• Journal of the Korean Data and Information Science Society
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• v.17 no.2
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• pp.279-289
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• 2006

This paper concerns with the effects of the specialized projected for the local W university on education which was planned and conducted in October 2004. From the empirical study using the correlation analysis, regression analysis, and structured equation model, we found some results that educational service satisfaction was highly correlated with general instruction factor and hard ware factor less correlated. Also we investigated that university educational service satisfaction was deeply correlated with word of mouth.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2001.06a
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• pp.93-96
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• 2001

Estimation of leaf wetness duration (LWD) facilitates assessment of the likelihood of outbreaks of many crop diseases. Models that estimate LWD may be more convenient and grower-friendly than measuring it with wetness sensors. Empirical models utilizing statistical procedures such as CART (Classification and Regression Tree; Gleason et al., 1994) have estimated LWD with accuracy comparable to that of electronic sensors.(omitted)

• International Commerce and Information Review
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• v.12 no.4
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• pp.151-170
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• 2010

The present study made an empirical analysis for investigating the competitiveness of technology trades in Korea. In particular, the study deduced the correlation between technology export and technology import using the variables of Gross Domestic Expenditure on R&D and Per capita industry value added Productivity and employed fixed effect model in panel linear regression model. It is found that the R&D expenditure of OECD countries made a significant effect on the technology import and the value-added labor productivity made a significant result on both technology export and import. Therefore, it showed that the technology trade in Korea made a sensitive response to labor productivity in OECD countries. By panel analysis, machine, construction, ICT, and service industry affect most on technology export in Korea for recent 5 years. For technology import, electric-electron, chemical, service, and construction industry have significant effects. This study contributed to understanding of industrial characteristics affecting technology trades in Korea and empirical analysis to show correlation between the factors affecting technology trade.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1996.06c
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• pp.219-227
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• 1996

Since the introduction of animal traction technologies(ATT) in many Sub Saharan Africa (SSA) countries, attempts to propagate its widespread use in the continent have suffered several setbacks. Many factors are responsible of this. However, developments in the African economies are believed to be a prominent factor. This study provides empirical evidence of the impact of economic growth on the performance of ATT-in Sub-Saharan Africa(SSa). The analysis uses 1971-1990 time-series data on ATT from botswana. The performance of ATT is measured on the basis of changes year covered under this study. The models used, are a regression model and a trend projection model. Although the regression model is representing a simplified view of the relationship between ATT and economic growth , it takes account of the influence of annual amounts of rainfall. It is concluded that economic growth has had a negative impact on the performance of ATT in Botwana. As the country's Gross Domestic Pro uct (GDP) steadily increased over the period of the analysis, the number of households using ATT declined at a rate of 2.5% per year. The impact of the GDP on ATT was directly associated with increases in the use of tractor, food imports and beef exports. The results have serious policy implications for agricultural development in many African countries that are not capable of sustaining their economic growth.

• Health Policy and Management
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• v.6 no.2
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• pp.58-90
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• 1996

This study provides an empirical analysis of location competition for demand maximization by central place theory among physicians in nonmetropolitan areas of Korea. The results show that the primary care physicians distribute themselves evenly from urban communities to rural communities. The subspecialists, however, cluster together in major cities rather than decentralize themselves in rural counties. This study establishes the three statistical models : Primary care physicians, subspecialist physicians, and total physicians. Two models of primary care physicians and total physicians have a strong significance in multiple regression analysis (p=-.0001). The primary care model explains approximately 45% of the variation and the total physicians model explains approximately 70% of the variation in physician/1,000 population ratios across national counties. The subspecialist physicians model analysze the tobit regreassion because of the left consored and truncated values(57 cases = 0). In all three models, analysis of the coefficiencts for physician centralization degrees in the 0- to 5- and 5- to 10-km rings around the core county reveals each a positive and negative association betwee these degrees and the physician/1,000 population ratios in the core county. Also, the results provide moderate evidence that the relationship between clinic physicians and community hospitals is competitive, and the relationship between clinic physicians and pharmaceutists is synergistic. This suggests that public policy makers and local self-governing bodies must take an active role to ensure procider availability and the regional health planning in all nonmetropolitan areas of Korea.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2000.05a
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• pp.135-143
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• 2000

The COCOMO Ⅱ model is well-suited for the new software development life cycle such as non-sequential and rapid-development processes. The traditional regression approach based on the least square criterion is the most commonly used technique for empirical calibration in the COCOMO Ⅱ model. But it has a few assumptions frequently violated by software engineering data sets. It is true that the source data is also generally imprecise in reporting size, effort, and cost-driver ratings, particularly across different organizations. And that the outlier for the source data is a peculiarity and indicates a data pint To cope with difficulties, in this paper, we propose a new regression method for calibrating COCOMO Ⅱ post-architecture model based on the minimum relative erro(MRE) criterion. The characteristic of the proposed method is insensitive to the extreme values of the data in the empirical calibration. As the experimental results, It is evident that our proposed calibration method MRE was shown to be superior to the traditional regression approach for model calibration, as illustrated by the values obtained for standard deviation(^σ), and prediction at level L PRED(L) measures.

• International journal of steel structures
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• v.18 no.4
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• pp.1125-1138
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• 2018

During earthquakes, braces behave in complex manners because of the asymmetric response nature of their responses in tension and compression. Hollow structural sections (HSS) have been popularly used for braces due to their sectional efficiency in compression. The purpose of this study is to accurately simulate the cyclic behavior of rectangular HSS braces using a computationally efficient numerical model. A conceptually efficient and simple physical theory model is used as a basis model. To improve the accuracy of the model, cyclic beam growth and buckling load, as well as the incidences of local buckling and brace fracture are estimated using empirical equations obtained from regression analyses using test data on rectangular HSS braces. The accuracy of the proposed model is verified by comparing actual and simulated cyclic curves of brace specimens with various slenderness and width-to-thickness ratios.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.136-136
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• 2022

This study investigates the possibility of coupling empirical mode decomposition (EMD) for runoff prediction from machine learning (ML) models. Here, support vector regression (SVR) and convolutional neural network (CNN) were considered for ML algorithms. Precipitation (P), minimum temperature (Tmin), maximum temperature (Tmax) and their intrinsic mode functions (IMF) values were used for input variables at a monthly scale from Jan. 1973 to Dec. 2020 in the Grand river basin, Canada. The support vector machine-recursive feature elimination (SVM-RFE) technique was applied for finding the best combination of predictors among input variables. The results show that the proposed method outperformed the individual performance of SVR and CNN during the training and testing periods in the study area. According to the correlation coefficient (R), the EMD-SVR model outperformed the EMD-CNN model in both training and testing even though the CNN indicated a better performance than the SVR before using IMF values. The EMD-SVR model showed higher improvement in R value (38.7%) than that from the EMD-CNN model (7.1%). It should be noted that the coupled models of EMD-SVR and EMD-CNN represented much higher accuracy in runoff prediction with respect to the considered evaluation indicators, including root mean square error (RMSE) and R values.

• Nuclear Engineering and Technology
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• v.54 no.11
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• pp.4159-4169
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• 2022

Hydrogen mitigation using Passive Autocatalytic Recombiners (PARs) has been widely accepted methodology inside reactor containment of accident struck Nuclear Power Plants. They reduce hydrogen concentration inside reactor containment by recombining it with oxygen from containment air on catalyst surfaces at ambient temperatures. Exothermic heat of reaction drives the product steam upwards, establishing natural convection around PAR, thus invoking homogenisation inside containment. CFD models resolving individual catalyst plate channels of PAR provide good insight about temperature and hydrogen recombination. But very thin catalyst plates compared to large dimensions of the enclosures involved result in intensive calculations. Hence, empirical correlations specific to PARs being modelled are often used in integral containment studies. In this work, an experimentally validated CFD model of PAR has been employed for developing an empirical correlation for Indian PAR. For this purpose, detailed parametric study involving different gas mixture variables at PAR inlet has been performed. For each case, respective values of gas mixture variables at recombiner outlet have been tabulated. The obtained data matrix has then been processed using regression analysis to obtain a set of correlations between inlet and outlet variables. The empirical correlation thus developed, can be easily plugged into commercially available CFD software.

• The Journal of Information Systems
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• v.27 no.1
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• pp.171-191
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• 2018

Purpose Corporate technology leakage is not only monetary loss, but also has a negative impact on the corporate image and further deteriorates sustainable growth. In particular, since SMEs are highly dependent on core technologies compared to large corporations, loss of technology leakage threatens corporate survival. Therefore, it is important for SMEs to "prevent and protect technology leakage". With the recent development of data analysis technology and the opening of public data, it has become possible to discover and proactively detect companies with a high probability of technology leakage based on actual company data. In this study, we try to construct profiles of enterprises with and without technology leakage experience through profiling analysis using data mining techniques. Furthermore, based on this, we propose a classification model that distinguishes companies that are likely to leak technology. Design/methodology/approach This study tries to develop the empirical model for prevention and protection of technology leakage through profiling method which analyzes each SME from the viewpoint of individual. Based on the previous research, we tried to classify many characteristics of SMEs into six categories and to identify the factors influencing the technology leakage of SMEs from the enterprise point of view. Specifically, we divided the 29 SME characteristics into the following six categories: 'firm characteristics', 'organizational characteristics', 'technical characteristics', 'relational characteristics', 'financial characteristics', and 'enterprise core competencies'. Each characteristic was extracted from the questionnaire data of 'Survey of Small and Medium Enterprises Technology' carried out annually by the Government of the Republic of Korea. Since the number of SMEs with experience of technology leakage in questionnaire data was significantly smaller than the other, we made a 1: 1 correspondence with each sample through mixed sampling. We conducted profiling of companies with and without technology leakage experience using decision-tree technique for research data, and derived meaningful variables that can distinguish the two. Then, empirical model for prevention and protection of technology leakage was developed through discriminant analysis and logistic regression analysis. Findings Profiling analysis shows that technology novelty, enterprise technology group, number of intellectual property registrations, product life cycle, technology development infrastructure level(absence of dedicated organization), enterprise core competency(design) and enterprise core competency(process design) help us find SME's technology leakage. We developed the two empirical model for prevention and protection of technology leakage in SMEs using discriminant analysis and logistic regression analysis, and each hit ratio is 65%(discriminant analysis) and 67%(logistic regression analysis).