• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.9C
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• pp.831-839
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• 2006

In this paper, we present an exact and general expression involving two-dimensional Gaussian Q-functions for the bit error rate (BER) of hierarchical MPSK with I/Q phase and amplitude imbalances over an additive white Gaussian noise (AWGN) channel. First we derive a BER expression for the k-th bit of hierarchical 4, 8, 16-PSK signal constellations when Gray code bit mapping is employed. Then, from the derived k-th bit BER expression, we present the exact and general average BER expression for hierarchical MPSK with I/Q phase and amplitude imbalances. This result can readily be applied to numerical evaluation for various cases of practical interest in an I/Q unbalanced hierarchical MPSK system, because the one- and two-dimensional Gaussian Q-functions can be easily and directly computed usinB commonly available mathematical software tools.

• Genomics & Informatics
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• v.17 no.4
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• pp.45.1-45.3
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• 2019

To identify pathways associated with survival phenotypes using gene expression data, we recently proposed the hierarchical structural component model for pathway analysis of gene expression data (HisCoM-PAGE) method. The HisCoM-PAGE software can consider hierarchical structural relationships between genes and pathways and analyze multiple pathways simultaneously. It can be applied to various types of gene expression data, such as microarray data or RNA sequencing data. We expect that the HisCoM-PAGE software will make our method more easily accessible to researchers who want to perform pathway analysis for survival times.

• Journal of KIISE:Computer Systems and Theory
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• v.30 no.10
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• pp.544-555
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• 2003

Since the result data from DNA microarray experiments contain a lot of gene expression information, adequate analysis methods are required. Hierarchical clustering is widely used for analysis of gene expression profiles. In this paper, we study leaf-ordering, which is a post-processing for the dendrograms output by hierarchical clusterings to improve the efficiency of DNA microarray data analysis. At first, we analyze existing leaf-ordering algorithms and then present new approaches for leaf-ordering. And we introduce a software HCLO(Hierarchical Clustering ＆ Leaf-Ordering Tool) that is our implementation of hierarchical clustering, some of existing leaf-ordering algorithms and those presented in this paper.

• KSBB Journal
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• v.16 no.4
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• pp.381-388
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• 2001

A program for integrated gene expression profile analysis such as hierarchical clustering, K-means, fuzzy c-means, self-organizing map(SOM), principal component analysis(PCA), and singular value decomposition(SVD) was made for DNA chip data anlysis by using Matlab. It also contained the normalization method of gene expression input data. The integrated data anlysis program could be effectively used in DNA chip data analysis and help researchers to get more comprehensive analysis view on gene expression data of their own.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2003.10a
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• pp.170-177
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• 2003

Gene expression data are the quantitative measurements of expression levels and ratios of numberous genes in different situations based on microarray image analysis results. The process to draw meaningful information related to genomic diseases and various biological activities from gene expression data is known as gene expression data analysis. In this paper, we present a hierarchical clustering method of gene expression data based on self organizing map which can analyze the clustering result of gene expression data more efficiently. Using our proposed method, we could eliminate the uncertainty of cluster boundary which is the inherited disadvantage of self organizing map and use the visualization function of hierarchical clustering. And, we could process massive data using fast processing speed of self organizing map and interpret the clustering result of self organizing map more efficiently and user-friendly. To verify the efficiency of our proposed algorithm, we performed tests with following 3 data sets, animal feature data set, yeast gene expression data and leukemia gene expression data set. The result demonstrated the feasibility and utility of the proposed clustering algorithm.

• ETRI Journal
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• v.29 no.6
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• pp.707-715
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• 2007

In-phase/quadrature (I/Q) imbalances, which are generated by non-ideal transceiver components, are inevitable physical phenomena that cause the performance of practical communication systems to be degraded. In this paper, we provide a new closed-form expression for the bit error rate of hierarchical M-ary phase shift keying with I/Q phase and amplitude imbalances and analyze the effect of I/Q imbalances on BER performance over an additive white Gaussian noise channel.

• Journal of Ocean Engineering and Technology
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• v.15 no.4
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• pp.108-114
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• 2001

Petri nets have been introduced as a powerful analyzing and design tool for the discrete systems such as sequential control systems. However, one of the important problems in its applications is that the model can be analyzed hardly when we deal with large scale systems because of increase of the number of Petri net components. To overcome this problem, some methods for dividing or reducing of Petri net have been suggested. In this paper, an approach for hierarchical expression of Petri net based on Sequential function Chart(SFC) and Bottom Up oriented Mehodology(BUM) is proposed. Especially, some definitions and rules are defined in order to divide and compose sub Petri nets. A measuring tank system will be described as a typical kind of discrete systems and modeled by some sub Petri nets based on the SFC and BUM by the proposed method in this paper.

• Communications for Statistical Applications and Methods
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• v.12 no.2
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• pp.335-348
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• 2005

The rapid development of microarray technologies enabled the monitoring of expression levels of thousands of genes simultaneously. Recently, the time course gene expression data are often measured to study dynamic biological systems and gene regulatory networks. For the data, biologists are attempting to group genes based on the temporal pattern of their expression levels. We apply the consensus clustering algorithm to a time course gene expression data in order to infer statistically meaningful information from the measurements. We evaluate each of consensus clustering and existing clustering methods with various validation measures. In this paper, we consider hierarchical clustering and Diana of existing methods, and consensus clustering with hierarchical clustering, Diana and mixed hierachical and Diana methods and evaluate their performances on a real micro array data set and two simulated data sets.

• Journal of Integrative Natural Science
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• v.7 no.1
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• pp.50-56
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• 2014

To explore effects on each the emotional awareness, emotional expression, emotional empathy, and emotional regulation, of the sub-factors of the child's emotional intelligence, to mural preference, emotion cultivation, and community connection, the hierarchical multiple regression analyses are performed(as in Table 1, 2, and 3). As the results, we found the following facts. Children's mural preference, emotion cultivation, and community connection were expressed by the following equations in order, respectively. Mural Preference = $.170{\times}$[Emotional Awareness](t=2.118, $p=.036^*$) - $.025{\times}$[Emotional Expression](t=-.275, p=.783) + $.088{\times}$[Emotional Empathy](t=.938, p=.350) + $.139{\times}$[Emotional Regulation] (t=1.529, p=.128). Mural Emotion Cultivation = $-.021{\times}$[Emotional Awareness](t=-.294, p=.769) - $.205{\times}$[Emotional Expression](t=-2.573, $p=.011^*$) + $.265{\times}$[Emotional Empathy](t=3.156, $p=.002^*$) + $.192{\times}$[Emotional Regulation](t=2.361, $p=.019^*$). Mural Community Connection = $-.001{\times}$[Emotional Awareness](t=-.007, p=.995) - $.132{\times}$[Emotional Expression](t=-1.478, p=.141) + $.172{\times}$[Emotional Empathy](t=1.732, $p=.027^*$) + $.098{\times}$[Emotional Regulation](t=1.072, p=.285).

• Lingua Humanitatis
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• v.5
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• pp.93-106
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• 2003

Professor Daihyun Chung has recently presented an intriguing view of Ban-mal(non-honorific expressions) in Korean. He criticizes uses of Ban-mal, on the grounds that they presuppose the existence of hierarchical structures in human beings. Professor Chung also advocates ″a relational theory of Ban-mal″ according to which ascriptions of 'n-mal' should be relativized to the speaker-hearer relationship. He employs this view in showing that uses of Ban-mal indeed presuppose the existence of hierarchical structures in human beings I claim that Professor Chung has not shown the credibility of his relational theory of Ban-mal, and that this very view leads to an unintended and undesirable consequence. Moreover, 1 show that even if we assume that this view is true for the sake of an argument, he is not able to show that uses of Ban-mal indeed presuppose the existence of hierarchical structures. All he can show is rather that uses of 'Ban-mal'(a meta-linguistic expression) presuppose the existence of hierarchical structures. Finally, 1 conclude that what really presupposes the existence of hierarchical structures is the asymmetrical uses of Ban-mal and honorific expressions.