut in the base rate. Research design, data, and methodology: The ARIMA intervention model can build a model without additional information with just one time series. Therefore, it is a time-series analysis method frequently used for short-term prediction. After the subprime mortgage, which had shocked since the global financial crisis in April 2007, the bank's interest rate in 2020 is set at a time point close to zero at 0.75%. After that, the model was estimated using the interest rate fluctuations for the Bank of Korea base interest rate, the house transaction price index, and the house rental price index as event variables. Results: In predicting the change in house transaction price due to interest rate intervention, the house transaction price index due to the fall in interest rates was predicted to change after 3 months. As a result, it was 102.47 in April 2020, 102.87 in May 2020, and 103.21 in June 2020. It was expected to rise in the short term. In forecasting the change in house rental price due to interest rate intervention, the house rental price index due to the drop in interest rate was predicted to change after 3 months. As a result, it was 97.76 in April 2020, 97.85 in May 2020, and 97.97 in June 2020. It was expected to rise in the short term. Conclusions: If low interest rates continue to stimulate the contracted economy caused by COVID-19, it seems that there is ample room for house transaction and rental prices to rise amid low growth. Therefore, In order to stabilize the house price due to the low interest rate situation, it is considered that additional measures are needed to suppress speculative demand.

• Spatial Information Research
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• v.15 no.3
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• pp.267-278
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• 2007

Internationally many models are developed and applied to predict the impact of the climate change, as occurring a lot of symptoms by climate change. Also, in Korea, according to increasing the application of the climate effect model in many research fields, it is required to study the method for preparing climate data and the characteristics of the climate. In this study IDSW (Inverse Distance Squared Weighting), one of the spatial statistic methods, is applied to interpolate. This method estimates a point of interest by assigning more weight to closer points, which are limited to be select by 3 in 100 km radius. As a result, annual average temperature and precipitation had increased by $0.4^{\circ}C$ and 412 mm during 1977 to 2006. They are also predicted to increase by $3.96^{\circ}C$, 319 mm in the 2100 compared to 2007. High variability of temperature and precipitation for last 30 years shows in some part of the Gangwon-do and in the southern part of Korea. Besides in the study of the variable trend, the variability of temperature and precipitation is inclined to increase in Gangwon-do and southern east part, respectively. However, during 2071 to 2100 variability of temperature is predicted to be high in midwest of Korea and variability of precipitation in the east. In the trend of variability, variability of temperature is apt to increase into west south, and variability of precipitation increase in midwest and a part of south.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.2B
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• pp.187-197
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• 2008

This study is to assess the impact of future land use change on hydrology and water quality in Gyungan-cheon watershed ($255.44km^2$) using SWAT (Soil and Water Assessment Tool) model. Using the 5 past Landsat TM (1987, 1991, 1996, 2004) and $ETM^+$ (2001) satellite images, time series of land use map were prepared, and the future land uses (2030, 2060, 2090) were predicted using CA-Markov technique. The 4 years streamflow and water quality data (SS, T-N, T-P) and DEM (Digital Elevation Model), stream network, and soil information (1:25,000) were prepared. The model was calibrated for 2 years (1999 and 2000), and verified for 2 years (2001 and 2002) with averaged Nash and Sutcliffe model efficiency of 0.59 for streamflow and determination coefficient of 0.88, 0.72, 0.68 for Sediment, T-N (Total Nitrogen), T-P (Total Phosphorous) respectively. The 2030, 2060 and 2090 future prediction based on 2004 values showed that the total runoff increased 1.4%, 2.0% and 2.7% for 0.6, 0.8 and 1.1 increase of watershed averaged CN value. For the future Sediment, T-N and T-P based on 2004 values, 51.4%, 5.0% and 11.7% increase in 2030, 70.5%, 8.5% and 16.7% increase in 2060, and 74.9%, 10.9% and 19.9% increase in 2090.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.36 no.2
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• pp.171-179
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• 2012

Cooling of hot sections, especially the turbine nozzle and rotor blades, has a significant impact on gas turbine performance. In this study, the influence of precooling of the cooling air on the performance of gas turbines and their combined cycle plants was investigated. A state-of-the-art F-class gas turbine was selected, and its design performance was deliberately simulated using detailed component models including turbine blade cooling. Off-design analysis was used to simulate changes in the operating conditions and performance of the gas turbines due to precooling of the cooling air. Thermodynamic and aerodynamic models were used to simulate the performance of the cooled nozzle and rotor blade. In the combined cycle plant, the heat rejected from the cooling air was recovered at the bottoming steam cycle to optimize the overall plant performance. With a 200K decrease of all cooling air stream, an almost 1.78% power upgrade due to increase in main gas flow and a 0.70 percent point efficiency decrease due to the fuel flow increase to maintain design turbine inlet temperature were predicted.