• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.1
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• pp.73-81
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• 2012

For planning and developing rural regions, it is very important to understand and utilize regional characteristics including social, demographic, and economic aspects. The purpose of this study is to find effective analysis techniques and provide a procedure design for mining regional characteristics in South Korea through reviewing and analyzing 41 related studies. The engaged research methods can be classified into five categories (PCA+CA, PCA, CA, GIS, and PCA+GIS) with the combination of three methodologies: principal component analysis (PCA), cluster analysis (CA), and geographical information system (GIS). The combination of PCA and CA occupied about 40 % of research methods used in related studies. The analysis tool of Korean Rural Information Supporting System (KRISS) is designed based on the outcomes of this study and applied to classify the regional capacity of agriculture using agricultural census data (2000) for evaluating its applicability.

• Journal of Engineering Education Research
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• v.8 no.1
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• pp.31-45
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• 2005

In this paper, selecting survey items was performed using three clustering methods: factor analysis, fuzzy c-Means algorithm and cluster analysis. The methods were used to extract key items from various questionnaires. The key item represents several similar questionnaires that form a cluster. Test survey was made of 120 items obtained from several surveys and it was answered by 646 students from 4 universities. Each item contains 6 choices. Applying the clustering method chose 25 items which is reduced from the original 120 items. The results yielded by three methods are very similar.

• Journal of Soil and Groundwater Environment
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• v.7 no.3
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• pp.19-32
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• 2002

This study is objected to identify the relations between surface- and shallow ground-water and the seasonal variation of their qualities in watersheds near Muju area. The water type shows mainly Ca-$HCO_3$type. Heavy-metal contamination of surface water is locally detected, due to the mixing with mine drainage. In October nitrate concentration is especially high in densely populated area. Cluster Analysis and Principal Component Analysis are implemented to interpret the complexity of the chemical variation of surface- and ground-water with large amount of chemical data. Based on the cluster analysis, surface-water was divided into five groups and ground-water into three groups. Principal Component Analysis efficiently supports the result of cluster analysis, allowing the identification of three main factors controlling the water quality. There are (1) hydrogeochemical factor, (2) anthropogenic factor and (3) heavy metal contaminated by mine drainage.

• Journal of Biosystems Engineering
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• v.27 no.3
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• pp.241-248
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• 2002

An electronic noses system including six metal oxide sensors was used to predict the characteristics of shelf-life of soybean curd. Soybean curd was stored at two different temperatures defined as low temperature(5$\^{C}$) and high temperature(25$\^{C}$). Resistance changes of the sensors were measured 13 times for 19 days at low temperature and 19 times for 120 hours at high temperature. Three different analytical methods such as graphical analysis(GA), principal component analysis(PCA), and cluster analysis(CA) were used to analyze sensors outputs. The ratio of resistance was decreased according to increasement of shelf-life. Using PCA it was possible to predict freshness and shelf-life time of soybean curds. Also, using CA it was possible to simplify an electronic nose system. Electronic nose system could be an efficient method to predict shelf-life and to evaluate quality in foods.

• Journal of Korean Society on Water Environment
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• v.37 no.6
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• pp.520-530
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• 2021

Multivariate statistical analysis and an environmental hydrological model were applied for investigating the causes of water pollution and providing best management practices for water quality improvement in urban and agricultural watersheds. Principal component analysis (PCA) and cluster analysis (CA) for water quality time series data show that chemical oxygen demand (COD), total organic carbon (TOC), suspended solids (SS) and total phosphorus (T-P) are classified as non-point source pollutants that are highly correlated with river discharge. Total nitrogen (T-N), which has no correlation with river discharge and inverse relationship with water temperature, behaves like a point source with slow and consistent release. Biochemical oxygen demand (BOD) shows intermediate characteristics between point and non-point source pollutants. The results of the PCA and CA for the spatial water quality data indicate that the cluster 1 of the watersheds was characterized as upstream watersheds with good water quality and high proportion of forest. The cluster 3 shows however indicates the most polluted watersheds with substantial discharge of BOD and nutrients from urban sewage, agricultural and industrial activities. The cluster 2 shows intermediate characteristics between the clusters 1 and 3. The results of hydrological simulation program-Fortran (HSPF) model simulation indicated that the seasonal patterns of BOD, T-N and T-P are affected substantially by agricultural and livestock farming activities, untreated wastewater, and environmental flow. The spatial analysis on the model results indicates that the highly-populated watersheds are the prior contributors to the water quality degradation of the river.

• Journal of Korean Society for Atmospheric Environment
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• v.18 no.1
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• pp.25-37
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• 2002

The purpose of the study was to survey the distribution patterns of inorganic elements of PM-10 in the various indoor environments and analyze the pollution patterns of aerosol in various places of indoor environment using a pattern recognition method based on cluster analysis and disjoint principal component analysis. A total of 40 samples in the indoor had been collected using mini-vol portable samplers. These samples were analyzed for their 19 bulk inorganic compounds such as B, Na, Mg, Al, K, Ca, Ti, V, Cr, Fe, Ni, Cu, Zn, As, Se, Cd, Ba, Ce, and Pb by using an ICP-MS. By applying a disjoint principal component analysis, four patterns of the indoor air pollutions were distinguished. The first pattern was identified as a group with high concentrations of PM-10, Na, Mg, and Ca. The second pattern was identified as a group with high concentrations B, Mg, At, Ca, Fe, Cu, and Ba. The third pattern was a group of sites with high concentrations of K, Zn. Cd. The fourth pattern was a group with low concentrations PM-10 and all inorganic elements. This methodology was found to be helpful enough to set the criteria standard of indoor air quality, corresponding pollutants, and classification of indoor environment categories when making an indoor air quality law.

• The Bulletin of The Korean Astronomical Society
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• v.43 no.1
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• pp.32.3-32.3
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• 2018

We report a possible discovery of a relic of a dissolved star cluster in the Sextans dwarf spheroidal galaxy. Using the hk index (${\equiv}$(Ca-b)-(b-y)) as a photometric metallicity indicator, we have successfully discriminated the metal-poor and metal-rich stars in the galaxy and found an unexpected number density peak of metal-poor stars near the galaxy center. The analysis of color-magnitude diagrams reveals that they appear to be originated from an old, metal-poor globular cluster which might be slightly farther than the bulk of field stars in the galaxy. This supports the presence of the star cluster remnants in the galaxy which have been suggested by previous studies. If confirmed, dissolution of a star cluster provides a piece of evidence of a cored dark-matter halo profile for the Sextans dwarf galaxy.

• Preventive Nutrition and Food Science
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• v.18 no.3
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• pp.214-217
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• 2013

Twenty-nine Korean wheat cultivars were analyzed for 8 important minerals (Cu, Fe, Mn, Zn, Ca, K, Mg and P) using Inductively Coupled Plasma Atomic Emission Spectrometry (ICP-AES). A hierarchical cluster analysis (HCA) was applied to classify wheat cultivars, which has a similarity in mineral compositions. The concentration ranges of the micro-minerals Cu, Fe, Mn, and Zn: 0.12~0.71 mg/100 g, 2.89~5.89 mg/100 g, 1.65~4.48 mg/100 g, and 2.58~6.68 mg/100 g, respectively. The content ranges of the macro-minerals Ca, K, Mg and P: 31.3~46.3 mg/100 g, 288.2~383.3 mg/100 g, 113.6~168.6 mg/100 g, and 286.2~416.5 mg/100 g, respectively. The HCA grouped 6 clusters from all wheat samples and a significant variance was observed in the mineral composition of each group. Among the 6 clusters, the second group was high in Fe and Ca, whereas the fourth group had high Cu, Mn and K concentrations; the fifth cluster was high in Zn, Mg and P. The variation in mineral compositions in Korean wheat cultivars can be used in the wheat breeding program to develop a new wheat cultivar with high mineral content, thus to improve the nutritional profile of wheat grains.

• Journal of agriculture & life science
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• v.45 no.3
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• pp.1-7
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• 2011

This study was carried out to provide the basic information for effective preservation and management of forest community of Nari basin in Ulleung Island. Forest community in Nari basin was classified into Fagus engleriana community, Sorbus amurensis community, Pinus densiflora community, Celtis jessoensis community and Alnus maximowiczii community. As the result of DCCA ordination analysis, sea level among environmental factors had high correlation with community distribution. Fagus engleriana community and Sorbus amurensis community correlated highly with aspect, Na content, and C/N ratio. There was a high correlation between Celtis jessoensis community and the content of Ca and K. Alnus maximowiczii community was distributed in site where CEC content is high. Pinus densiflora community was distributed in site where the content of Ca and CEC is high.

• KCID journal
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• v.17 no.2
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• pp.17-27
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• 2010

In order to manage the water quality in reservoir, it is necessary to understand the temporal and spatial variation of reservoirs and to classify the reservoirs. In this research, agricultural reservoirs are classified according to physical characteristics (depth, residence time, shape of the reservoir etc) and water quality using multivatriate analysis (PCA and CA). CA (Cluster Analysis) method classify reservoirs into several groups as a similarity of the reservoirs, but it is difficult to indicate a full list to the one table. In case of PCA (Principle Component Analysis) method, it has the advantage for the classification on the reservoirs depending on the water quality similarity and also it is useful to analyze the relationship between related factors through correlation analysis. However PCA is limited to classify into several groups based on the characteristics of the reservoirs and each user should be classified as randomly subjective according to the relative position of the reservoir in the figure. In conclusions, compared to conventional reservoirs classification methods, both CA and PCA methods are considered to be a classification method that describes the nature of the reservoir well, but classification results has a restriction on use, so further research will be needed to complement.