• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.6
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• pp.1449-1461
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• 2024

As technology advances, processor speeds are increasing at a rapid pace and digital systems require a significant amount of data bandwidth. As a result, careful consideration of signal integrity is required to ensure reliable and high-speed data processing. Crosstalk has become a vital area of research in signal integrity for electronic packages, mainly because of the high level of integration. Analytic formulas were analyzed in this study to identify the features that can predict crosstalk in multi-conductor transmission lines. Through the analysis, five variables were found and obtained a dataset consisting of 302,500, data points. The study evaluated the performance of various regression models for optimization via automatic machine learning by comparing the machine learning predictions with the analytic solution. Extra tree regression consistently outperformed other algorithms, with coefficients of determination exceeding 0.9 and root mean square logarithmic errors below 0.35. The study also notes that different algorithms produced varied predictions for the two metrics.

• Clinics in Shoulder and Elbow
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• v.16 no.1
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• pp.63-72
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• 2013

In medical research, multivariate analysis, especially multiple regression analysis, is used to analyze the influence of multiple variables on the result. Multiple regression analysis should include variables in the model and the problem of multi-collinearity as there are many variables as well as the basic assumption of regression analysis. The multiple regression model is expressed as the coefficient of determination, $R^2$ and the influence of independent variables on result as a regression coefficient, ${\beta}$. Multiple regression analysis can be divided into multiple linear regression analysis, multiple logistic regression analysis, and Cox regression analysis according to the type of dependent variables (continuous variable, categorical variable (binary logit), and state variable, respectively), and the influence of variables on the result is evaluated by regression coefficient${\beta}$, odds ratio, and hazard ratio, respectively. The knowledge of multivariate analysis enables clinicians to analyze the result accurately and to design the further research efficiently.

• Journal of Korea Water Resources Association
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• v.49 no.5
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• pp.399-410
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• 2016

In case of the application of Hazen-Williams C for design, operation or maintenance of water supply system, field situations always should be reflected on the factors. In this study, the relationships between C factors and influencing factors are analyzed using statistical techniques with 174 measured C factor data collected in periodic inspection for safety diagnosis in multi-regional water supply systems. To analyze their relationships, cross analysis, one-way ANOVA, correlation analysis were conducted. Analysis results showed that C factors had high correlations with both of elapsed year and pipe diameter and were relatively highly affected by coating material among influencing factors with the categorical type. On the other hand, elapsed year, pipe diameter and water type were meaningful influencing factors according to the results of multiple regression analysis. The Cluster analysis revealed that C factors had a tendency of being fundamentally classified on the basis of the elapsed year of about 20 years and the pipe diameter of 1500mm. Although C factors were generally greatly affected by elapsed year, size of pipe diameter relatively had an large influence on values of them in case of large diameter pipes. Lastly, It can be suggested that C factor estimation formulas using multiple regression analysis and clustering analysis in this study, can be applied as decision standards of C factor in multi-regional water supply systems.

• Journal of IKEEE
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• v.20 no.1
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• pp.94-102
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• 2016

This paper proposes a new prediction method to reduce times and labor of repetitive multi-physics simulation. To achieve exact results from the whole simulation processes, complex modeling and huge amounts of time are required. Current multi-physics analysis focuses on the simulation method itself and the simulation environment to reduce times and labor. However this paper proposes an alternative way to reduce simulation times and labor by exploiting machine learning algorithm trained with data set from simulation results. Through comparing each machine learning algorithm, Gaussian Process Regression showed the best performance with under 100 training data and how similar results can be achieved through machine-learning without a complex simulation process. Given trained machine learning algorithm, it's possible to predict the result after changing some features of the simulation model just in a few second. This new method will be helpful to effectively reduce simulation times and labor because it can predict the results before more simulation.

• Journal of Fisheries and Marine Sciences Education
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• v.21 no.3
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• pp.409-419
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• 2009

The purpose of this study was the examination of delinquency according to, narcissism, self-esteem, hostility and alienation of juvenile. For this, setting 172 students of an academic high school and 366 students of a vocational school(total 538) as the object of this study, the measures of Narcissistic Personality Scales, Self-Esteem Scales, Alienation Scales, and Delinquency Scales were inquired. The method of statistical analysis about these materials was composed of Two-way Analysis of Variance, One-way Analysis of Variance, and Multi-regression Analysis by using SPSS 10.0. The result, when delinquency was examined according to narcissism and self-esteem, in the case that narcissism was highest, self-esteem was lowest, delinquency was highest. When delinquency was examined according to narcissism and hostility, in the case that narcissism was highest, hostility was highest, delinquency was highest. When delinquency was examined according to narcissism and alienation, in the case that narcissism was highest, alienation was highest, delinquency was highest. And, when Multi-regression Analysis about the effect of narcissism, self-esteem, hostility and alienation on delinquency was administrated, the variation that affected delinquency significantly was narcissism, hostility and alienation. That is, we could look forward that the more narcissists feel hostility and alienation, the higher they have delinquency.

• 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.

• Journal of Information Technology Applications and Management
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• v.17 no.3
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• pp.135-150
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• 2010

Interactivity has been referred to as an important element promoting students' active participation in virtual classes. Assuming that interactivity cannot be defined by a single dimension, this study proposes multi-dimensional interactivity. Multi-dimensional interactivity includes all types of interactivity in e-learning. This study explored multi-dimensional interactivity which affects learners' satisfaction with e-learning. Data were collected from 132 students who had attended e-learning courses and the relationship between multi-dimensional interactivity and learners' satisfaction levels were tested through regression analysis. The result of this study showed that mechanical, reactive, and creative interactivity were positively related to learners' satisfaction. However, social interactivity seemed not to be related to learners' satisfaction. This study provides new insights on interactivity and verifies the importance of the multi-dimensional interactivity. The result of this study is expected to provide practical implications for interactivity strategies in e-learning.

• Journal of the Korean Home Economics Association
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• v.45 no.1
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• pp.51-61
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• 2007

The purpose of this study was to examine if multi-shop's store image affects store loyalty and brand switching. Two hundred fifty females and males who have purchased fashion products in multi-shop participated in this survey. For data analysis, descriptive statistics, factor analysis, Pearson's correlation and regression analysis were used for this study. The results were as followed. First, respondents' the most favorite multi-shop was MUE, followed by Boon the shop and ABC mart. Second, store image was classified into four factors such as store atmosphere, service of store, store recognition and product variety. Store loyalty was classified into five factors such as emotional relationship, pursue of novelty, trust about salesperson, satisfaction about service, and active loyalty. Third, result revealed that 'product variety' and 'store atmosphere', 'store recognition', 'service of store' accounted for 39.6% of the explained varience in store loyalty, and 'store recognition' accounted for 4% of the explained varience in brand switching behavior, while 'trust about salesperson', 'pursue of novelty' accounted for 5% of the explained varience in brand switching behavior. Based on these results, multi-shop's fashion marketing strategy would be suggested.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.393-393
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• 2021

도시화는 도시 호우 유출 발생으로 인한 수질 악화를 초래했고 문제를 해결하기 위해 본 연구에서는 보다 정확한 설계를 위해 그린인프라(Green Infrastructure, GI)의 구조적 특성과 수문학적인 특성을 이용해 어떤 인자들이 설계에 필요한지 상관관계를 통해 분석하였다. GI의 종류 중 저류지와 저류연못의 총부유사량(Total Suspended Solids, TSS)와 총인 (Total Phosphorous, TP)의 유입수, 유출수, 비점오염원 농도, 수문학적인 특성 그리고 GI의 구조적 특성을 Ordinary Least Squares regression(OLS)과 Multi Linear Regression(MLR) 방법을 적용하였다. GI의 구조적인 특성은 한 BMP마다 달라지지 않으나 호우사상의 데이터 개수에 의한 편향이 있을 수 있다. 이런 문제를 해결하기 위해 일정한 범위를 가지고 무작위로 데이터를 추출하는 방법과 이상치를 제외하는 방법을 사용하여 모델에 적용하였다. 이러한 OLS와 MLR 모델들의 정확도를 PBIAS(Percent Bias), NSE(Nash-Sutcliffe efficiency), RSR(RMSE-observations standard deviation ratio)을 통해 분석할 수 있다. 연구 결과 유입수의 비점오염원의 농도뿐만 아니라 수문학적 특성과 GI의 구조적 특성이 함께 들어갈 시 더 좋은 상관관계를 가지고 있음을 알 수 있다. 저류지가 저류연못보다 모델의 성능평가 면에서 좋은 값을 가지고 있지만 특성별 상관관계는 저류연못이 더 뚜렷한 결과를 보여준다.