• The Korean Journal of Applied Statistics
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• v.36 no.6
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• pp.547-559
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• 2023

Proper handling of nonresponse in sample survey improves the accuracy of the parameter estimation. Various studies have been conducted to properly handle MAR (missing at random) nonresponse or MCAR (missing completely at random) nonresponse. When nonresponse occurs, the PSA (propensity score adjusted) estimator is commonly used as a mean estimator. The PSA estimator is known to be unbiased when known sample weights and properly estimated response probabilities are used. However, for MNAR (missing not at random) nonresponse, which is affected by the value of the study variable, since it is very difficult to obtain accurate response probabilities, bias may occur in the PSA estimator. Chung and Shin (2017, 2022) proposed a post-stratification method to improve the accuracy of mean estimation when MNAR nonresponse occurs under a non-informative sample design. In this study, we propose a double post-stratification method to improve the accuracy of the naive PSA estimator for MNAR nonresponse under an informative sample design. In addition, we perform simulation studies to confirm the superiority of the proposed method.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.45 no.11
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• pp.45-54
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• 2008

In this paper, we use particle swarm optimization (PSO) to design a randomly excited and randomly spaced linear array with either the lowest side lobe level (SLL) or the narrowest beamwidth. The positions and the excitation amplitudes of the array elements are considered as variables to be controlled. The beam pattern is optimized by controlling the two variables simultaneously and randomly. The best beam patterns are obtained using PSO in the fitness function where performance is improved by the random assignment of weight coefficients to each angular sector of the beam Pattern. The weight coefficients and angles are obtained through several trial runs. Also, an extra term, ${\beta}{\ast}BW$, is added to the fitness function to account for the beamwidth as well as the SLL. Is produces the best result for the beam pattern with either the lowest SLL or the narrowest beamwidth. In the former case, the SLL and beamwidth are about -43dB and $32.2^{\circ}$, respectively, with only 10 elements. In the latter case, the SLL and beamwidth are about -26dB and $24.2^{\circ}$, respectively.

• Journal of Cadastre & Land InformatiX
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• v.47 no.1
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• pp.37-51
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• 2017

Despite the improvement in accuracy of heavy rain forecasting, socioeconomic costs due to heavy rain hazards continue to increase. This is due to a lack of understanding of the effects of weather. In this study, the risk of heavy rain hazard was analyzed using the concepts of hazard, vulnerability, and exposure, which are key concepts of impact forecast presented by WMO. The potential impacts were constructed by the exposure and vulnerability variables, and the hazard index was calculated by selecting three variables according to the criteria of heavy rain warning. Weights of the potential impact index were calculated by using PCA and hazard index was calculated by applying the same weight. Correlation analysis between the potential impact index and damages showed a high correlation and it was confirmed that the potential impact index appropriately reflects the actual damage pattern. The heavy rain hazard risk was estimated by using the risk matrix consisting of the heavy rain potential impact index and the hazard index. This study provides a basis for the impacts analysis study for weather warning with spatial/temporal variation and it can be used as a useful data to establish the local heavy rain hazard prevention measures.

• Survey Research
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• v.11 no.2
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• pp.1-28
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• 2010

This paper reports the results of the 2009 Internet volunteer panel version of the social survey conducted by Statistics Korea (Korea National Statistical Office). Authors identify socio-psychological characteristics of Internet survey volunteers and present quantitative evaluation of the propensity adjustment weighting method intended to remove Internet sample bias. The nine criteria used for propensity adjustment were regions, urban/rural, gender, age, education, consumer satisfaction, views on income distribution, newspaper access and Internet news access. Propensity adjustment weighting based on the logit model and rim weights were applied to the online survey of 2,903 respondents using the face-to-face area sample data of 37,049 respondents as reference. A total of 106 items were used for evaluating the propensity adjustment weighting methods. The results showed that in 80% of survey items the propensity adjustment weighting yielded better estimates compared to simple demographic weighting. This suggests that Internet surveys by volunteer panels are useful for conducting the general social study in Korea. The reference survey data for this study contains several items on social-psychological behaviors and attitudes, is large in size and obtained by probability sampling. Thus it may be utilized in propensity adjustment of other Internet surveys.

• Journal of Environmental Impact Assessment
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• v.22 no.6
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• pp.627-637
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• 2013

기후변화가 산업단지에 미치는 영향은 지자체에까지 확산될 수 있으며 물리적, 경제적인 영향이 미쳐지므로 어떠한 물리적, 경제적 변수에 의해 산업단지의 피해가 초래되는지 규명될 필요가 있다. 따라서 본 연구는 국가 산업단지를 대상으로 산업단지 및 입지 시군구의 물리적, 경제적 특성을 기반으로 한 기후변화 취약성 평가를 수행하고 산업단지 입지에 영향을 미치는 시간, 공간별 극한기후를 규명하고자 하였다. 산업단지의 극한기후에 의한 취약성은 IPCC에서 제안한 상향식 취약성 평가 방법을 따라 분석되었으며 전력요구도, 위험지역유무 등 단지의 물리적 입지조건과 입지 지자체의 기반시설 현황, 지자체와 산업단지의 경제적 특성 등이 평가 기준으로 연구되었다. 기후노출, 민감도, 적응 능력의 항목별 가중치와 항목내 변수들의 가중치 분석에 AHP가 적용되었다. 본 연구는 홍수, 가뭄, 혹서, 혹한, 해수면상승에 대해 취약 입지 시군구를 밝혔고 홍수와 가뭄, 혹서와 혹한별로 대비되는 취약성 결과가 나옴을 고찰하였다. 또한, 각 극한기후별로 적응능력 함양이 시급한 시군구를 밝혀 지자체 차원의 노력이 필요한 시군구를 규명하였다. 서울시 금천구는 산업단지 입지에 가장 취약한 지역으로 나타났으며 홍수, 혹한에서 높은 취약성을 보임이 분석되었다. 한편, 극한기후의 발생에도 불구하고 전반적으로 낮은 취약성을 보인 산업단지 입지 지자체는 광주광역시, 전라북도 익산, 제주시 등으로 분석되었다. 따라서 본 연구는 산업단지의 업종에 따라 적합하지 않은 입지 지역을 규명하였으며 향후 산업단지 입지의 잠재 기후변화 영향을 고려한 의사결정을 지원할 수 있다.

• Journal of KIISE:Software and Applications
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• v.34 no.1
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• pp.1-10
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• 2007

This paper proposes a new indexing mechanism for MPEG-7 visual descriptors, especially Dominant Color and Contour Shape descriptors, that guarantees an efficient similarity search for the multimedia database whose visual meta-data are represented with MPEG-7. Since the similarity metric used in the Dominant Color descriptor is based on Gaussian mixture model, the descriptor itself could be transform into a color histogram in which the distribution of the color values follows the Gauss distribution. Then, the transformed Dominant Color descriptor (i.e., the color histogram) is indexed in the proposed indexing mechanism. For the indexing of Contour Shape descriptor, we have used a two-pass algorithm. That is, in the first pass, since the similarity of two shapes could be roughly measured with the global parameters such as eccentricity and circularity used in Contour shape descriptor, the dissimilar image objects could be excluded with these global parameters first. Then, the similarities between the query and remaining image objects are measured with the peak parameters of Contour Shape descriptor. This two-pass approach helps to reduce the computational resources to measure the similarity of image objects using Contour Shape descriptor. This paper also proposes two integration schemes of visual descriptors for an efficient retrieval of multimedia database. The one is to use the weight of descriptor as a yardstick to determine the number of selected similar image objects with respect to that descriptor, and the other is to use the weight as the degree of importance of the descriptor in the global similarity measurement. Experimental results show that the proposed indexing and integration schemes produce a remarkable speed-up comparing to the exact similarity search, although there are some losses in the accuracy because of the approximated computation in indexing. The proposed schemes could be used to build a multimedia database represented in MPEG-7 that guarantees an efficient retrieval.

• Land and Housing Review
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• v.4 no.1
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• pp.33-42
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• 2013

This study which are recognized that the lack of empirical research about the efficiency of the elements of environmentally friendly housing development planned presented housing design elements and policies to revitalize for the reduction of greenhouse gas emissions by analyzing the effectiveness of reduction of greenhouse gas output. In addition, it used various models of DEA which are accepted until now effective technique to evaluate the performance of the organization. In conclusion, there are effective 5 regionals which are Seoul, Incheon, Ulsan, South Chungcheong Province, South Gyeongsang Province. other regionals was analyzed to be inefficient. The conclusion from this study are as follows: First, in case of 11 regionals which are analyzed to be inefficient, they have to difference plan elements to make up. So each region should establish strategy to complement vulnerability. Second, not only internal architectural factors but institutional, and external environmental factors also affect the reduction of greenhouse gas emissions. And weighted scores also were moderately high. But levels of weighted scores still less than the ratio of Good quality housing. So it can be determined that evaluation of individual architecture still considered important. It need to pay more attention to the operating system and the external environmental factors.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.3
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• pp.129-141
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• 2019

In recent years, fast growing cities in Korea are showing signs of being vulnerable to more disasters as their population and facilities increase and intensify. In particular, fire is one of the most common disasters in Korea's cities, along with traffic accidents. Therefore, in this study, we analyze what type of factors affect the fire that threatens urban people. Fire data were acquired for 10 years, from 2007 to 2017, in Jinju, Korea. Spatial distribution pattern of fire occurrence in Jinju was assessed through the spatial autocorrelation analysis. First, spatial autocorrelation analysis was carried out to grasp the spatial distribution pattern of fire occurrence in Jinju city. In addition, correlation and multiple regression analysis were used to confirm spatial dependency and abnormality among factors. Based on this, OLS (Ordinary Least Square) regression analysis was performed using space weighting considering fire location and spatial location of each facility. As a result, First, LISA (Local Indicator of Spatial Association) analysis of the occurrence of fire in Jinju shows that the most central commercial area are fire department, industrial area, and residential area. Second, the OLS regression model was analyzed by applying spatial weighting, focusing on the most derived factors of multiple regression analysis, by integrating population and social variables and physical variables. As a result, the second kind of neighborhood living facility showed the highest correlation with the fire occurrence, followed by the following in the order of single house, sales facility, first type of neighborhood living facility, and number of households. The results of this study are expected to be useful for analyzing the fire occurrence factors of each facility in urban areas and establishing fire safety measures.

• Economic and Environmental Geology
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• v.52 no.2
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• pp.199-212
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• 2019

In the machine learning techniques, the sampling strategy of the training data affects a performance of the prediction model such as generalizing ability as well as prediction accuracy. Especially, in landslide susceptibility analysis, the data sampling procedure is the essential step for setting the training data because the number of non-landslide points is much bigger than the number of landslide points. However, the previous researches did not consider the various sampling methods for the training data. That is, the previous studies selected the training data randomly. Therefore, in this study the authors proposed several different sampling methods and assessed the effect of the sampling strategies of the training data in landslide susceptibility analysis. For that, total six different scenarios were set up based on the sampling strategies of landslide points and non-landslide points. Then Random Forest technique was trained on the basis of six different scenarios and the attribute importance for each input variable was evaluated. Subsequently, the landslide susceptibility maps were produced using the input variables and their attribute importances. In the analysis results, the AUC values of the landslide susceptibility maps, obtained from six different sampling strategies, showed high prediction rates, ranges from 70 % to 80 %. It means that the Random Forest technique shows appropriate predictive performance and the attribute importance for the input variables obtained from Random Forest can be used as the weight of landslide conditioning factors in the susceptibility analysis. In addition, the analysis results obtained using specific sampling strategies for training data show higher prediction accuracy than the analysis results using the previous random sampling method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.5B
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• pp.364-378
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• 2008

Data Envelopment Analysis(DEA) models can be used for performance evaluation on governmental funding projects for IT small and medium-sized enterprises associated with multiple-outputs/multiple-inputs. In order to enhance the accuracy of DEA efficiency scores, DEA models with exogenously fixed variables are required where the corporate competency is taken into account. Additionally, it is necessary to use multiple DEA basic as well as extended models so as to relax the restriction on the performance evaluation to relying on a single DEA model. In this study; 1)a DEA data structure is designed including exogenously fixed variables representing corporate asset, revenue and the number of employees at the point in time that the governmental funding project concerned is initiated; 2)DEA basic as well as extended models are established according to the DEA data structure presented abovementioned; and 3)a case study is illustrated with an empirical testbed dataset. As for the DEA basic models, CCR, BCC, Super-efficiency model are adopted. The DEA extended models are developed based on the models associated with noncontrollable and nondiscretionary variables. In the case study, it is explained a comparison of DEA models and also major numerical outcomes such as efficiency scores, ranks derived from each DEA model are integrated using Analytic Hierarchy Process(AHP) weights. Performance significance with DEA efficiency scores between technical categories are tested based not only on parametric but also nonparametric single-factor analysis of variance method.