• Proceedings of the Plant Resources Society of Korea Conference
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• 2019.10a
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• pp.84-84
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• 2019

본 연구는 전국 임의의 산양삼 재배지를 선정하여 재배지 내의 토양 특성 및 토양세균군집을 분석하고, 토양 특성, 세균군집 및 산양삼 생육특성 간의 상관관계를 구명하기 위하여 수행되었다. 토양 이화학성 분석은 농촌진흥청의 종합분석실 매뉴얼에 따라 분석하였고, 토양세균군집 분석은 pyrosequencing analysis (Illumina platform)를 이용하였다. 토양세균군집과 생육특성 간의 상관관계는 Spearman's rank correlation을 이용하여 분석하였다. 전국 8개 산양삼 재배지로부터 분리한 토양세균군집은 2개의 cluster로 군집화를 이루는 것을 확인하였다. 모든 토양 샘플에서 Proteobacteria와 Alphaproteobacteria가 각각 평균 상대적 빈도수가 35.4%, 24.4%로 우점종으로 나타났다. 나타났다. 두 개의 cluster 간 토양세균군집의 상대적 빈도수를 비교 분석한 결과, 먼저 Proteobacteria (p = 0.03), Actinobacteria (p = 0.02), Ahlpaproteobacteria (p = 0.029), Betaproteobacteria (p = 0.021)는 cluster 1에서 cluster 2에 비해 상대적 빈도수가 유의적으로 높았고, Fimicutes (p = 0.004), Cyanobacteria (p = 0.004), Acidobacteriia (p = 0.041), Ktedonobacteria (p = 0.019), Gammaproteobacteria (p = 0.034), Bacilli (p = 0.009)은 cluster 2에서 유의적으로 상대적 빈도수가 높은 것으로 나타났다. 토양세균군집 cluster 간 산양삼의 생육특성을 비교 분석한 결과, cluster 2 재배지에서 수집한 산양삼 시료의 지하부 생중량은 cluster 1 재배지에서 수집한 산양삼 시료에 비해 cluster 2에서 유의적 (p = 0.04)으로 높았다. 산양삼 생육특성과 토양세균군집 간의 상관관계를 분석한 결과, 산양삼의 생육은 토양 pH가 낮고 Acidobacteria의 상대적 빈도수가 높은 토양에서 증가하였으며, Acidobacteriia와 Koribacteraceae의 상대적 빈도수는 산양삼의 생육과 유의적인 정의 상관관계를 보이는 것으로 나타났다. 본 연구 결과는 토양미생물군집과 산양삼 생육 간의 상관관계를 구명하는 중요한 자료가 될 것으로 생각되고, 나아가 산양삼 재배적지를 선정하는데 있어 보다 명확한 정보를 제공할 수 있을 것으로 사료된다.

• The Korean Journal of Applied Statistics
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• v.33 no.5
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• pp.591-601
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• 2020

The self-organizing map (SOM) is a unsupervised learning method projecting high-dimensional data into low-dimensional nodes. It can visualize data in 2 or 3 dimensional space using the nodes and it is available to explore characteristics of data through the nodes. To understand the structure of data, cluster analysis is often used for nodes obtained from SOM. In cluster analysis, the optimal number of clusters is one of important issues. To help to determine it, various cluster validity indexes have been developed and they can be applied to clustering outcomes for nodes from SOM. However, while SOM has an advantage in that it reflects the topological properties of original data in the low-dimensional space, these indexes do not consider it. Thus, we propose a new cluster validity index for SOM based on connectivity between nodes which considers topological properties of data. The performance of the proposed index is evaluated through simulations and it is compared with various existing cluster validity indexes.

• The Korean Journal of Applied Statistics
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• v.29 no.5
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• pp.949-960
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• 2016

This paper uses all sections of highway data (VDS) for two years (Jan. 2014-Dec. 2015), with 15 minute units. The first purpose of this study is to find clusters with similar patterns that appear repeatedly with time variables of month, week and hour. The cluster analysis results indicate a variety of patterns of average traffic speeds by time variables depending on the clusters; subsequently, these can be utilized to model for the forecast of the speed at a specific time. The second purpose is to do cluster analysis for grouping sections by effect nets that are closely related to each other. For the similarity measure we use cross-correlation functions calculated after pre-whitening the speed of each section. The cluster analysis gets 19 clusters, and sections within a cluster are geographically close. These results are expected to help to forecast a real-time speed.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.1
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• pp.107-114
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• 2020

This paper studies a method for estimating the scale of a Hadoop cluster to process big data as a cost-effective manner. In the case of medical institutions, demands for cloud-based big data analysis are increasing as medical records can be stored outside the hospital. This paper first analyze the Amazon EMR framework, which is one of the popular cloud-based big data framework. Then, this paper presents a efficiency model for scaling the Hadoop cluster to execute a Mapreduce application more cost-effectively. This paper also analyzes the factors that influence the execution of the Mapreduce application by performing several experiments under various conditions. The cost efficiency of the analysis of the big data can be increased by setting the scale of cluster with the most efficient processing time compared to the operational cost.

• The Korean Journal of Applied Statistics
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• v.19 no.1
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• pp.13-32
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• 2006

Biologists are attempting to group genes based on the temporal pattern of gene expression levels. So far, a number of methods have been proposed for clustering microarray data. However, the results of clustering depends on the genes selection, therefore the gene selection with significant expression difference is also very important to cluster for microarray data. Thus, this paper present the results of broad comparative studies to time course microarray data by considering methods of gene selection, clustering and cluster validation.

• Journal of the Korea Society of Computer and Information
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• v.26 no.6
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• pp.29-35
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• 2021

In this paper, we propose a method that combines KNN(K-Nearest Neighbor), Local Map Classification and Bayes Filter as a way to increase the accuracy of location positioning. First, in this technique, Local Map Classification divides the actual map into several clusters, and then classifies the clusters by KNN. And posterior probability is calculated through the probability of each cluster acquired by Bayes Filter. With this posterior probability, the cluster where the robot is located is searched. For performance evaluation, the results of location positioning obtained by applying KNN, Local Map Classification, and Bayes Filter were analyzed. As a result of the analysis, it was confirmed that even if the RSSI signal changes, the location information is fixed to one cluster, and the accuracy of location positioning increases.

• Journal of Applied Biological Chemistry
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• v.61 no.4
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• pp.357-365
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• 2018

The quality of Korean doenjang, which was traditionally made for this study, was monitored for physicochemical properties, antioxidant capacity, and sensory properties at six months intervals for three years. The collected data were comprehensively analyzed using the k-means clustering via principal component analysis (PCA) to determine the optimal intake duration and sensory factors associated with acceptance. Doenjang samples were classified with every year interval based on PCA, and then the classified doenjang samples were further grouped into cluster one, two, and three based on the k-means clustering. In Cluster three, doenjang that was aged for thirty and thirty-six months, respectively, showed high total phenolic content, antioxidant capacity, superoxide dismutase like activity, and 2,2-diphenyl-1-picryl-hydrazyl radical scavenging capacity. Interestingly, along with acceptance, the levels of free amino acids and organic acids were higher in Cluster 3. The sensory factors found to be associated with acceptance included umami taste and brown color. In conclusion, this study proposes the intake of doenjang aged for thirty months based on its antioxidant activity and sensory properties although doenjang is usually ready after twelve months of aging.

• The Journal of the Korea Contents Association
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• v.14 no.4
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• pp.314-324
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• 2014

Aerospace industry is a combination of high technologies which has several characteristics such as product reliability, precision, light weight, and energy efficiency. Nowadays, each country is trying to invigorating knowledge and information sharing between the companies for the synergy effect of aerospace industry. However, the research and empirical analysis on the vitalization of aerospace industry cluster are insufficient. Therefore, this study aims to firstly classify the supporting functions of government for aerospace industry cluster into five types by analyzing existing literatures and status reports issued by government. Secondly, companies are surveyed on the five classified types of supporting functions by questionnaire. Questionnaire survey responded by 30 aerospace companies in Gyeongnam aerospace industry cluster are analyzed. Quantitative analysis methods were used for statistical analysis. Based on the analysis, improvement directions of government supporting functions are suggested. The results of this study is expected to help policy making for invigorating the aerospace industry cluster.

• Land and Housing Review
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• v.11 no.2
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• pp.25-32
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• 2020

This study classified and analyzed characteristics of residents in public rental housing based on data from the 2018 Housing Survey. First, in order to classify the type of public rental housing resident, the criteria were derived through factor analysis based on the satisfaction evaluation index. Next, based on the factor value, the group was classified by type through cluster analysis, and the satisfaction, characteristics of residential households, and characteristics of rental housing types were analyzed for each cluster. As a result of factor analysis, evaluation of housing facilities, accessibility, and residential comfort was selected as the cluster classification criteria, and a total of four clusters were derived through cluster analysis. As a result of analyzing the characteristics of each cluster, it was found that there was a statistically significant difference in the level of residential satisfaction, characteristics of residents, and detailed types of rental housing. The results of this analysis are expected to be used to improve existing public rental housing or develop new types of rental housing to match the characteristics of residential housing for public rental housing. In addition, in the type integration of rental housing currently being promoted, it is necessary to develop a method of providing differentiated services in consideration of the characteristics of tenants as well as the integration of physical housing types.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.355-357
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• 2016

Korea Information Security Industry growth rate (7.1%) declined compared to the previous three years growth (15%). The government has announced a 2020 K-ICT security. According to the policy of promoting the competitiveness of industry consolidation and data protection enable entrepreneurship, job creation, there is a need for the ICT industry in conjunction with cyber security cluster composition. In this paper, I research for cybersecurity cluster. Investigate a successful cyber clusters of foreign and analyzed. In addition, I analysis of existing cluster in the domestic and identify the problem. Consequently, building a cyber-research cluster and the expected effects of cyber-research and on how to operate the cluster.