• Journal of Korean Society for Geospatial Information Science
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• v.18 no.3
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• pp.49-56
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• 2010

The Genetic Algorithm is one of the population surface modelling tool in the field of urban and environmental research based on the gridded population data. Taking the East-Hwasung area as the case, this study first builds a gridded population data based on the GIS databases as well as municipal population survey data. The study then constructs the attribute values of the explanatory variables by way of GIS tools. The regression model constructed with the same variables is also run as a comparative purpose at the same time. It is shown that the GenAlg output predicted as much consistent and meaningful coefficient estimates for the explanatory variables as the regression model, indicating that it is a very useful interdisciplinary research tool to find optimal solutions in urban problems.

• Journal of the Korean Data and Information Science Society
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• v.28 no.6
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• pp.1471-1479
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• 2017

The variability of trade price index of apartment influences on the various aspect, especially economics, social phenomenon, industry, and culture of the country. In this article, the autoregressive error (ARE) model has been considered for analyzing the monthly trading price index of apartment data. About 16 years of the monthly data have been used from September 2001 to May 2017. In the ARE model, six macroeconomic variables are used as the explanatory variables for the rade price index of apartment. The six explanatory variables are mortgage rate, oil import price index, consumer price index, KOSPI stock index, GDP, and GNI. The result has shown that trading price index of apartment explained about 76% by the mortgage rate, and KOSPI stock index.

• The Journal of Fisheries Business Administration
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• v.34 no.2
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• pp.131-139
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• 2003

The products of the quality certificated fisheries which are protected from environmental pollution, decomposition are hygienically safe and convenient for food. However, consumers have not yet understood the recognition of the system of quality certfication so far because of lack of demand on the quality certification fisheries. Above all, to put the system of the quality QC in place sucessfully, to understand the variation of consumer's inclination efficiently, the empirical study must be carried out by both consumer's take part in the market of the quality certificated fisheries products and how much the amount of consumption is in this market. The purpose of this study, under the preconditions where these have limited fisheries items in consumer's inclination survey, is to analyze the demand of QC though the Double Hurdle Model. Explanatory variables included were household characteristics such as housewives' age and education, her job, household income as well as their health perceptions and food purchase behaviors. Survey from 530 household was collected in Pusan City in 2003, of 502 were actually used for empirical analysis. The Double-hurdle framework proved to a better representation of the factors influencing the separate decision participation and consumption levels. According to the results of this study, whether or not, participating In the market of quality certicipating in the market of qualify certificated fisheries products is affected by how much experience and confidence these have got. housewives' having a job or not. Furthermore, the amount of consumption is mostly affected income. This value is attributed to the safety of QC fisheries products in comparison with regular fisheries. Findings suggest that the consumers put substantially high monetary value on safe food, such as high quality fisheries products. Therefore, first of all, legal and institutional systems should be clearly and strictly identified for the QC products.

• Journal of Korean Public Health Nursing
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• v.36 no.3
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• pp.361-374
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• 2022

Purpose: This study applies an ecological model to investigate individual and organizational levels to identify factors influencing the HRQOL of industrial employees. Methods: Totally, 133 industrial workers of a vehicle company were enrolled, who understood the purpose and consented to participate in the study. The collected data were analyzed by frequency, percentage, mean, standard deviation, independent t-test, one-way ANOVA, Scheffe Test and hierarchical regression analysis using the SPSS 20.0 program. Results: Hierarchical regression analysis showed that job Stress(β=-.44, p<.001), and hobbies(β=-.21, p=.013) were the major influencing factors of the Physical Component Summary of HRQOL, which had an additional explanatory power of 11.5%. The influencing factors for the Mental Component Summary of HRQOL were job stress(β=-.43, p<.001), and coronary artery disease(β=.17, p=.034) with an additional explanatory power of 13.5%. Conclusion: Results of this study, reveal that a multidimensional approach based on an ecological model is suitable as a health promotion intervention strategy to improve the HRQOL. We further propose developing a multi-dimensional health promotion program that consider the individual and organizational factors such as job stress, activation of in-house clubs, and assessing and managing of the risk of cerebral and cardiovascular diseases.

• Journal of the Korean Geosynthetics Society
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• v.21 no.2
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• pp.21-30
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• 2022

Shear strength is the most important indicator in the evaluation of rock slope stability. It is generally estimated by comparing the results of existing literature data, back analysis, experiments and etc. There are additional variables related to the state of discontinuity to consider in the shear strength of the rock slope. It is difficult to determine whether these variables exist through drilling, and it is also difficult to find an exact relationship with shear strength. In this study, the data calculated through back analysis were used. The relationship between previously considered variables was applied to deep learning and the possibility for estimating shear strength of rock slope was explored. For comparison, an existing simple linear regression model and a deep learning algorithm, a deep neural network(DNN) model, were used. Although each analysis model derived similar prediction results, the explanatory power of DNN was improved with a small differences.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.2
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• pp.44-53
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• 2023

Recently, serious parking problems are occurring due to the difficulty of securing sufficient parking space, and it may lead to other traffic or social problems. In order to solve the parking problem in areas and districts beyond a certain range, a study on-roads and off-street parking lots reflecting regional characteristics is necessary. Therefore, this study establishing a parking demand calculation model for use as a basic study in establishing on-road and off-road characteristics. In order to conduct the study, Dong-fu, Daegu Metropolitan City was divided into dongs, and parking facilities and parking demand were investigated. The survey time was divided into daytime and nighttime on weekdays, and the types of vehicles were divided into three types: passenger car, small trucks and buses, large trucks and buses. As explanatory variables for calculating parking demand, the total floor area of buildings for each of six purposes was used, including detached houses, apartment houses, neighborhood living facilities, cultural and assembly facilities, business facilities, and sales facilities. As a result of the correlation analysis, among the six explanatory variables, the total area of neighborhood living facilities showed a significant correlation with on- and off-street parking demand. A regression analysis model was constructed using the total area of neighborhood living facilities as an explanatory variable, and statistically significant results were obtained.

• IE interfaces
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• v.23 no.3
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• pp.229-238
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• 2010

Virtual metrology (VM), a critical component of semiconductor manufacturing, is an efficient way of assessing the quality of wafers not actually measured. This is done based on a model between equipment sensor data (obtained for all wafers) and the quality characteristics of wafers actually measured. This paper considers principal component regression (PCR), partial least squares regression (PLSR), kernel PCR (KPCR), and kernel PLSR (KPLSR) as VM models. For each regression model, two cases are considered. One utilizes all explanatory variables in developing a model, and the other selects significant variables using the genetic algorithm (GA). The prediction performances of 8 regression models are compared for the short- and long-term etch process data. It is found among others that the GA-KPLSR model performs best for both types of data. Especially, its prediction ability is within the requirement for the short-term data implying that it can be used to implement VM for real etch processes.

• Journal of Forest and Environmental Science
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• v.24 no.3
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• pp.137-142
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• 2008

In this paper, we apply a logistic regression model to the data of snow damage on sugi (Cryptomeria japonica) occurred in Toyama prefecture (in Japan) in 2004 for estimating the risk probability. In order to specify the factors effecting snow damage, we apply a model selection procedure determining optimal subset of explanatory variables. In this process we consider the following 3 information criteria, 1) Akaike's information criterion, 2) Baysian information criterion, 3) Bias-corrected Akaike's information criterion. For the selected variables, we give a proper interpretation from the viewpoint of natural disaster.

• Journal of Korean Academy of Nursing
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• v.35 no.4
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• pp.737-744
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• 2005

Purpose. This study was done to identify and test a model of the psychosocial variables that influence adherence to therapeutic regimens in patients with hypertension. Method. A convenience sample of 219 patients with hypertension who were enrolled in an outpatient clinic of a cardiovascular center in Korea participated in the study. They completed self-administered questionnaires anonymously. The questionnaire was based on the Social Action Theory model and a literature review. The explanatory model was constructed and tested using structural equation modeling in order to examine the effects within the model. Results. The results of this study showed that perceived self-efficacy was the strongest factor influencing patient adherence in this sample. Adherence to therapeutic regimens in patients with hypertension was influenced by self-efficacy, patient-provider relationship, social support, and depression. Conclusions. Adherence to therapeutic regimens in patients with hypertension was most strongly influenced by self-efficacy. These findings suggest that nursing interventions to promote patient adherence should focus on the promotion of self-efficacy including improvement in patient-provider relationship and social support, and reduction in depression.

• Communications for Statistical Applications and Methods
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• v.29 no.2
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• pp.203-223
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• 2022

We propose a Bayesian approach to cumulative logistic regression model for the ordinal response based on the orthogonal principal components via singular value decomposition considering the multicollinearity among predictors. The advantage of the suggested method is considering dimension reduction and parameter estimation simultaneously. To evaluate the performance of the proposed model we conduct a simulation study with considering a high-dimensional and highly correlated explanatory matrix. Also, we fit the suggested method to a real data concerning sprout- and scab-damaged kernels of wheat and compare it to EM based proportional-odds logistic regression model. Compared to EM based methods, we argue that the proposed model works better for the highly correlated high-dimensional data with providing parameter estimates and provides good predictions.