• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2004.11a
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• pp.290-295
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• 2004

Along with social changes, school buildings are getting complex and diversified unlike the past. However, objective data analysis on construction costs fall short. In particular, ordering agencies are in a great need of objective and practical construction cost management for on-budget construction and procurement of quality goods. This paper analyzes the design diagram for a newly built school with an order from the Daejeon Metropolitan Office of Education, and compares the analysis with those of other kinds of buildings. The results are: the total construction cost of one school unit is 8,017,596,000 won on average; the cost is in the order of building, machinery and equipment, electricity, communications and civil engineering; as to activity, RC construction takes account of $30.3\%$ of the total construction cost. 1'he cost of school construction per M2 is 838,000 won, which is 6th highest of 11 kinds of constructions and slightly lower than 950,000 won, the average price of comparative constructions. When it comes to the percentage, school building takes mote percentage of the total cost than comparative building while machinery and equipment, electricity and communications takes slightly less percentage. Through simple regression analysis of gross coverage, this paper suggests a model formula with which the total construction cost, construction cost in accordance with activity, how much main construction materials are to be used are predictable.

• Journal of Preventive Medicine and Public Health
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• v.38 no.2
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• pp.208-214
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• 2005

Objectives: This study was performed to examine the factors affecting re-smoking in male workers. Methods: A self-administrated questionnaire survey was conducted during April 2003 to examine the smoking state of 1,154 employees of a company that launched a smoking cessation campaign in1998. Five hundred and eighty seven persons, who had stopped smoking for at least one week, were selected as the final study subjects. This study collected data on smoking cessation success or failure for 6 months, and looked at the factors having an effect on re-smoking within this period. This study employed the Health Belief Model as its theoretical basis. Results: The re-smoking rate of the 587 study subjects who had stopped smoking for at least one week was 44.8% within the 6 month period. In a simple analysis, the re-smoking rates were higher in workers with a low age, on day and night shifts, blue collar, of a low rank, where this was their second attempt at smoking cessation and for those with a shorter job duration (p<0.05). Of the cues to action variables in the Heath Belief Model, re-smoking was significantly related with the perceived susceptibility factor, economic advantages of smoking cessation among the perceived benefits factor, the degree of cessation trial's barrier of the perceived barriers factor, smoking symptom experience, recognition of the degree of harmfulness of environmental tobacco smoke and the existence of chronic disease due to smoking (p<0.05). In the multiple logistic regression analysis for re-smoking, the significant variables were age, perceived susceptibility for disease, economic advantages due to smoking cessation, the perceived barrier for smoking cessation, recognition on the degree of harmfulness of environmental tobacco smoke, the existence of chronic disease due to smoking and the number of attempts at smoking cessation (p<0.05). Conclusion: From the result of this study, for an effective smoking ban policy within the work place, health education that improves the knowledge of the adverse health effects of smoking and the harmfulness of environmental tobacco smoke will be required, as well as counter plans to reduce the barriers for smoking cessation.

• Journal of the Korean Society of Child Welfare
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• no.27
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• pp.121-151
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• 2008

The purpose of this study is to examine the moderated mediation effects of parenting functioning on the relations between deviant peer's influences and delinquent behaviors in adolescence. To investigate the moderated mediation effects, simple slope test and bootstrapping method based on multiple linear regression(MLR) model were used. This study used data from wave 1-2(2003-2004) of the Korea Youth Panel Survey(KYPS). Results showed that poorer parenting increased the probability that adolescents would affiliated with deviant peers, and more association with deviant peers, in turn, was related to delinquency. There was statistical significant interaction between affiliation with deviant peers and parenting in the model for delinquency. It implied that the relation between deviant peers and delinquency depends on the quality of parenting. Finally, indirect effect of earlier parenting on delinquent behavior through affiliation with deviant peers was moderated by later parenting. These results help clarify the conditions under which exposure to parenting behaviors can buffer the negative effect of deviant best friends on delinquent behaviors in adolescence. Practice and policy implications as well as further research topic were discussed to aid the search for highly effective preventive and treatment interventions.

• Pediatric Infection and Vaccine
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• v.17 no.2
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• pp.101-107
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• 2010

Purpose : In evaluation of patients, laboratory results are crucial in determination of a treatment plan. Obtaining venous blood from infants and children is a difficult procedure. Substitution of a capillary blood sample for a venous blood sample has been suggested. However, there are few studies showing mutual correlation between C-reactive protein (CRP) results in capillary and venous blood. This study was designed to determine whether the result of the capillary sample is the same as the result of the venous blood sample. Methods : After informed consent, a pair of venous and fingertip capillary blood samples were simultaneously collected from 100 children. The LC-178CRPTM was used for analysis of capillary blood and the Hitachi 7180 automatic hematology analyzer was used for analysis of venous blood. We compared CRP of both venous and capillary blood samples. Results were analyzed by crosstabulation analysis, simple regression analysis and the Bland Altman Plot method. Results : A close correlation (90.63%) was observed between capillary and venous blood analyzed by crosstabulation analysis. CRP results were similar between the two groups and showed a high coefficient correlation ($\beta$=1.3434, $R^2$=0.9888, P<0.0001) when analyzed by a simple regression model. The average value in venous blood was also higher compared to capillary blood. According to Bland Altman Plot analysis, lab results were measured at a 95% confidence interval. Conclusion : CRP results from capillary blood showed close correlation with venous blood sampling. At present, venous blood sampling is the preferred method. However, due to difficulty in venous blood sampling, capillary sampling could be considered as an alternative technique for use with children.

• Journal of the Korean Society of Radiology
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• v.17 no.2
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• pp.257-265
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• 2023

The purpose of this study was to vaccinate every year according to the general characteristics of COVID-19, whether to vaccinate every year according to the vaccination experience, whether to vaccinate every year according to knowledge/attitude about vaccination, and negative responses to the vaccinate every year In order to understand the factors affecting the vaccination physician every year by identifying the factors of Statistical analysis is based on general characteristics, variables based on vaccination experience, and knowledge/attitudes related to vaccination. The doctor calculates the frequency and percentage, A square test (-test) was performed, and if the chi-square test was significant but the expected frequency was less than 5 for 25% or more, a ratio difference test was performed with Fisher's exact test. Through multiple logistic regression analysis using variables that were significant in simple analysis, a predictive model for future vaccination and the effect size of each independent variable were estimated. As statistical analysis software, SAS 9.4 (SAS Institute Inc., Cary, NC, USA) was used, and because the sample size was not large, the significance level was set at 10%, and when the p-value was less than 0.10, it was interpreted as statistically significant. In the simple logistic regression analysis, the reason why they answered that they would not be vaccinated every year was that they answered 'to prevent infection of family and hospital guests' rather than 'to prevent my infection' as the reason for the vaccination. It was 11.0 times higher and 3.67 times higher in the case of 'for the formation of collective immunity of the local community and the country'. The adverse reactions experienced after the 1st and 2nd vaccination were 8.42 times higher in those who did not experience pain at the injection site than those who did not, 4.00 times higher in those who experienced swelling or redness, and 5.69 times higher in those who experienced joint pain. There was a 5.57 times higher rate of absenteeism annually than those who did not. In addition, the more anxious they felt about vaccination, the more likely they were to not get the vaccine every year by 2.94 times.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.31 no.3
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• pp.158-169
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• 2019

A probabilistic model that can predict the residual useful lifetime of structure is formulated by using the gamma process which is one of the stochastic processes. The formulated stochastic model can take into account both the sampling uncertainty associated with damages measured up to now and the temporal uncertainty of cumulative damage over time. A method estimating several parameters of stochastic model is additionally proposed by introducing of the least square method and the method of moments, so that the age of a structure, the operational environment, and the evolution of damage with time can be considered. Some features related to the residual useful lifetime are firstly investigated into through the sensitivity analysis on parameters under a simple setting of single damage data measured at the current age. The stochastic model are then applied to the rubble-mound breakwater straightforwardly. The parameters of gamma process can be estimated for several experimental data on the damage processes of armor rocks of rubble-mound breakwater. The expected damage levels over time, which are numerically simulated with the estimated parameters, are in very good agreement with those from the flume testing. It has been found from various numerical calculations that the probabilities exceeding the failure limit are converged to the constraint that the model must be satisfied after lasting for a long time from now. Meanwhile, the expected residual useful lifetimes evaluated from the failure probabilities are seen to be different with respect to the behavior of damage history. As the coefficient of variation of cumulative damage is becoming large, in particular, it has been shown that the expected residual useful lifetimes have significant discrepancies from those of the deterministic regression model. This is mainly due to the effect of sampling and temporal uncertainties associated with damage, by which the first time to failure tends to be widely distributed. Therefore, the stochastic model presented in this paper for predicting the residual useful lifetime of structure can properly implement the probabilistic assessment on current damage state of structure as well as take account of the temporal uncertainty of future cumulative damage.

• Journal of Bio-Environment Control
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• v.21 no.3
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• pp.243-246
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• 2012

The study was aimed at the development of the simple linear regression model to estimate the fruit yield of sweet pepper and to support decision-making management for growing sweet pepper crop in Korea. For quantitative analysis of relationship between environmental data and periodical yield of sweet pepper the data obtained from the commercial Venlo-type glasshouse for 2 years. Obtained periodical yield data of five different cultivars and radiation data were accumulated and fitted by linear regression. A significant linear relationship was found between radiation integral and fruit yield, whereas the production per unit of radiation was different between cultivars. The slope of linear regression could indicate as light use efficiency for fruit production ($LUE_F$, $g{\cdot}MJ^{-1}$). $LUE_F$ of 'Ferrari' was $5.85g{\cdot}MJ^{-1}$, 'Fiesta' 5.32 for first year and $4.75g{\cdot}MJ^{-1}$ and for second year, 'President' was $4.66g{\cdot}MJ^{-1}$, 'Cupra' was $3.86g{\cdot}MJ^{-1}$, and 'Boogie' was $6.48g{\cdot}MJ^{-1}$. The amount of light requirement for the unit gram of fruit was between $25.88J{\cdot}g^{-1}$, for 'Cupra' and $15.42J{\cdot}g^{-1}$ for 'Boogie'. Although we found the linear relationship between radiation and fruit yield, $LUE_F$ was varied between cultivars and as well as year. The linear relationship could describe the fruit yield as function of radiation, but it needed more variable to generalization of the production, such as cultivar specifications, temperature, and number of fruits set per plant or unit of ground.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.59-77
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• 2012

This study focuses on predicting which firms will increase capital by issuing new stocks in the near future. Many stakeholders, including banks, credit rating agencies and investors, performs a variety of analyses for firms' growth, profitability, stability, activity, productivity, etc., and regularly report the firms' financial analysis indices. In the paper, we develop predictive models for rights issues using these financial analysis indices and data mining techniques. This study approaches to building the predictive models from the perspective of two different analyses. The first is the analysis period. We divide the analysis period into before and after the IMF financial crisis, and examine whether there is the difference between the two periods. The second is the prediction time. In order to predict when firms increase capital by issuing new stocks, the prediction time is categorized as one year, two years and three years later. Therefore Total six prediction models are developed and analyzed. In this paper, we employ the decision tree technique to build the prediction models for rights issues. The decision tree is the most widely used prediction method which builds decision trees to label or categorize cases into a set of known classes. In contrast to neural networks, logistic regression and SVM, decision tree techniques are well suited for high-dimensional applications and have strong explanation capabilities. There are well-known decision tree induction algorithms such as CHAID, CART, QUEST, C5.0, etc. Among them, we use C5.0 algorithm which is the most recently developed algorithm and yields performance better than other algorithms. We obtained data for the rights issue and financial analysis from TS2000 of Korea Listed Companies Association. A record of financial analysis data is consisted of 89 variables which include 9 growth indices, 30 profitability indices, 23 stability indices, 6 activity indices and 8 productivity indices. For the model building and test, we used 10,925 financial analysis data of total 658 listed firms. PASW Modeler 13 was used to build C5.0 decision trees for the six prediction models. Total 84 variables among financial analysis data are selected as the input variables of each model, and the rights issue status (issued or not issued) is defined as the output variable. To develop prediction models using C5.0 node (Node Options: Output type = Rule set, Use boosting = false, Cross-validate = false, Mode = Simple, Favor = Generality), we used 60% of data for model building and 40% of data for model test. The results of experimental analysis show that the prediction accuracies of data after the IMF financial crisis (59.04% to 60.43%) are about 10 percent higher than ones before IMF financial crisis (68.78% to 71.41%). These results indicate that since the IMF financial crisis, the reliability of financial analysis indices has increased and the firm intention of rights issue has been more obvious. The experiment results also show that the stability-related indices have a major impact on conducting rights issue in the case of short-term prediction. On the other hand, the long-term prediction of conducting rights issue is affected by financial analysis indices on profitability, stability, activity and productivity. All the prediction models include the industry code as one of significant variables. This means that companies in different types of industries show their different types of patterns for rights issue. We conclude that it is desirable for stakeholders to take into account stability-related indices and more various financial analysis indices for short-term prediction and long-term prediction, respectively. The current study has several limitations. First, we need to compare the differences in accuracy by using different data mining techniques such as neural networks, logistic regression and SVM. Second, we are required to develop and to evaluate new prediction models including variables which research in the theory of capital structure has mentioned about the relevance to rights issue.

• Knowledge Management Research
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• v.22 no.4
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• pp.71-85
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• 2021

The purpose of this study is to find out which artificial intelligence methodology is most suitable for creating a foreign exchange rate prediction model using the indicators of bond market and interest rate market. KTBs and MSBs, which are representative products of the Korea bond market, are sold on a large scale when a risk aversion occurs, and in such cases, the USD/KRW exchange rate often rises. When USD liquidity problems occur in the onshore Korean market, the KRW Cross-Currency Swap price in the interest rate market falls, then it plays as a signal to buy USD/KRW in the foreign exchange market. Considering that the price and movement of products traded in the bond market and interest rate market directly or indirectly affect the foreign exchange market, it may be regarded that there is a close and complementary relationship among the three markets. There have been studies that reveal the relationship and correlation between the bond market, interest rate market, and foreign exchange market, but many exchange rate prediction studies in the past have mainly focused on studies based on macroeconomic indicators such as GDP, current account surplus/deficit, and inflation while active research to predict the exchange rate of the foreign exchange market using artificial intelligence based on the bond market and interest rate market indicators has not been conducted yet. This study uses the bond market and interest rate market indicator, runs artificial neural network suitable for nonlinear data analysis, logistic regression suitable for linear data analysis, and decision tree suitable for nonlinear & linear data analysis, and proves that the artificial neural network is the most suitable methodology for predicting the foreign exchange rates which are nonlinear and times series data. Beyond revealing the simple correlation between the bond market, interest rate market, and foreign exchange market, capturing the trading signals between the three markets to reveal the active correlation and prove the mutual organic movement is not only to provide foreign exchange market traders with a new trading model but also to be expected to contribute to increasing the efficiency and the knowledge management of the entire financial market.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.95-108
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• 2011

TThis is a study of the personalization method that intelligently adapts the level of clustering considering purchasing index of a customer. In the e-biz era, many companies gather customers' demographic and transactional information such as age, gender, purchasing date and product category. They use this information to predict customer's preferences or purchasing patterns so that they can provide more customized services to their customers. The previous Customer-Segmentation method provides customized services for each customer group. This method clusters a whole customer set into different groups based on their similarity and builds predictive models for the resulting groups. Thus, it can manage the number of predictive models and also provide more data for the customers who do not have enough data to build a good predictive model by using the data of other similar customers. However, this method often fails to provide highly personalized services to each customer, which is especially important to VIP customers. Furthermore, it clusters the customers who already have a considerable amount of data as well as the customers who only have small amount of data, which causes to increase computational cost unnecessarily without significant performance improvement. The other conventional method called 1-to-1 method provides more customized services than the Customer-Segmentation method for each individual customer since the predictive model are built using only the data for the individual customer. This method not only provides highly personalized services but also builds a relatively simple and less costly model that satisfies with each customer. However, the 1-to-1 method has a limitation that it does not produce a good predictive model when a customer has only a few numbers of data. In other words, if a customer has insufficient number of transactional data then the performance rate of this method deteriorate. In order to overcome the limitations of these two conventional methods, we suggested the new method called Intelligent Customer Segmentation method that provides adaptive personalized services according to the customer's purchasing index. The suggested method clusters customers according to their purchasing index, so that the prediction for the less purchasing customers are based on the data in more intensively clustered groups, and for the VIP customers, who already have a considerable amount of data, clustered to a much lesser extent or not clustered at all. The main idea of this method is that applying clustering technique when the number of transactional data of the target customer is less than the predefined criterion data size. In order to find this criterion number, we suggest the algorithm called sliding window correlation analysis in this study. The algorithm purposes to find the transactional data size that the performance of the 1-to-1 method is radically decreased due to the data sparity. After finding this criterion data size, we apply the conventional 1-to-1 method for the customers who have more data than the criterion and apply clustering technique who have less than this amount until they can use at least the predefined criterion amount of data for model building processes. We apply the two conventional methods and the newly suggested method to Neilsen's beverage purchasing data to predict the purchasing amounts of the customers and the purchasing categories. We use two data mining techniques (Support Vector Machine and Linear Regression) and two types of performance measures (MAE and RMSE) in order to predict two dependent variables as aforementioned. The results show that the suggested Intelligent Customer Segmentation method can outperform the conventional 1-to-1 method in many cases and produces the same level of performances compare with the Customer-Segmentation method spending much less computational cost.