• Journal of Korean Society of Forest Science
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• v.88 no.3
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• pp.379-388
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• 1999

As a first step for delineating the provenance regions of the forest trees in Korea, horizontal zones have been deduced primarily from the various climatic factors such as annual mean temperature, extremely low temperature, relative humidity, annual gum of possible growing days, duration of sunshine and dry index. The basic concept to the delineation of the provenance regions was based on the ecological regions, which was likely to be more practical than that on the basis of the typical provenance regions at the species level. Primary classification of the regions has been based on the forest zones(sub-tropical, warm-temperate, mid-temperate and cool-temperate) as a broad geographic region. Further classification has been carried out using cluster analyses among the basic regions within forest zone. On the basis of clustering, a total of 19 regions including 3 from sub-tropical, 6 from warm-temperate, 8 from mid-temperate and 2 from cool-temperate was horizontally delineated. Of the mean values of 6 climate factors at the broad geographic region level, three factors such as annual mean temperature, extremely low temperature, annual growing days showed directional tendencies from subtropical to cool-temperate, while the others didn't. The values of relative humidity, duration of sunshine and dry index varied among the provenance regions within forest zone. These three factors might he more sensitive by the micro-environment condition than by the macro-environment condition. Present study aimed to delineate the primary provenance regions for tentative application to forest practices. These will be stepwise revised through the supplement using accumulated information regard to genecological data.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.716-720
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• 2006

Fuzzy sets are good for abstracting and unifying information using natural language like terms. However, fuzzy sets embody vagueness and users may have different attitude to the vagueness, each user may choose difference one as the best among several fuzzy values. In this paper, we develop a method teaming a user's, preference on fuzzy values and select one which fits to his preference. Users' preferences are modeled with artificial neural networks. We gather learning data from users by asking to choose the best from two fuzzy values in several representative cases of comparing two fuzzy sets. In order to establish tile representative comparing cases, we enumerate more than 600 cases and cluster them into several groups. Neural networks ate trained with the users' answer and the given two fuzzy values in each case. Experiments show that the proposed method produces outputs closet to users' preference than other methods.

• Journal of Broadcast Engineering
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• v.22 no.1
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• pp.28-41
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• 2017

In this paper, the fast encoding algorithm in High Efficiency Video Coding (HEVC) encoder was studied. For the encoding efficiency, the current HEVC reference software is divided the input image into Coding Tree Unit (CTU). then, it should be re-divided into CU up to maximum depth in form of quad-tree for RDO (Rate-Distortion Optimization) in encoding precess. But, it is one of the reason why complexity is high in the encoding precess. In this paper, to reduce the high complexity in the encoding process, it proposed the method by determining the maximum depth of the CU using a hierarchical clustering at the pre-processing. The hierarchical clustering results represented an average combination of motion vectors (MV) on neighboring blocks. Experimental results showed that the proposed method could achieve an average of 16% time saving with minimal BD-rate loss at 1080p video resolution. When combined the previous fast algorithm, the proposed method could achieve an average 45.13% time saving with 1.84% BD-rate loss.

• Korean Journal of Plant Resources
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• v.29 no.3
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• pp.322-330
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• 2016

Understanding the genetic variation among landrace collections is important for crop improvement and utilization of valuable genetic resources. The present study was carried out to analyse the genetic diversity and associated population structure of 621 foxtail millet accessions of Korean landraces using 22 EST-SSR markers. A total of 121 alleles were detected from all accessions with an average of 5.5 alleles per microsatellite locus. The average values of gene diversity, polymorphism information content, and expected heterozygosity were 0.518, 0.594, and 0.034, respectively. Following the unweighted neighbor-joining method with arithmetic mean based clustering using binary data of polymorphic markers, the genotypes were grouped into 3 clusters, and population structure analysis also separated into 3 populations. Principal coordinate analysis (PCoA) explained a variation of 13.88% and 10.99% by first and second coordinates, respectively. However, in PCoA analysis, clear population-level clusters could not be found. This pattern of distribution might be the result of gene flow via germplasm exchanges in nearby regions. The results indicate that these Korean landraces of foxtail millet exhibit a moderate level of diversity. This study demonstrated that molecular marker strategies could contribute to a better understanding of the genetic structure in foxtail millet germplasm, and provides potentially useful information for developing conservation and breeding strategies.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.34 no.5
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• pp.493-501
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• 2001

A study on the community structure of macrobenthos was accomplished around Kadugdo, a south coast of Korea. Sampling was performed bimonthly using a Smith-McIntyre grab ($0.05 m^2$) at ten stations from January to November, 1998. A total of 260 species was sampled with mean density of $1,729 ind./m^2$and biomass of $154.7 gwwt./m^2$during the study periods. Of these species, there were 96 species of arthropods ($37.0\%$), 89 species of annelids ($34.2\%$), 45 species of molluscs ($17.3\%$) and 30 species of others ($11.5\%$). Annelids were density-dominant faunal group with a mean density of $1,263 ind./m^2$which occupied $73.0\%$ of the total individual of benthic animals. Molluscs were represented as biomass-dominant faunal group with a mean biomass of $99.5 gwwt./m^2$ ($64.3\%$ of total biomass). The density-dominant species were a bivalvia Theora fragilis ($194 ind./m^2$) and five species of polychaetes, Lumbrineris longifolia ($177 ind./m^2$), Chaetozone setosa ($150 ind./m^2$), Sternaspis scutata ($116 ind./m^2$), Sigambra tentaculata ($106 ind./m^2$) and Hemipodus yenourensis ($94 ind./m^2$). And major biomass-dominant species was a bivalvia Ruditapes philippinarum ($45.6 gwwt./m^2$). Clustering analysis showed that the study area could be divided into two station groups and three stations: southwestern part of Kadugdo effected on Chinhae Bay, fisheries farming area and eastern part of Kadugdo effected on Nakdong River estuary.

• Journal of Korean Society of Forest Science
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• v.101 no.4
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• pp.619-625
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• 2012

This study was conducted to investigate the variation of fruit, seed and germination characteristics among populations of Serrateleaf Pearlbush (Exochorda serratifolia S. Moore) distributed in Korea. Fruits collected from 96 trees in five natural populations and their five fruit, seven seed and three germination characteristics were analyzed. In 14 characteristics except for mean germination time, there were significant differences among populations and among individuals within population. Generally, fruit and seed characteristics showed higher values among population in total variance component. Coefficients of variations in weight of fruit (27.0%), length/thickness of seed (28.1%) and germination rate (52.5%) were relatively high compared to other traits. In seed germination behaviors, germination percentage, mean germination time and germination rate showed 62.9%, 64.0 days and 0.40 ea./day, respectively. As a result of simple correlation analysis, mean germination time showed a significant positive correlation with seed thickness, germination rate showed a significant positive correlation with height of parent tree and latitude, respectively. Also, latitude showed a positive correlation with fruit weight. The populations close geographically did not show the tendency of clustering into the same group. The results of principal component analysis showed that the first for principal components (PC's) explained 63.0% of the total variation. Primary 3 principal components appeared to be major variables because of the loading contribution of 97.0%.

• Asian-Australasian Journal of Animal Sciences
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• v.27 no.10
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• pp.1399-1405
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• 2014

The effective management of endangered animal genetic resources is one of the most important concerns of modern breeding. Evaluation of genetic diversity and relationship of local breeds is an important factor towards the identification of unique and valuable genetic resources. This study aimed to analyze the genetic diversity and population structure of six Korean native chicken breeds (n = 300), which were compared with three imported breeds in Korea (n = 150). For the analysis of genetic diversity, 30 microsatellite markers from FAO/ISAG recommended diversity panel or previously reported microsatellite markers were used. The number of alleles ranged from 2 to 15 per locus, with a mean of 8.13. The average observed heterozygosity within native breeds varied between 0.46 and 0.59. The overall heterozygote deficiency ($F_{IT}$) in native chicken was $0.234{\pm}0.025$. Over 30.7% of $F_{IT}$ was contributed by within-population deficiency ($F_{IS}$). Bayesian clustering analysis, using the STRUCTURE software suggested 9 clusters. This study may provide the background for future studies to identify the genetic uniqueness of the Korean native chicken breeds.

• International Journal of Oral Biology
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• v.43 no.2
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• pp.101-111
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• 2018

Understanding the classification of malocclusion is a crucial issue in Orthodontics. It can also help us to diagnose, treat, and understand malocclusion to establish a standard for definite class of patients. Principal component analysis (PCA) and k-means algorithms have been emerging as data analytic methods for cephalometric measurements, due to their intuitive concepts and application potentials. This study analyzed the macro- and meso-scale classification structure and feature basis vectors of 1020 (415 male, 605 female; mean age, 25 years) orthodontic patients using statistical preprocessing, PCA, random matrix theory (RMT) and k-means algorithms. RMT results show that 7 principal components (PCs) are significant standard in the extraction of features. Using k-means algorithms, 3 and 6 clusters were identified and the axes of PC1~3 were determined to be significant for patient classification. Macro-scale classification denotes skeletal Class I, II, III and PC1 means anteroposterior discrepancy of the maxilla and mandible and mandibular position. PC2 and PC3 means vertical pattern and maxillary position respectively; they played significant roles in the meso-scale classification. In conclusion, the typical patient profile (TPP) of each class showed that the data-based classification corresponds with the clinical classification of orthodontic patients. This data-based study can provide insight into the development of new diagnostic classifications.

• Ecology and Resilient Infrastructure
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• v.7 no.3
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• pp.189-198
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• 2020

Wetlands, which provide various ecological services, have been regarded as an important nature-based solution for, for example, sustainable water quality improvement and buffering of impacts from climate change. Although the importance of conserving wetlands to reduce the impacts of various perturbations (e.g., changes of land use, climate, and hydrology) has been acknowledged, the possibility of applying these efforts as a nature-based solution in a macro-scale (e.g., landscape) has been insufficient. In this study, we examine the possibility of ecological network analysis that provides an engineering solution as a nature-based solution. Specifically, we analyzed how land use change affects the structural and functional characteristics (connectivity, network efficiency, and clustering coefficient) of the ecological networks by using the ecological networks generated by multiple dispersal models of the hypothetical inhabiting species in wetlandscape. Changes in ecological network characteristics were analyzed through simultaneously removing wetlands, with two initial conditions for surface area, in the zones where land use change occurs. We set a total number of four zones of land use change with different wetland densities. All analyses showed that mean degree and network efficiency were significantly reduced when wetlands in the zones with high wetland density were removed, and this phenomenon was intensified especially when zones contained hubs (nodes with high degree). On the other hand, we observed the clustering coefficient to increase. We suggest our approach for assessing the impacts of land use change on ecological networks, and with additional analysis on betweenness centrality, we expect it can provide a nature-based engineering solution for creating alternative wetlands.

• Journal of the Korea Convergence Society
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• v.13 no.3
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• pp.33-41
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• 2022

In this article, we proposed to predict natural gas (NG) leakage levels through feature selection based on a factor analysis (FA) of the integrating the Korean Meteorological Agency data and natural gas leakage data for considering complex factors. The paper has been divided into three modules. First, we filled missing data based on the linear interpolation method on the integrated data set, and selected essential features using FA with OrdinalEncoder (OE)-based normalization. The dataset is labeled by K-means clustering. The final module uses four algorithms, K-nearest neighbors (KNN), decision tree (DT), random forest (RF), Naive Bayes (NB), to predict gas leakage levels. The proposed method is evaluated by the accuracy, area under the ROC curve (AUC), and mean standard error (MSE). The test results indicate that the OrdinalEncoder-Factor analysis (OE-F)-based classification method has improved successfully. Moreover, OE-F-based KNN (OE-F-KNN) showed the best performance by giving 95.20% accuracy, an AUC of 96.13%, and an MSE of 0.031.