• Korean Journal of Medicinal Crop Science
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• v.26 no.1
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• pp.32-41
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• 2018

Background: To obtain useful safflower resources for breeding and research of functional materials, the present study was conducted to determine fatty acid compositions and antioxidant activities of 281 safflower accessions collected from East Asia including South Korea. Methods and Results: Lipid contents and fatty acids compositions were evaluated using soxhlet extraction and gas chromatography, respectively. A antioxidant activities were analyzed using a spectrophotometer. The evaluation range of safflower accessions showed very wide variation. In terms of lipid contents, the China accessions were higher than the collection from other regions, whereas antioxidant activities did not differ among regions. The result of the principal component analysis showed that the first and second principal component cumulatively explained 90.6% of the total variation. In clustering the safflower accessions, the tree showed four major clades. Group II (16 accessions) was high in lipid content, oleic acid and linoleic acid, whereas group III (50 accessions) exhibited higher antioxidant activities than other groups. Conclusions: It was recommended that the China collections be utilized as a useful resource for research on functional oil materials. These results provided valuable information for safflower breeders and researchers of functional food.

• The Transactions of the Korea Information Processing Society
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• v.3 no.4
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• pp.733-744
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• 1996

Content-based image retrieval(CBIR) is an image data retrieval methodology using characteristic values of image data those are generated by system automatically without any caption or text information. In this paper, we propose a content-based image data retrieval method using shape and color features of image data as characteristic values. For this, we present some image processing techniques used for feature extraction and indexing techniques based on trie and R tree for fast image data retrieval. In our approach, image query result is more reliable because both shape and color features are considered. Also, we how an image database which implemented according to our approaches and sample retrieval results which are selected by our system from 200 sample images, and an analysis about the result by considering the effect of characteristic values of shape and color.

• Spatial Information Research
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• v.20 no.6
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• pp.45-57
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• 2012

The street trees in the urban areas provide an comfortable environment to the pedestrians and drivers and play important roles to absorb the carbons. Therefore, it is necessary to acquire and manage efficiently the location, height, and crown width of street trees. This study suggests a methodology to provide quantitative information of the street trees in urban areas including the quantity, location, height, and crown width of the trees. Therefore, it is more appropriate to add functionality of changing size of the crown width of the trees in the method. In addition, the positions of the street trees were selected using the fact that street trees are generally planted along the road in a straight line. An experiment on extracting street trees was conducted in parts of Osan-si, Gyeonggi-do and the suitability of the suggested methodology was evaluated by comparing the results to a 1/1,000 digital map. Through the experimental results, the minimum, maximum, and the root mean square errors of the position of street trees were 0.5m, 1.9m, and approximately ${\pm}0.4m$, respectively.

• Journal of Korean Society for Geospatial Information Science
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• v.13 no.3 s.33
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• pp.25-32
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• 2005

This paper presents a method for point-based 3D building reconstruction using Lidar data and digital map. The proposed method consists of three processes: extraction of building roof points, identification of roof types, and 3D building reconstruction. After extracting points inside the polygon of building, the ground surface, wall and tree points among the extracted points are removed through the filtering process. The filtered points are then fitted into the flat plane using ODR(Orthogonal Distance Regression) in the first place. If the fitting error is within the predefined threshold, the surface is classified as a flat roof. Otherwise, the surface is fitted and classified into a gable or arch roof through RMSE analysis. Experimental results showed that the proposed method classified successfully three different types of roof and that the fusion of LIDAR data and digital map could be a feasible method of modeling 3D building reconstruction.

• Journal of the Korean Society of Clothing and Textiles
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• v.10 no.3
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• pp.37-48
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• 1986

The purpose of this study was to analyze 'the natural groupings' of subjects in order to classify highly similar somatotype for clothing construction. The sample for the study was drawn randomly out of senior high school boys in Seoul urban area. The sample size was 425 boys between age 16 and 18. Cluster analysis was more concerned with finding the hierarchical structure of subjects by three dimensional distance of stature. bust girth and sleeve length. The groups forming a partition can be subdivided into 5 and 6 sets by the hierarchical tree of the given subjects. Ward's Minimum Variance Method was applied after extraction of distance matrix by the Standardized Euclidean Distance. All of the above data was analyzed by the computer installed at Korea Advanced Institute of Science and Technology. The major findings, take for instance, of 16 age group can be summarized as follows. The results of cluster analysis of this study: 1. Cluster 1 (32 persons means $18.29\%$ of the total) is characterized with smaller bust girth than that of cluster 5, but stature and sleeve length of the cluster 1 are the largest group. 2. Cluster 2 (18 Persons means $10.29\%$ of the total) is characterized with the group of the smallest stature and sleeve length, but bust girth larger than that of cluster 3. 3. Cluster 3(35persons means $20\%$ of the total) is classified with the smallest group of all the stature, bust girth and sleeve length. 4. Cluster 4(60 persons means $34.29\%$ of the total) is grouped with the same value of sleeve length with the mean value of 16 age group, but the stature and bust girth is smaller than the mean value of this age group. 5. Cluster 5(30 persons means $17.14\%$ of the total) is characterized with smaller stature than that of cluster 1, and with larger bust girth than that of cluster 1, but with the same value of the sleeve length with the mean value of the 16 age group.

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.1
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• pp.83-91
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• 2012

Crown density, which is defined as the proportion of the forest floor concealed by tree crown, is important and useful information in various fields. Previous methods of measuring crown density have estimated crown density by interpreting aerial photographs or through a ground survey. These are time-consuming, labor-intensive, expensive and inconsistent approaches, as they involve a great deal of subjectivity and rely on the experience of the interpreter. In this study, the crown density of a forest in Korea was estimated using KOMPSAT-2 high-resolution satellite images. Using the image segmentation technique and stand information of the digital forest map, the forest area was divided into zones. The crown density for each segment was determined using the discriminant analysis method and the forest ratio method. The results showed that the accuracy of the discriminant analysis method was about 60%, while the accuracy of the forest ratio method was about 85%. The probability of extraction of candidate to update was verified by comparing the result with the digital forest map.

• Journal of Korea Multimedia Society
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• v.5 no.2
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• pp.167-175
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• 2002

Image Mosaicing creates a new image by composing video frames or still images that are related, and performed by arrangement, composition and redundancy analysis of images. This paper proposes a hierarchical image mosaicing system using camera and object parameters far efficient video database construction. A tree-based image mosiacing has implemented for high-speed computation time and for construction of static and dynamic image mosaic. Camera parameters are measured by using least sum of squared difference and affine model. Dynamic object detection algorithm has proposed for extracting dynamic objects. For object extraction, difference image, macro block, region splitting and 4-split detection methods are proposed and used. Also, a dynamic positioning method is used for presenting dynamic objects and a blurring method is used for creating flexible mosaic image.

• The Plant Pathology Journal
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• v.20 no.2
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• pp.127-130
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• 2004

Grapevine leaf roll-associated virus 3 (GLRaV-3) is one of the most important viral diseases of grapevine in the world. In this study, GLRaV-3 Ampelovirus was identi-fied from grapevines in Korea by analyzing viral coat protein size, nucleotide, and amino acid sequences. The molecular weight of viral coat protein from virus-infected in vitro plantlets was determined by western blot using a commercial GLRaV-3 polyclonal antibody. Western blot analysis showed a coat protein of about 43 kDa. RT-PCR product of about 942 bp which encoded the coat protein (CP) gene was amplified with specific primers. When the viruses existed at low titers in the host plant, the dsRNA had very specific template in RT- PCR amplification of fruit tree viruses. Especially, small-scale dsRNA extraction method was very reliable and rapid. Sequence analysis revealed that the CP of the GLRaV-3 Ko consisted of 942 bp nucleotide, which encoded 314 amino acid residues. The CP gene of GLRaV-3 Ko had 98.9% nucleotide sequence and 98.7% amino acid sequence identities with earlier reported GLRaV-3. This is the first report on molecular assay of GLRaV-3 Ampelovirus identified from Korea. The GLRaV-3 Ko CP clone would be very useful for breeding of virus resistant grapevines.

• Journal of Food Hygiene and Safety
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• v.25 no.1
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• pp.79-82
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• 2010

The pomegranate (Punica granatum), especially its fruit, possesses a vast ethnomedical history and represents a phytochemical reservoir of heuristic medical value. The tree and fruit can be divided into several anatomical compartments, and the fruit juice, peel and oil are known to be weakly estrogenic and heuristically of interest for treatment of menopausal symptoms and sequellae. In this study, analysis of estrogen in pomegranate extract was carried out with LC/MS/MS. Three batches of pomegranate extract samples were used to analysis the target compounds (estrogen). The contents of estrogen derivatives in the samples were 38.6 ppb of estriol, 83.5 ppb of estrone, and 10.9 ppb of estradiol. This result suggests that the pomegranate extract can used for treatment of menopause symptoms in the woman.

• Journal of Information Technology Services
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• v.16 no.3
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• pp.167-183
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• 2017

The rapid growth of information technology and mobile service platforms, i.e., internet, google, and facebook, etc. has led the abundance of data. Due to this environment, the world is now facing a revolution in the process that data is searched, collected, stored, and shared. Abundance of data gives us several opportunities to knowledge discovery and data mining techniques. In recent years, data mining methods as a solution to discovery and extraction of available knowledge in database has been more popular in e-commerce service fields such as, in particular, movie recommendation. However, most of the classification approaches for predicting the movie popularity have used only several types of information of the movie such as actor, director, rating score, language and countries etc. In this study, we propose a classification-based support vector machine (SVM) model for predicting the movie popularity based on movie's genre data and social network data. Social network analysis (SNA) is used for improving the classification accuracy. This study builds the movies' network (one mode network) based on initial data which is a two mode network as user-to-movie network. For the proposed method we computed degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality as centrality measures in movie's network. Those four centrality values and movies' genre data were used to classify the movie popularity in this study. The logistic regression, neural network, $na{\ddot{i}}ve$ Bayes classifier, and decision tree as benchmarking models for movie popularity classification were also used for comparison with the performance of our proposed model. To assess the classifier's performance accuracy this study used MovieLens data as an open database. Our empirical results indicate that our proposed model with movie's genre and centrality data has by approximately 0% higher accuracy than other classification models with only movie's genre data. The implications of our results show that our proposed model can be used for improving movie popularity classification accuracy.