• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.88-88
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• 2011

수자원에서 일강수량 모의기법은 다양한 목적으로 활용되고 있으며 기본적으로 수공구조물 설계 및 수자원계획을 수립하기 위한 입력 자료로서 이용된다. 수자원계획은 장기적인 목적을 가지고 수행되는 것이 일반적이며 우리가 목표로 하는 장기간의 일강수량자료의 획득이 어렵기 때문에 단기간의 일강수량자료를 장기 모의하여 이용하게 된다. 일강수량을 모의하는데 있어서 강수계열의 단기간의 기억(memory)을 활용한 Markov Chain 모형이 가장 일반적이며, 기존 Markov Chain 모형을 통한 일강수량 모의에서 발생하는 가장 큰 문제점은 극치강수량을 재현하기 어렵다는 점이다. 이러한 문제점으로 인해 수자원 계획을 수립하는데 있어서 불확실성을 가중시키고 있다. 특히 일강수량 모의기법을 통해서 추정되는 빈도강수량의 과소추정으로 인해 수공구조물 설계 시에 신뢰성을 확보하는 데 문제점이 있다. 이러한 점에서 본 연구에서는 기존 Markov Chain 모형에서 일강수량에 평균적인 특성과 극치특성을 동시에 재현할 수 있도록 불연속 Kernel-Pareto Distribution 기반에 일강수량모의기법을 개발하였다. 한강유역의 3개 강수지점에 대해서 기존 Markov Chain 모형과 본 연구에서 제안한 방법을 적용한 결과 여름의 일강수량 모의 시 1차모멘트인 평균과 2-3차 모멘트 모두 효과적으로 재현하지 못하는 문제점이 나타났다. 그러나 본 연구에서 제안한 불연속 Kernel-Pareto 분포형 기반 Markov Chain 모형은 여름의 일강수량 모의 시 강수계열의 평균적인 특성뿐만 아니라 표준편차 및 왜곡도의 경우에도 관측치의 통계특성을 매우 효과적으로 재현하는 것으로 나타났다. 본 연구에서 제시한 방법론은 전체적으로 기존 Markov Chain 모형에 비해 극치강수량을 재현하는데 유리한 기법으로 판단되며, 또한 극치강수량을 일반강수량으로부터 분리하여 모의함으로서 평균 및 중간값 등 낮은 차수에 모멘트 등 일강수량에 전체적인 분포특성을 더욱 효과적으로 모의할 수 장점을 확인하였다.

• Korean Journal of Agricultural and Forest Meteorology
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• v.4 no.2
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• pp.95-102
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• 2002

Spatial interpolation has become a common procedure in converting temperature forecasts and observations at irregular points for use in regional scale ecosystem modeling and the model based decision support systems for resource management. Neglection of terrain effects in most spatial interpolations for short term temperatures may cause erroneous results in mountainous regions, where the observation network hardly covers full features of the complicated terrain. A spatial interpolation model for daytime hourly temperature was formulated based on error analysis of unsampled site with respect to the site topography. The model has a solar irradiance correction scheme in addition to the common backbone of the lapse rate - corrected inverse distance weighting. The solar irradiance scheme calculates the direct, diffuse and reflected components of shortwave radiation over any surfaces based on the sun-slope geometry and compares the sum with that over a reference surface. The deviation from the reference radiation is used to calculate the temperature correction term by an empirical conversion formula between the solar energy and the air temperature on any sloped surfaces at an hourly time scale, which can be prepared seasonally for each land cover type. When this model was applied to a 14 km by 22 km mountainous region at a 10 m horizontal resolution, the estimated hourly temperature surfaces showed a better agreement with the observed distribution than those by a conventional method.

• Journal of the Korean association of regional geographers
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• v.14 no.6
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• pp.736-751
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• 2008

Although a growing number of studies have commonly used a simple areal weighting interpolation method to quantify demographic characteristics of impacted areas in environmental equity analysis, the results obtained are inevitably imprecise because of the method's unrealistic assumption that population is evenly distributed within a census enumeration unit. Two alternative areal interpolation methods such as intelligent areal weighting and regression methods can account for the distributional biases in the estimation of impacted populations by making use of additional information about the geographic distribution of population. This research explores five areal interpolation methods for estimating the population characteristics of impacted areas in environmental equity analysis and evaluates the sensitivity of the outcomes of environmental equity analysis to areal interpolation methods. This study used GIS techniques to allow areal interpolation to be informed by the distribution of land cover types, as inferred from a satellite image. in both the source and target units. Independent samples t-test statistics were measured to verify the environmental equity hypothesis while coefficients of variation were calculated to compare the relative variability and consistency in the socioeconomic characteristics of populations at risk over different areal interpolation methods. Results show that the outcomes of environmental equity analysis in the study area are not sensitive to the areal interpolation methods used in estimating affected populations, but the population estimates within the impacted areas are largely variable as different areal interpolation methods are used. This implies that the use of different areal interpolation methods may to some degree alter the statistical results of environmental equity analysis.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.3B
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• pp.277-284
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• 2011

The limitations of existing Markov chain model for reproducing extreme rainfalls are a known problem, and the problems have increased the uncertainties in establishing water resources plans. Especially, it is very difficult to secure reliability of water resources structures because the design rainfall through the existing Markov chain model are significantly underestimated. In this regard, aims of this study were to develop a new daily rainfall simulation model which is able to reproduce both mean and high order moments such as variance and skewness using a piecewise Kernel-Pareto distribution. The proposed methods were applied to summer and fall season rainfall at three stations in Han river watershed in Korea. The proposed Kernel-Pareto distribution based Markov chain model has been shown to perform well at reproducing most of statistics such as mean, standard deviation and skewness while the existing Gamma distribution based Markov chain model generally fails to reproduce high order moments. It was also confirmed that the proposed model can more effectively reproduce low order moments such as mean and median as well as underlying distribution of daily rainfall series by modeling extreme rainfall separately.

• Journal of Korean Society of Forest Science
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• v.101 no.3
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• pp.374-380
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• 2012

The k-Nearest Neighbor (kNN) technique is popularly applied to assess forest resources at the county level and to provide its spatial information by combining large area forest inventory data and remote sensing data. In this study, two approaches such as distance-weighting and stratification of training dataset, were compared to improve kNN-based forest growing stock estimates. When compared with five distance weights (0 to 2 by 0.5), the accuracy of kNN-based estimates was very similar ranged ${\pm}0.6m^3/ha$ in mean deviation. The training dataset were stratified by horizontal reference area (HRA) and forest cover type, which were applied by separately and combined. Even though the accuracy of estimates by combining forest cover type and HRA- 100 km was slightly improved, that by forest cover type was more efficient with sufficient number of training data. The mean of forest growing stock based kNN with HRA-100 and stratification by forest cover type when k=7 were somewhat underestimated ($5m^3/ha$) compared to statistical yearbook of forestry at 2011.

• Korean Journal of Agricultural and Forest Meteorology
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• v.12 no.4
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• pp.217-224
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• 2010

This study was carried out to quantify potential effects of the surrounding ocean on the observed air temperature at coastal weather stations in the Korean Peninsula. Daily maximum and minimum temperature data for 2001-2009 were collected from 66 Korea Meteorological Administration (KMA) stations and the monthly averages were calculated for further analyses. Monthly data from 27 inland sites were used to generate a gridded temperature surface for the whole Peninsula based on an inverse distance weighting and the local temperature at the remaining 39 sites were estimated by recent techniques in geospatial climatology which are widely used in correction of small - scale climate controls like cold air drainage, urban heat island, topography as well as elevation. Deviations from the observed temperature were regarded as the 'apparent' sea effect and showed a quasi-logarithmic relationship with the distance of each site from the nearest coastline. Potential effects of the sea on daily temperature might exceed $6.0^{\circ}C$ cooling in summer and $6.5^{\circ}C$ warming in winter according to this relationship. We classified 25 sites within the 10 km distance from the nearest coastline into 'coastal sites' and the remaining 15 'fringe sites'. When the average deviations of the fringe sites ($0.5^{\circ}C$ for daily maximum and $1.0^{\circ}C$ for daily minimum temperature) were used as the 'noise' and subtracted from the 'apparent' sea effects of the coastal sites, maximum cooling effects of the sea were identified as $1.5^{\circ}C$ on the west coast and $3.0^{\circ}C$ on the east and the south coast in summer months. The warming effects of the sea in winter ranged from $1.0^{\circ}C$ on the west and $3.5^{\circ}C$ on the south and east coasts.

• The Korean Journal of Financial Management
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• v.25 no.4
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• pp.183-212
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• 2008

The purpose of this paper is to analyze the efficiency change and determinants of the korean non-life insurance companies. we use DEA (Data Envelopment Analysis) model to measure company efficiency change and use GLS, Tobit model, FIixed effect model, Random effect model, GMM to measure efficiency determinants. we utilize ten non-life insurance companies in korea and the panel data for five from 2001 to 2005. The empirical results show the following findings. First, technical efficiency shows that approximately 15.5% of inefficiency exists on the non-life insurance companies and it reveals that the cause for technical inefficiency is due to scale inefficiency. Second, Dea Window results show that the stable dissimilarity by standard deviation, LDP of CCR. Third, the results of efficiency determinants show that increase efficiency is depend on the premium income and real estates.

• Korean Journal of Construction Engineering and Management
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• v.18 no.5
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• pp.59-67
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• 2017

At the completion of the construction, various finishing processes are concentrated. This imposes a burden on the on-site manager and imposes on experience based quality control, thereby causing deviations in the quality of construction depending on supervisor or worker's individual competence. In addition, the information related to quality control is frequently scattered in various types of documents such as specifications and drawings, and checkpoints are frequently omitted. It is necessary to provide a tool that can effectively provide the practitioner before or during the inspection work by systematically storing the information related to the defect prevention and linking them in a mutually referential state. This paper proposes an quality inspection support model that can systematically store necessary information on activity or room basis for the quality check of the apartment house finishing work. Establish a defect prevention information base and a information nexus by linking specifications, design standards, checklists, regulations, defect cases, and drawings to the finishing process and the rooms. Based on this, information registration and search interface are presented. It can contribute to securing a certain level of construction quality or more by suggesting a frame that can be utilized by linking various defects prevention information with the focus on closing activity and room.

• Journal of the Korean Geotechnical Society
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• v.38 no.3
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• pp.35-42
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• 2022

The N-value from the Standard Penetration Test (SPT), which is one of the representative in-situ test, is an important index that provides basic geological information and the depth of the bearing layer for the design of geotechnical structures. In the aspect of time and cost-effectiveness, there is a need to carry out a representative sampling test. However, the various variability and uncertainty are existing in the soil layer, so it is difficult to grasp the characteristics of the entire field from the limited test results. Thus the spatial interpolation techniques such as Kriging and IDW (inverse distance weighted) have been used for predicting unknown point from existing data. Recently, in order to increase the accuracy of interpolation results, studies that combine the geotechnics and deep learning method have been conducted. In this study, based on the SPT results of about 22,000 holes of ground survey, a comparative study was conducted to predict the depth of the bearing layer using deep learning methods and IDW. The average error among the prediction results of the bearing layer of each analysis model was 3.01 m for IDW, 3.22 m and 2.46 m for fully connected network and PointNet, respectively. The standard deviation was 3.99 for IDW, 3.95 and 3.54 for fully connected network and PointNet. As a result, the point net deep learing algorithm showed improved results compared to IDW and other deep learning method.

• The Journal of the Korea Contents Association
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• v.22 no.8
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• pp.347-357
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• 2022

This study was attempted to identify the health-related quality of life of Chronic Obstructive Pulmonary Disease (COPD) patients and factors influencing the quality of life, focusing on Health-related quality of life with 8 items (HINT-8). The subjects of this study were 451 adults aged 40 years or older who performed lung function tests and whose ratio is less than 0.7 by measuring forced respiratory volume in 1 second [FEV₁] to forced vital capacity in the 2019 National Health and Nutrition Examination Survey, It was analyzed using SAS program. As a result, both the HINT-8 index and EuroQol five-dimensions 3-level version (EQ-5D-3L) index were appropriate as tools to measure the health-related quality of life in COPD patients, and the factors affecting the health-related quality of life were age, gender, income, and smoking status, comorbidities, stress, and subjective health status. Therefore, in order to improve the health-related quality of life of COPD patients, an individualized management program suitable for the characteristics of subjects such as the low-income class and the elderly, including smoking cessation education and stress management, should be developed and applied.