• Genomics & Informatics
• /
• v.18 no.1
• /
• pp.11.1-11.3
• /
• 2020

In genome-wide association studies, pathway-based analysis has been widely performed to enhance interpretation of single-nucleotide polymorphism association results. We proposed a novel method of hierarchical structural component model (HisCoM) for pathway analysis of common variants (HisCoM for pathway analysis of common variants [HisCoM-PCA]) which was used to identify pathways associated with traits. HisCoM-PCA is based on principal component analysis (PCA) for dimensional reduction of single nucleotide polymorphisms in each gene, and the HisCoM for pathway analysis. In this study, we developed a HisCoM-PCA software for the hierarchical pathway analysis of common variants. HisCoM-PCA software has several features. Various principle component scores selection criteria in PCA step can be specified by users who want to summarize common variants at each gene-level by different threshold values. In addition, multiple public pathway databases and customized pathway information can be used to perform pathway analysis. We expect that HisCoM-PCA software will be useful for users to perform powerful pathway analysis.

• The Korean Journal of Applied Statistics
• /
• v.26 no.6
• /
• pp.933-942
• /
• 2013

Common factor analysis and principal component analysis represent two technically distinctive approaches to exploratory factor analysis. Much of the psychometric literature recommends the use of common factor analysis instead of principal component analysis. Nonetheless, factor analysts use principal component analysis more frequently because they believe that principal component analysis could yield (relatively) less accurate estimates of factor loadings compared to common factor analysis but most often produce similar pattern of factor loadings, leading to essentially the same factor interpretations. A simulation study is conducted to evaluate the relative performance of these two approaches in terms of factor pattern recovery under different experimental conditions of sample size, overdetermination, and communality.The results show that principal component analysis performs better in factor recovery with small sample sizes (below 200). It was further shown that this tendency is more prominent when there are a small number of variables per factor. The present results are of practical use for factor analysts in the field of marketing and the social sciences.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.8 no.2
• /
• pp.177-188
• /
• 2012

GIS provides the various analyzing and displaying using diverse spatial data have supported the powerful functionality and friendly user-interface. But, early GIS software is developed as package tool, it have many difficulties with reducing the cost of developing GPS application and satisfying the various user requirements. At present, the developed GPS applications across multiple domains, despite the common features are built separately for each domain in terms of software engineering development followed out waste of time and money expenditure. However, common features between GPS applications, if deployed as a component assembly and reuse components in terms of enabling the two kinds of component-based development can bring out the beneficial results. In this paper, we described the Analysis and design of GPS ApplicationsS based on Component. Each GPS component is composed of many objects accomplish the atomic service processing and cooperate with each other. And, GPS components meets the qualifications of thc low cost of developing GPS application because of the reusability and re-composition.

• Journal of the Korean Data and Information Science Society
• /
• v.19 no.3
• /
• pp.751-759
• /
• 2008

Mixtures of factor analyzers(MFA) is useful to model the distribution of high-dimensional data on much lower dimensional space where the number of observations is very large relative to their dimension. Mixtures of common factor analyzers(MCFA) can reduce further the number of parameters in the specification of the component covariance matrices as the number of classes is not small. Moreover, the factor scores of MCFA can be displayed in low-dimensional space to distinguish the groups. We propose the factor scores of MCFA as new low-dimensional features for classification of high-dimensional data. Compared with the conventional dimension reduction methods such as principal component analysis(PCA) and canonical covariates(CV), the proposed factor score was shown to have higher correct classification rates for three real data sets when it was used in parametric and nonparametric classifiers.

• Korean Institute of Interior Design Journal
• /
• v.20 no.3
• /
• pp.161-171
• /
• 2011

The purpose of this study is to examine the preferences about component elements of Public Space of apartment housing in Daegu. This study is based on the survey of thirty nine apartment complex in seventeen construction companies to create images and component elements types. Also, by using an questionnaire analysis derived from a precedent study, component elements in the porch and interior common space of apartment residents are comprehended. The survey analysis of the porch is researched and divided according to a form of porch, designs and lights of ceiling. Interior common space is analyzed by several elements; forms of core, designs and lights of ceiling, design of walls and floors, necessity of windows, and necessity of public facilities. The questionnaire is composed of 40 items and it was returned by one hundred sixty two respondents. As a result of the study, After the enforcement of price deregulation, the porch and interior common space of apartments has had improved quality. However, by questionnaire survey of respondents showed preferences of each element and necessary facilities. Thus, i hope that this study is an excellent source for apartment design.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.22 no.4
• /
• pp.515-520
• /
• 2012

In brain computer interface (BCI) system, the most important part is classification of human thoughts in order to translate into commands. The more accuracy result in classification the system gets, the more effective BCI system is. To increase the quality of BCI system, we proposed to reduce noise and artifact from the recording data to analyzing data. We used auditory stimuli instead of visual ones to eliminate the eye movement, unwanted visual activation, gaze control. We applied independent component analysis (ICA) algorithm to purify the sources which constructed the raw signals. One of the most famous spatial filter in BCI context is common spatial patterns (CSP), which maximize one class while minimize the other by using covariance matrix. ICA and CSP also do the filter job, as a raw filter and refinement, which increase the classification result of linear discriminant analysis (LDA).

• Journal of KIISE:Software and Applications
• /
• v.29 no.9
• /
• pp.605-618
• /
• 2002

The CBD (Component Based Development) is a requisite technique for the Time-To-Market, and a highly reusable component should be provided to develop a variety of domain applications with the use of components. To increase the reusability of components, they should be developed by analyzing requirements of many different kinds of domains. However, to analyze requirements of a variety of domains related to the components to be developed and to include them inside the components will give burden to developers. Also, providing only general components that have common facilities for the several domains is not easy to accomplish the time-to-market since there are other domains that the developers have to develop. As such, developing common component through the analysis of several domains at the time of the CD (Component Development) does not always guarantee high reusability of the component, but gives burden to developers to develop another development since such components have common functions. Considering this, this paper proposes the component customization technique to reuse common components as well as special components. The reusability of the component can be increased by providing changeability of the attribute, behavior and message flow of the component. This customization technique can change the message flow to integrate developed components or to provide new functions within the component. Also, provides a technique to replace the class existing within the component with other class or to exchange the integrated component with the component having a different function so that requirements from a variety of domains may be satisfied. As such, this technique can accept the requirements of several domains. As such, this customization technique is not only the component with a common function, but it also secures reusability components in the special domain.

• Journal of the Korean Data and Information Science Society
• /
• v.17 no.2
• /
• pp.301-309
• /
• 2006

In this paper, we show the improved fuzzy c-means clustering method. To improve, we use the double clustering as principal component analysis from objects which is located on common region of more than two clusters. In addition we use the degree of membership (probability) of fuzzy c-means which is the advantage. From simulation result, we find some improvement of accuracy in data of the probability 0.7 exterior and interior of overlapped area.

• Asia-Pacific Journal of Business
• /
• v.11 no.4
• /
• pp.37-48
• /
• 2020

Purpose - This study empirically investigates whether the risk property included in fat-tails of return distributions is systematic or unsystematic based on the devised statistical methods. Design/methodology/approach - This study devised empirical designs based on two traditional methods: principal component analysis (PCA) and the testing method of portfolio diversification effect. The fatness of the tails in return distributions is quantitatively measured by statistical probability. Findings - According to the results, the risk property in the fat-tails of return distributions has the economic meanings of eigenvalues having a value greater than 1 through PCA, and also systematic risk that cannot be removed through portfolio diversification. In other words, the fat-tails of return distributions have the properties of the common factors, which may explain the changes of stock returns. Meanwhile, the fatness of the tails in the portfolio return distributions shows the asymmetric relationship of common factors on the tails of return distributions. The negative tail in the portfolio return distribution has a much closer relation with the property of common factors, compared to the positive tail. Research implications or Originality - This empirical evidence may complement the existing studies related to tail risk which is utilized in pricing models as a common factor.

• The Journal of the Korea Contents Association
• /
• v.6 no.1
• /
• pp.54-63
• /
• 2006

In this paper we will propose a domain analysis methodology that uses an extended workflow mechanism based on dynamic modeling to solve problems of a traditional domain analysis on legacy systems. This methodology is called WODA(Workflow Oriented Domain Analysis). Following procedures on WODA, we can identify common/uncommon component, and also extract the cluster of components. It will be effectively reusable on developing new systems with these components. With our proposed component testing metrics, we can determine highly reusable component/scenario on identifying possible scenarios of the particular system. We can also recognize most critical/most frequent reusable components and prioritize possible component scenarios of the system. This paper contains one application of UPS that illustrates our autonomous modeling tool, WODA.