• Journal of Veterinary Clinics
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• v.28 no.4
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• pp.422-426
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• 2011

For a national survey in which wide geographic region or an entire country is targeted, multi-stage sampling approach is widely used to overcome the problem of simple random sampling, to consider both herd- and animallevel factors associated with disease occurrence, and to adjust clustering effect of disease in the population in the calculation of sample size. The aim of this study was to establish sample size for estimating bovine tuberculosis (TB) in Korea using stratified two-stage sampling design. The sample size was determined by taking into account the possible clustering of TB-infected animals on individual herds to increase the reliability of survey results. In this study, the country was stratified into nine provinces (administrative unit) and herd, the primary sampling unit, was considered as a cluster. For all analyses, design effect of 2, between-cluster prevalence of 50% to yield maximum sample size, and mean herd size of 65 were assumed due to lack of information available. Using a two-stage sampling scheme, the number of cattle sampled per herd was 65 cattle, regardless of confidence level, prevalence, and mean herd size examined. Number of clusters to be sampled at a 95% level of confidence was estimated to be 296, 74, 33, 19, 12, and 9 for desired precision of 0.01, 0.02, 0.03, 0.04, 0.05, and 0.06, respectively. Therefore, the total sample size with a 95% confidence level was 172,872, 43,218, 19,224, 10,818, 6,930, and 4,806 for desired precision ranging from 0.01 to 0.06. The sample size was increased with desired precision and design effect. In a situation where the number of cattle sampled per herd is fixed ranging from 5 to 40 with a 5-head interval, total sample size with a 95% confidence level was estimated to be 6,480, 10,080, 13,770, 17,280, 20.925, 24,570, 28,350, and 31,680, respectively. The percent increase in total sample size resulting from the use of intra-cluster correlation coefficient of 0.3 was 22.2, 32.1, 36.3, 39.6, 41.9, 42.9, 42,2, and 44.3%, respectively in comparison to the use of coefficient of 0.2.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.2
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• pp.142-149
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• 2004

It is important to retrieve exact information coinciding with user's need from lots of Usenet news and filter desired information quickly. Differently from email system, we must previously register our interesting news group if we want to get the news information. However, it is not easy for a novice to decide which news group is relevant to his or her interests. In this work, we present a service classifying user preferred news groups among various news groups by the use of Kohonen network. We first extract candidate terms from example documents and then choose a number of representative keywords to be used in Kohonen network from them through fuzzy inference. From the observation of training patterns, we could find the sparsity problem that lots of keywords in training patterns are empty. Thus, a new method to train neural network through reduction of unnecessary dimensions by the statistical coefficient of determination is proposed in this paper. Experimental results show that the proposed method is superior to the method using every dimension in terms of cluster overlap defined by using within cluster distance and between cluster distance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.2
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• pp.277-284
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• 2017

In a flood of information, most users will want to get a proper recommendation. If a recommender system fails to give appropriate contents, then quality of experience (QoE) will be drastically decreased. In this paper, we propose a recommender system based on the intra-cluster users' item preference for improving recommendation accuracy indices such as precision, recall, and F1 score. To this end, first, users are divided into several clusters based on the actual rating data and Pearson correlation coefficient (PCC). Afterwards, we give each item an advantage/disadvantage according to the preference tendency by users within the same cluster. Specifically, an item will be received an advantage/disadvantage when the item which has been averagely rated by other users within the same cluster is above/below a predefined threshold. The proposed algorithm shows a statistically significant performance improvement over the item-based collaborative filtering algorithm with no clustering in terms of recommendation accuracy indices such as precision, recall, and F1 score.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.10 no.2
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• pp.117-123
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• 2011

The purpose of this study is to show the friction and wear characteristics on the vapor deposited coating layers on the SCM415 steel. In this research, frictional wear characteristic of coating materials such as Ti-series, Cr-series & WC/C and TiAlN+WC/C multilayer coating was investigated under room temperature, normal air pressure and no lubricating condition. Therefore, this study carried out research on the friction coefficient, micro hardness(Hv), surface roughness and wear quantity on the vapor deposited coating layers on the SCM415 steel. As the wear experimental result, the excellence of TiAlN+WC/C multilayer coating has been proven by high micro-hardness, low friction coefficient and wear quantity.

• Asian-Australasian Journal of Animal Sciences
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• v.19 no.6
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• pp.784-788
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• 2006

Microsatellite polymorphism and the genetic relationship were estimated using genotype information of 305 horses from 11 microsatellite loci. The breeds include the indigenous Korean breeds, Korean native horse (102) and Jeju racing horse (56) together with Japan Hokkaido horse (5), Mongolian horse (19), Thoroughbred horse (108), Quarter horse (11) and Przewalskii horse (4). Allelic frequencies, the number of alleles per locus were estimated by direct counting from observed genotype, and genetic variability was computed using the CERVUX software and DISPAN. The number of alleles per locus varied from 6 (HMS6) to 18 (ASB17) with an average value of 10.45 in horse breeds. The expected total heterozygosity ($H_T$) and coefficient of gene differentiation ($G_{ST}$) ranged 0.764-0.921 (the average value was 0.830) and 0.102-0.266 (the average value was 0.180) in horse breeds, respectively. Four populations (Przewalskii horse, Japan Hokkaido horse, Quarter horse, Thoroughbred horse) showed lower heterozygosity than the average value (the average value was 0.710). The expected heterozygosity within breed ($H_S$) and mean no. of observed alleles ranged from $0.636{\pm}0.064$ (Japan Hokkaido horse) to $0.809{\pm}0.019$ (Mongolian horse), and from 2.73 (Przewalskii horse) to 8.27 (Korean native horse), respectively. The polymorphic information content (PIC) ranged from 0.490 (Przewalskii horse) to 0.761 (Mongolian horse) with an average value of 0.637 in horse breeds. The results showed three distinct clusters with high bootstrap support: the Korean native horse cluster (Korean native horse, Mongolian horse), the European cluster (Przewalskii horse, Thoroughbred horse), and other horse cluster (Jeju racing horse, Japan Hokkaido horse, and Quarter horse). A relatively high bootstrap value was observed for the Korean native horse cluster and European cluster (87%), and the Korean native horse and Mongolian horse (82%). Microsatellite polymorphism data were shown to be useful for estimating the genetic relationship between Korean native horse and other horse breeds, and also be applied for parentage testing in those horse breeds.

• Journal of Korean Society of Water and Wastewater
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• v.36 no.3
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• pp.149-165
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• 2022

The water consumption data of individual consumers must be analyzed and forecast to establish an effective water demand management plan. A k-mean cluster model that can monitor water use characteristics based on hourly water consumption data measured using automated meter reading devices and demographic factors is developed in this study. In addition, the quantification model that can estimate the daily water consumption is developed. K-mean cluster analysis based on the four clusters shows that the average silhouette coefficient is 0.63, also the silhouette coefficients of each cluster exceed 0.60, thereby verifying the high reliability of the cluster analysis. Furthermore, the clusters are clearly classified based on water usage and water usage patterns. The correlation coefficients of four quantification models for estimating water consumption exceed 0.74, confirming that the models can accurately simulate the investigated demographic data. The statistical significance of the models is considered reasonable, hence, they are applicable to the actual field. Because the use of automated smart water meters has become increasingly popular in recent year, water consumption has been metered remotely in many areas. The proposed methodology and the results obtained in this study are expected to facilitate improvements in the usability of smart water meters in the future.

• Journal of the Economic Geographical Society of Korea
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• v.10 no.1
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• pp.19-43
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• 2007

The purpose of this study is to distinguish industrial clusters in Korea. Based on I/O table in 2000, coefficient matrix was calculated and factor analysis was performed on the matrix. Among 38 derived industrial clusters, 5 of them were unreported clusters, which were indistinguishable. Once these 38 industrial clusters were differentiated according to I/O table, final results of clusters were produced as I/O table was converted into KSIC (Korean Standard Industry Code). Since existing studies on industrial cluster have been focused on qualitative methods such as case studies and interviews, it is expected that this study could contribute to carry out more systematic and efficient methodology.

• Journal of Microbiology and Biotechnology
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• v.7 no.6
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• pp.397-401
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• 1997

The present research program was conducted to characterize a strain of actinomycetes producing an anti methicillin-resistant Staphylococcus aureus (MRSA) antibiotic. Soil samples were collected from various sites in Korea and a number of actinomycetes were isolated from the soil samples by applying selective agar for actinomycetes. Among over 400 isolates, a strain (AMLK-135) producing anti-MRSA antibiotic against S. aureus TK 784 was selected. According to the morphological and physiological characteristics, the strain AMLK-135 was confirmed to belong to the genus Streptomyces. From the results of species identification with the TAXON program, the strain AMLK-135 was shown to belong to major cluster 5 (Streptomyces exfoliatus), but it had a low simple matching coefficient ($S_{SM}$ SM/) value to member organisms of major cluster 5. Percentage ($\%$) of strain further away of the strain AMLK-135 was low (1.9400) and it was placed further away than the outer-most members in major cluster 5. Therefore, the strain AMLK-135 was identified as a new species of the genus Streptomyces.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.583-586
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• 1999

In this paper, the wavelet transform is performed in the input 256$\times$256 color image and decomposes a image into low-pass and high-pass components. Since the high-pass band contains the components of three directions, edges are detected by combining three parts. After finding the position of face using the histogram of the edge component, a face region in low-pass band is cut off. Since RGB color image is sensitively affected by luminances, the image of low pass component is normalized, and a facial region is detected using face color informations. As the wavelet transform decomposes the detected face region into three layer, the dimension of input image is reduced. In this paper, we use the 3000 images of 10 persons, and KL transform is applied in order to classify face vectors effectively. FCM(Fuzzy C-Means) algorithm classifies face vectors with similar features into the same cluster. In this case, the number of cluster is equal to that of person, and the mean vector of each cluster is used as a codebook. We verify the system performance of the proposed algorithm by the experiments. The recognition rates of learning images and testing image is computed using correlation coefficient and Euclidean distance.

• Plant Resources
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• v.6 no.3
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• pp.178-182
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• 2003

This study was conducted to investigate the relationship between yearly variations of climatic elements and yearly variations of productivity in perilla. In addition, correlation coefficients among yield and yield components were estimated. The data of yield and yield components were investigated for 10 years from 1991 to 2000. The meteorological data gathered at the Yeosu Weather Station for the same period were used to find out the relationships between climatic elements and productivity. Yearly variation of the amount of precipitation in September was large with coefficients of variation(c. v.) of 11.1%, but the coefficient of variance(c. v.) in July and August were relative small with 1.8, 2.1%, respectively. Number of cluster per hill and weight of 1,000 grains were greatly with c. v. of 76.1, 79.3%, respectively, but the coefficients of variance(c. v.) of plant height and seed yield were more less with 9.58, 10.60%, respectively. Correlation coefficients between precipitation of September and seed yield were positively significant correlation at the level of 5.1%, respectively, but the duration of sunshine in September and seed yield were negatively significant at the level of 5.1%, respectively. Correlation coefficients of these, the plant height, number of branches per plant, cluster length, number of cluster per hill, weight of 1,000 grains and seed yield were positively significant at the level of 5.1% respectively.