• International Journal of Safety
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• v.2 no.1
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• pp.45-49
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• 2003

In this paper, we investigated the relationship between the variables of the industrial accident factors and the manufacturing performances such as production quantity, quality, cost, and delivery. For this investigation, we collected the real data from 30 small/medium-sized manufacturing industries by performing a questionnaire survey and a on-site inter-view with the workers. Thirty industries were made up of 10 from each of the following three industries: metal processing, machinery manufacturing, and chemical products manufacturing. The data analysis was made using SPSS PC+. Based on the result of the analysis, we came to the tentative conclusion that only two variables such as work skill and load affected all four manufacturing performances and the rest of them two or three performances.

• Journal of Korea Technology Innovation Society
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• v.5 no.2
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• pp.129-140
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• 2002

This study is try to do the empirical tests on the relationship among innovation cycle, investment cycle, and business cycle suggested in recent economic growth models. We apply co-spectra analysis to estimate dynamic correlations in the extraction HP filtered variables and first difference filtered variables in our data set. Our empirical results are; (i) an existing asynchronization between innovation cycle and investment cycle, (ii) in the long frequency, an existing positive correlation between innovation cycle and business cycle, (iii) in the short frequency, however, a finding the high negative correlation between the two cycle. Our empirical findings support the recent growth through cycle models and suggest some economic policy implementations for economic stabilization during a severe business cycle.

• Journal of the military operations research society of Korea
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• v.22 no.1
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• pp.114-128
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• 1996

In this paper, a life distribution fitting method based on generalized phase-type distributions(GPH) is presented. By fitting the life distribution to a GPH, we can utilize various useful properties of the GPH. Two different approaches are used according to the properties of the given failure time data. One is an approximation to a GPH through the piecewise Weibull failure rate(PWF) model and the other is a direct approximation to a GPH using the empirical distribution function. Two numerical examples are also presented. In the first example, both of the two approaches are utilized and compared for an incomplete data set. And in the second example, the direct approximation method from an empirical distribution is utilized for the analysis of a complete data set. In both cases, we could confirm the validity of the proposed method.

• Journal of the Korean Data Analysis Society
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• v.20 no.6
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• pp.2733-2746
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• 2018

The two inverse regression estimation methods, SIR and SAVE to estimate the central space are computationally easy and are widely used. However, SIR and SAVE may have poor performance in finite samples and need strong assumptions (linearity and/or constant covariance conditions) on predictors. The two non-parametric estimation methods, MAVE and dMAVE have much better performance for finite samples than SIR and SAVE. MAVE and dMAVE need no strong requirements on predictors or on the response variable. MAVE is focused on estimating the central mean subspace, but dMAVE is to estimate the central space. This paper explores and compares four methods to explain the dimension reduction. Each algorithm of these four methods is reviewed. Empirical study for simulated data shows that MAVE and dMAVE has relatively better performance than SIR and SAVE, regardless of not only different models but also different distributional assumptions of predictors. However, real data example with the binary response demonstrates that SAVE is better than other methods.

• Korean Institute of Interior Design Journal
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• v.20 no.6
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• pp.192-199
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• 2011

The purposes of this study are to present the criteria for a sensibility rating scale for measuring the general women's perception of museums' spatial environment, through an empirical analysis; and to clarify the characteristics of the presented rating scale in terms of each rating element and factor. For this study, a survey was conducted during August 19 - September 16, 2010, and a total of 342 museum visitors participated in the survey. A sensibility rating scale used for the survey consisted of a total of 32 adjectives selected from a literature review of previous studies. To specify the dimensions of semantic space using the semantic adjectives, words with opposite meanings were analyzed with the semantic differential technique developed by Osgood et al. Using SPSS, a reliability analysis, factor analysis were conducted on the data obtained from the survey. The results of this study can be summarized as follows: According to the women's perception of museums' spatial environment, six factors were found from the 25 semantic ratings of the Museum. The summarized criteria were: 'aesthetic', 'pleasant', 'valuable', 'function', 'affinity', and 'material.' The derived criteria were verified through an empirical test using emotional adjectives. In the coming years, the results of this study will serve as valuable data for constructing a sensibility rating scale for evaluating spatial environments of museums.

• Communications for Statistical Applications and Methods
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• v.9 no.2
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• pp.515-520
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• 2002

In general, we use the hierarchical Poisson-gamma model for the Poisson data in generalized linear model. Time effect will be emphasized for the analysis of the observed data to be collected annually for the time period. An extended model with time effect for estimating the effect is proposed. In particularly, we discuss the Quasi likelihood function which is used to numerical approximation for the likelihood function of the parameter.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.07a
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• pp.407-408
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• 2019

This thesis determines the research framework and scale design of leisure food E-Commerce consumer satisfaction degree by referring to previous theoretical model of customer satisfaction degree, the universal satisfaction evaluation index system, and the characteristics of leisure food E-Commerce in China. In this research, consumers who have bought leisure food online are taken as the research object, data are collected by questionnaires, and exploratory factor method is used to screen valid sample data. Through the Empirical Analysis which includes Descriptive Statistical Analysis, Reliability and Validity Analysis and Structural Equation Modeling, it is concluded that website design, logistics delivery service, commodity quality, and after-sales service are the main service quality on which the Leisure food E-Commerce enterprises should take focus. The service quality has significant positive influences on satisfaction degree. On the other hand consumer satisfaction has a significant positive influence on customer loyalty, which will create more earnings for the Leisure food E-Commerce enterprises.

• Knowledge Management Research
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• v.18 no.1
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• pp.67-81
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• 2017

As the important breakthrough continues in the field of machine learning and artificial intelligence recently, there has been a growing interest in the analysis and the utilization of the big data which constitutes a foundation for the field. In this background, while the economic value of the data held by the corporates and public institutions is well recognized, the research on the evaluation of its economic value is still insufficient. Therefore, in this study, as a part of the economic value evaluation of the data, we have conducted the economic value measurement of the data generated through the small business guarantee program of Korean Federation of Credit Guarantee Foundations (KOREG). To this end, by examining the previous research related to the economic value measurement of the data and intangible assets at home and abroad, we established the evaluation methods and conducted the empirical analysis. For the data value measurements in this paper, we used 'cost-based approach', 'revenue-based approach', and 'market-based approach'. In order to secure the reliability of the measured result of economic values generated through each approach, we conducted expert verification with the employees. Also, we derived the major considerations and issues in regards to the economic value measurement of the data. These will be able to contribute to the empirical methods for economic value measurement of the data in the future.

• Tunnel and Underground Space
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• v.6 no.3
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• pp.234-238
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• 1996

Three empirical criteria of rock failure are analyzed in order to understand the meaning of coefficients. Transformation of equations is discussed to apply in the numerical analysis. New failure criterion for intact rocks is proposed in this study, which can be used directly in programming. New equation has the form of parabolic curve($\alpha$=0.5~1.0), and is based on Mohr's shear failure using data from triaxial tests. Its validity will be discussed in the next report.

• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1812-1824
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• 2016

The neural dynamics underlying the causal network during motor planning or imagery in the human brain are not well understood. The lack of signal processing tools suitable for the analysis of nonlinear and nonstationary electroencephalographic (EEG) hinders such analyses. In this study, noise-assisted multivariate empirical mode decomposition (NA-MEMD) is used to estimate the causal inference in the frequency domain, i.e., partial directed coherence (PDC). Natural and intrinsic oscillations corresponding to the motor imagery tasks can be extracted due to the data-driven approach of NA-MEMD, which does not employ predefined basis functions. Simulations based on synthetic data with a time delay between two signals demonstrated that NA-MEMD was the optimal method for estimating the delay between two signals. Furthermore, classification analysis of the motor imagery responses of 29 subjects revealed that NA-MEMD is a prerequisite process for estimating the causal network across multichannel EEG data during mental tasks.