• Korean Journal of Plant Resources
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• v.35 no.5
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• pp.641-651
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• 2022

The biodiversity research of barley, a functional food, is proceeding to conserve germplasms and develop new cultivar of barley to improve its functional effects. In this study, with 25,104 barley germplasms in the National Agrobiodiversity Center, South Korea, the biodiversity index of species was much lower (1.17) than the origins (24.73) because of the presence of a biased species, Hordeum vulgare subsp. vulgare, but the species and origin of germplasms were significantly different with regard to genetic traits. In the clustering analysis based on genetic traits, we found that 97% barley germplasms could mostly be distributed between 1~7 clusters out of a total of 15 clusters; 'normal and uzu type', 'lodging', and 'loose smut' were commonly represented in the 1~7 clusters and some clusters showed specific differences in five genetic traits including 'growth habit'. In correlation of each genetic trait, the infection of 'barley yellow mosaic virus' was highly correlated to 'number of grains per spike'. '1000 grain weight' was weakly correlated with seven genetic traits including 'number of grains per spike'. Our analysis for barley's biodiversity can provide a useful guide to the species' phenotypes that need to be collected to conserve biodiversity and to breed new barley varieties.

• Survey Research
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• v.7 no.1
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• pp.55-84
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• 2006

This study has classified development stages (Embryonic-Growth-Maturity) of mobile telecommunication industry based on Industry Life Cycle theory. There are two steps to be analyzed in this study, In the first step, cluster was investigated through cluster analysis using mobile density to categorize development stages of mobile telecommunication industry. In the second step, we compared on indexes of market structure, market efficiency and market performance to find out characteristics of each stage of development. The results are as follows. First, HHI is higher at embryonic stage than at growth and maturity stages, Second, ARPU(Average Revenue Per User) and RPM(Revenue Per Minute) are getting higher as the stages move on. Third, EBITDA margins, an index of market performance, is decreasing along the three stages. Finally, this study presents a clue to define the stage of development of mobile telecommunication industry and build a proper strategy for the market change.

• Korean Journal of Environmental Biology
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• v.23 no.3 s.59
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• pp.238-249
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• 2005

The present study was carried out to apprehend that the pollutants originating from Jinhae Industrial Complex affect benthic polychaetous community in the northern Jinhae bay. An investigation on the macrobenthic community in Jinhae bay was conducted in September, December of 2002 and March of 2003. The benthic fauna showed mean density of 2,151 ind. $m^{-2}$ in September of 2002, 2,427 ind. $m^{-2}$ in December of 2002 and 2,394 ind. $m^{-2}$ in March of 2003. Major faunal groups are polychaetes, crustaceans and mollusks, corresponding to $73.7\%,\;12.0\%\;and\;11.7\%,$ in total mean density during all of the sampling season, respectively. The most abundant species was Lumbrineris longifolia $(24.85\%),$ followed by Tharyx sp. $(21.70\%),$ Mesochaetopterus sp. $(6.20\%),$ Heteromastus filiformis $(5.39\%),$ Prionospio sp. $(5.18\%),$ Clycinde sp. $(4.29\%),$ etc. Tharyx sp. was the highest abundant species in September of 2002, and Lumbrineris longifolia was the dominant species in another sampling seasons. The density and the species number of polychaetes were high around Chori Is. and poor near Jinhae Industrial Complex area. Cluster analysis based on the species composition showed that Jinhae bay could be divided into three regions except in March of 2003. In December of 2002, there are very distinct regions by the cluster analysis. The density of benthic polychaetes in Jinhae bay was higher than that in the other coastal area of Korea, due to the predominance by some of opportunistic species, such as Lumbrineris longifolia, Tharyx sp. and Heteromastus filiformis, etc. It means that the study area were in the process of organic enrichment.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.1
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• pp.87-94
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• 2019

A wireless sensor network is emerging technology and intelligent wireless communication paradigm that is dynamically aware of its surrounding environment. It is also able to respond to it in order to achieve reliable and efficient communication. The dynamical cognition capability and environmental adaptability rely on organizing dynamical networks effectively. However, optimally clustering the cognitive wireless sensor networks is an NP-complete problem. The objective of this paper is to develop an optimal sensor network design for maximizing the performance. This proposed Ranking Artificial Bee Colony (RABC) is developed based on Artificial Bee Colony (ABC) with ranking strategy. The ranking strategy can make the much better solutions by combining the best solutions so far and add these solutions in the solution population when applying ABC. RABC is designed to adapt to topological changes to any network graph in a time. We can minimize the total energy dissipation of sensors to prolong the lifetime of a network to balance the energy consumption of all nodes with robust optimal solution. Simulation results show that the performance of our proposed RABC is better than those of previous methods (LEACH, LEACH-C, and etc.) in wireless sensor networks. Our proposed method is the best for the 100 node-network example when the Sink node is centrally located.

• Journal of Korean Society for Quality Management
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• v.47 no.2
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• pp.381-395
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• 2019

Purpose: This study aims to conduct a systematic literature review to suitably identify wide and specific issues and topics on service quality in supply chain. Methods: This study is to investigate service quality in supply chain research using a systematic literature review methodology. In order to extract influential journals and papers, we used the SJR impact factor provided by the SCOPUS database. The collected 169 papers were analyzed using bibliometric analysis, citation analysis as well as keywords network. Results: We conducted a bibliometric analysis to identify top authors contributing to service quality in supply chain and their issues, and further examined important keywords and new emerging keywords. In addition, we extracted five influential papers by PageRank to clarify critical issues and divided into five clusters to identify topics of service quality in supply chain by using network-based approach. In order to examine comprehensive issues and topics of service quality in supply chain, we constructed a keyword network to observe difference in the classification of important keywords across network centrality measures. Conclusion: Our study reviewed literature on service quality in supply chain and explored the future directions and trends of service quality in supply chain.

• Fashion & Textile Research Journal
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• v.23 no.4
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• pp.480-490
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• 2021

Sales forecasting is crucial for many retail operations. For apparel retailers, accurate sales forecast for the next season is critical to properly manage inventory and plan their supply chains. The challenge in this increases because apparel products are always new for the next season, have numerous variations, short life cycles, long lead times, and seasonal trends. In this study, a sales forecasting model is proposed for apparel products using machine learning techniques. The sales data pertaining to outerwear items for four years were collected from a Korean sports brand and filtered with outliers. Subsequently, the data were standardized by removing the effects of exogenous variables. The sales patterns of outerwear items were clustered by applying K-means clustering, and outerwear attributes associated with the specific sales-pattern type were determined by using a decision tree classifier. Six types of sales pattern clusters were derived and classified using a hybrid model of clustering and decision tree algorithm, and finally, the relationship between outerwear attributes and sales patterns was revealed. Each sales pattern can be used to predict stock-keeping-unit-level sales based on item attributes.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.6D
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• pp.557-563
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• 2012

The purpose of this study is to analyze the spatial structure of Mega-Economic Region particularly in Daegu using Freight Origin-Destination (O/D) Data which comes from KTDB. To diagnose the appropriate separation of Regions, the mean of three standardized indices was calculated. The indicates measured are Freight Occupancy Ratio (FOR), Freight Dependancy Ratio (FDR), Scale Parameter (SP), respectively. The result of analysis showed that FOR FDR SP indicators gave effective explanation about characteristic of Regions depending on Freight moving patterns. Especially, Gyeongsan and Gumi had high correlation Regions with FOR FDR indicator. Also, the major industries of Daegu Metropolitan based on the SP indicator are Chemical and Metal machinery industry.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.1-19
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• 2018

Since stock movements forecasting is an important issue both academically and practically, studies related to stock price prediction have been actively conducted. The stock price forecasting research is classified into structured data and unstructured data, and it is divided into technical analysis, fundamental analysis and media effect analysis in detail. In the big data era, research on stock price prediction combining big data is actively underway. Based on a large number of data, stock prediction research mainly focuses on machine learning techniques. Especially, research methods that combine the effects of media are attracting attention recently, among which researches that analyze online news and utilize online news to forecast stock prices are becoming main. Previous studies predicting stock prices through online news are mostly sentiment analysis of news, making different corpus for each company, and making a dictionary that predicts stock prices by recording responses according to the past stock price. Therefore, existing studies have examined the impact of online news on individual companies. For example, stock movements of Samsung Electronics are predicted with only online news of Samsung Electronics. In addition, a method of considering influences among highly relevant companies has also been studied recently. For example, stock movements of Samsung Electronics are predicted with news of Samsung Electronics and a highly related company like LG Electronics.These previous studies examine the effects of news of industrial sector with homogeneity on the individual company. In the previous studies, homogeneous industries are classified according to the Global Industrial Classification Standard. In other words, the existing studies were analyzed under the assumption that industries divided into Global Industrial Classification Standard have homogeneity. However, existing studies have limitations in that they do not take into account influential companies with high relevance or reflect the existence of heterogeneity within the same Global Industrial Classification Standard sectors. As a result of our examining the various sectors, it can be seen that there are sectors that show the industrial sectors are not a homogeneous group. To overcome these limitations of existing studies that do not reflect heterogeneity, our study suggests a methodology that reflects the heterogeneous effects of the industrial sector that affect the stock price by applying k-means clustering. Multiple Kernel Learning is mainly used to integrate data with various characteristics. Multiple Kernel Learning has several kernels, each of which receives and predicts different data. To incorporate effects of target firm and its relevant firms simultaneously, we used Multiple Kernel Learning. Each kernel was assigned to predict stock prices with variables of financial news of the industrial group divided by the target firm, K-means cluster analysis. In order to prove that the suggested methodology is appropriate, experiments were conducted through three years of online news and stock prices. The results of this study are as follows. (1) We confirmed that the information of the industrial sectors related to target company also contains meaningful information to predict stock movements of target company and confirmed that machine learning algorithm has better predictive power when considering the news of the relevant companies and target company's news together. (2) It is important to predict stock movements with varying number of clusters according to the level of homogeneity in the industrial sector. In other words, when stock prices are homogeneous in industrial sectors, it is important to use relational effect at the level of industry group without analyzing clusters or to use it in small number of clusters. When the stock price is heterogeneous in industry group, it is important to cluster them into groups. This study has a contribution that we testified firms classified as Global Industrial Classification Standard have heterogeneity and suggested it is necessary to define the relevance through machine learning and statistical analysis methodology rather than simply defining it in the Global Industrial Classification Standard. It has also contribution that we proved the efficiency of the prediction model reflecting heterogeneity.

• Journal of Digital Convergence
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• v.20 no.5
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• pp.627-635
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• 2022

As the non-face-to-face situation continues for a long time due to COVID-19, the underlying technologies of the 4th industrial revolution such as IOT, AR, VR, and big data are affecting the metaverse platform overall. Such changes in the external environment such as society and culture can affect the development of academics, and it is very important to systematically organize existing achievements in preparation for changes. The Korea Educational Research Information Service (RISS) collected data including the 'metaverse platform' in the keyword and used the text mining technique, one of the big data analysis. The collected data were analyzed for word cloud frequency, connection strength between keywords, and semantic network analysis to examine the trends of metaverse platform research. As a result of the study, keywords appeared in the order of 'use', 'digital', 'technology', and 'education' in word cloud analysis. As a result of analyzing the connection strength (N-gram) between keywords, 'Edue→Tech' showed the highest connection strength and a total of three clusters of word chain clusters were derived. Detailed research areas were classified into five areas, including 'digital technology'. Considering the analysis results comprehensively, It seems necessary to discover and discuss more active research topics from the long-term perspective of developing a metaverse platform.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.6
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• pp.259-268
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• 2018

The purpose of this study is to propose a training method customized to the level of cognitive abilities to increase users' interest and engagement while using cognitive function training contents. The level of cognitive ability of the users was based on the clustering based on the users' information and Mini-Mental Statue Examination-Korea Child test score using the K-means algorithm applied collaborative filtering. The results were applied to the integrated cognitive function training system, and the contents order and difficulty level of the cognitive function training area were recommended to the user's cognitive ability level. Particularly, the contents difficulty control was designed to give a high immersion feeling by applying the 'flow theory' method that users can repeatedly feel tension and comfort. In conclusion, the user-customized cognitive function training method proposed in this paper can be expected to be more effective and rehabilitative results than existing therapists' subjective setting of contents order and difficulty level.