• Journal of Animal Science and Technology
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• v.53 no.4
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• pp.325-332
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• 2011

This research was conducted to analyze the effects of raising farm on the heritability and breeding values of Hanwoo cows for their carcass traits, including cold carcass weight (CWT), back-fat thickness (BFT), eye-muscle area (EMA) and marbling score (MAR). The carcass data and pedigree data were collected from steers raised on Hanwoo farms in Pyeongchang-gun, Gangwon-do, South Korea. Three analytical models were applied for the estimation of heritabilities and breeding values. The first model (model 1) included slaughter house-year-month combination as fixed effects and age at slaughter was fitted as linear and quadratic covariates. The second model (model 2) was similar to model 1, but raising farm was additionally included as random effect. The third model (model 3) was similar to model 1 but farm effects were additionally included as fixed effect. The comparisons between the model 1 and the models including farm effect (model 2 and model 3) revealed that heritability estimates from model 2 or model 3 were smaller to those from model 1 for all carcass traits. Especially, obvious decrease of heritability was observed in CWT where heritability was 0.23 from model 1, 0.15 from model 2 and 0.18 from model 3. The maximum log likelihood of the model 2 and 3 were higher than those of model 1 for all traits. In model 2 that raising farm was included as a random effect, the ratio of farm variance to the total phenotypic variance were ranged from 4% (EMA) to 18% (CWT). Top 10% and bottom 10% of female cows were selected based on the breeding values from model 1, and the Spearman's rank correlation coefficients among models were estimated for each trait within selected group. The correlation coefficients were ranged from 0.57 to 0.95 in top 10% group and from 0.68 to 0.95 in bottom 10% group. These results show that the discrepancies in the rankings of breeding values can be based on the models applied. In conclusion, the results obtained in this study suggest that the herd effect or farm effect should be included in the analytical model when breeding values are estimated with the purpose of improvement of carcass traits of Hanwoo breeding cows.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5D
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• pp.885-894
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• 2006

The purpose of this study is focused on the development of the effective classification technique using ultra multiband of hyperspectral image. This study suggests the classification technique using canonical correlation analysis, one of multivariate statistical analysis in hyperspectral image classification. High accuracy of classification result is expected for this classification technique as the number of bands increase. This technique is compared with Maximum Likelihood Classification(MLC). The hyperspectral image is the EO1-hyperion image acquired on September 2, 2001, and the number of bands for the experiment were chosen at 30, considering the band scope except the thermal band of Landsat TM. We chose the comparing base map as Ground Truth Data. We evaluate the accuracy by comparing this base map with the classification result image and performing overlay analysis visually. The result showed us that in MLC's case, it can't classify except water, and in case of water, it only classifies big lakes. But Canonical Correlation Classification (CCC) classifies the golf lawn exactly, and it classifies the highway line in the urban area well. In case of water, the ponds that are in golf ground area, the ponds in university, and pools are also classified well. As a result, although the training areas are selected without any trial and error, it was possible to get the exact classification result. Also, the ability to distinguish golf lawn from other vegetations in classification classes, and the ability to classify water was better than MLC technique. Conclusively, this CCC technique for hyperspectral image will be very useful for estimating harvest and detecting surface water. In advance, it will do an important role in the construction of GIS database using the spectral high resolution image, hyperspectral data.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1211-1224
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• 2023

Clouds cause many difficult problems in observing land surface phenomena using optical satellites, such as national land observation, disaster response, and change detection. In addition, the presence of clouds affects not only the image processing stage but also the final data quality, so it is necessary to identify and remove them. Therefore, in this study, we developed a new cloud detection technique that automatically performs a series of processes to search and extract the pixels closest to the spectral pattern of clouds in satellite images, select the optimal threshold, and produce a cloud mask based on the threshold. The cloud detection technique largely consists of three steps. In the first step, the process of converting the Digital Number (DN) unit image into top-of-atmosphere reflectance units was performed. In the second step, preprocessing such as Hue-Value-Saturation (HSV) transformation, triangle thresholding, and maximum likelihood classification was applied using the top of the atmosphere reflectance image, and the threshold for generating the initial cloud mask was determined for each image. In the third post-processing step, the noise included in the initial cloud mask created was removed and the cloud boundaries and interior were improved. As experimental data for cloud detection, CAS500-1 L2G images acquired in the Korean Peninsula from April to November, which show the diversity of spatial and seasonal distribution of clouds, were used. To verify the performance of the proposed method, the results generated by a simple thresholding method were compared. As a result of the experiment, compared to the existing method, the proposed method was able to detect clouds more accurately by considering the radiometric characteristics of each image through the preprocessing process. In addition, the results showed that the influence of bright objects (panel roofs, concrete roads, sand, etc.) other than cloud objects was minimized. The proposed method showed more than 30% improved results(F1-score) compared to the existing method but showed limitations in certain images containing snow.

• Journal of Korea Water Resources Association
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• v.45 no.8
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• pp.827-837
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• 2012

The estimation of the rainfall quantile is of great importance in designing hydrologic structures. Conventionally, the rainfall quantile is estimated by univariate frequency analysis with an appropriate probability distribution. There is a limitation in which duration of rainfall is restrictive. To overcome this limitation, bivariate frequency analysis by using 3 copula models is performed in this study. Annual maximum rainfall events in 5 stations are used for frequency analysis and rainfall depth and duration are used as random variables. Gumbel (GUM), generalized logistic (GLO) distributions are applied for rainfall depth and generalized extreme value (GEV), GUM, GLO distributions are applied for rainfall duration. Copula models used in this study are Frank, Joe, and Gumbel-Hougaard models. Maximum pseudo-likelihood estimation method is used to estimate the parameter of copula, and the method of probability weighted moments is used to estimate the parameters of marginal distributions. Rainfall quantile from this procedure is compared with various marginal distributions and copula models. As a result, in change of marginal distribution, distribution of duration does not significantly affect on rainfall quantile. There are slight differences depending on the distribution of rainfall depth. In the case which the marginal distribution of rainfall depth is GUM, there is more significantly increasing along the return period than GLO. Comparing with rainfall quantiles from each copula model, Joe and Gumbel-Hougaard models show similar trend while Frank model shows rapidly increasing trend with increment of return period.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.8
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• pp.3748-3759
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• 2013

The purpose of this study was to examine the classification accuracies of ML, MAP, and EAP methods under DINA model. For this purpose, this study examined the classification accuracies of the classification methods under the various conditions: the number of attributes, the ability distribution of examinees, and test length. To accomplish this purpose, this study used a simulation method. For the simulation study, data was simulated under the various simulation conditions including the number of attributes (K= 5, 7), the ability distribution of examinees (high, middle, low), and test length (J= 15, 30, 45). Additionally, the percent of agreements between true skill patterns(true ${\alpha}$) and skill patterns estimated by the ML, MAP, and EAP methods were calculated. The summary of the main results of this study is as follows: First, When the number of attributes was 5 and 7, the EAP method showed relatively higher average in the percent of exact agreement than the ML and MAP methods. Second, under the same conditions, as the number of attributes increased, the average percent of exact agreement decreased in ML, MAP, and EAP methods. Third, when the prior distribution of examinees ability was different from low to high under the conditions of the same test length, the EAP method showed relatively higher average in the percent of exact agreement than those of the ML and MAP methods. Fourth, the average percent of exact agreement increased in all methods, ML, MAP, and EAP when the test length increased from 15 to 30 and 45 under the conditions of the same the ability distribution of examinees.

• Journal of the Korean Geophysical Society
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• v.3 no.3
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• pp.161-174
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• 2000

Bayesian inversion is a stable approach to infer the subsurface structure with the limited data from geophysical explorations. In geophysical inverse process, due to the finite and discrete characteristics of field data and modeling process, some uncertainties are inherent and therefore probabilistic approach to the geophysical inversion is required. Bayesian framework provides theoretical base for the confidency and uncertainty analysis for the inference. However, most of the Bayesian inversion require the integration process of high dimension, so massive calculations like a Monte Carlo integration is demanded to solve it. This method, though, seemed suitable to apply to the geophysical problems which have the characteristics of highly non-linearity, we are faced to meet the promptness and convenience in field process. In this study, by the Gaussian approximation for the observed data and a priori information, fast Bayesian inversion scheme is developed and applied to the model problem with electric well logging and dipole-dipole resistivity data. Each covariance matrices are induced by geostatistical method and optimization technique resulted in maximum a posteriori information. Especially a priori information is evaluated by the cross-validation technique. And the uncertainty analysis was performed to interpret the resistivity structure by simulation of a posteriori covariance matrix.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1995.04a
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• pp.282-286
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• 1995

일반적으로 관성계 내의 물체에 대한 동적특성의 파악을 위해서는 속도, 가속도 및 각속도, 각가속도에 대한 정보를 필요로 하며 자이로는 이중에서 각속도를 측정하는 장치이다. 운동하는 질량에 회전각속도가 인가될 때 발생되는 코리올리힘을 측정하여 회전각속도를 검출하는 개념의 각속도 센서인 진동자이로는 성능이 회전형 자이로에 비해 떨어지나 구조가 간단하고 소형이며 대량생산이 가능한 장점이 있다. 진동자이로의 효시로는 1950년 영국의 Sperry Gyroscope Company의 "Gyroton"이며, 전자기력을 이용한 가진과 측정이 그 특징으로서 실험실 조건에서 지구의 자전속도를 측정할 수 있었다. 그후 1960년대에 General Electric에서 "VYRO"라는 모델을 개발했는데 압전소자를 이용하여 가진과 측정을 하는 방법이 사용되었다. 1980년대에 Watson Ind., Soderkvist등은 센서자체가 압전물질로 만들어진 자이로를 실험하였고 1990년도에 들어서는 진동자이로의 원리를 마이크로 머시닝 기술과 연계시켜서 소형 경량화와 대량생산을 목표로 연구가 일부 진행되고 있다. 현재 제품화되어 실제 응용되고 있는 예로는 무라다사의 삼각프리즘 형태의 자이로, 토킨사의 원통형 자이로 등이 있으며 이러한 자이로는 캠코더 화면의 안정화 장치에 주로 사용되고 있다. 본 논문에서는 압전소자의 압전, 전왜 방정식으로 출발하여 자이로헤드의 동적 거동을 해석하였다. 진동자이로는 물체의 공진주파수에서의 진동현상을 이용하며, 두 방향의 고유진동수를 일치시켜야 하는 등의 설계조건이 있다. 이러한 조건을 만족하도록 사각보 구조를 기본으로 하여 새로운 형태의 자이로헤드를 고안하였다. 자이로헤드의 구동회로를 설계, 해석하고 각속도를 측정할 수 있는 검출회로를 설계하여 설계된 진동자이로의 동적 특성을 확인하고 보정회로를 이용하여 사용 주파수 영역을 넓혔다.이용하여 사용 주파수 영역을 넓혔다.러한 강이성들이 보장되는 제어이론들 중 H$_{\infty}$ 제어이론이 많이 연구/응용 되고 있다. 특히 공칭 플랜트 모델과 함께 사용되는 플랜트 모델과 함께 사용되는 플랜트 불확실성 모델은 직접적으로 성능 및 안정도에 영향을 미치므로 주의 깊게 선정해야 한다. 방법의 실질적인 적용에는 어려움이 있다. 본 연구에서는 기존의 방법들의 단점을 극복할 수 있는 새로운 회귀적 모우드 변수 규명 방법을 개발하였다. 이는 Fassois와 Lee가 ARMAX모델의 계수를 효율적으로 추정하기 위하여 개발한 뱉치방법인 Suboptimum Maximum Likelihood 방법[5]를 기초로 하여 개발하였다. 개발된 방법의 장점은 응답 신호에 유색잡음이 존재하여도 모우드 변수들을 항상 정확하게 구할 수 있으며, 또한 알고리즘의 안정성이 보장된 것이다.. 여기서는 실험실 수준의 평 판모델을 제작하고 실제 현장에서 이루어질 수 있는 진동제어 구조물에 대 한 동적실험 및 FRS를 수행하는 과정과 동일하게 따름으로써 실제 발생할 수 있는 오차나 error를 실험실내의 차원에서 파악하여 진동원을 있는 구조 물에 대한 진동제어기술을 보유하고자 한다. 이용한 해마의 부피측정은 해마경화증 환자의 진단에 있어 육안적인 MR 진단이 어려운 제한된 경우에만 실제적 도움을 줄 수 있는 보조적인 방법으로 생각된다.ofile whereas relaxivity at high field is not affected by τS. On the other hand, the change in τV does not affect low field profile but strongly in fluences on both inflection fie이 and the maximum relaxivity value. The results shows a fluences on both inflection field and the

• Asian-Australasian Journal of Animal Sciences
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• v.31 no.4
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• pp.473-479
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• 2018

Objective: The study was designed to perform a genome-wide association (GWA) and partitioning of genome using Illumina's PorcineSNP60 Beadchip in order to identify variants and determine the explained heritability for the total number of teats in Yorkshire pig. Methods: After screening with the following criteria: minor allele frequency, $MAF{\leq}0.01$; Hardy-Weinberg equilibrium, $HWE{\leq}0.000001$, a pair-wise genomic relationship matrix was produced using 42,953 single nucleotide polymorphisms (SNPs). A genome-wide mixed linear model-based association analysis (MLMA) was conducted. And for estimating the explained heritability with genome- or chromosome-wide SNPs the genetic relatedness estimation through maximum likelihood approach was used in our study. Results: The MLMA analysis and false discovery rate p-values identified three significant SNPs on two different chromosomes (rs81476910 and rs81405825 on SSC8; rs81332615 on SSC13) for total number of teats. Besides, we estimated that 30% of variance could be explained by all of the common SNPs on the autosomal chromosomes for the trait. The maximum amount of heritability obtained by partitioning the genome were $0.22{\pm}0.05$, $0.16{\pm}0.05$, $0.10{\pm}0.03$ and $0.08{\pm}0.03$ on SSC7, SSC13, SSC1, and SSC8, respectively. Of them, SSC7 explained the amount of estimated heritability along with a SNP (rs80805264) identified by genome-wide association studies at the empirical p value significance level of 2.35E-05 in our study. Interestingly, rs80805264 was found in a nearby quantitative trait loci (QTL) on SSC7 for the teat number trait as identified in a recent study. Moreover, all other significant SNPs were found within and/or close to some QTLs related to ovary weight, total number of born alive and age at puberty in pigs. Conclusion: The SNPs we identified unquestionably represent some of the important QTL regions as well as genes of interest in the genome for various physiological functions responsible for reproduction in pigs.

• The KIPS Transactions:PartB
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• v.12B no.3 s.99
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• pp.273-282
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• 2005

Real-time Object tracking is an important field in developing vision applications such as surveillance systems and vision based navigation. mean-shift algerian and Condensation algorithm are widely used in robust object tracking systems. Since the mean-shift algorithm is easy to implement and is effective in object tracking computation, it is widely used, especially in real-time tracking systems. One of the drawbacks is that it always converges to a local maximum which may not be a global maximum. Therefore, in a cluttered environment, the Mean-shift algorithm does not perform well. On the other hand, since it uses multiple hypotheses, the Condensation algorithm is useful in tracking in a cluttered background. Since it requires a complex object model and many hypotheses, it contains a high computational complexity. Therefore, it is not easy to apply a Condensation algorithm in real-time systems. In this paper, by combining the merits of the Condensation algorithm and the mean-shift algorithm we propose a new model which is suitable for real-time tracking. Although it uses only a few hypotheses, the proposed method use a high-likelihood hypotheses using mean-shift algorithm. As a result, we can obtain a better result than either the result produced by the Condensation algorithm or the result produced by the mean-shift algorithm.

• Asian-Australasian Journal of Animal Sciences
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• v.30 no.7
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• pp.912-919
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• 2017

Objective: Identification of the candidate genes that play key roles in phenotypic variations can provide new information about evolution and positive selection. Interleukin (IL)-32 is involved in many biological processes, however, its role for the immune response against various diseases in mammals is poorly understood. Therefore, the current investigation was performed for the better understanding of the molecular evolution and the positive selection of single nucleotide polymorphisms in IL-32 gene. Methods: By using fixation index ($F_{ST}$) based method, IL-32 (9375) gene was found to be outlier and under significant positive selection with the provisional combined allocation of mean heterozygosity and $F_{ST}$. Using nucleotide sequences of 11 mammalian species from National Center for Biotechnology Information database, the evolutionary selection of IL-32 gene was determined using Maximum likelihood model method, through four models (M1a, M2a, M7, and M8) in Codeml program of phylogenetic analysis by maximum liklihood. Results: IL-32 is detected under positive selection using the $F_{ST}$ simulations method. The phylogenetic tree revealed that goat IL-32 was in close resemblance with sheep IL-32. The coding nucleotide sequences were compared among 11 species and it was found that the goat IL-32 gene shared identity with sheep (96.54%), bison (91.97%), camel (58.39%), cat (56.59%), buffalo (56.50%), human (56.13%), dog (50.97%), horse (54.04%), and rabbit (53.41%) respectively. Conclusion: This study provides evidence for IL-32 gene as under significant positive selection in goat.