• Journal of the Korea Society of Computer and Information
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• v.12 no.1 s.45
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• pp.231-242
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• 2007

This study was conducted to identify the relation of bottleneck and satisfaction factors in Korean succession companies. The final goal of this paper is finding some strategies and supporting system for Korean succession companies. According to the results of the study, we found four latent variables of the cause variables and two latent variables of the result variables. Three latent variables of the cause variables have an direct effect on two latent variables of the result variables.

• Journal of Korean Society for Quality Management
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• v.39 no.2
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• pp.244-255
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• 2011

In this study, we try to evaluate the efficiency of the photonics industry using a data envelopment analysis(DEA) model. We first develope four stage procedures for selecting proper input and output variables which consist of selecting the first candidate variables from literature survey, selecting the second candidate variables through experts' discussion, measuring the partial efficiency of the selected variables based on Tofallis' profiling, and clustering some variables through the rank correlation analysis of partial efficiency proposed by Min and Kim(l998). With this procedure, we select 4 input variables(capital, number of employee, R&D cost, operating cost) and 2 output variables(sales, growth of sales) and then utilize CCR and BCC model to measure efficiencies of 26 photonics companies in Gwangju. Moreover, we perform the reference group analysis to figure out what causes inefficiencies and to provide the desirable values for input and output variables at which inefficient photonics companies become efficient. Finally, we classify 26 photonics companies into three groups such as optical communications, optical applications, and optical sources, and perform the Kruskal-Wallis test to check if there exist some differences between efficiencies of three groups.

• Communications for Statistical Applications and Methods
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• v.26 no.3
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• pp.261-272
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• 2019

In this paper, we propose a procedure to build a prediction interval of the sum of dependent binary random variables over a graph to account for the dependence among binary variables. Our main interest is to find a prediction interval of the weighted sum of dependent binary random variables indexed by a graph. This problem is motivated by the prediction problem of various elections including Korean National Assembly and US presidential election. Traditional and popular approaches to construct the prediction interval of the seats won by major parties are normal approximation by the CLT and Monte Carlo method by generating many independent Bernoulli random variables assuming that those binary random variables are independent and the success probabilities are known constants. However, in practice, the survey results (also the exit polls) on the election are random and hardly independent to each other. They are more often spatially correlated random variables. To take this into account, we suggest a spatial auto-regressive (AR) model for the surveyed success probabilities, and propose a residual based bootstrap procedure to construct the prediction interval of the sum of the binary outcomes. Finally, we apply the procedure to building the prediction intervals of the number of legislative seats won by each party from the exit poll data in the $19^{th}$ and $20^{th}$ Korea National Assembly elections.

• Journal of Navigation and Port Research
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• v.46 no.2
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• pp.92-98
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• 2022

Maritime accidents result in enormous economic loss and loss of life; thus, such accidents must be prevented, and risks must be managed to prevent these occurrences Risk management must be based on statistical evidence such as variables. Because calculating when variables increase statistically can be difficult, compressing the designated variables is necessary to use the maritime accident data in Korea. Thus, in this study, variables of marine accident data are compressed using statistical methods. The date, ship type, and marine accident type included in all maritime accident data were extracted, the number of optimal variables was confirmed using the hierarchical clustering analysis method, and the data were compressed. For the compressed variables, the validity of the data use was statistically confirmed using analysis of variance, and the data of the variables identified using the variable compression method were designated. Consequently, among the monthly and yearly data, statistical significance was confirmed in yearly data, and compression was possible. The significance of the data was confirmed in six and eight types of ships and accidents, respectively, and these were compressed. These results can be directly used for prevention or prediction based on past maritime accident data. Additionally, the data range extracted from past maritime accidents and the number of applicable data will be studied in the future.

• Journal of the Korean Society of Clothing and Textiles
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• v.48 no.2
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• pp.367-381
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• 2024

The sharing economy (SE) describes an economic system in which consumers share a product or service via collaborative consumption. Reasonably, the SE receives much attention in the fashion industry as a sustainable form of consumption. However, a systematic review of the antecedents and outcomes of consumer participation in SE is limited by researchers' and practitioners' hindered understanding of what consumers expect and gain from the SE. This study offers a systematic review of the SE research conducted from January 2016 to July 2021 and proposes a conceptual model. In terms of antecedents, three factors, composed of nine categories and 153 variables, were identified: 1) consumer factors (80 variables), 2) platform/product factors (69 variables), and 3) environmental factors (4 variables). The outcomes included 14 variables divided into two categories: 1) positive outcomes (8 variables) and 2) negative outcomes (6 variables). The results provide recommendations for future research on applying the SE to the fashion sector. First, to more thoroughly investigating antecedents of consumer participation in fashion sharing, the research must focus on barriers and environmental factors, in addition to demographic and psychological variables. Secondly, research on the outcomes of participating in fashion sharing, including economic and social benefits, is needed.

• Journal of Families and Better Life
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• v.27 no.3
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• pp.239-251
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• 2009

This study aims to examine how much Korean's family value orientations and gender role attitudes are different from those of U.S.A., Sweden, and Japan, and how demographic variables influence family value orientations and gender role attitudes across the countries. By using 2004 Korea General Social Survey data and 2002 International Social Survey Program family module, multiple regression analyses showed that Korean's family value orientations and gender role attitudes were much more traditional than those of U.S.A., Sweden, and Japan, even after controlling demographic variables. Furthermore, each country showed a distinct pattern in the impact of demographic variables on family value orientations and gender role attitudes. Among the demographic variables, age and marital status were statistically significant indicators of family value orientations for all the countries. However, gender, the year of education, and employment status effected on family value orientations only in some countries. The findings of this study showed that Korea was still traditional in terms of family value orientations and gender role attitudes, compared with U.S.A, Sweden, and Japan. Although family value orientations were more traditional in Korea than in the other countries, all the countries showed similar patterns of explaining mechanism in the effect of demographic variables on family value orientations. People who were men and married were likely to be more traditional than those who were women and unmarried. However, gender role attitudes showed interesting results. All the demographic variables were significant predictors of gender role attitudes for Korea, whereas only some of demographic variables were statistically significant indicators of gender role attitudes for other countries. That is, Korean society showed strong attitudinal differences on the basis of demographic variables. The implication of these differences was discussed.

• Journal of Environmental Impact Assessment
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• v.20 no.6
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• pp.891-905
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• 2011

Climate vulnerability index is usually defined as a function of the climate exposure, sensitivity, and adaptive capacity, which requires adequate selection of proxy variables of each variable. We selected and used 9 proxy variables related to climate exposure in the literature, and diagnosed the adequacy of them for application in Korean peninsula. The selected proxy variables are: four variables from temperature, three from precipitation, one from wind speed, and one from relative humidity. We collected climate data over both previous year (1981~2010) and future climate scenario (A1B scenario of IPCC SERES) for 2020, 2050, and 2100. We introduced the spatial and temporal diagnostic statistical parameters, and evaluated both spatial and time variabilities in the relative scale. Of 9 proxy variables, effective humidity indicated the most sensitive to climate change temporally with the biggest spatial variability, implying a good proxy variable in diagnostics of climate change vulnerability in Korea. The second most sensitive variable is the frequency of strong wind speed with a decreasing trend, suggesting that it should be used carefully or may not be of broad utility as a proxy variable in Korea. The A1B scenario of future climate in 2020, 2050 and 2100 matches well with the extension of linear trend of observed variables during 1981~2010, indicating that, except for strong wind speed, the selected proxy variables can be effectively used in calculating the vulnerability index for both past and future climate over Korea. Other local variabilities for the past and future climate in association with climate exposure variables are also discussed here.

• Journal of Korean Society for Quality Management
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• v.39 no.3
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• pp.400-411
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• 2011

Process variables are factors in an experiment that are not mixture components but could affect the blending properties of the mixture ingredients. For example, the effectiveness of an etching solution which is measured as an etch rate is not only a function of the proportions of the three acids that are combined to form the mixture, but also depends on the temperature of the solution and the agitation rate. Efficient designs for the mixture components-process variables experiments depend on the mixture components-process variables model which is called a combined model. We often use the product model between the canonical polynomial model for the mixture and process variables model as a combined model. In this paper we propose three starting models for the mixture components-process variables experiments. One of the starting model we are considering is the model which includes product terms up to cubic order interactions between mixture effects and the linear & pure quadratic effect of the process variables from the product model. In this paper, we propose a method for finding robust designs and practical designs with respect to D-, G-, and I-optimality for the various starting combined models and then, we find practically efficient and robust designs for estimating the regression coefficients for those models. We find the prediction capability of those recommended designs in the case of three components and three process variables to be good by checking FDS(Fraction of Design Space) plots.

• Archives of Plastic Surgery
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• v.38 no.3
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• pp.279-286
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• 2011

Purpose: Chin is located in a prominent position, and is important to balance and harmony of the face. Genioplasty is widely performed with patients' high satisfaction, yet being relatively simple procedure. Recently in analysis of dentofacial trait, three rotational variables of yaw, pith, and roll are considered with three translational variables (forward/backward, up/down, right/left). And we could correct chin deformity effectively by applying the three rotational variables with three translational variables in genioplasty. Methods: Twenty-eight patients who have chin deformity underwent osseous genioplasty. Preoperative photography, facial three dimensional computed tomography, and cephalography were taken while chin deformities were accessed. The chin deformity was classified into four categories; macrogenia, microgenia, asymmetric chin deformity, and combined chin deformity groups. According to the nature of chin deformities and the patients' desire, preoperative plans were formulated, in consideration of three rotational variables and translational variables. Through intraoral approach, anterior mandible was exposed in the subperiosteal plane between the mental foramens and beneath the mental foramens. In the anterior mandible, vertical and horizontal grid lines with 5 mm intervals were marked to confirm the spatial location of osteomized bone segment after osteotomy. Chin repositioning was done in consideration of axial rotation and planar translation. Results: Most of the patients had achieved satisfactory results with few complications. By considering the three rotational variables, it was possible to make the chin repositioning effectively. One of the patients complained about insufficient chin correction. In other case, persistent sensory impairment around chin was observed. Conclusion: In conclusion, it is worthwhile to apply preoperative analysis and operative procedures in consideration of a three rotational variables with three translational variables in genioplasty.

• Journal of the Korea Society for Simulation
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• v.21 no.3
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• pp.71-78
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• 2012

This research discusses the application of the control variables to achieve a more precise estimation for the target response in queueing network simulation. The efficiency of control variable method in estimating the response depends upon how we choose a set of control variables strongly correlated with the response and how we construct a function of selected control variables. For a class of queuing network simulations, the random variables that drive the simulation are basically the service-time and routing probability random variables. Most of applications of control variable method focus on utilization of the service time random variables for constructing a controlled estimator. This research attempts to suggest a controlled estimator which uses these two kinds of random variables and explore the efficiency of these estimators in estimating the reponses for computer network system. Simulation experiments on this model show the promising results for application of routing probability control variables. We consider the applications of the routing probability control variables to various simulation models and combined control variables using information of service time and routing probability together in constructing a control variable as future researches.