• Horticultural Science & Technology
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• v.32 no.4
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• pp.525-534
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• 2014

Microsatellite markers were used to identify 58 major commercial melon cultivars, and to assess hybrid seed purity of a melon breeding line known as '10H08'. A set of 412 microsatellite primer pairs were utilized for fingerprinting of the melon cultivars. Twenty-nine markers showed hyper-variability and could discriminate all cultivars on the basis of marker genotypes, representing the genetic variation within varietal groups. Cluster analysis based on Jaccard's distance coefficients using the UPGMA algorithm categorized 2 major groups, which were in accordance to morphological traits. The DNA bulks of female and male parents of breeding line '10H08' were tested with 29 primer pairs based on microsatellites to investigate purity testing of $F_1$ hybrid seeds, and 5 primer pairs exhibited polymorphism. One microsatellite primer pair (CMGAN12) produced unambiguous polymorphic bands among the parents. Among 192 seeds tested with CMGAN12, progeny possibly generated by self-pollination of the female parent were clearly distinguished from the hybrid progeny. These markers will be useful for fingerprinting melon cultivars and can help private seed companies to improve melon seed purity.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.5
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• pp.57-70
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• 2009

A CAD design drawing based on vector data that is very important art work in industrial fields has been considered to content that the copyright protection is urgently needed. This paper presents a watermarking scheme based on k-means++ for CAD design drawing. One CAD design drawing consists of several layers and each layer consists of various geometric objects such as LINE, POLYLINE, CIRCLE, ARC, 3DFACE and POLYGON. POLYLINE with LINE, 3DFACE and ARC that are fundamental objects make up the majority in CAD design drawing. Therefore, the proposed scheme selects the target object with high distribution among POLYLINE, 3DFACE and ARC objects in CAD design drawing and then selects layers that include the most target object. Then we cluster the target objects in the selected layers by using k-means++ and embed the watermark into the geometric distribution of each group. The geometric distribution is the normalized length distribution in POLYLINE object, the normalized area distribution in 3DFACE object and the angle distribution in ARC object. Experimental results verified that the proposed scheme has the robustness against file format converting, layer attack as well as various geometric editing provided in CAD editing tools.

• Korean Journal of Breeding Science
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• v.50 no.4
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• pp.497-503
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• 2018

A strawberry variety 'Arihyang' was derived as an artificial cross between 'Tochiotome' and 'Seolhyang' in 2014. The seedling and line selections were conducted from 2014 to 2015. Preliminary and advanced yield trials of '14-5-5,' which was the final selected line, were conducted from 2015 to 2017. 'Arihyang' is suitable for forced cultivation and has strong plant vigor, uniformly large-sized fruit, and a high yield compared to those of the check variety, 'Seolhyang' and 'Maehyang.' Especially, vitamin C was at a significant level, which was approximately 15% higher than that of 'Seolhyang.' The average number of flowers per first flower cluster was 10.5, which could reduce the labor of thinning fruit. Its fruit has a conical shape, dark red color, and glossy skin. The fruit was of good quality but has recommendations for harvest at the fully ripened stage. 'Arihyang' has intermediate resistant to phytophthora crown rot, but is susceptible to powdery mildew, gray mold, anthracnose, and fusarium wilt. It is reguired to manage major diseases and pests using optimum cultivation techniques and chemical control.

• Journal of the Korean earth science society
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• v.43 no.1
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• pp.60-76
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• 2022

This study presents a software full setup and the following test execution times in a Linux cluster for the United Kingdom Earth System Model (UKESM) and then compares the model results from control and experimental simulations of the UKESM relative to various observations. Despite its low resolution, the latest version of the UKESM can simulate tropospheric chemistry-aerosol processes and the stratospheric ozone chemistry using the United Kingdom Chemistry and Aerosol (UKCA) module. The UKESM with UKCA (UKESM-UKCA) can treat atmospheric chemistryaerosol-cloud-radiation interactions throughout the whole atmosphere. In addition to the control UKESM run with the default CMIP5 SO₂ emission dataset, an experimental run was conducted to evaluate the aerosol effects on meteorology by changing atmospheric SO₂ loading with the newest REAS data over East Asia. The simulation period of the two model runs was 28 years, from January 1, 1982 to December 31, 2009. Spatial distributions of monthly mean aerosol optical depth, 2-m temperature, and precipitation intensity from model simulations and observations over East Asia were compared. The spatial patterns of surface temperature and precipitation from the two model simulations were generally in reasonable agreement with the observations. The simulated ozone concentration and total column ozone also agreed reasonably with the ERA5 reanalyzed one. Comparisons of spatial patterns and linear trends led to the conclusion that the model simulation with the newest SO₂ emission dataset over East Asia showed better temporal changes in temperature and precipitation over the western Pacific and inland China. Our results are in line with previous finding that SO₂ emissions over East Asia are an important factor for the atmospheric environment and climate change. This study confirms that the UKESM can be installed and operated in a Linux cluster-computing environment. Thus, researchers in various fields would have better access to the UKESM, which can handle the carbon cycle and atmospheric environment on Earth with interactions between the atmosphere, ocean, sea ice, and land.

• Korean Journal of Environment and Ecology
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• v.35 no.2
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• pp.135-153
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• 2021

The demilitarized zone (DMZ) is a border barrier with 248 kilometers in length and about 4 kilometers in width crossing east to west to divide the Korean Peninsula about in half. The boundary at 2 kilometers to the south is called the southern limit line. The DMZ has formed a unique ecosystem through a natural ecological succession after the Armistice Agreement and has high conservation value. However, the use of facilities for the military operation and the unchecked weeding often damage the areas in the vicinities of the southern limit line's iron-railing. This study aimed to prepare basic data for the restoration of damaged barren vegetation. As a result of classifying vegetation communities based on indicator species, 10 communities were identified as follows: Duchesnea indica Community, Hosta longipes Community, Sedum kamtschaticum-Sedum sarmentosum Community, Potentilla anemonefolia Community, Potentilla fragarioides var. major Community, Prunella vulgaris var. lilacina Community, Dendranthema zawadskii var. latilobum-Carex lanceolata Community, Dendranthema zawadskii Community, Plantago asiatica-Trifolium repens Community, and Ixeris stolonifera-Kummerowia striata Community. Highly adaptable species can characterize vegetation in barren areas to environment disturbances because artificial disturbances such as soil erosion, soil compaction, topography change, and forest fires caused by military activities frequently occur in the barren areas within the southern limit line. Most of the dominant species in the communities are composed of plants that are commonly found in the roads, roadsides, bare soil, damaged areas, and grasslands throughout South Korea. Currently, the vegetation in barren areas in the vicinities of the DMZ is in the early ecological succession form that develops from bare soil to herbaceous vegetation. Since dominant species distributed in barren land can grow naturally without special maintenance and management, the data can be useful for future restoration material development or species selection.

• Journal of Korea Game Society
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• v.4 no.4
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• pp.11-18
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• 2004

The on-line games in the past were played by only two persons exchanging data based on one-to-one connections, whereas recent ones (e.g. MMORPG: Massively Multi-player Online Role-playings Game) enable tens of thousands of people to be connected simultaneously. Specifically, Korea has established an excellent network infrastructure that can't be found anywhere in the world. Almost every household has a high-speed Internet access. What made this possible was, in part, high density of population that has accelerated the formation of good Internet infrastructure. However, this rapid increase in the use of on-line games may lead to surging traffics exceeding the limited Internet communication capacity so that the connection to the games is unstable or the server fails. expanding the servers though this measure is very costly could solve this problem. To deal with this problem, the present study proposes the load distribution technology that connects in the form of local clustering the game servers divided by their contents used in each on-line game reduces the loads of specific servers using the load balancer, and enhances performance of sewer for their efficient operation. In this paper, a cluster system is proposed where each Game server in the system has different contents service and loads are distributed efficiently using the game server resource information such as CPU utilization. Game sewers having different contents are mutually connected and managed with a network file system to maintain information consistency required to support resource information updates, deletions, and additions. Simulation studies show that our method performs better than other traditional methods. In terms of response time, our method shows shorter latency than RR (Round Robin) and LC (Least Connection) by about 12%, 10% respectively.

• Journal of KIISE:Computer Systems and Theory
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• v.34 no.5_6
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• pp.202-213
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• 2007

A network is an important model widely used in natural and social science as well as engineering. To analyze these networks easily it is necessary that we should layout the features of networks visually. These Graph-Layout researches have been performed recently according to the development of the computer technology. Among them, the Scale-free Network that stands out in these days is widely used in analyzing and understanding the complicated situations in various fields. The Scale-free Network is featured in two points. The first, the number of link(Degree) shows the Power-function distribution. The second, the network has the hub that has multiple links. Consequently, it is important for us to represent the hub visually in Scale-free Network but the existing Graph-layout algorithms only represent clusters for the present. Therefor in this thesis we suggest Graph-layout algorithm that effectively presents the Scale-free network. The Hubity(hub+ity) repulsive force between hubs in suggested algorithm in this thesis is in inverse proportion to the distance, and if the degree of hubs increases in a times the Hubity repulsive force between hubs is ${\alpha}^{\gamma}$ times (${\gamma}$??is a connection line index). Also, if the algorithm has the counter that controls the force in proportion to the total node number and the total link number, The Hubity repulsive force is independent of the scale of a network. The proposed algorithm is compared with Graph-layout algorithm through an experiment. The experimental process is as follows: First of all, make out the hub that exists in the network or not. Check out the connection line index to recognize the existence of hub, and then if the value of connection line index is between 2 and 3, then conclude the Scale-free network that has a hub. And then use the suggested algorithm. In result, We validated that the proposed Graph-layout algorithm showed the Scale-free network more effectively than the existing cluster-centered algorithms[Noack, etc.].

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.47-64
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• 2021

The global spread of COVID-19 around the world has not only affected many parts of our daily life but also has a huge impact on many areas, including the economy and society. As the number of confirmed cases and deaths increases, medical staff and the public are said to be experiencing psychological problems such as anxiety, depression, and stress. The collective tragedy that accompanies the epidemic raises fear and anxiety, which is known to cause enormous disruptions to the behavior and psychological well-being of many. Long-term negative emotions can reduce people's immunity and destroy their physical balance, so it is essential to understand the psychological state of COVID-19. This study suggests a method of monitoring medial news reflecting current days which requires striving not only for physical but also for psychological quarantine in the prolonged COVID-19 situation. Moreover, it is presented how an easier method of analyzing social media networks applies to those cases. The aim of this study is to assist health policymakers in fast and complex decision-making processes. News plays a major role in setting the policy agenda. Among various major media, news headlines are considered important in the field of communication science as a summary of the core content that the media wants to convey to the audiences who read it. News data used in this study was easily collected using "Bigkinds" that is created by integrating big data technology. With the collected news data, keywords were classified through text mining, and the relationship between words was visualized through semantic network analysis between keywords. Using the KrKwic program, a Korean semantic network analysis tool, text mining was performed and the frequency of words was calculated to easily identify keywords. The frequency of words appearing in keywords of articles related to COVID-19 emotions was checked and visualized in word cloud 'China', 'anxiety', 'situation', 'mind', 'social', and 'health' appeared high in relation to the emotions of COVID-19. In addition, UCINET, a specialized social network analysis program, was used to analyze connection centrality and cluster analysis, and a method of visualizing a graph using Net Draw was performed. As a result of analyzing the connection centrality between each data, it was found that the most central keywords in the keyword-centric network were 'psychology', 'COVID-19', 'blue', and 'anxiety'. The network of frequency of co-occurrence among the keywords appearing in the headlines of the news was visualized as a graph. The thickness of the line on the graph is proportional to the frequency of co-occurrence, and if the frequency of two words appearing at the same time is high, it is indicated by a thick line. It can be seen that the 'COVID-blue' pair is displayed in the boldest, and the 'COVID-emotion' and 'COVID-anxiety' pairs are displayed with a relatively thick line. 'Blue' related to COVID-19 is a word that means depression, and it was confirmed that COVID-19 and depression are keywords that should be of interest now. The research methodology used in this study has the convenience of being able to quickly measure social phenomena and changes while reducing costs. In this study, by analyzing news headlines, we were able to identify people's feelings and perceptions on issues related to COVID-19 depression, and identify the main agendas to be analyzed by deriving important keywords. By presenting and visualizing the subject and important keywords related to the COVID-19 emotion at a time, medical policy managers will be able to be provided a variety of perspectives when identifying and researching the regarding phenomenon. It is expected that it can help to use it as basic data for support, treatment and service development for psychological quarantine issues related to COVID-19.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.85-107
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• 2019

Online consumers browse products belonging to a particular product line or brand for purchase, or simply leave a wide range of navigation without making purchase. The research on the behavior and purchase of online consumers has been steadily progressed, and related services and applications based on behavior data of consumers have been developed in practice. In recent years, customization strategies and recommendation systems of consumers have been utilized due to the development of big data technology, and attempts are being made to optimize users' shopping experience. However, even in such an attempt, it is very unlikely that online consumers will actually be able to visit the website and switch to the purchase stage. This is because online consumers do not just visit the website to purchase products but use and browse the websites differently according to their shopping motives and purposes. Therefore, it is important to analyze various types of visits as well as visits to purchase, which is important for understanding the behaviors of online consumers. In this study, we explored the clustering analysis of session based on click stream data of e-commerce company in order to explain diversity and complexity of search behavior of online consumers and typified search behavior. For the analysis, we converted data points of more than 8 million pages units into visit units' sessions, resulting in a total of over 500,000 website visit sessions. For each visit session, 12 characteristics such as page view, duration, search diversity, and page type concentration were extracted for clustering analysis. Considering the size of the data set, we performed the analysis using the Mini-Batch K-means algorithm, which has advantages in terms of learning speed and efficiency while maintaining the clustering performance similar to that of the clustering algorithm K-means. The most optimized number of clusters was derived from four, and the differences in session unit characteristics and purchasing rates were identified for each cluster. The online consumer visits the website several times and learns about the product and decides the purchase. In order to analyze the purchasing process over several visits of the online consumer, we constructed the visiting sequence data of the consumer based on the navigation patterns in the web site derived clustering analysis. The visit sequence data includes a series of visiting sequences until one purchase is made, and the items constituting one sequence become cluster labels derived from the foregoing. We have separately established a sequence data for consumers who have made purchases and data on visits for consumers who have only explored products without making purchases during the same period of time. And then sequential pattern mining was applied to extract frequent patterns from each sequence data. The minimum support is set to 10%, and frequent patterns consist of a sequence of cluster labels. While there are common derived patterns in both sequence data, there are also frequent patterns derived only from one side of sequence data. We found that the consumers who made purchases through the comparative analysis of the extracted frequent patterns showed the visiting pattern to decide to purchase the product repeatedly while searching for the specific product. The implication of this study is that we analyze the search type of online consumers by using large - scale click stream data and analyze the patterns of them to explain the behavior of purchasing process with data-driven point. Most studies that typology of online consumers have focused on the characteristics of the type and what factors are key in distinguishing that type. In this study, we carried out an analysis to type the behavior of online consumers, and further analyzed what order the types could be organized into one another and become a series of search patterns. In addition, online retailers will be able to try to improve their purchasing conversion through marketing strategies and recommendations for various types of visit and will be able to evaluate the effect of the strategy through changes in consumers' visit patterns.

• Journal of Korean Society of Forest Science
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• v.105 no.1
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• pp.19-41
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• 2016

This study examined the flora and community classification in the Forest Genetic Resources Reserves (FGRR) at Mt. Daeseng and Juparyeong to understand the degree of disturbance and the state of forest stand development within the Civilian Control Line (CCL) and to provide baseline data for preservation and management. Of the vascular plants dispersed throughout the study site, there were 98 families, 311 genera, 507 species, 6 subspecies, 65 varieties, and 10 forms, for a total of 588 taxa. Of these, 92 families, 290 genera, 459 species, 6 subspecies, 58 varieties, and 9 forms, for a total of 532 taxa of native plants, were determined to be dispersed around the FGRR at Mt. Daeseng, and 94 families, 259 genera, 364 species, 6 subspecies, 50 varieties, and 8 forms, for a total of 428 taxa of native plants, were confirmed to be dispersed around the FGRR at Juparyeong. There were 21 taxa of endemic Korean plants growing in the FGRR at Mt. Daeseng, with 14 taxa growing in the FGRR at Juparyeong. In terms of rare plants, 26 taxa were confirmed in the FGRR at Mt. Daeseng, and 10 taxa were confirmed in the FGRR at Juparyeong. A Cluster analysis was performed using vegetation data taken from 58 sample plots in each FGRR. The results showed a total of three representative community classifications from Mt. Daeseng: mixed mesophytic forest, Quercus mongolica-Acer pseudosieboldianum, and Quercus mongolica-Fraxinus rhynchophylla communities. Four representative community classifications were observed from Juparyeong: Quercus mongolica-broad leaved forest, Fraxinus rhynchophylla-broad leaved forest, Quercus mongolica, and Quercus communities. On the whole, the species diversity of the communities in Mt. Daeseng FGRR was greater than for the communities in Juparyeong FGRR. At Mt. Daeseng, the mixed mesophytic forest had the highest species diversity index at 1.590, while at Juparyeong, the Fraxinus rhynchophylla-broad leaved forest had the highest species diversity index at 1.319. These study results should serve as useful baseline data for future preservation and management of the demilitarized zone (DMZ) and the surrounding area.