• Journal of Electrical Engineering and information Science
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• v.1 no.1
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• pp.45-50
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• 1996

This paper mainly describes the wind load assumption of 765kV transmission towers. We analyzed wind velocity data a meteorological observatories to get the wind velocity of 50 years return period by using Gumbel I type extreme value distribution. By multi-correlative regression analysis method, wind velocity at no observation site was obtained. Reference dynamics wind pressure map was obtained from above analysis and the wind pressure was classified as three regio in high temperature season.

• Journal of the Korean Institute of Landscape Architecture
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• v.29 no.4
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• pp.91-99
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• 2001

This study was carried out to investigate the effects of mowing height, rolling, N-fertilizing, and season on green speed(i.e., ball-roll distance) for developing and implementing a program of increasing green speed in Korean golf courses. Data were subjected to multi-regression analysis using SPSSWIN(Statistical Package for the Social Science), which collected from Yong-Pyong golf course greens selected to investigate. The results was as follows. 1) The multi-regression analysis of mowing height, rolling times, and N-fertilizer application rates on spring green speed was as follows; $Y_1$(spring green speed)=4.287+0.155X$_1$(rolling times)-0.131X$_2$(the amount of N-fertilizing)-0.251X$_3$(mowing height). 2) The multi-regression analysis of mowing height, rolling times, and N-fertilizer application rates on summer green speed was as follows; $Y_2$(summer green speed)=4.833-0.423X$_3$(mowing height)+0.146X$_1$(rolling times)-0.107X$_2$(the amount of N-fertilizing). 3) The multi-regression analysis of mowing height, rolling times, and N-fertilizer application rates on fall green speed was as follows; $Y_3$(fall green speed)=4.651-0.383X$_3$(mowing height)+0.142X$_1$(rolling times)-0.103X$_2$(the amount of N-fertilizing). 4) As mowing height was lowered by 1mm, green speed increased by 0.251~0.423m. As rolling times increased by 1(one), green speed increased by0.142~0.15m. As the amount of N-fertilizing increased by 1g/$m^2$, green speed decreased by 0.103~0.131m. The season also affected green speed. In comparison with spring green speed, summer green speed decreased by 0.145m and fall green speed decreased by 0.144m.

• Proceedings of the ESK Conference
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• 1993.10a
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• pp.124-132
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• 1993

This study compared two prediction methods-regression and artificial neural network (ANN) on the visual search performance when monitoring a multi-parameter screen with different occurrence frequencies. Under the highlighting condition for the highest occurrence frequency parameter as a search cue, it was found from the requression analysis that variations of mean search time (MST) could be expained almost by three factors such as the number of parameters, the target occurrence frequency of a highlighted parameter, and the highlighted parameter size. In this study, prediction performance of ANN was evaluated as an alternative to regression method. Backpropagation method which was commonly used as a pattern associator was employed to learn a search behavior of subjects. For the case of increased number of parameters and incresed target occurrence frequency of a highlighted parameter, ANN predicted MST's moreaccurately than the regression method (p<0.000). Only the MST's predicted by ANN did not statistically differ from the true MST's. For the case of increased highlighted parameter size. both methods failed to predict MST's accurately, but the differences from the true MST were smaller when predicted by ANN than by regression model (p=0.0005). This study shows that ANN is a good predictor of a visual search performance and can substitute the regression method under certain circumstances.

• Journal of Convergence for Information Technology
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• v.11 no.10
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• pp.322-331
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• 2021

This study attempted to identify the multi-cultural acceptance level of freshmen in nursing colleges and to analyze the factors influencing it. For the research method, data were collected from 410 first-year nursing students at K University in W City through a questionnaire from March 1 to 28, 2021, and frequency, reliability analysis, t-test, ANOVA, correlation, and multiple regression were conducted using the open-source statistical package R. As a result of the study, the multi-cultural acceptance level of freshman in nursing colleges averaged 77.36 points, indicating that they have a slightly higher multi-cultural acceptance capacity, and as a result of analyzing the influence of multi-cultural acceptance related factors, Korean recognition requirements(𝛽=0.34, p<.001), perceived threat recognition for migrants (𝛽=0.29, p<.001), Experience in multi-cultural education(𝛽=0.14, p<.001), Recognition of the appropriate age for multi-cultural education (𝛽=0.20, p<.001) was statistically significant. According to results, it is necessary to develop and actively utilize regular curriculum and programs related to multi-culturalism for nursing students.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.5
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• pp.101-108
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• 2019

The domestic duck industry is the sixth-largest among the livestock industries. However, 34.3% of duck houses were the duck houses arbitrarily converted from plastic greenhouses. This type of duck house was difficult to properly manage internal air temperature and humidity environment. Humidity environment inside duck houses is an important factor that directly affects the productivity and disease occurrence of the duck. Although the humidity environments of litters (bedding materials) affect directly the inside environment of duck houses, there are only few studies related to humidity environment of litters. In this study, evaporation rates from litters were evaluated according to air temperature, relative humidity, water contents of litters, and wind speed. The experimental chamber was made to measure evaporation rates from litters. Temperature and humidity controlled chamber was utilized during the conduct of the laboratory experiments. Using the measured data, a multi linear regression analysis was carried out to derive the calculation formula of evaporation rates from litters. In order to improve the accuracy of the multi linear regression model, the partial vapor pressure directly related to evaporation was also considered. Variance inflation factors of air temperature, relative humidity, partial vapor pressure, water contents of litters, and wind speed were calculated to identify multicollinearity problem. The Multiple $R^2$ and adjusted-$R^2$ of regression model were calculated at 0.76 and 0.71, respectively. Therefore, the regression models were developed in this study can be used to estimate evaporation rates from the litter of duck houses.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.173-179
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• 2012

Recently, the requirements of large-dynamic management systems are increasing in multi-PC convergence environment because such system are out. In this research, it has been studied that the regression algorithm was applied instantly for abnormal state and the priority threshold process module was implemented. The system was optimized and become efficiently through the software design and implementation for multi-PC management. As a result, it has been possible for least person and system to manage a lot of requirements.

• Fashion & Textile Research Journal
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• v.10 no.3
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• pp.316-321
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• 2008

The purpose of this study is to find what male consumers value in their clothing behaviors, as well as to investigate how the consumers' shopping values and store VM impact on their clothing purchase behaviors. For data collection, research questionnaires were responded by 202 male students living in Busan. The collected data were analyzed according to the frequency-factor analysis using SPSS for win 10.1 Package, the factor analysis using Varimax, reliability analysis, and multi-regression analysis. The results of this study are as follows; First, the shopping values were composed of hedonic, utilitarian, and economic value, and VM was divided into store facility, store image, layout, and fashion information. Second, multi-regression analysis was conducted to find the impact of consumers' shopping values on their clothing purchase behaviors. The result showed that the hedonic shopping value and utilitarian shopping value significantly affected the consumers' clothing purchase behaviors, while economics shopping value did not show any statistical significance. Third, multi-regression analysis was conducted to find the impact of store VM on consumers' clothing purchase behaviors. The result showed that store image, layout, and fashion information had a significant impact on consumers' clothing purchase behaviors.

• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.38-50
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• 2018

Spatio-temporal load forecasting (STLF) is a foundation for building the prediction-based power map, which could be a useful tool for the visualization and tendency assessment of urban energy application. Constructing one point-forecasting model for each electricity cell in the geographic space is possible; however, it is unadvisable and insufficient, considering the aggregation features of electricity cells and uncertainties in input variables. This paper presents a new STLF method, with a data-driven framework consisting of 3 subroutines: multi-level clustering of cells considering their aggregation features, load regression for each category of cells based on SLS-SVRNs (sparse least squares support vector regression networks), and interval forecasting of spatio-temporal load with sampled blind number. Take some area in Pudong, Shanghai as the region of study. Results of multi-level clustering show that electricity cells in the same category are clustered in geographic space to some extent, which reveals the spatial aggregation feature of cells. For cellular load regression, a comparison has been made with 3 other forecasting methods, indicating the higher accuracy of the proposed method in point-forecasting of spatio-temporal load. Furthermore, results of interval load forecasting demonstrate that the proposed prediction-interval construction method can effectively convey the uncertainties in input variables.

• Genomics & Informatics
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• v.20 no.4
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• pp.48.1-48.7
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• 2022

Penalized regression has been widely used in genome-wide association studies for joint analyses to find genetic associations. Among penalized regression models, the least absolute shrinkage and selection operator (Lasso) method effectively removes some coefficients from the model by shrinking them to zero. To handle group structures, such as genes and pathways, several modified Lasso penalties have been proposed, including group Lasso and sparse group Lasso. Group Lasso ensures sparsity at the level of pre-defined groups, eliminating unimportant groups. Sparse group Lasso performs group selection as in group Lasso, but also performs individual selection as in Lasso. While these sparse methods are useful in high-dimensional genetic studies, interpreting the results with many groups and coefficients is not straightforward. Lasso's results are often expressed as trace plots of regression coefficients. However, few studies have explored the systematic visualization of group information. In this study, we propose a multi-level polar Lasso (MP-Lasso) chart, which can effectively represent the results from group Lasso and sparse group Lasso analyses. An R package to draw MP-Lasso charts was developed. Through a real-world genetic data application, we demonstrated that our MP-Lasso chart package effectively visualizes the results of Lasso, group Lasso, and sparse group Lasso.

• Communications for Statistical Applications and Methods
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• v.14 no.2
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• pp.377-388
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• 2007

As one of multi-class classification methods, ECOC (Error Correcting Output Coding) method is known to have low classification error rate. This paper aims at suggesting effective multi-class classification method (1) by comparing various encoding methods and decoding methods in ECOC method and (2) by comparing ECOC method and direct classification method. Both SVM (Support Vector Machine) and logistic regression model were used as binary classifiers in comparison.