• Journal of Korea Water Resources Association
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• v.45 no.4
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• pp.393-407
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• 2012

This study suggests the results of temporal and spatial variations for rainfall data in the Korean Peninsula. We got the index of the rainfall amount, frequency and extreme indices from 65 weather stations. The results could be easily understood by drawing the graph, and the Mann-Kendall trend analysis was also used to determine the tendency (up & downward/no trend) of rainfall and temperature where the trend could not be clear. Moreover, by using the FARD, frequency probability rainfalls could be calculated for 100 and 200 years and then compared each other value through the moment method, maximum likelihood method and probability weighted moments. The Average Rainfall Index (ARI) which is meant comprehensive rainfalls risk for the flood could be obtained from calculating an arithmetic mean of the RI for Amount (RIA), RI for Extreme (RIE), and RI for Frequency (RIF) and as well as the characteristics of rainfalls have been mainly classified into Amount, Extremes, and Frequency. As a result, these each Average Rainfall Indices could be increased respectively into 22.3%, 26.2%, and 5.1% for a recent decade. Since this study showed the recent climate change trend in detail, it will be useful data for the research of climate change adaptation.

• Journal of the Korean Data and Information Science Society
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• v.25 no.1
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• pp.53-64
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• 2014

One of the major problems to forecast election, especially based on survey, is nonresponse. We may have different forecasting results depend on method of imputation. Handling nonresponse is more important in a survey about sensitive subject, such as presidential election. In this research, we consider a model based method of nonresponse imputation. A model based imputation method should be constructed based on assumption of nonresponse mechanism and may produce different results according to the nonresponse mechanism. An assumption of the nonresponse mechanism is very important precondition to forecast the accurate results. However, there is no exact way to verify assumption of the nonresponse mechanism. In this paper, we compared the accuracy of prediction and assumption of nonresponse mechanism based on the result of presidential election exit poll. We consider maximum likelihood estimation method based on EM algorithm to handle assumption of the model of nonresponse. We also consider modified within precinct error which Bautista (2007) proposed to compare the predict result.

• Korean Journal of Remote Sensing
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• v.28 no.5
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• pp.489-499
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• 2012

In this paper, a classifier ensemble framework for remote sensing data classification is presented that combines classification results generated from both different training sets and different classifiers. A core part of the presented framework is to increase a diversity between classification results by using both different training sets and classifiers to improve classification accuracy. First, different training sets that have different sampling densities are generated and used as inputs for supervised classification using different classifiers that show different discrimination capabilities. Then several preliminary classification results are combined via a majority voting scheme to generate a final classification result. A case study of land-cover classification using multi-temporal ENVISAT ASAR data sets is carried out to illustrate the potential of the presented classification framework. In the case study, nine classification results were combined that were generated by using three different training sets and three different classifiers including maximum likelihood classifier, multi-layer perceptron classifier, and support vector machine. The case study results showed that complementary information on the discrimination of land-cover classes of interest would be extracted within the proposed framework and the best classification accuracy was obtained. When comparing different combinations, to combine any classification results where the diversity of the classifiers is not great didn't show an improvement of classification accuracy. Thus, it is recommended to ensure the greater diversity between classifiers in the design of multiple classifier systems.

• Asian-Australasian Journal of Animal Sciences
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• v.21 no.8
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• pp.1109-1115
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• 2008

The reproduction component traits are important components of sow efficiency. The objective of this study was to evaluate the phenotypic and genetic parameters of four reproduction component traits (age at puberty (AP), preweaning number dead (PND), weaning to service interval (WSI), and intra-individual SD in litter size (IISDLS)) of sows in two Chinese indigenous pig breeds. Available reproductive records including 22,591 piglets born from 2,054 litters by 574 Jiangquhai sows and 464 Meishan sows were used in this investigation. A set of mixed models and restricted maximum likelihood methodology were used for the multiple trait analyses of these traits. The results showed that the estimates of heritabilities (${\pm}$standard error) for AP, PND, WSI and IISDLS were $0.40{\pm}0.05$, $0.06{\pm}0.03$, $0.20{\pm}0.02$ and 0.09{\pm}0.03 in Jiangquhai sows, and $0.35{\pm}0.06$, $0.05{\pm}0.03$, $0.18{\pm}0.03$ and $0.10{\pm}0.04$ in Meishan sows, respectively. There was moderate genetic correlation between AP and WSI, while there were low genetic correlations between the other pairwise traits. The genetic correlations were positive for most of the pairwise traits, except for the one between AP and IISDLS. The results indicated that all traits except for AP were difficult to make genetic improvement by traditional selection methods due to low heritabilities and the favorable improvement of AP might result in unfavorable changes of IISDLS due to the trend of genetic antagonism.

• Asian-Australasian Journal of Animal Sciences
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• v.24 no.11
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• pp.1514-1524
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• 2011

A total of 4,752 monthly lactation records of Friesian cows during the period from 2000 to 2005 were used to estimate genetic parameters and to determine the effect of udder health on milk production traits. Three milk production traits were studied: 305-day milk yield (305-dMY), 305-day fat yield (305-dFY) and 305-day protein yield (305-dPY). Four udder health traits were studied: somatic cell count (SCC), mastitis (MAST), udder health status (UDHS) with 10 categories and udder quarter infection (UDQI) with 7 categories. Mixed model least square analysis was used to estimate the fixed effects of month and year of calving and parity (P) on different studied traits. Sire and dam within sire were included in the model as random effects. Data were analyzed using Multi-trait Derivative Free Restricted Maximum Likelihood methodology (MTDFREML) to estimate genetic parameters. Unadjusted means of 305-dMY, 305-dFY, 305-dPY and SCC were 3,936, 121, 90 kg and 453,000 cells/ml, respectively. Increasing SCC from 300,000 to 2,000,000 cells/ml increased UDQI from 5.51 to 23.2%. Losses in monthly and lactationally milk yields per cow ranged from 17 to 93 and from 135 to 991 kg, respectively. The corresponding losses in monthly and lactationally milk yields return per cow at the same level of SCC ranged from 29.8 to 163 and from 236 to 1,734 Egyptian pounds, respectively. Heritability estimates of 305-dMY, 305-dFY, 305-dPY, SCC, MAST, UDHS, UDQI were 0.31${\pm}$0.4, 0.33${\pm}$0.03, 0.35${\pm}$0.05, 0.23${\pm}$0.02, 0.14${\pm}$0.02, 0.13${\pm}$0.03, and 0.09${\pm}$0.01, respectively. All milk production traits showed slightly unfavorable negative phenotypic and genetic correlations with SCC, MAST, UDHS and UDQI. There were positive and high genetic correlations between SCC and each of MAST (0.85${\pm}$0.7), UDHS (0.87${\pm}$0.10) and UDQI (0.77${\pm}$0.06) and between MAST and each of UDHS (0.91${\pm}$0.11) and UDQI (0.83${\pm}$0.07). It could be concluded that the economic losses from mastitis and high SCC are considerable. The high genetic correlation between SCC and clinical mastitis (CM) suggest that the selection for lower SCC would help to reduce or eliminate the undesirable correlated responses of clinical mastitis associated with selection for increasing milk yield. Additionally, it is recommended also that if direct information on under health traits is not available, measures of SCC can be inclusion in a selection criteria to improve the income from dairy cows.

• Korean Journal of Agricultural and Forest Meteorology
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• v.19 no.3
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• pp.93-101
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• 2017

The distribution of inter-annual variation in temperature would help evaluate the likelihood of a climatic risk and assess suitable zones of crops under climate change. In this study, we evaluated two methods to estimate the standard deviation of temperature in the areas where weather information is limited. We calculated the monthly standard deviation of temperature by collecting temperature at 0600 and 1500 local standard time from 10 automated weather stations (AWS). These weather stations were installed in the range of 8 to 1,073m above sea level within a mountainous catchment for 2011-2015. The observed values were compared with estimates, which were calculated using a geospatial correction scheme to derive the site-specific temperature. Those estimates explained 88 and 86% of the temperature variations at 0600 and 1500 LST, respectively. However, it often underestimated the temperatures. In the spring and fall, it tended to had different variance (e.g., increasing or decreasing pattern) from lower to higher elevation with the observed values. A regression analysis was also conducted to quantify the relationship between the standard deviation in temperature and the topography. The regression equation explained a relatively large variation of the monthly standard deviation when lapse-rate corrected temperature, basic topographical variables (e.g., slope, and aspect) and topographical variables related to temperature (e.g., thermal belt, cold air drainage, and brightness index) were used. The coefficient of determination for the regression analysis ranged between 0.46 and 0.98. It was expected that the regression model could account for 70% of the spatial variation of the standard deviation when the monthly standard deviation was predicted by using the minimum-maximum effective range of topographical variables for the area.

• Journal of Embryo Transfer
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• v.31 no.3
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• pp.243-247
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• 2016

Growth traits, such as body weight, directly influence productivity and economic efficiency in the swine industry. In this study, we estimate heritability for body weight traits usinginformation from pedigree and genome-wide single nucleotide polymorphism (SNP) chip data. Four body weight phenotypes were measured in 1,105 $F_2$ progeny from an intercross between Landrace and Jeju native black pigs. All experimental animals were subjected to genotypic analysis using PorcineSNP60K BeadChip platform, and 39,992 autosomal SNP markers filtered by quality control criteria were used to construct genomic relationship matrix for heritability estimation. Restricted maximum likelihood estimates of heritability were obtained using both genomic- and pedigree- relationship matrix in a linear mixed model. The heritability estimates using SNP information were smaller (0.36-0.55) than those which were estimated using pedigree information (0.62-0.97). To investigate effect of common environment, such as maternal effect, on heritability estimation, we included maternal effect as an additional random effect term in the linear mixed model analysis. We detected substantial proportions of phenotypic variance components were explained by maternal effect. And the heritability estimates using both pedigree and SNP information were decreased. Therefore, heritability estimates must be interpreted cautiously when there are obvious common environmental variance components.

• Journal of the military operations research society of Korea
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• v.36 no.1
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• pp.77-90
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• 2010

Adapting commercial equipments to military operations may provide the advantage of low cost, reduced acquisition time, and technology advancement. On the other hand, it may also offer the opportunity for a reliability and logistics risk because commercial products, standards, and practices may not meet military requirements. In addition to this, commercial vendors have little experience in providing the technical data required to support military deployment logistics. As more companies are equipped with data aquisition systems for their products, considerable amount of field warranty data has been accumulated. Typically, the field data for a given product comprise with the sales volume and the number of the claims for each period. Three types of product data are considered in this study: military designed equipment operating in a military environment, commercial equipment operating in a military environment, and commercial equipment operating in a commercial environment. We construct a estimation model for each type of data and propose an reliability transform method from a commercial environment to a military environment. Parametric methods for estimating the product reliability are proposed based on maximum likelihood criteria and least square criteria. Then a reliability transform procedure for handling different types of data is proposed in a consistent fashion. A case study is investigated to characterize our model based on a real field warranty data set.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1999.11b
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• pp.99-104
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• 1999

영상을 획득하는 과정에 있어, 영상획득 장치 또는 피사체의 흔들림으로 인해 발생되는 움직임 열화(motion-blur)현상은 영상의 선명도를 크게 떨어뜨리는 주된 원인이 된다. 손상된 영상은 그 영상자체로부터 움직임의 각도와 길이를 추출 함으로서 복원될 수 있다. 본 논문에서는 움직임 열화의 각도와 길이를 추정하기 위한 방법 중, 본 저자가 제안 했던 극점자취방법에, 확률적인 개념을 적용한 새로운 확률적 극점자취 방법을 소개한다. 기존의 방법은 신호지배영역이 올바로 지정되지 않았을 경우, 오차를 수반하기도 한다. 이러한 문제를 해결 하기 위해, 본 연구에서는 maximum likelihood(ML) 분류방법을 이용해 적절하지 않은 극점자취점의 영향을 선택적으로 작게 하여, 신호지배 영역의 설정 없이, 저주파 영역에서의 올른 극점자취의 검출이 가능하도록 하였다. 또한, Auto-regressive(Ah) 모델을 이용한 선형예측방법을 통해 극점 검출과정에서 불규칙하게 발생하는 특이점들이 극점으로 검출되지 못하도록 하여, 정밀한 움직임 방향의 추정이 가능하게 하였다. 또한, 움직임 길이의 검출에 있어서는, 노이즈에 의해 영향을 무시할 수 없는 기존의 영점교차점 방법을 보완한, 새로운 이동평균최소(MALM)법을 정의하였다 이 방법은 움직임 열화가 발생한 영상의 주파수 영역단면 패턴을 이용한 것으로서, 2차원적인 sinc함수를 1차원적인 표현으로 바꾸어주는 이동평균함수를 사용하여, 쉽게 부극점(sub-peak point)을 찾을 수 있도록 한다 부극점 또한 노이즈에 의한 영향을 받지 않고, 이동평균최소법 자체에 노이즈를 제거하는 과정에 포함되어있으므로. 이 방법을 사용하게 되면, 심한 노이즈 환경에서도 적절한 움직임의 길이 값을검출할 수 있다. 이렇게 얻어진 길이와 방향의 파라메터를 이용하여, 실제 실험에 사용된 손상되어진 영상을 효과적으로 복원할 수 있었다.>$\bigcirc$ 펄라이트 : 합섬A(비스코스+레이온)급액천의 유입은 소(1$\times$60cm)에서 21.8ml, 중(2$\times$60cm) 33.5ml, 대(3$\times$60cm) 43.4ml가 통과되었고 합섬(폴리에스텔)에서는 19.0~30.7ml로서 급액천의 규격에 따라 통과되는 차이가 있었다. 배지가 규격화되어 있어 급액천의 규격별로 일정하게 유입되었으며 급액천의 재질이 유입에 영향을 미친 것으로 사료되었다. (2) 급액관과 베드상과의 높이에 따른 유출양 : 급액과 베드상과의 낙차가 클수록 유출이 증가함을 알수 있었으나 합섬C(인견)실험구에서는 낙차가 유출에 영향을 미치지 않았다. (4) 급액된 양액의 EC 및 pH조사 : 급액된 양액의 EC 및 pH에 전혀 변화가 없어 재배 적응에 문제가 없을것으로 사료되었다.이가 가장 이상적인 것으로 생각된다.세포수에 대한 내부세포괴세포(ICM/total cells)가 20~40% 범주에 드는 비율은 처리구가 대조구보다 낮은 결과를 나타냈다. 결론적으로 돼지난포란을 이용하여 체외성숙을 유기할 때 효과적인 cysteamine의 농도는 50$\mu$M이 적당하며, 초기배발달을 유기할 때의 효과적인 cysteamine의 농도는 25~50$\mu$M인 것으로 판단된다.N)A(N)/N을 제시하였다(A(N)=N에 대한 A값). 위의 실험식을 사용하여 헝가리산 Zempleni 시료(15%$S_{XRD}$)의 기본입자분포로부터 %$S_{XRD}$를 계산한 결과, 16%$S_{XRD}$의 결과값을 얻을 수 있었다. 따라서, 본 연구에서 도출한 관계식들이 유효함을 확인할 수 있었다.계식들이 유효함을 확인할 수 있었다.할 때 약간의 증가를 나타냈다.". And

• Journal of the Korean Association of Geographic Information Studies
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• v.17 no.3
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• pp.54-67
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• 2014

This study was performed to estimate Greenhouse gases(GHGs) emissions from biomass burning at large forest fire(Ulju, Pohang and Bonghwa) in 2013. The extended methodology to estimate GHGs adopted the IPCC(Intergovermental Panel on Climate Change) Guidelines(2006) equation. For classifying fire damaged area and analyzing burn severity of total three large-fire area damaged, this study used post-fire imagery from Rapideye imagery to compute the Maximum Likelihood Classifiction (MLC). The result of accuracy assessment on burn severity from imagery showed that average overall accuracy was 75.93% and Kapp coefficient was 0.67 Finally, GHGs emissions from biomass burning in the three large-fire area 2013 were estimated as follows: Ulju $CO_2$ 63,260, CO 5.207, $CH_4$ 360, $N_2O$ 28.0 and $NO_x$ $4.4g/kg^{-1}{\cdot}ha^{-1}$, Pohang $CO_2$ 28,675, CO 2.359, $CH_4$ 163, $N_2O$ 12.7 and $NO_x$ $1.9g/kg^{-1}{\cdot}ha^{-1}$ and Bonghwa $CO_2$ 53,086, CO 1,655, $CH_4$ 114, $N_2O$ 23.5 and $NO_x$ $3.6g/kg^{-1}{\cdot}ha^{-1}$.