• Journal of Environmental Science International
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• v.5 no.1
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• pp.15-23
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• 1996

During the last two decades, many industrial complexes for heavy and chemical industries have been established along the Korean coastline, thereby increasing the pollution materials burden on the coastal environment of seawater. Masan Bay is one of the most polluted coastal areas in Korea and the main soures of pollutants are domestic and industrial wastewater from Masan, Changwon. This study was aimed to evaluate relationships among the physicochemical parameters in the bottom water of Masan bay and to examine environmental factors affecting to pollutions of seawater by factor analysis. 'rife factor loading, 1 is showed higher increasing inclination after 1989 year in station 1. The variance of pollutant materials is showed 43.7% in which the coastal inflow water is indicated external loadings(factor 1 : NO3--N, TN, factor 4 : SiO2-Si) corresponded to domestic sewage, industrial wastewater, and earth-sands in the bottom water of Masan bay And the internal loadings(factor 2 : SS, salinity, factor 3 . W.T., DO) are explained 33.8%'corresponded the phenomena of sedimentary layer and oxygen concentration. Therefore, The external loadings are explained by the higher factor pollutantal variance in Masan bay.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.8
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• pp.3726-3734
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• 2012

This study evaluated the behavior of pollutants based on the seasonal change by selecting the branch river's factors that influence the outflow of pollutants in Soyang lake basin. The analysis method was the factor analysis that classified the factors of the drainage area influencing the outflow of pollutants, and evaluated selected representative factors. As a result of the study, SS and T-P factors should be classified as similar factors to the storm water runoff, and the improvement of water must be strived through managing source of pollution at the time of no rain. Second, as the result of the influence from the factors, spring and winter seasons usually exert 36% influence and summer and fall exert over 90% significant influence that the improvement of water through managing source of water seems possible. At last, the prediction about delivery pollution load considering the outflow characteristic of pollutants at the drainage area based on seasonal change by regarding selected factors as independent variables is possible.

• The KIPS Transactions:PartB
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• v.11B no.4
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• pp.403-410
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• 2004

Recently, studies of image analysis, as the preprocessing stage for medical image analysis or image retrieval, are actively carried out. This paper intends to propose a way of utilizing color components for image retrieval. For image retrieval, it is based on color components, and for analysis of color, CLCM (Color Level Co-occurrence Matrix) and statistical techniques are used. CLCM proposed in this paper is to project color components on 3D space through geometric rotate transform and then, to interpret distribution that is made from the spatial relationship. CLCM is 2D histogram that is made in color model, which is created through geometric rotate transform of a color model. In order to analyze it, a statistical technique is used. Like CLCM, GLCM (Gray Level Co-occurrence Matrix)[1] and Invariant Moment [2,3] use 2D distribution chart, which use basic statistical techniques in order to interpret 2D data. However, even though GLCM and Invariant Moment are optimized in each domain, it is impossible to perfectly interpret irregular data available on the spatial coordinates. That is, GLCM and Invariant Moment use only the basic statistical techniques so reliability of the extracted features is low. In order to interpret the spatial relationship and weight of data, this study has used Principal Component Analysis [4,5] that is used in multivariate statistics. In order to increase accuracy of data, it has proposed a way to project color components on 3D space, to rotate it and then, to extract features of data from all angles.

• Journal of the Korean Data and Information Science Society
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• v.26 no.1
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• pp.41-53
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• 2015

In meteorological field, many researchers have tried to develop micro scale weather analysis modules for providing real-time weather information service in the metropolitan area. This effort enables us to cope with various economic and social harms coming from serious change in the micro meteorology of a metropolitan area due to rapid urbanization such as quantitative expansions in its urban activity, growth of population, and building concentration. The accuracy of the micro scale weather analysis modules (MSWAM) directly related to usefulness and quality of the real-time weather information service in the metropolitan area. This paper design a evaluation system along with verification tools that sufficiently accommodate spatio-temporal characteristics of the outputs of the MSWAM. For this we proposes a test for the equality of mean vectors of the output series of the MSWAM and corresponding observed time series by using a spectral analysis technique. As a byproduct, a time series cluster analysis method, using a function of the test statistic as the distance measure, is developed. A real data application is given to demonstrate the utility of the method.

• The Korean Journal of Applied Statistics
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• v.27 no.1
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• pp.31-41
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• 2014

Biplot is a useful graphical method to simultaneously explore the rows and columns of a two-way data matrix. In particular, principal component factor biplot is a graphical method to describe the interrelationship among many variables in terms of a few underlying but unobservable random variables called factors. If we consider the unobservable variables (which are mutually independent and also non-Gaussian), we can apply the independent component analysis decomposing a mixture of non-Gaussian in its independent components. In this case, if we apply the principal component factor analysis, we cannot clearly describe the interrelationship among many variables. Therefore, in this study, we apply the independent component analysis of Jutten and Herault (1991) decomposing a mixture of non-Gaussian in its independent components. We suggest an independent component biplot to interpret the independent component analysis graphically.

• Korean Journal of Food Science and Technology
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• v.28 no.6
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• pp.1171-1176
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• 1996

The changes in volatiles of the model system were analyzed by GC and GC-MS before and after retorting. The GC data were analyzed statistically by applying the analysis of variance, and 42 peaks were selected at 5% significance level. Multivariate statistical analysis was performed with these 42 peaks as independent variables. Through the stepwise discriminant analysis, 8 peaks, which corresponded to the compounds such as 2-heptanone, cis-3-hexenal, 2-pentyl-furan, 1-methyl-trans-1,2-cyclohexanediol, 2-hexanone, 3-octanone, trans, trans-nona-2,4-dienal and 1-octen-3-ol, were obtained in sequence to distinguish the samples with and without retorting. The principal component analysis of a set of 8 independent variables resulted in 3 principal components which accounted for 96.1% of the variance, while the first principal component (PC 1) explained 76.5% of the total variance. In addition, through the factor analysis of the principal components, the peaks 11, 20 and 21 could be grouped togather in accordance with the direction and the size while the peaks 9, 33 and 39 constituted the second group in the direction.

• International Area Studies Review
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• v.21 no.4
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• pp.223-245
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• 2017

This study is examined interaction between carbon market with stock market using a multivariate GARCH(DCC) model. Carbon market is EU ETS EUA price, stock market is the iron and cement stock price which has relatively energy intensive and massive carbon emissions sector in the industrial sector. It also analyzed changes in the correlation between the markets through an analysis of correlation coefficients. Moreover, it checked whether there was marketability expansion(or expansion of carbon emissions reduction) through the analysis above. As a result of empirical tests, it showed that the price spillover effect was insignificant. In addition, it represented that there was a weak correlation between the two markets since the volatility spillover effect disappeared in the second phase by an external shock(a financial crisis). Moreover, it was revealed that there were no significant changes although there was a weak upward trend in terms of the correlation between the carbon market and the stock market. This implies that emission rights could not expand marketability to financial market as a commodity(or did not play its natural role of the reduction of carbon emission).

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.541-549
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• 2018

This paper deals with the braking dynamic behavior of diesel electric locomotive pulling domestic cargo and passenger vehicles. Friction coefficient, pneumatic pressure, and running resistance affecting the braking system were tested. For the friction coefficient, the Dynamo test was performed with reference to UIC 541-4. The results are analyzed by multivariate regression and the relationship between braking force and ititial velocity is presented. The pneumatic pressure were classified into service braking and emergency braking. In order to reflect the characteristics of the brake valve and piping, the pressure rising over time was measured in the vehicle. In order to reflect the external force acting on the vehicle, we carried out the test of EN 14067-4 and presented the second order polynomial formula on a running resistance. The running resistance test results were compared with other countries. The dynamic behavior of a diesel electric locomotive running on a straight flat track based on vehicle resources, friction coefficient, braking pressure, and running resistance is simulated using the time integration presented in EN 14531-1. The simulation results were compared and verified with the vehicle braking test results. The results of this study can be used to analyze the dynamic braking behavior of a train. Also, it is expected that various parameters affecting braking in vehicle design can be analyzed and used as basic data for braking performance improvement.

• Conservation Science in Museum
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• v.2
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• pp.69-76
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• 2000

This study, a part of scientific analysis of the 23 Neolithic red earthenwares excavated from southeastern coast and islands. We analyzed earthenware through statistical method and hardness analysis of clay, following the analysis of clay composition, its firing temperature, pigment composition and its thickness. Clay composition was analyzed by using ICP-AES and XRF, and then Principal Component Analysis (one of multivariate methods) was used for classification. As a result, clay of the earthenware was classified into 3 groups(Bumbang, Youkjido and other sites). In addition, hardness analysis of pigment and clay based on the depth of earthenware surface showed that pigment layer containing lots of Fe₂O₃ had higher hardness than clay part, which can be interpreted that Fe₂O₃ contributes to raising hardness in case of high temperature firing. The fact that pigment hardness is higher than that of clay part implies that pigment was applied before firing.

• Korean Journal of Remote Sensing
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• v.15 no.2
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• pp.91-105
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• 1999

Spatial data integration using multiple geo-based data sets has been regarded as one of the primary GIS application issues. As for this issue, several integration schemes have been developed as the perspectives of mathematical geology or geo-mathematics. However, research-based approaches for statistical/quantitative assessments between integrated layer and input layers are not fully considered yet. Related to this niche point, in this study, spatial data integration using multiple geoscientific data sets by known integration algorithms was primarily performed. For spatial integration by using raster-based GIS functionality, geological, geochemical, geophysical data sets, DEM-driven data sets and remotely sensed imagery data sets from the Ogdong area were utilized for geological thematic mapping related by mineral potential mapping. In addition, statistical/quantitative information extraction with respective to relationships among used data sets and/or between each data set and integrated layer was carried out, with the scope of multiple data fusion and schematic statistical assessment methodology. As for the spatial integration scheme, certainty factor (CF) estimation and principal component analysis (PCA) were applied. However, this study was not aimed at direct comparison of both methodologies; whereas, for the statistical/quantitative assessment between integrated layer and input layers, some statistical methodologies based on contingency table were focused. Especially, for the bias reduction, jackknife technique was also applied in PCA-based spatial integration. Through the statistic analyses with respect to the integration information in this case study, new information for relationships of integrated layer and input layers was extracted. In addition, influence effects of input data sets with respect to integrated layer were assessed. This kind of approach provides a decision-making information in the viewpoint of GIS and is also exploratory data analysis in conjunction with GIS and geoscientific application, especially handing spatial integration or data fusion with complex variable data sets.