• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.12 no.6
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• pp.461-468
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• 2002

Computer simulation is essential to design the suspension elements of railway vehicle. By computer simulation, engineers can assess the feasibility of the given design factors and change them to get a better design. But if one wishes to perform complex analysis on the simulation, such as railway vehicle dynamic, the computational time can become overwhelming. Therefore, many researchers have used a surrogate model that has a regression model performed on a data sampling of the simulation. In general, metamodels(surrogate model) take the form y($\chi$)＝f($\chi$)＋$\varepsilon$, where y($\chi$) is the true output, f($\chi$) is the metamodel output, and is the error. In this paper, a second order polynomial equation is used as the RSM(response surface model) for high speed train that have twenty-nine design variables and forty-six responses. After the RSM is constructed, multi-objective optimal solutions are achieved by using a nonlinear programming method called VMM(variable matric method) This paper shows that the RSM is a very efficient model to solve the complex optimization problem.

• Journal of Korea Technology Innovation Society
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• v.10 no.4
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• pp.629-653
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• 2007

Korean government drives various programs to improve the performance of university-industry cooperation since 1998 such as BK 21(Brain Korea), NURI(New University for Regional Innovation), Connect Korea Program, and so on. We analyse the relationships between performance of university-industry cooperations and university competency factors(research competency and management competency) through multi-regression model, and propose policy implication. We used the basic data related to the performance of university-industry cooperation and university competency factors from Korean 61 universities. We set up some hypotheses and try to verify them with the method of multi-variable regression analysis including dependent variable(licensing fee, the number of technology transfers, the number of spin-offs) and independent variables(research competency, management competency). We, through this analysis, find both the research competency variables and management competency variables are significant to the performance of university-industry cooperation. Firstly, for licensing fee and the number of technology transfers, research competency variables such as the number of SCIE papers, the number of patent registration were significant, but management competency variables such as the scale of technology leasing organization, the number of specialist were not significant. Secondly, for the number of spin-offs management variables are significant, but research competency varialbles are not. These results imply that both the research competency and management competency of universities are the critical factors for the effective commercialization of university technology not only in United States but also in Korea. In the conclusion, we propose government drive university-industry cooperation policy to enhance the quality of research papers and patent as well as management capabilities of technology leasing organization.

• Biomedical Science Letters
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• v.16 no.3
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• pp.169-177
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• 2010

Osteoporosis is a disease that increases the fracture rates and a major cause of increased mortality and morbidity in the elderly people. This study is to determine which components of body composition and metabolic syndrome risk factors are important to bone health, we analysed the relationship among bone mineral density (BMD), body composition and metabolic syndrome risk factors in females. Totally 630 females participated in a medical check-up program (mean age 47 years) were selected for this study. Body composition analysis was performed by segmental bioelectrical impedance method, muscle mass, and percent body fat were measured. We also measured metabolic syndrome risk factors including abdominal obesity, HDL-cholesterol, triglyceride, blood pressure and fasting glucose level. Metabolic syndrome was defined by NCEP-ATP III criteria. The lumbar spine and femoral neck BMD were measured using the dual energy X-ray absorptiometry. Osteopenia and osteoporosis were observed in 180 and 51 persons, respectively. Muscle mass and HDL-cholesterol decreased in osteopenia and osteoporosis groups compared to the control group, and the grade was shown progressively by the symptoms. Significant positive correlation between BMD and muscle mass was observed. Multi variable regression analyses showed that % body fat and muscle mass were independent predictors of BMD after adjustment of age, height and weight. In conclusion, the BMD showed negative correlation with the metabolic and body composition was associated with BMD.

• The Journal of Korea Robotics Society
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• v.4 no.1
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• pp.10-16
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• 2009

This paper is concerned with Korean students' attitudes towards robots, presenting two survey studies. The first study was concerned with a group of college students, taking the perspective of international comparison. Data were collected by administering an online survey, where 106 volunteer students had participated. In the survey, the Negative Attitude towards Robot Scale(NARS) was adopted to compare the Korean students' scores with those of multi-national groups (U.S.A, Germany, Netherland, Japan, Mexico, and China) who responded to the same scale in Bartneck et al.'s research. The analysis of the data reveals that Korean students tend to be more concerned about social impacts that robots might bring to future society and are very conscious about the uncertain influences of robots on human life. The second study investigated factors that may affect K-12 students' attitudes towards robots, with survey data garnered from 298 elementary, middle, and high school students. The data were analyzed by the method of multiple regression analysis to test the hypothesis that a student's gender, age, the extent of interest in robots, and the extent of experiences with robots may influence his or her attitude towards robots. The hypothesis was partially supported in that variables of a student's gender, age, and the extent of interest in robots were statistically significant with regard to the attitude variable. Given the results, this paper suggests three points of discussions to better understand Korean students' attitudes towards robots: social and cultural context, individual differences, and theory of mind.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.05a
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• pp.1086-1092
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• 2002

Computer simulation is essential to design the suspension elements of railway vehicle. By computer simulation, engineers can assess the feasibility of a given design factors and change them to get a better design. But if one wishes to perform complex analysis on the simulation, such as railway vehicle dynamic, the computational time can become overwhelming. Therefore, many researchers have used a mega model that has a regression model made by sampling data through simulation. In this paper, the neural network is used a mega model that have twenty-nine design variables and forty-six responses. After this mega model is constructed, multi-objective optimal solutions are achieved by using the differential evolution. This paper shows that this optimization method using the neural network and the differential evolution is a very efficient tool to solve the complex optimization problem.

• Journal of Ocean Engineering and Technology
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• v.35 no.4
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• pp.257-265
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• 2021

Many studies have been performed to predict a reliable and accurate stress-range distribution and fatigue damage regarding the Gaussian wide-band stress response due to multi-peak waves and multiple dynamic loads. So far, most of the approximation models provide slightly inaccurate results in comparison with the rain-flow counting method as an exact solution. A step-by-step study was carried out to develop new approximate spectral moments that are close to the rain-flow counting moment, which can be used for the development of a fatigue damage model. Using the special parameters and bandwidth parameters, four kinds of parameter-based combinations were constructed and estimated using the R-squared values from regression analysis. Based on the results, four candidate empirical formulas were determined and compared with the rain-flow counting moment, probability density function, and root mean square (RMS) value for relative distance. The new approximate spectral moments were finally decided through comparison studies of eight response spectra. The new spectral moments presented in this study could play an important role in improving the accuracy of fatigue damage model development. The present study shows that the new approximate moment is a very important variable for the enhancement of Gaussian wide-band fatigue damage assessment.

• Journal of the Korean Geographical Society
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• v.49 no.3
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• pp.405-422
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• 2014

Two important issues in hedonic model are to specify accurate model and delineate submarkets. While the former has experienced much improvement over recent decades, the latter has received relatively little attention. However, the accuracy of estimates from hedonic model will be necessarily reduced when the analysis does not adequately address market segmentation which can capture the spatial scale of price formation process in real estate. Placing emphasis on improvement of performance in hedonic model, this paper tried to segment real estate markets in Gangnam-gu and Jungrang-gu, which correspond to most heterogeneous and homogeneous ones respectively in 25 autonomous districts of Seoul. First, we calculated variable coefficients from mixed geographically weighted regression model (mixed GWR model) as input for clustering, since the coefficient from hedonic model can be interpreted as shadow price of attributes constituting real estate. After that, we developed a spatially constrained data-driven methodology to preserve spatial contiguity by utilizing the SKATER algorithm based on a minimum spanning tree. Finally, the performance of this method was verified by applying a multi-level model. We concluded that submarket does not exist in Jungrang-gu and five submarkets centered on arterial roads would be reasonable in Gangnam-gu. Urban infrastructure such as arterial roads has not been considered an important factor for delineating submarkets until now, but it was found empirically that they play a key role in market segmentation.

• Korean Journal of Veterinary Service
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• v.32 no.3
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• pp.265-273
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• 2009

A cross-sectional survey was undertaken to identify risk factors and clinical signs associated with parasitic helminth infections of cattle in Mymensignh district of Bangladesh. A nonrandom convenience sampling method was used to select 138 animals from 40 farmers/herds. The eggs per gram of faeces (epg) for nematodes and trematodes were determined by McMaster and Stoll's methods respectively. Animal-level and herd-level data were recorded by means of a questionnaire. Multi-collinearity amongst explanatory variables were assessed using $2{\times}2{\times}\;X^2$ test and one variable in a pair was dropped if $P{\leq}0.05$ formultiple logistic regression models. Association study between outcome and explanatory variables was conducted using classification tree, random forests and multiple logistic regression. A positive epg was considered as infected. Analyses were performed using $STATA^{(R)}$, version 8.0/Intercooled and $R^{(R)}$, Version 2.3.0. Seventy eight percent of the cattle were found to be infected with at least one type of helminth. Twenty four pairs of combinations of explanatory variables showed significant associations. Male animals (OR=3.3, P=.006, 95% CI=1.4, 7.7) were associated with significantly increased prevalence of nematode infection. Female cattle of the study area are mostly cross-breed, kept indoor, fed relatively good diet and not used for draught purpose. Males are used for draught purpose thereby more exposed to nematode infective stage and provided with relatively poor diet. So stressed male cattle may become more susceptible to nematode infection. All of the three statistical techniques selected gender and lumen motility as most important variables in association with nematode infection in cattle. The result of this survey can only be extrapolated to the periurban cattle population of traditional management system.

• Ocean and Polar Research
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• v.40 no.1
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• pp.1-13
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• 2018

Previous studies have indicated a great regional difference in Sea Level Rise (SLR) in the Pacific and it has been suggested that this is linked to climate variability over the past two decades. In this study, we seek to identify the possible linkage between regional sea level and Pacific climate variability from altimetry-based sea level data (1993-2012) and further investigate how the Pacific sea level has changed spatially and temporally over the past 60 years from long-term sea level reconstruction data (1953-2008). Based on the same method as Zhang and Church (2012), the Inter-annual Climate Index (ICI) associated with the El $Ni{\tilde{n}}o-Southern$ Oscillation (ENSO) and the Decadal Climate Index (DCI) associated with Pacific Decadal Oscillation (PDO) are defined and then the multiple variable linear regression is used to analyze quantitatively the impact of inter-annual and decadal climate variability on the regional sea levels in the Pacific. During the altimeter period, the ICI that represents ENSO influence on inter-annual time scales strongly impacts in a striking east-west "see-saw mode" on sea levels across the tropical Pacific. On the other hand, the decadal sea level pattern that is linked to the DCI has a broad meridional structure that is roughly symmetric in the equator with its North Pacific expression being similar to the PDO, which largely contributes to a positive SLR trend in the western Pacific and a negative trend in the eastern Pacific over the two most recent decades. Using long-term sea level reconstruction data, we found that the Pacific sea levels have fluctuated in the past over inter-annual and decadal time scales and that strong regional differences are presented. Of particular interest is that the SLR reveals a decadal shift and presents an opposite trend before and after the mid-1980s; i.e., a declining (rising) trend in the western (eastern) Pacific before the mid-1980s, followed by a rising (declining) trend from the mid-1980s onward in the western (eastern) Pacific. This result indicates that the recent SLR patterns revealed from the altimeters have been persistent at least since the mid-1980s.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.23-45
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• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.