• Journal of Korea Water Resources Association
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• v.43 no.1
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• pp.15-24
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• 2010

Regional frequency analysis is often used to overcome the limitation of point frequency analysis to estimate probability rainfall depths. However, point frequency analysis is still used in drought analyses. This study proposed a practical method to categorize the homogeneous regions of drought characteristics for the analyses of regional characteristics of droughts in Korea. Using rainfall data from 58 observation stations managed by the Korea Meteorological Administration, this study calculated drought attributes, i.e., mean drought indices for various durations using the Standardized Precipitation Index (SPI) and drought severities expressed by durations, depth, and intensity. The drought attributes provided useful information for categorizing stations into the hydrological homogeneous regions. This study introduced a cluster analysis with K-means techniques to group observation stations. The cluster analysis grouped observation stations into 6 regions in Korea. The data in the hydrological homogeneous region would be used in spatial analysis of drought characteristics and drought regional frequency analysis.

• Journal of the Korean earth science society
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• v.22 no.2
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• pp.138-146
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• 2001

The land cover over Korean peninsula was classified using a multi-temporal NOAA/AVHRR (Advanced Very High Resolution Radiometer) data. Four types of phenological data derived from the 10-day composited NDVI (Normalized Differences Vegetation Index), maximum and annual mean land surface temperature, and topographical data were used not only reducing the data volume but also increasing the accuracy of classification. Self organizing feature map (SOFM), a kind of neural network technique, was used for the clustering of satellite data. We used a decision tree for the classification of the clusters. When we compared the classification results with the time series of NDVI and some other available ground truth data, the urban, agricultural area, deciduous tree and evergreen tree were clearly classified.

• Asian-Australasian Journal of Animal Sciences
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• v.29 no.12
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• pp.1696-1701
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• 2016

Japanese quail is still used as a model for poultry research because of their usefulness as laying, meat, and laboratory animals. Microsatellite markers are the most widely used molecular markers, due to their relative ease of scoring and high levels of polymorphism. The objective of the research was to determine genetic diversity and population genetic structures of selected Japanese quail lines (high body weight 1 [HBW1], HBW2, low body weight [LBW], and layer [L]) throughout 15th generations and an unselected control (C). A total of 69 individuals from five quail lines were genotyped by fifteen microsatellite markers. When analyzed profiles of the markers the observed ($H_o$) and expected ($H_e$) heterozygosity ranged from 0.04 (GUJ0027) to 0.64 (GUJ0087) and 0.21 (GUJ0027) to 0.84 (GUJ0037), respectively. Also, $H_o$ and $H_e$ were separated from 0.30 (L and LBW) to 0.33 (C and HBW2) and from 0.52 (HBW2) to 0.58 (L and LBW), respectively. The mean polymorphic information content (PIC) ranged from 0.46 (HBW2) to 0.52 (L). Approximately half of the markers were informative ($PIC{\geq}0.50$). Genetic distances were calculated from 0.09 (HBW1 and HBW2) to 0.33 (C and L). Phylogenetic dendrogram showed that the quail lines were clearly defined by the microsatellite markers used here. Bayesian model-based clustering supported the results from the phylogenetic tree. These results reflect that the set of studied markers can be used effectively to capture the magnitude of genetic variability in selected Japanese quail lines. Also, to identify markers and alleles which are specific to the divergence lines, further generations of selection are required.

• Journal of the Korean Society of Food Culture
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• v.31 no.1
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• pp.33-41
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• 2016

The purpose of this study was to examine the difference in nutritional knowledge and food preferences according to food-related lifestyle among 400 married women. Using the K-average clustering method, food-related lifestyles of subjects were categorized into three clusters: rational and diversity-oriented group, convenience-oriented group, and health-oriented group. The nutritional knowledge level and food preferences among three clusters were compared to each other using ANOVA test. The findings were summarized as follows: For the nutritional knowledge level, health-oriented group showed the highest mean score, whereas the lowest score was detected in the convenience-oriented group. The convenience-oriented group showed higher preferences for fish, meat, eggs, fruits, milk/dairy products, seaweed, grains, etc. among natural food than the other groups. Meanwhile, the rational and diversity-oriented group preferred legumes, and green vegetables, whereas the health-oriented group showed preferences for other vegetables. However, the convenience-oriented group reported more preferences for breads, noodles, pancakes, fried/stir-fried food, and processed food such as sausage, ham, and fast food, This study found that nutritional knowledge level and food preferences were significantly different according to food-related lifestyles of married women living in Seoul and Gyonggie areas. Thus, it is suggested that nutritional education targeting married women needs to be carefully designed by considering their food-related lifestyle.

• Journal of Korea Multimedia Society
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• v.11 no.11
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• pp.1501-1514
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• 2008

The face shape extracted by the depth values has different appearance as the most important facial information. The face images decomposed into frequency subband are signified personal features in detail. In this paper, we develop a method for recognizing the range face images by multiple frequency domains for each depth image using the modified fuzzy c-mean algorithm. For the proposed approach, the first step tries to find the nose tip that has a protrusion shape on the face from the extracted face area. And the second step takes into consideration of the orientated frontal posture to normalize. Multiple contour line areas which have a different shape for each person are extracted by the depth threshold values from the reference point, nose tip. And then, the frequency component extracted from the wavelet subband can be adopted as feature information for the authentication problems. The third step of approach concerns the application of eigenface to reduce the dimension. And the linear discriminant analysis (LDA) method to improve the classification ability between the similar features is adapted. In the last step, the individual classifiers using the modified fuzzy c-mean method based on the K-NN to initialize the membership degree is explained for extracted coefficient at each resolution level. In the experimental results, using the depth threshold value 60 (DT60) showed the highest recognition rate among the extracted regions, and the proposed classification method achieved 98.3% recognition rate, incase of fuzzy cluster.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.6
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• pp.212-217
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• 2020

The purpose of this study was to analyze the volatility and properties of a time series for tangerine prices in Jeju using the GARCH model of Bollerslev(1986). First, it was found that the time series for the rate of change in tangerine prices had a thicker tail rather than a normal distribution. At a significance level of 1%, the Jarque-Bera statistic led to a rejection of the null hypothesis that the distribution of the time series for the rate of change in tangerine prices is normally distributed. Second, the correlation between the time series was high based on the Ljung-Box Q statistic, which was statistically verified through the ARCH-LM test. Third, the results of the GARCH(1,1) model estimation showed statistically significant results at a significance level of 1%, except for the constant of the mean equation. The persistence parameter value of the variance equation was estimated to be close to 1, which means that there is a high possibility that a similar level of volatility will be present in the future. Finally, it is expected that the results of this study can be used as basic data to optimize the government's tangerine supply and demand control policy.

• Journal of Radiation Industry
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• v.17 no.1
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• pp.19-32
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• 2023

The amount of radioactive waste is expected to dramatically increase with decommissioning of nuclear power plants such as Kori-1, the first nuclear power plant in South Korea. Accurate nuclide analysis is necessary to manage the radioactive wastes safely, but research on verification of radionuclide analysis has yet to be well established. This study aimed to develop the technology that can verify the results of radionuclide analysis based on artificial intelligence. In this study, we propose an anomaly detection algorithm for inspecting the analysis error of radionuclide. We used the data from 'Updated Scaling Factors in Low-Level Radwaste' (NP-5077) published by EPRI (Electric Power Research Institute), and resampling was performed using SMOTE (Synthetic Minority Oversampling Technique) algorithm to augment data. 149,676 augmented data with SMOTE algorithm was used to train the artificial neural networks (classification and anomaly detection networks). 324 NP-5077 report data verified the performance of networks. The anomaly detection algorithm of radionuclide analysis was divided into two modules that detect a case where radioactive waste was incorrectly classified or discriminate an abnormal data such as loss of data or incorrectly written data. The classification network was constructed using the fully connected layer, and the anomaly detection network was composed of the encoder and decoder. The latter was operated by loading the latent vector from the end layer of the classification network. This study conducted exploratory data analysis (i.e., statistics, histogram, correlation, covariance, PCA, k-mean clustering, DBSCAN). As a result of analyzing the data, it is complicated to distinguish the type of radioactive waste because data distribution overlapped each other. In spite of these complexities, our algorithm based on deep learning can distinguish abnormal data from normal data. Radionuclide analysis was verified using our anomaly detection algorithm, and meaningful results were obtained.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.18 no.6
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• pp.61-71
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• 2015

The objectives of this study are to construct an ecoregion map and to extract ecological factors from each ecoregion to adapt FLR (Forest Landscape Restoration) of North Korea. An ecological map was constructed by PCA(Principal Component Analysis) and MGC(Multivatiate Geographical Clustering). An ANOVA test verified the differences among ecoregions, and post-hoc pair wise comparisons were performed to determine similarities between them. Factor analysis was conducted to extract ecoregional characteristics. Ecoregions were distributed into clusters reflecting differences of south and north and of east and west of their ecological factors. About 12% of land area in North Korea shared similar ecological factors with South Korea, but the remaining 88% was found to be ecologically different. The ANOVA test showed a p-value of 0.000, indicating significant differences between the regions. Post-hoc pair wise comparisons indicated statistically significant similarities in annual mean temperature between ecoregion D and G, precipitation seasonality between ecoregion H and O, and precipitation of the warmest quarter between ecoregion K and O. Because ecoregion A and N showed same in their soil water contents, they were assumed that the dense of forest cover in the Southern ecoregion A is similar to that in the Northern ecoregion N of Korean peninsular. Based on the results of this study, it is necessary to accommodate quantitative and spatial based planning, when South Korea aids forest restoration projects in North Korea. In addition, it is recommended for both South and North Korea to share on Forest Landscape Restoration methodologies with each other.

• Journal of the Korean Home Economics Association
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• v.43 no.10 s.212
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• pp.69-79
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• 2005

The purpose of this study was to determine restaurant patrons' preference differences in interior design style of restaurants according to their lifestyles. Written questionnaires were handed out to 500 adults in Seoul and surroundings and the results were sampled by convenience sampling. The questionnaire was composed of respondents' general characteristics, lifestyles, and preference for 10 types of interior design style. A total of 415 questionnaires were usable for data analysis, resulting in a response rate of $83\%$. To analyze the collected data, frequency, factor, reliability, quick clustering K- means and One-Way ANOVA analysis were conducted using SPSS 10.0. The results showed that there were preference differences in 10 types of interior design style of restaurants according to lifestyle types which were categorized into 4 groups. The conservative and self-convinced group showed the lowest preference scores in the 10 types of interior design style which are Romantic, Ethnic, Classic, High-Tech, Elegant, Country, Modem, Minimal, Natural, and Casual style. The quality life pursuing group and extroverted individuality groups showed the high preference scores in most of the styles, especially in the Classic and Elegant styles. The realistic self-centered group showed the highest preference scores in Casual style among the 4 groups. These study findings indicate that restaurants should take into account their patrons' lifestyles as a mean of market segmentation, and respond to their taste and preference when they have established suitable servicescape.

• Journal of the Korea Society for Simulation
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• v.27 no.2
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• pp.1-10
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• 2018

This paper presents an autonomous detection and aiming point computation of a battle tank by using RGB images. Maximally stable extremal regions algorithm was implemented to find features of the tank, which are matched with images extracted from streaming video to figure out the region of interest where the tank is present. The median filter was applied to remove noises in the region of interest and decrease camouflage effects of the tank. For the tank segmentation, k-mean clustering was used to autonomously distinguish the tank from its background. Also, both erosion and dilation algorithms of morphology techniques were applied to extract the tank shape without noises and generate the binary image with 1 for the tank and 0 for the background. After that, Sobel's edge detection was used to measure the outline of the tank by which the aiming point at the center of the tank was calculated. For performance measurement, accuracy, precision, recall, and F-measure were analyzed by confusion matrix, resulting in 91.6%, 90.4%, 85.8%, and 88.1%, respectively.