• Ocean and Polar Research
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• v.29 no.4
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• pp.391-400
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• 2007

To identify the spatial distribution pattern of water quality in Masan Bay, Pearson's correlation as a common statistic method and Moran's I as a spatial autocorrelation statistics were applied to the hydrological data seasonally collected from Masan Bay for two years ($2004{\sim}2005$). Spatial distribution of salinity, DO and silicate among the hydrological parameters clustered strongly while chlorophyll a distribution displayed a weak clustering. When the similarity matrix of Moran's I was compared with correlation matrix of Pearson's r, only the relationships of temperature vs. salinity, temperature vs. silicate and silicate vs. total inorganic nitrogen showed significant correlation and similarity of spatial clustered pattern. Considering Pearson's correlation and the spatial autocorrelation results, water quality distribution patterns of Masan Bay were conceptually simplified into four types. Based on the simplified types, Moran's I and Pearson's r were compared respectively with spatial distribution maps on salinity and silicate with a strong clustered pattern, and with chlorophyll a having no clustered pattern. According to these test results, spatial distribution of the water quality in Masan Bay could be summed up in four patterns. This summation should be developed as spatial index to be linked with pollutant and ecological indicators for coastal health assessment.

• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1449-1466
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• 2014

In this paper, we consider a hedge portfolio based on futures of underlying asset. A classical way to estimate a hedge ratio for a hedge portfolio of a spot and futures is a regression analysis. However, a regression analysis is not capable of reflecting long-run equilibrium between a spot and futures and volatility clustering in the conditional variance of financial time series. In order to overcome such defects, we analyzed KOSPI200 index and futures using VECM-CC-GARCH model and computed a hedge ratio from the estimated conditional covariance-variance matrix. In real data analysis, we compared a regression and VECM-CC-GARCH models in terms of hedge effectiveness based on variance, value at risk and expected shortfall of log-returns of hedge portfolio. The empirical results show that the multivariate GARCH models significantly outperform a regression analysis and improve hedging effectiveness in the period of high volatility.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.3
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• pp.310-315
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• 2004

Image taken in the night can be low-contrast images because of poor environment and image transmission. We propose an algorithm that improves the acquired low-contrast image. MPEG-2 separates chrominance and illuminance, and compresses respectively because human vision is more sensitive to luminance. We extracted illumination and used K-means algorithm to find a proper crossover point automatically. We used K-means algorithm in the viewpoint that the problem of crossover point selection can be considered as the two-category classification problem. We divided an image into two subimages using the crossover point, and applied the histogram equalization method respectively. We used the index of fuzziness to evaluate the degree of improvement. We compare the results of the proposed method with those of other methods.

• Korean Journal of Remote Sensing
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• v.39 no.4
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• pp.425-440
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• 2023

Monitoring water surface has become one of the most prominent areas of research in addressing environmental challenges.Accurate and automated detection of watersurface in remote sensing imagesis crucial for disaster prevention, urban planning, and water resource management, particularly for a country where water plays a vital role in human life. However, achieving precise detection poses challenges. Previous studies have explored different approaches,such as analyzing water indexes, like normalized difference water index (NDWI) derived from satellite imagery's visible or infrared bands and using k-means clustering analysis to identify land cover patterns and segment regions based on similar attributes. Nonetheless, challenges persist, notably distinguishing between waterspectralsignatures and cloud shadow or terrain shadow. In thisstudy, our objective is to enhance the precision of water surface detection by constructing a comprehensive water database (DB) using existing digital and land cover maps. This database serves as an initial assumption for automated water index analysis. We utilized 1:5,000 and 1:25,000 digital maps of Korea to extract water surface, specifically rivers, lakes, and reservoirs. Additionally, the 1:50,000 and 1:5,000 land cover maps of Korea aided in the extraction process. Our research demonstrates the effectiveness of utilizing a water DB product as our first approach for efficient water surface extraction from satellite images, complemented by our second and third approachesinvolving NDWI analysis and k-means analysis. The image segmentation and binary mask methods were employed for image analysis during the water extraction process. To evaluate the accuracy of our approach, we conducted two assessments using reference and ground truth data that we made during this research. Visual interpretation involved comparing our results with the global surface water (GSW) mask 60 m resolution, revealing significant improvements in quality and resolution. Additionally, accuracy assessment measures, including an overall accuracy of 90% and kappa values exceeding 0.8, further support the efficacy of our methodology. In conclusion, thisstudy'sresults demonstrate enhanced extraction quality and resolution. Through comprehensive assessment, our approach proves effective in achieving high accuracy in delineating watersurfaces from satellite images.

• Animal cells and systems
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• v.3 no.2
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• pp.153-159
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• 1999

In an artificial pH-gradient batch culture system, the effects of acidification on the species composition of a heterotrophic bacterial community were analyzed. As a result of this study, it was found that total bacteria numbers were not affected by acidification and that the population of hetero-trophic bacteria decreased as pH became lower. The heterotrophic bacteria isolated from the entire pH gradient were 12 genera and 22 species. Among them, 64% were gram negative and 36% were gram positive bacteria. As pH decreased, the distribution rate of gram negative bacteria increased while that of gram positive bacteria decreased. The diversity of genera decreased from 13 to 5 as pH decreased from 7 to 3. The G+C content of all of the 202 isolated strains varied from 22.8 to 77.0%, and increased in interspecies of same genus as pH decreased. As a result of clustering analysis, the diversity index of species ranged from 1.13 to 2.37, and it had lower indices as pH decreased. In order to evaluate the diversity of numbers of sample of different size, a rarefaction method was used to analyze the expected number of species appearance according to pH. The statistical significance of species diversity was verified by the fact that the number decreased at lower pH.

• Atmosphere
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• v.17 no.2
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• pp.159-169
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• 2007

A comparison of the three land cover data sets (United States Geological Survey: USGS, International Geosphere Biosphere Programme: IGBP, and University of Maryland: UMd), derived from 1992-1993 Advanced Very High Resolution Radiometer(AVHRR) data sets, was performed over the Asian continent. Preprocesses such as the unification of map projection and land cover definition, were applied for the comparison of the three different land cover data sets. Overall, the agreement among the three land cover data sets was relatively high for the land covers which have a distinct phenology, such as urban, open shrubland, mixed forest, and bare ground (>45%). The ratios of triple agreement (TA), couple agreement (CA) and total disagreement (TD) among the three land cover data sets are 30.99%, 57.89% and 8.91%, respectively. The agreement ratio between USGS and IGBP is much greater (about 80%) than that (about 32%) between USGS and UMd (or IGBP and UMd). The main reasons for the relatively low agreement among the three land cover data sets are differences in 1) the number of land cover categories, 2) the basic input data sets used for the classification, 3) classification (or clustering) methodologies, and 4) level of preprocessing. The number of categories for the USGS, IGBP and UMd are 24, 17 and 14, respectively. USGS and IGBP used only the 12 monthly normalized difference vegetation index (NDVI), whereas UMd used the 12 monthly NDVI and other 29 auxiliary data derived from AVHRR 5 channels. USGS and IGBP used unsupervised clustering method, whereas UMd used the supervised technique, decision tree using the ground truth data derived from the high resolution Landsat data. The insufficient preprocessing in USGS and IGBP compared to the UMd resulted in the spatial discontinuity and misclassification.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.7
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• pp.339-349
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• 2001

This paper suggests an optimal identification method for complex and nonlinear system modeling that is based on Fuzzy-Neural Networks(FNN). The proposed Hybrid Identification Algorithm is based on Yamakawa's FNN and uses the simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. In this paper, the FNN modeling implements parameter identification using HCM algorithm and hybrid structure combined with two types of optimization theories for nonlinear systems. We use a HCM(Hard C-Means) clustering algorithm to find initial apexes of membership function. The parameters such as apexes of membership functions, learning rates, and momentum coefficients are adjusted using hybrid algorithm. The proposed hybrid identification algorithm is carried out using both a genetic algorithm and the improved complex method. Also, an aggregated objective function(performance index) with weighting factor is introduced to achieve a sound balance between approximation and generalization abilities of the model. According to the selection and adjustment of a weighting factor of an aggregate objective function which depends on the number of data and a certain degree of nonlinearity(distribution of I/O data), we show that it is available and effective to design an optimal FNN model structure with mutual balance and dependency between approximation and generalization abilities. To evaluate the performance of the proposed model, we use the time series data for gas furnace, the data of sewage treatment process and traffic route choice process.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.25 no.1
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• pp.295-318
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• 2014

The purpose of this study is to analyze the intellectual structure of reading studies by using Co-Word Analysis based on the mixed weight in which the level of academic journals and the position of keywords are calculated. To achieve it, 838 academic articles relating to reading studies from KCI during the period from 2003 to 2012 were retrieved and 56 keywords were extracted. The results of clustering analysis, MDS, network analysis are that the network based on the mixed weight has a better performance in above three methods and reading studies can be divided into 4 bigger divisions and 11 subdivisions. Finally, the result of document analysis shows reading studies changes its research tendency from theoretical studies to empirical studies.

• Tunnel and Underground Space
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• v.15 no.1 s.54
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• pp.47-54
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• 2005

One of the standard procedures of discontinuity survey is the joint set identification from the population of field orientation data. Discontinuity set identification is fundamental to rock engineering tasks such as rock mass classification, discrete element analysis, key block analysis. and discrete fracture network modeling. Conventionally, manual method using contour plot had been widely used for this task, but this method has some short-comings such as yielding subjective identification results, manual operations, and so on. In this study, the method of discontinuity set identification using genetic algorithm was introduced, but slightly modified to handle the orientation data. Finally, based on the genetic algorithm, we developed a FORTRAN program, Genetic Algorithm based Clustering(GAC) and applied it to two different discontinuity data sets. Genetic Algorithm based Clustering(GAC) was proved to be a fast and efficient method for the discontinuity set identification task. In addition, fitness function based on variance showed more efficient performance in finding the optimal number of clusters when compared with Davis - Bouldin index.

• Journal of the Korean Society for information Management
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• v.32 no.3
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• pp.221-236
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• 2015

The purpose of this study is to analyze the research fields constituting the knowledge structure of the Korean Studies by applying hierarchical clustering method to domestic journal papers in Korean Studies. We analyzed 3,800 papers containing Korean author keyword that were listed in 14 kinds of Korean Studies journals published in 2004-2013, which have average impact factor more than 0.5 in 2011-2013. The results of the analysis show that the central research fields are the subjects related to political & social problems based on Confucian ideas focusing on Neo-Confucianism (Seonglihak) and Realist School of Confucianism (Silhak), to the political situation associated with territorial division of the Korean peninsula, and to the history from the period of japanese colonialism to modern and contemporary. It has been also found that the temporal backgrounds of researches in domestic Korean Studies were related to the modern times and the Joseon Dynasty periods, rather than the time of the ancient and contemporary.