• The Journal of Information Technology
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• v.3 no.1
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• pp.177-189
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• 2000

It was investigated this study how much individual's job stress would be explained by formalized task environment. also, would be explained locus of control as moderate variable. The data used in the study were collected from employees in domestic corporate. After sending the total of 150 questionnaires, 113 responses were returned. Among the returned questionnaires, 9 poor responses were excluded, and the remained 104 copies were analyzed. The final regression model included formalized task environment as statistically significant, but locus of control as statistically non-significant. it was relied on a lack of sample. This study was to open job stress studies up multi level study(individual - organizational level).

• The Journal of Korean Society for School & Community Health Education
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• v.20 no.2
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• pp.109-124
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• 2019

Purpose: The purpose of this study was to compare the happiness level of one-person households according to gender in Korea and identify factors influencing householders' happiness. Methods: This was a descriptive correlational study design using the 2017 Community Health Survey data. The participants were 8,724 male and 16,810 female individuals in one-person households. Complex samples descriptive statistics, cross analysis, general linear model, and multi-order regression were conducted to identify the health status, health behavior, and factors influencing happiness. Results: The mean score of happiness was higher in female than male individuals. The main factors of happiness of male householders were suicide thought experience, subjective health level, and experience of having necessary medical services. The main factors of happiness of female householders were suicide thought experience, household income, depression experience. Conclusion: It is necessary to develop and implement nursing interventions and policies according to priorities for the happiness of one-person householders.

• Computers and Concrete
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• v.24 no.1
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• pp.37-49
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• 2019

In this study, the shear behaviour of reinforced concrete (RC) beams that were retrofitted using precast panels of ultra-high performance fiber reinforced concrete (UHPFRC) is presented. The precast UHPFRC panels were glued to the side surfaces of RC beams using epoxy adhesive in two different configurations: (i) retrofitting two sides, and (ii) retrofitting three sides. Experimental tests on the adhesive bond were conducted to estimate the bond capacity between the UHPFRC and normal concrete. All the specimens were tested in shear under varying levels of shear span-to-depth ratio (a/d=1.0; 1.5). For both types of configuration, the retrofitted specimens exhibited a significant improvement in terms of stiffness, load carrying capacity and failure mode. In addition, the UHPFRC retrofitting panels glued in three-sides shifted the failure from brittle shear to a more ductile flexural failure with enhancing the shear capacity up to 70%. This was more noticeable in beams that were tested with a/d=1.5. An approach for the approximation of the failure capacity of the retrofitted RC beams was evolved using a multi-level regression of the data obtained from the experimental work. The predicted values of strength have been validated by comparing them with the available test data. In addition, a 3-D finite element model (FEM) was developed to estimate the failure load and overall behaviour of the retrofitted beams. The FEM of the retrofitted beams was conducted using the non-linear finite element software ABAQUS.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.

• Korean Journal of Remote Sensing
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• v.22 no.6
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• pp.581-593
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• 2006

Various geological thematic maps have been generated by interpolating sparsely sampled ground survey data and geostatistical kriging that can consider spatial correlation between neighboring data has widely been used. This paper applies multi-variate geostatistical algorithms to integrate secondary information with sparsely sampled ground survey data for geological thematic mapping. Simple kriging with local means and kriging with an external drift are applied among several multi-variate geostatistical algorithms. Two case studies for spatial mapping of groundwater level and grain size have been carried out to illustrate the effectiveness of multi-variate geostatistical algorithms. A digital elevation model and IKONOS remote sensing imagery were used as secondary information in two case studies. Two multi-variate geostatistical algorithms, which can account for both spatial correlation of neighboring data and secondary data, showed smaller prediction errors and more local variations than those of ordinary kriging and linear regression. The benefit of applying the multi-variate geostatistical algorithms, however, depends on sampling density, magnitudes of correlation between primary and secondary data, and spatial correlation of primary data. As a result, the experiment for spatial mapping of grain size in which the effects of those factors were dominant showed that the effect of using the secondary data was relatively small than the experiment for spatial mapping of groundwater level.

• Structural Engineering and Mechanics
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• v.45 no.4
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• pp.543-568
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• 2013

This study explores the floor micro-vibrations induced by the automated guided vehicles (AGVs) in liquid-crystal-display (LCD) factories. The relationships between moving loads and both the vehicle weights and speeds were constructed by a modified Kanai-Tajimi (MKT) power spectral density (PSD) function whose best-fitting parameters were obtained through a regression analysis by using experimental acceleration responses of a small-scale three-span continuous beam model obtained in the laboratory. The AGV induced floor micro-vibrations under various AGV weights and speeds were then assessed by the proposed regressional MKT model. Simulation results indicate that the maximum floor micro-vibrations of the target LCD factory fall within the VC-B and VC-C levels when AGV moves at a lower speed of 1.0 m/s, while they may exceed the acceptable VC-B level when AGV moves at a higher speed of 1.5 m/s. The simulated floor micro-vibration levels are comparable to those of typical LCD factories induced by AGVs moving normally at a speed between 1.0 m/s and 2.0 m/s. Therefore, the numerical algorithm that integrates a simplified sub-structural multi-span continuous beam model and a proposed regressional MKT moving force model can provide a satisfactory prediction of AGV-induced floor micro-vibrations in LCD factories, if proper parameters of the MKT moving force model are adopted.

• Journal of Environmental Health Sciences
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• v.38 no.1
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• pp.42-50
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• 2012

The purpose of this study was to investigate the concentration and distribution characteristics of $PM_{10}$ at high schools classrooms in Ulsan and its surroundings. This study evaluated $PM_{10}$ levels in classrooms at 79 high schools in Ulsan from May 2008 to May 2009. The average $PM_{10}$ level was 63.8 ${\mu}g/m^3$, and the rate of exceeding the statutory maintenance limit was 16.0%. The average $PM_{10}$ level was higher in general schools (64.9 ${\mu}g/m^3$) compared to professional schools (59.2 ${\mu}g/m^3$), and private schools (66.6 ${\mu}g/m^3$) was higher than that of public schools (62.5 ${\mu}g/m^3$). The average $PM_{10}$ level (68.4 ${\mu}g/m^3$) in general classrooms was significantly (p < 0.01) higher than that in multi-purpose ones (54.6 ${\mu}g/m^3$), and first-year student classrooms (73.3 ${\mu}g/m^3$) was significantly (p < 0.05) higher than that in second or third grade ones (67.6 ${\mu}g/m^3$, 51.5 ${\mu}g/m^3$, respectively). The $PM_{10}$ level in schools in Dong-Gu in the vicinity of assorted industrial complexes was higher than that of schools located in other districts around the Ulsan Metropolitan Area. The regression model showed that $PM_{10}$ level was positively associated with number of students and relative humidity.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.2
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• pp.1-14
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• 2021

It is highly challenging to measure the efficiency of electric vehicle charging stations (EVCSs) because factors affecting operational characteristics of EVCSs are time-varying in practice. For the efficiency measurement, environmental factors around the EVCSs can be considered because such factors affect charging behaviors of electric vehicle drivers, resulting in variations of accessibility and attractiveness for the EVCSs. Considering dynamics of the factors, this paper examines the technical efficiency of 622 electric vehicle charging stations in Seoul using data envelopment analysis (DEA). The DEA is formulated as a multi-period output-oriented constant return to scale model. Five inputs including floating population, number of nearby EVCSs, average distance of nearby EVCSs, traffic volume and traffic congestion are considered and the charging frequency of EVCSs is used as the output. The result of efficiency measurement shows that not many EVCSs has most of charging demand at certain periods of time, while the others are facing with anemic charging demand. Tobit regression analyses show that the traffic congestion negatively affects the efficiency of EVCSs, while the traffic volume and the number of nearby EVCSs are positive factors improving the efficiency around EVCSs. We draw some notable characteristics of efficient EVCSs by comparing means of the inputs related to the groups classified by K-means clustering algorithm. This analysis presents that efficient EVCSs can be generally characterized with the high number of nearby EVCSs and low level of the traffic congestion.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.29 no.6
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• pp.14-26
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• 2015

This study has been conducted so as to analyse user's sensibility on lighting method, correlated color temperature and illumination by composing surface light source, which was projected onto a unit side of interior wall, ceiling and floor. 1) As an analyzed results of the sensibility images, it showed that the "snug & tender" value had got higher when the correlated color temperature got lower. And the "energetic, cheerful" value had got higher when the level of illuminance got lower. Furthermore, the "unusual, unique" showed higher value on the illuminated floor circumstance. Finally, the higher correlated color temperature had been, "energetic, cheerful" value also got higher. 2) As a result of multi-regression analysis, it was found that 3000K and 100lx had the biggest influence on 'snug' image while 5,500K, 500lx had the biggest influence on 'energetic' image. In addition, it was found that the illuminated floor had a big influence on 'unusual' image while 500lx had the biggest influence on 'refined' image.

• Journal of Distribution Science
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• v.12 no.5
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• pp.27-32
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• 2014

Purpose - Since Iran's economy is only now developing, and its stock market is only now emerging, we should deal with the relationship between ownership structure and conservative accounting of companies to see whether such a relationship exists in Iran's market. This study aims to investigate the relationship between ownership structure and accounting conservatism of listed companies on the Tehran Stock Exchange. Research design, data, and methodology - All listed companies on the Tehran Stock Exchange, for which the required information financial statements (balance sheet, profit and loss account) could be acquired for the period 2007-2012, were studied. A total of 123 companies from various industries was selected. Results - In order to test the hypotheses, multi variate regression (inter procedure), with their meaningful t- and f-statistics, and a Durbin-Watson autocorrelation model were used. Conclusions - The research results show that the ownership of major shareholders and ownership concentration have a negative significant relationship with accounting conservatism. Therefore, as a significant negative relationship between concentration of ownership and accounting conservatism at the 95% confidence level was found, the second hypothesis was confirmed.