• 대한토목학회논문집
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• 제34권1호
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• pp.155-165
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• 2014

본 연구에서는 낙동강 유역을 대상으로 물리, 화학, 생물학적의 복합적인 평가요소를 이용하여 수생태 관련 기후변화 취약구간을 선정하였다. 먼저 SWAT 모형을 이용하여 A1B 기후변화 시나리오에 따라 각 소유역별로 유출량, 유사량, 갈수량 변화를 모의하였다. 또한 환경부의 수질 측정망과 수생태 건강성 측정 지점이 서로 일치하는 48개 지점을 대상으로, BOD 및 TP 등의 수질 데이터와 IBI, KSI의 수생물 데이터를 평가에 반영하였다. 한편, 미래 기온 상승에 따른 낙동강 유역 하천에서의 수온 상승 폭을 예측하였고, 이로 인한 수생물 서식처 영향을 분석하여 평가에 반영하였다. 각 평가요소를 종합하여 가장 취약한 상위 10개 지점을 제시하였다. 본 연구는 하천 생태복원을 위한 취약구간 평가 및 종합적인 평가 결과를 토대로 각 하천 특성에 맞는 하천 관리 계획을 수립하는데 있어 효과적일 것으로 사료된다.

• 대한기계학회논문집A
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• 제24권2호
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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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• 제15권2호
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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.

• 대한원격탐사학회지
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• 제33권1호
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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.

• 한국경영과학회지
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• 제37권4호
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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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• 한국균학회 2018년도 춘계학술대회 및 임시총회
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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.

• 산경연구논집
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• 제11권8호
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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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• 제15권3호
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• pp.267-278
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• 2007

국제사회는 기후변화에 따른 이상 현상의 징후가 발생함에 따라 이의 영향 예측을 위해 많은 모델들을 개발 및 적용하고 있다. 현재 우리나라에서도 여러 분야의 기후 영향모델 활용이 증가하면서 모델의 입력자료 중 특히 기후자료의 구축 방법 및 한반도 기후의 특성 파악에 대한 연구가 절실히 요구되고 있다. 본 연구에서는 보간을 위하여 공간통계학방법 중 IDSW(Inverse Distance Squared Weighting:거리자승역산가중)를 적용하였다. 이 방법은 미관측지점의 값을 추정하기 위하여 주변 관측지점들을 고려하며, 그 영향은 거리에 반비례함을 반영한다. 여기서 주변 관측지점 선정시 반경 100km내의 가장 인접한 순으로 최대 3개의 관측지점을 선택하게 제약을 두었다. 그 결과 한국의 기온과 강수량 모두 과거 30년 동안에 연평균 약 $0.4^{\circ}C$, 412mm 증가하는 것으로 나타났다. 또한 미래에도 2007년에 비해 2100년의 기온이 $3.96^{\circ}C$, 강수량이 319mm 증가하는 것으로 나타났다. 기후변이성의 특성은 과거 30년 동안 기온의 경우 강원도 일부지역이 높게 나타났으며 강수량의 경우 남부지역이 높게 나타났다. 변화 경향은 기온의 경우 강원도 지역이 변이성이 증가하는 경향을 보였으며, 강수량의 경우 남동부부지역이 변이성이 증가하는 경향을 보였다. 미래 30년간의 변이성 분석결과 기온은 중서부 지역에서, 강수량은 동부지역에서 높은 것으로 나타났고, 변화경향은 기온의 경우 남서부로 갈수록 변이의 정도가 증가되는 경향을 보였으며, 강수량의 경우 중서부와 남부 일부가 변이가 증가되는 경향을 보였다.

• 대한토목학회논문집
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• 제28권2B호
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• pp.187-197
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• 2008

본 연구에서는 SWAT(Soil and Water Assessment Tool) 모형을 이용하여 경안천 유역($255.44km^2$)을 대상으로 미래 토지이용변화가 수문-수질에 미치는 영향을 분석하고자 하였다. Landsat TM(1987, 1991, 1996, 2004), $ETM^+$(2001) 위성영상으로부터 시계열 토지이용도를 작성하고, CA-Markov 기법을 이용하여 2030, 2060, 2090년도의 미래 토지이용변화를 예측하였다. 모형의 입력 자료인 수문 기상자료와 지형자료(DEM, 토양도, 하천도 등), 수질자료(SS, T-N, T-P)를 구축하고 1999, 2000년 자료를 이용하여 모형의 보정을 실시하였으며, 2001, 2002년에 대하여 검증하였다. 검보정 결과, 유출량에 대해 모형 효율성 계수는 0.59, 수질항목(Sediment, T-N, T-P)에 대한 결정계수는 0.88, 0.72, 0.68로 분석되었다. 미래 토지이용변화에 따른 유출량과 비점오염 부하량의 변화를 분석한 결과, 도시화가 진행되면서 2004년을 기준(76.3)으로 유역 평균 CN값이 2030년 76.9, 2060년 77.1, 2090년 77.4로 증가하면서 유출량이 1.4%, 2.0%, 2.7% 증가하는 것으로 분석되었다. 또한, 비점오염원의 증가로 유사량과 T-N, T-P 부하량은 2004년을 기준으로 2030년 51.4%, 5.0%, 11.7% 증가하였으며, 2060년 70.5%, 8.5%, 16.7% 2090년에 74.9%, 10.9%, 19.9% 증가하는 것으로 분석되었다.

• 대한기계학회논문집B
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• 제36권2호
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• pp.171-179
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• 2012

고온부에 해당하는 터빈 노즐과 로터의 냉각은 가스터빈의 성능에 큰 영향을 미친다. 본 연구에서는 냉각 공기의 예냉각이 가스터빈과 복합화력 발전 성능에 미치는 영향을 알아보았다. 계산에 사용된 모델은 F-Class 가스 터빈이며 냉각을 고려한 터빈의 구성요소를 사용해 냉각공기의 변화에 대해 보다 정확한 모사를 구사하였다. 냉각공기의 예냉각에 따른 가스터빈의 성능변화를 나타내기 위해 탈설계 해석이 수행되었다. 노즐 및 로터의 냉각에 따른 성능 변화를 보다 정확하게 나타내기 위해 열역학적 냉각모델과 속도삼각형을 고려한 모델이 고려되었다. 또한 복합발전의 경우 냉각공기에서 추출된 열을 하부사이클에서 회수하여 스팀터빈을 구동하는데 추가적인 열을 공급하는 시스템이 구성되었다. 복합발전 시스템의 모든 냉각공기의 온도를 200K 예냉각하는 경우에 주유동가스의 유량증가로 인해 약 1.78%의 출력 증가를 나타내었으며 동일한 터빈 입구온도 유지를 위한 연료소모의 증가로 효율은 0.70% 포인트 감소하였다.