• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.3
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• pp.58-72
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• 2021

In July 2021, UNCTAD classified Korea as a developed country. After the Korean War in the 1950s, economic development was promoted despite difficult conditions, resulting in epoch-making national growth. However, in order to respond to the rapidly changing global economy, it is necessary to continuously study the domestic industrial ecosystem and prepare strategies for continuous change and growth. This study analyzed the industrial ecosystem of the automobile industry where it is possible to obtain transaction data between companies by applying complexity spatial network analysis. For data, 295 corporate data(node data) and 607 transaction data (link data) were used. As a result of checking the spatial distribution by geocoding the address of the company, the automobile industry-related companies were concentrated in the Seoul metropolitan area and the Southeastern(Dongnam) region. The node importance was measured through degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality, and the network structure was confirmed by identifying density, distance, community detection, and assortativity and disassortivity. As a result, among the automakers, Hyundai Motor, Kia Motors, and GM Korea were included in the top 15 in 4 indicators of node centrality. In terms of company location, companies located in the Seoul metropolitan area were included in the top 15. In terms of company size, most of the large companies with more than 1,000 employees were included in the top 15 for degree centrality and betweenness centrality. Regarding closeness centrality and eigenvector centrality, most of the companies with 500 or less employees were included in the top 15, except for automakers. In the structure of the network, the density was 0.01390522 and the average distance was 3.422481. As a result of community detection using the fast greedy algorithm, 11 communities were finally derived.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.1
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• pp.45-50
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• 2019

With the gradual advancement of IT, passive work processes are automated and the overall quality of life has greatly improved. This is made possible by the formation of an organic topology between a wide variety of real-life smart devices. To serve these diverse smart devices, businesses or users are using the cloud. The services in the cloud are divided into Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS). SaaS runs on PaaS, and PaaS runs on IaaS. Since IaaS is the basis of all services, an algorithm is required to operate virtualization resources efficiently. Among them, desktop resource virtualization is used for resource high availability of unused state time of existing desktop PC. Clustering of hierarchical structures is important for high availability of these resources. In addition, it is very important to select a suitable algorithm because many clustering algorithms are mainly used depending on the distribution ratio and environment of the desktop PC. If various attempts are made to find an algorithm suitable for desktop resource virtualization in an operating environment, a great deal of power, time, and manpower will be incurred. Therefore, this paper proposes a resource clustering simulator for cluster selection of desktop virtualization. This provides a clustering simulation to properly select clustering algorithms and apply elements in different environments of desktop PCs.

• Korean Journal of Ecology and Environment
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• v.51 no.4
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• pp.354-364
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• 2018

In this study, we surveyed the fish community at the four sites(St. 1, 3, 5, 7) in Jungrang Stream from 2008 to 2016. We found 37 species grouped into nine families. There were three exotic species; Cyprinus carpio, Carassius cuvieri and Micropterus salmoides after rainfall. Dominant species was Zacco platypus (57.3%) and subdominant species was Carassius auratus(10.4%) in a community. The water quality, surveyed at the six sites(St. 1, 2, 3, 4, 5, 6), based on eight factors(Water temperature, pH, DO, BOD, SS, EC, TN and TP), largely varied depending on each site and period. Minimum values in four factors(BOD, EC, TN and TP) were observed in rainy season, indicating an ionic and nutrient dilution of stream water by precipitation. In contrast, a maximum value in SS was occurred in rainy season at St. 2 and St. 5. The precipitation, Maximum value observed in July ($497.5{\pm}297.2mm$), minimum value in January ($12.9{\pm}8.6mm$). In July and August, the precipitation was divided into before and after the rainfall season in Jungrang stream. Using cluster analysis three fish sites (St. 1, 3, 5) were identified as significantly influence 11 fish species; Hemiculter eigenmanni, Squalidus japonicus coreanus, Hemibarbus labeo, Gnathopogon strigatus, Pungtungia herzi, Rhynchocypris oxycephalus, Pseudogobio esocinus, Pseudorasbora parva, Cyprinus carpio, Carassius auratus and Zacco platypus by rainfall.

• Journal of Korean Society of Forest Science
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• v.110 no.2
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• pp.141-154
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• 2021

The change in vegetation structure along elevational gradients on the southeastern part of Mt. Hallasan was studied. Vegetation data were collected with 59 quadrates located from 16 to 1,565 m with 100 m intervals. Community types were classified using cluster analysis, and species composition and diversity were analyzed along elevational gradients. The vegetation was classified into seven, namely, type 1 Quercus serrata community, type 2 Carpinus tschonoskii community, type 3 Carpinus laxiflora community, type 4 Pinus densiflora community, type 5 Abies koreana community, type 6 Castanopsis sieboldii community, and type 7 Quercus acuta community. The species with a high importance value in tree layer in each elevational zone were C. sieboldii and Q. acuta at 100-600 m; C. laxiflora, Q. serrata, and C. tschonoskii at 700-800 m; P. densiflora at 1,100-1,200 m; and Abies koreana at 1,500-1,600 m. The species diversity indicated higher value at 700-800 m, 1,200-1,300 m and 1,400-1,500 m than at the other elevation.

• Journal of Environmental Impact Assessment
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• v.30 no.4
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• pp.237-246
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• 2021

The similarity of hourly PM₁₀ and PM_2.5 concentration profiles of the atmospheric monitoring stations in Gyeonggi-do was evaluated through the multilateral analysis between stations. The existing category for most stations in the regions shows relatively low Pearson correlation values of 0.68 and 0.7 for PM₁₀ and PM_2.5 on average respectively, and some monitoring stations revealed high relationships over 0.8 to other regions. Since the current regions are mainly categorized by cluster analysis based on the number of occurrence of high concentration events and geological factors, it is necessary to reclassify them by concentration characteristics for precise fine dust management. In accordance, multi-dimensional scaling being able to visualize could categorize the regions based on regional emission contribution rate and hourly fine dust concentration. As a result of the current analysis, PM₁₀ and PM_2.5 could be reclassified into five regions and fourregions, respectively.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.26 no.3
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• pp.1-17
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• 2023

Korea's natural parks are managed by dividing them into four use districts: nature preservation district, natural environment district, cultural heritage district, and park village district within the park under the goal of 'conservation and sustainable use of natural parks'. However, the use districts divided in this way are designated by reflecting the results derived from the simple drawing overlapping method, and there is a limit in that objective and scientific evidence for this is insufficient. In addition, in Taeanhaean National Park, the case of this study, only a very small area of less than 1% of the nature preservation district is designated, and the natural environment district that serves as a buffer space is designated on an excessively wide scale, making it difficult to efficiently manage the national park. Therefore, the use district is not fulfilling its role. In this study, the purpose of this study was to present a method for analyzing the spatial characteristics of natural parks using environmental indicators and unsupervised learning analysis methods to set the use districts of natural parks. In this study, evaluation indicators that can evaluate the natural and human environments were derived, and the distribution patterns for each indicator were analyzed. Afterwards, by applying Self-Organizing Map (SOM) analysis, one of the unsupervised learning analysis methods, districts with similar characteristics were derived in Taeanhaean National Park, and the characteristics of each district were analyzed. As a result of the study, 7 districts with different characteristics were derived in Taeanhaean National Park, and by examining the contribution of each indicator together, it was possible to reveal that each district had different representative characteristics even though it was an adjacent area. This study evaluated natural parks by comprehensively considering the indicators of the natural and human environments. In addition, the SOM method used in the study is meaningful in that it can provide scientific and objective grounds for the existing zoning and apply it to the management plan.

• Composites Research
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• v.37 no.1
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• pp.46-52
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• 2024

The development of transportation methods has improved human transportation convenience and made it possible to expand the travel radius of people with disabilities who have difficulty moving. However, in the case of WAV (wheelchair Accessible Vehicle), the safety that may occur in a vehicle accident is still lower than that of regular passenger seats. In particular, in the case of a rear-end collision that may occur in a defenseless situation, it can cause fatal neck injuries to disabled passengers. Therefore, a more detailed design plan must be reflected in the headrest to be applied to WAV. In this study, a multi-cell headrest was proposed to implement local compression characteristic distribution of the headrest during rear-end collision of WAV. Afterwards, a correlation analysis was performed between the passenger's NIC (Neck Injury Criterion) and impact energy absorption using the data set construction through analysis and the clustering results using k-means clustering. As a result of clustering, it was confirmed that data clusters with similar characteristics were formed, and a correlation analysis between NIC and impact energy absorption through the characteristics of each cluster was performed. As a result of the analysis, it was confirmed that the softer the cell compression characteristics in Mid3 and Mid6, the more impact energy absorption increases, and the harder the cell compression characteristics in Front2, Mid3, and Mid6, the more effective it is in reducing NIC.

• Journal of the Korean Society of Marine Environment & Safety
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• v.30 no.1
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• pp.20-31
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• 2024

This study was conducted to investigate the changes in the structure of benthic communities resulting from aquaculture activities and to assess the benthic health status of surface sediment in Gangjin Bay, a region known for concentrated shellfish aquaculture on the southern coast of Korea. Survey stations were divided into longline culture, bottom culture, and non-cultivation areas. The spatiotemporal distribution of physiochemical factors such as the grain size, water temperature, salinity, and total organic carbon in Gangjin Bay showed no significant differences between sampling stations. However, the species number, density, and diversity were relatively lower at the sampling stations in the bottom culture areas than at the other stations throughout the entire survey period. Cluster analysis and principal coordinates analysis also clearly distinguished the benthic communities in the bottom culture areas from those in the other sampling areas. At the sampling stations in the longline culture and non-cultivation areas, Scolectoma longifolia and Sigambra tentaculata, which are indicator species of organically enriched areas, appeared as dominant species. However, excluding some stations influenced by physical factors such as the water depth and current speed, the occupancy rate was not high. The health assessment results, conducted using the fisheries environment assessment method, revealed good conditions with Grades 1 and 2 across the entire area. However, an examination of the spatiotemporal changes in benthic communities and the benthic health index indicated that the benthic environment in the bottom culture areas was affected by physical disturbances.

• Korean Journal of Environmental Biology
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• v.41 no.4
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• pp.720-734
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• 2023

To understand the spatiotemporal distribution pattern of zooplankton and the environmental factors influencing zooplankton abundance in Gomso Bay, major harvesting area of Manila clam (Venerupis philippinarum) in South Korea, zooplankton sampling was conducted four times in autumn (October 2022), winter (January 2023), early spring (March 2023), and spring (May 2023). Among the environmental factors of Gomso Bay, water temperature, chlorophyll a concentration (Chl-a), dissolved oxygen (DO), and pH observed different patterns, while salinity and suspended particulate matter(SPM) showed no significant statistical differences between the survey periods. The zooplankton in Gomso Bay occurred 33, 29, 27, and 29 taxonomic groups during each respective survey period. In October 2022 and May 2023, arthropod plankton were dominated, while in January and March 2023, protozoa were primarily dominant. Among the Arthropods, copepods including Acartia hongi, Paracalanus parvus s. l., Corycaeus spp., and Oithona spp. commonly found along Korean coastal areas of the Yellow Sea, were dominated. Cluster analysis based on zooplankton abundance indicated a single community (stable condition) in each season, attributed to low dissimilarity distances, while three distinct clusters (autumn, winter-early spring, spring) between seasons indicated a highly seasonal environment in Gomso Bay.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.77-110
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• 2017

Recently, the diversification and individualization of consumption patterns through the web and mobile devices based on the Internet have been rapid. As this happens, the efficient operation of the offline store, which is a traditional distribution channel, has become more important. In order to raise both the sales and profits of stores, stores need to supply and sell the most attractive products to consumers in a timely manner. However, there is a lack of research on which SKUs, out of many products, can increase sales probability and reduce inventory costs. In particular, if a company sells products through multiple in-store stores across multiple locations, it would be helpful to increase sales and profitability of stores if SKUs appealing to customers are recommended. In this study, the recommender system (recommender system such as collaborative filtering and hybrid filtering), which has been used for personalization recommendation, is suggested by SKU recommendation method of a store unit of a distribution company that handles a homogeneous brand through a plurality of sales stores by country and region. We calculated the similarity of each store by using the purchase data of each store's handling items, filtering the collaboration according to the sales history of each store by each SKU, and finally recommending the individual SKU to the store. In addition, the store is classified into four clusters through PCA (Principal Component Analysis) and cluster analysis (Clustering) using the store profile data. The recommendation system is implemented by the hybrid filtering method that applies the collaborative filtering in each cluster and measured the performance of both methods based on actual sales data. Most of the existing recommendation systems have been studied by recommending items such as movies and music to the users. In practice, industrial applications have also become popular. In the meantime, there has been little research on recommending SKUs for each store by applying these recommendation systems, which have been mainly dealt with in the field of personalization services, to the store units of distributors handling similar brands. If the recommendation method of the existing recommendation methodology was 'the individual field', this study expanded the scope of the store beyond the individual domain through a plurality of sales stores by country and region and dealt with the store unit of the distribution company handling the same brand SKU while suggesting a recommendation method. In addition, if the existing recommendation system is limited to online, it is recommended to apply the data mining technique to develop an algorithm suitable for expanding to the store area rather than expanding the utilization range offline and analyzing based on the existing individual. The significance of the results of this study is that the personalization recommendation algorithm is applied to a plurality of sales outlets handling the same brand. A meaningful result is derived and a concrete methodology that can be constructed and used as a system for actual companies is proposed. It is also meaningful that this is the first attempt to expand the research area of the academic field related to the existing recommendation system, which was focused on the personalization domain, to a sales store of a company handling the same brand. From 05 to 03 in 2014, the number of stores' sales volume of the top 100 SKUs are limited to 52 SKUs by collaborative filtering and the hybrid filtering method SKU recommended. We compared the performance of the two recommendation methods by totaling the sales results. The reason for comparing the two recommendation methods is that the recommendation method of this study is defined as the reference model in which offline collaborative filtering is applied to demonstrate higher performance than the existing recommendation method. The results of this model are compared with the Hybrid filtering method, which is a model that reflects the characteristics of the offline store view. The proposed method showed a higher performance than the existing recommendation method. The proposed method was proved by using actual sales data of large Korean apparel companies. In this study, we propose a method to extend the recommendation system of the individual level to the group level and to efficiently approach it. In addition to the theoretical framework, which is of great value.