• The KIPS Transactions:PartD
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• v.9D no.4
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• pp.613-618
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• 2002

The component-based development methodology becomes famous as the new technology for reuse. That technology can help us easily develop a complex and large system by composing reusable components in short period with high-quality and low-cost. The component-based system nay be developed by composing more than one component. So, the quality of component-based system is determined by individual component duality. Therefore, it is necessary to measure individual component quality for the improvement in quality of component-based system. Hence, in this paper, we propose new component metrics for measuring the cohesion as relationship between classes and interfaces or among classes. Those can be applied in the early stage of software development life cycle. So, we can measure the functional cohesion of component which will be developed. Predicting functional independence of a component, we expect to reduce the software developing cost & effort and improve software quality by reusing a component.

• The Korean Journal of Applied Statistics
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• v.32 no.4
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• pp.607-618
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• 2019

A new and interesting task in statistics is to effectively analyze functional data that frequently comes from advances in modern science and technology in areas such as meteorology and biomedical sciences. Functional linear regression with scalar response is a popular functional data analysis technique and it is often a common problem to determine a functional association if a functional predictor variable affects the scalar response in the models. Recently, Kong et al. (Journal of Nonparametric Statistics, 28, 813-838, 2016) established classical testing methods for this based on functional principal component analysis (of the functional predictor), that is, the resulting eigenfunctions (as a basis). However, the eigenbasis functions are not generally suitable for regression purpose because they are only concerned with the variability of the functional predictor, not the functional association of interest in testing problems. Additionally, eigenfunctions are to be estimated from data so that estimation errors might be involved in the performance of testing procedures. To circumvent these issues, we propose a testing method based on fixed basis such as B-splines and show that it works well via simulations. It is also illustrated via simulated and real data examples that the proposed testing method provides more effective and intuitive results due to the localization properties of B-splines.

• Communications for Statistical Applications and Methods
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• v.20 no.4
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• pp.271-281
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• 2013

This paper considers a problem of classification of variable stars based on functional data analysis. For a better understanding of galaxy structure and stellar evolution, various approaches for classification of variable stars have been studied. Several features that explain the characteristics of variable stars (such as color index, amplitude, period, and Fourier coefficients) were usually used to classify variable stars. Excluding other factors but focusing only on the curve shapes of variable stars, Deb and Singh (2009) proposed a classification procedure using multivariate principal component analysis. However, this approach is limited to accommodate some features of the light curve data that are unequally spaced in the phase domain and have some functional properties. In this paper, we propose a light curve estimation method that is suitable for functional data analysis, and provide a classification procedure for variable stars that combined the features of a light curve with existing functional data analysis methods. To evaluate its practical applicability, we apply the proposed classification procedure to the data sets of variable stars from the project STellar Astrophysics and Research on Exoplanets (STARE).

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.399-402
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• 2000

The reduction of chip area and delay is important purpose of Scheduling in High-Level Synthesis. This paper presents a scheduling approach with component selection. After obtaining a initial schedule taking only single-functional u-nits, the component selection of our approach attempts the reduction of chip area and/or delay by the selection more suitable components in a component library using Simulated Annealing.

• Molecules and Cells
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• v.42 no.2
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• pp.166-174
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• 2019

Bacterial species in the genus Xanthomonas infect virtually all crop plants. Although many genes involved in Xanthomonas virulence have been identified through molecular and cellular studies, the elucidation of virulence-associated regulatory circuits is still far from complete. Functional gene networks have proven useful in generating hypotheses for genetic factors of biological processes in various species. Here, we present a genome-scale co-functional network of Xanthomonas oryze pv. oryzae (Xoo) genes, XooNet (www.inetbio.org/xoonet/), constructed by integrating heterogeneous types of genomics data derived from Xoo and other bacterial species. XooNet contains 106,000 functional links, which cover approximately 83% of the coding genome. XooNet is highly predictive for diverse biological processes in Xoo and can accurately reconstruct cellular pathways regulated by two-component signaling transduction systems (TCS). XooNet will be a useful in silico research platform for genetic dissection of virulence pathways in Xoo.

• Journal of Korean Society for Quality Management
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• v.22 no.4
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• pp.79-101
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• 1994

In modern manufacturing, a product consists of many components created by different processes. Variations in the individual component dimensions and in the processes may result in unacceptable final assemblies. Thus, engineers have increased pressure to properly set tolerance specifications for individual components and to control manufacturing processes. When a proper variation stack-up analysis is not performed for all of the components in a functional system, all component parts can be within specifications, but the final assembly may not be functional. Thus, in order to improve the performance of the final assembly, a proper variation stack-up analysis is essential for specifying dimensional tolerances and process control. This research provides a detailed case example of the use of variation stack-up analysis using a Monte Carlo simulation method to improve the defect rate of a complex process, which is the commutator brush track undercut process of an armature assembly of a small motor. Variations in individual component dimensions and process mean shifts cause high defect rate, Since some dimensional characteristics have non-normal distributions and the stack-up function is non-linear, the Monte Carlo simulation method is used.

• Journal of the Korean Data and Information Science Society
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• v.21 no.2
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• pp.379-385
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• 2010

This paper considers Bayesian approach to modeling a flexible regression function under functional measurement error model. The regression function is modeled based on semiparametric regression with penalized splines. Model fitting and parameter estimation are carried out in a hierarchical Bayesian framework using Markov chain Monte Carlo methodology. Their performances are compared with those of the estimators under functional measurement error model without semiparametric component.

• Communications for Statistical Applications and Methods
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• v.30 no.3
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• pp.311-330
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• 2023

The present work describes simulation studies to compare the performances in terms of averaged mean squared error of bayesian wavelet shrinkage methods in estimating component curves from aggregated functional data. Five bayesian methods available in the literature were considered to be compared in the studies: The shrinkage rule under logistic prior, shrinkage rule under beta prior, large posterior mode (LPM) method, amplitude-scale invariant Bayes estimator (ABE) and Bayesian adaptive multiresolution smoother (BAMS). The so called Donoho-Johnstone test functions, logit and SpaHet functions were considered as component functions and the scenarios were defined according to different values of sample size and signal to noise ratio in the datasets. It was observed that the signal to noise ratio of the data had impact on the performances of the methods. An application of the methodology and the results to the tecator dataset is also done.

• Journal of the Korean Society of Clothing and Textiles
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• v.38 no.4
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• pp.495-505
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• 2014

This study investigated the type of fashion brand concepts and derived the components of fashion brand concepts. A total of 125 brand concept texts of women's wear brands were collected from "2012/2013 Korea Fashion Brand Annual" (S. M. Kim, 2012). A qualitative research method was employed. To investigate the types of fashion brand concepts, the texts were classified into three types such as functional, symbolic, and experiential concepts, and four complex types such as functional/symbolic, functional/experiential, symbolic/experiential, and functional/symbolic/experiential concepts. Open coding and axial coding provided the components of fashion brand concepts. The results were as follows. First, an investigation of the types of fashion brand concepts indicated differences in the types of fashion brand concepts and the types of general product brand concepts. One content of a fashion brand concept could be interpreted as more than two concept types; consequently, many fashion brand concepts did not fit the notion of the types of general product brand concept. Most fashion brand concepts simultaneously encompassed more than two types of brand concepts at once. Second, the components of fashion brand concepts consisted of 55 subjects, 7 sub-categories (physical/intrinsic product characteristics, symbolic/conceptual product characteristics, target demographics, target consumer behavior, brand capability, brand values, and brand management/marketing) and 3 categories (product, target consumer, and brand).

• Journal of the Korean Society of Physical Medicine
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• v.10 no.2
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• pp.47-54
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• 2015

PURPOSE: The purpose of this study is to examine the relationship between the physical activities of individuals post-stroke and their HRQL, as well as to determine whether their functional abilities contribute to their amounts of physical activity. METHODS: The study's subjects included 90 individuals post-stroke. Their amounts of physical activity were measured using the International Physical Activity Questionnaire (IPAQ), and their HRQL was measured using the Medical Outcomes Study 36-Item Short-form Health Survey (SF-36). In addition, the functional abilities of the subjects were measured. For the measures of physical activities and the HRQL, Pearson's correlation coefficients were used to identify the strengths of the associations between the measures. A hierarchical linear regression model was used to determine whether physical activities had independent impacts on the HRQL. RESULTS: This study found that the physical activities performed by the subjects affected the SF-36 physical component score (PCS) (12%). However, the physical activities and the SF-36 mental component score (MCS) showed no statistically significant relationship, whereas functional abilities and physical activities had a statistically significant relationship (r = .57~.86, p<.001). CONCLUSION: The present study identified a correlation between physical activity and the PCS. Therefore, individuals post-stroke should be encouraged to carry out more physical activities, including more frequent walking activities.