• Korean Journal of Breeding Science
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• v.42 no.1
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• pp.28-34
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• 2010

Diversity of 30 Korean potato cultivars was evaluated using 14 microsatellite markers. Twelve microsatellite markers representing 12 loci in the potato genome detected 84 polymorphisms among 30 cultivars and revealed alleles with a mean of 7.00 alleles per primer. The polymorphism information content (PIC) value ranged from 0.57 to 0.93 with average of 0.82. Based on polymorphism, cluster analysis was conducted by the unweighted pair-group method with arithmetic average (UPGMA) methods. Thirty potato varieties were distinctly separated into 2 groups and similarity coefficient of cluster ranged from 0.58 to 0.95. Thirty tetraploid cultivars were evaluated for six important agronomic traits. One-way analysis of variance was done to look for the degree of relationships between individual markers and traits. K1 and K2 markers showed a significant association with amylose contents, starch contents, and specific gravity.

• Journal of the Korea Society of Computer and Information
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• v.23 no.11
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• pp.25-30
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• 2018

Pattern classification system is often an important component of intelligent systems. In this paper, we present a pattern classification system consisted of the feature selection module, knowledge base construction module and decision module. We introduce a feature impact evaluation selection method based on fuzzy cluster analysis considering computational approach and generalization capability of given data characteristics. A fuzzy neural network, OFUN-NET based on unsupervised learning data mining technique produces knowledge base for representative clusters. 240 blemish pattern images are prepared and applied to the proposed system. Experimental results show the feasibility of the proposed classification system as an automating defect inspection tool.

• Communications for Statistical Applications and Methods
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• v.29 no.1
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• pp.103-125
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• 2022

In this paper, we analyze the time series data of the case and death counts of COVID-19 that broke out in China in December, 2019. The study period is during the lockdown of Wuhan. We exploit functional data analysis methods to analyze the collected time series data. The analysis is divided into three parts. First, the functional principal component analysis is conducted to investigate the modes of variation. Second, we carry out the functional canonical correlation analysis to explore the relationship between confirmed and death cases. Finally, we utilize a clustering method based on the Expectation-Maximization (EM) algorithm to run the cluster analysis on the counts of confirmed cases, where the number of clusters is determined via a cross-validation approach. Besides, we compare the clustering results with some migration data available to the public.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.1
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• pp.74-96
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• 2023

At present, the smart grid has become one of the indispensable infrastructures in people's lives. As a commonly used communication method, wireless communication is gradually, being widely used in smart grid systems due to its convenient deployment and wide range of serious challenges to security. For the insecurity of the schemes based on large integer factorization and discrete logarithm problem in the quantum environment, an identity-based key management scheme for smart grid over lattice is proposed. To assure the communication security, through constructing intra-cluster and inter-cluster multi-hop routing secure mechanism. The time parameter and identity information are introduced in the relying phase. Through using the symmetric cryptography algorithm to encrypt improve communication efficiency. Through output the authentication information with probability, the protocol makes the private key of the certification body no relation with the distribution of authentication information. Theoretic studies and figures show that the efficiency of keys can be authenticated, so the number of attacks, including masquerade, reply and message manipulation attacks can be resisted. The new scheme can not only increase the security, but also decrease the communication energy consumption.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1056-1059
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• 2003

Self-Oranizing Map(SOM) is an unsupervised neural network providing cluster analysis of high dimensional input data. The output from the SOM is represented in map that help us to explore data. The weak point of conventional SOM is when the map is large, it take a long time to train the data. The computing time is known to be O(MN) for trainning to find the winning node (M,N are the number of nodes in width and height of the map). This paper presents a new method to reduce the computing time by creating new map. Each node in a new map is the centroid of nodes' group that are in the original map. After create a new map, we find the winning node of this map, then find the winning node in original map only in nodes that are represented by the winning node from the new map. This new method is called "High Speed Self-Oranizing Map"(HS-SOM). Our experiment use HS-SOM to cluster documents and compare with SOM. The results from the experiment shows that HS-SOM can reduce computing time by 30%-50% over conventional SOM.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.3
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• pp.1-7
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• 2012

Recently, researches are studied in developing efficient techniques for accessing, querying, and managing XML documents which are frequently used in the Internet. In this paper, we propose a new method to cluster XML documents efficiently. An element of a XML document corresponds to a node of the corresponding tree and an inclusion relationship of the document corresponds to a relationship between parent and child node of the tree. Therefore, similar XML documents are similar to the node's name and level of the corresponding trees. We make evaluation function with this characteristic to cluster XML documents by genetic algorithm. The experiment shows that our proposed method has better performance than other existing methods.

• Horticultural Science & Technology
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• v.31 no.3
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• pp.322-327
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• 2013

The genetic diversity and phylogenetic relationships of eleven herbaceous peonies grown in Korea were analyzed by random amplified polymorphic DNA (RAPD). Twenty-four decamer RAPD primers were used in a comparative analysis of these Korean peony species. Of the 142 total RAPD fragments amplified, 124 (87.3%) were found to be polymorphic. The remaining 18 fragments were found to be monomorphic (12.7%) shared by individuals of all 11 peony species. Cluster analysis based on the presence or absence of bands was performed by Jaccard's similarity coefficient, based on Unweighted Pair Group Method with Arithmetic Averages. Genetic similarity range was 0.39 to 0.90 with a mean of 0.64. This study offered a rapid and reliable method for the estimation of variability among different peony species which could be utilized by the breeders for further improvement of the local peony species. Also, the results propose that the RAPD marker technique is a useful tool for evaluation of genetic diversity and relationship amongst different peony species.

• Journal of Internet Computing and Services
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• v.10 no.3
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• pp.141-150
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• 2009

XML is increasingly important in data exchange and information management. A large amount of efforts have been spent in developing efficient techniques for accessing, querying, and storing XML documents. In this paper, we propose a new method to cluster XML documents efficiently. A new prepresentative path called a virtul path which can represent both the structure and the contents of a XML document is proposed for the feature of a XML document. A method to apply the well known hierarchical clustering techniques to the representative paths to cluster XML documents is also proposed. The experiment shows that the true clusters are formed in a compact shape when a virtual path is used for the feature of a XML document.

• Journal of Korean Society for Quality Management
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• v.46 no.3
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• pp.441-452
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• 2018

Purpose: The purpose of this paper is to study quantitative risk grade assessment for objective government quality assurance activities based on risk management in initial mass production for weapon systems. Methods: The Defense quality management regulations and foreign risk assessment documents are referred to analyze problems performing quality assurance actives. The failure rate data, maintainability and cost of products have been studied to quantify the risk Likelihood and impact. The analyzed data were classified as risk grade assessment through K-means Cluster Analysis method. Results: Results show that a proposed method can objectively evaluate risk grade. The analyzed results are clustered into three levels such as high, middle and low. Two products are allocated high, eleven low and seven middle. Conclusion: In this paper, quantitative risk grade assessment methods were presented by analyzing risk ratings based on objective data. The findings showed that the methods would be effective for initial mass production for weapon systems.

• The Korean Journal of Applied Statistics
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• v.24 no.6
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• pp.1213-1224
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• 2011

In high dimensionality where the number of variables are excessively larger than observations, it is required to remove the noninformative variables to cluster observations. Most model-based approaches for variable selection have been considered under the assumption of homoscedasticity and their models are mainly estimated by a penalized likelihood method. In this paper, a different approach is proposed to remove the noninformative variables effectively and to cluster based on the modified normal mixture model simultaneously. The validity of the model was provided and an EM algorithm was derived to estimate the parameters. Simulation studies and an experiment using real microarray dataset showed the effectiveness of the proposed method.