• The Korean Journal of Applied Statistics
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• v.22 no.4
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• pp.759-770
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• 2009

A problem of separating signals from noises is considered, when they are randomly mixed in the observation. It is assumed that the noise follows a Gaussian distribution and the signal follows a Gamma distribution, thus the underlying distribution of an observation will be a mixture of Gaussian and Gamma distributions. The parameters of the mixture model will be estimated from the EM algorithm. Then the signals and noises will be classified by a fixed threshold approach based on multiple testing using positive false discovery rate and Bayes error. The proposed method is applied to a real optical emission spectroscopy data for the quantitative analysis of inclusions. A simulation is carried out to compare the performance with the existing method using 3 sigma rule.

• Journal of Korean Society of Water and Wastewater
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• v.28 no.4
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• pp.377-389
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• 2014

In recent years, Smart Water Grid (SWG) concept has globally emerged over the last decade and also gained significant recognition in South Korea. Especially, there has been growing interest in water demand forecast and this has led to various studies regarding energy saving and improvement of water supply reliability. In this regard, this study aims to develop a nonlinear ensemble model for hourly water demand forecasting which allow us to estimate uncertainties across different model classes. The concepts was demonstrated through application to observed from water plant (A) in the South Korea. Various statistics (e.g. the efficiency coefficient, the correlation coefficient, the root mean square error, and a maximum error rate) were evaluated to investigate model efficiency. The ensemble based model with an cross-validate prediction procedure showed better predictability for water demand forecasting at different temporal resolutions. In particular, the performance of the ensemble model on hourly water demand data showed promising results against other individual prediction schemes.

• Research in Community and Public Health Nursing
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• v.18 no.2
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• pp.211-222
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• 2007

Purpose: This study was conducted to identify relations among health behaviors, health symptoms and physical fitness of people in the same community. Method: The study was designed as a descriptive survey and the participants were 1,621 healthy people. Data were collected from March to December, 2003 using a structured questionnaire on health behaviors, health symptoms and physical fitness. Data were analyzed by descriptive statistics, $\chi^2$-test, t-test, two-way ANOVA, Pearson correlation coefficients and multiple regression using SPSS/PC. Results: The result of multiple regression showed that sex, age, and BMI were related to physical fitness. In health-related physical fitness, regular exercise was related to cardiorespiratory endurance and flexibility, and daily caffeine intake was related to muscular endurance. In performance-related physical fitness, having breakfast regularly was related to power, daily caffeine intake was related to agility, and regular lifestyle and health symptoms were related to balance. Conclusion: In conclusion, this study suggests that for better physical fitness, nursing strategies and intervention programs should include physical fitness-related factors, and education programs should be developed based on the above parameters to promote positive perception of physical fitness.

• Journal of Korean Society for Quality Management
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• v.32 no.4
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• pp.15-29
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• 2004

This paper is interested in analyzing the CSFs of 3 different kinds of on-line digital products. They are on-line digital products of (1) transaction, (2) relations, and (3) interests. This study first tried to define the on-line digital products based in the above three categories and then to find the factors that might effect the customer acceptability in each product groups. Independent variables used in this study are characters of products, characters of users, and system environments. This paper that on-line survey has been conducted for the study's specialty and the collected resource was analyzed by using the SPSS 10.0K statistics package. Using multiple regression, this study finds that major factors effecting the user acceptability of each group are different from one another. For example, system environment variables such as speediness, system performance effects the customer acceptability of digital products of Interest(game, avartar, etc). Much more then that of products of relations(information service, etc). Therefore empirical findings are suggested in the conclusion.

• The Korean Journal of Applied Statistics
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• v.15 no.2
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• pp.297-310
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• 2002

Liang and Zeger proposed generalized estimating equations(GEE) for analyzing repeated data which is discrete or continuous. GEE model can be extended to model for repeated categorical data and its estimator has asymptotic multivariate normal distribution in large sample sizes. But GEE is based on large sample asymptotic theory. In this paper, we study the properties of GEE estimators for repeated ordinal data in small sample sizes. We generate ordinal repeated measurements for two groups using two methods. Through Monte Carlo simulation studies we investigate the empirical type 1 error rates, powers, relative efficiencies of the GEE estimators, the effect of unequal sample size of two groups, and the performance of variance estimators for polytomous ordinal response variables, especially in small sample sizes.

• Journal of the Korean Society for Nondestructive Testing
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• v.37 no.2
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• pp.106-114
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• 2017

This paper presents an automated subsurface micro void detection technique based on pulsed infrared thermography for inspecting epoxy molding compounds (EMC) used in electronic device packaging. Subsurface micro voids are first detected and visualized by extracting a lock-in amplitude image from raw thermal images. Binary imaging follows to achieve better visualization of subsurface micro voids. A median filter is then applied for removing sparse noise components. The performance of the proposed technique is tested using 36 EMC samples, which have subsurface (below $150{\mu}m{\sim}300{\mu}m$ from the inspection surface) micro voids ($150{\mu}m{\sim}300{\mu}m$ in diameter). The experimental results show that the subsurface micro voids can be successfully detected without causing any damage to the EMC samples, making it suitable for automated online inspection.

• Wind and Structures
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• v.9 no.3
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• pp.217-230
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• 2006

This paper describes a procedure to develop fragility curves for woodframe structures subjected to lateral wind loads. The fragilities are cast in terms of horizontal displacement criteria (maximum drift at the top of the shearwalls). The procedure is illustrated through the development of fragility curves for one and two-story residential woodframe buildings in high wind regions. The structures were analyzed using a monotonic pushover analysis to develop the relationship between displacement and base shear. The base shear values were then transformed to equivalent nominal wind speeds using information on the geometry of the baseline buildings and the wind load equations (and associated parameters) in ASCE 7-02. Displacement vs. equivalent nominal wind speed curves were used to determine the critical wind direction, and Monte Carlo simulation was used along with wind load parameter statistics provided by Ellingwood and Tekie (1999) to construct displacement vs. wind speed curves. Wind speeds corresponding to a presumed limit displacement were used to construct fragility curves. Since the fragilities were fit well using a lognormal CDF and had similar logarithmic standard deviations (${\xi}$), a quick analysis to develop approximate fragilities is possible, and this also is illustrated. Finally, a compound fragility curve, defined as a weighted combination of individual fragilities, is developed.

• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.489-503
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• 2000

Data mining means data analysis and model selection using various types of data in order to explore useful information and knowledge for making decisions. Examples of data mining include scoring for credit analysis of a new customer and scoring for churn management, where the customers with high scores are given special attention. In this paper, scoring is interpreted as a modeling process of the conditional probability and polyclass scoring method is described. German credit data, a PC communication company data and a mobile communication company data are used to compare the performance of polyclass scoring method with that of the scoring method based on a tree model.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.37 no.1
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• pp.76-82
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• 2000

In this paper, we consider nonparametric signal detection problems under the presence of additive noise exhibiting weak dependence We derive the test statistics of the locally optimum rank detectors under a weakly dependent noise model for known and random signal cases The performance characteristic of the locally optimum rank detectors are analyzed in terms of asymptotic relative efficiency.

• Journal of the Korean Home Economics Association
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• v.49 no.6
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• pp.67-83
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• 2011

The purposes of this study were to a develop a preventive tailored early intervention program and to investigate the effects of the program on the quality of parenting for low income families. The subjects were eighty five infants, aged 13 to 32 months, and their mothers. Of the 85 mother-infant dyads, 42 dyads were randomly assigned to the experimental group and 43 dyads to the control group. To prevent the risks of the adverse development of infants from low income families a tailored intervention program was created based upon the transactional model. This emphasized the quality of the home environment and of the daily mother-infant interaction. The risk factors were assessed and progress monitoring was undertaken for the preventive tailored intervention treatment. In order to assess the effects of the program, HOME, the NCAST Teaching Scale and the Parenting Stress Index/Short From were used. The collected data were analyzed with descriptive statistics, ANCOVA, and the McNemar test. The experimental group showed a significant increase in performance and higher scores than the control group on the scores from HOME, and the NCAST Teaching Scale and significant decrease and lower scores in the Parenting Stress Index(SF).