• Korean Journal of Food Science and Technology
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• v.36 no.1
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• pp.75-80
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• 2004

This research was conducted to predict taste of barley kanjang using multiple regression analysis between taste components and sensory score. In the analysis of single correlation, the correlation coefficient of proline, alanine, Methionine, lysine, histidine, lavulinic acid, ${\alpha}$-ketogutaric acid was significant in 5% level. On the other hand, the taste of barley kanjang was not significantly effected by threonine, serine, cystein, phenylalanine, succinic acid, arabinose, xylose, and sucrose. It was impossible to measure taste of kanjang with barley bran to use simple correlation analysis. The 93% of barley kanjang taste was predicted using multiple regression analysis with taste components and sensory evaluation scores.

• Journal of Environmental Science International
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• v.16 no.11
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• pp.1301-1312
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• 2007

The objective of this study is to propose a critical storm duration forecasting model on storm runoff in small river basin. The critical storm duration data of 582 sub-basin which introduced disaster impact assessment report on the National Emergency Management Agency during the period from 2004 to 2007 were collected, analyzed and studied. The stepwise multiple regression method are used to establish critical storm duration forecasting models(Linear and exponential type). The results of multiple regression analysis discriminated the linear type more than exponential type. The results of multiple linear regression analysis between the critical storm duration and 5 basin characteristics parameters such as basin area, main stream length, average slope of main stream, shape factor and CN showed more than 0.75 of correlation in terms of the multi correlation coefficient.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.4
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• pp.249-255
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• 2022

The COVID-19 virus appeared in 2019 and is extremely contagious. Because it is very infectious and has a huge impact on people's mobility. In this paper, multiple linear regression and random forest models are used to predict the number of COVID-19 cases using COVID-19 infection status data (open source data provided by the Ministry of health and welfare) and Google Mobility Data, which can check the liquidity of various categories. The data has been divided into two sets. The first dataset is COVID-19 infection status data and all six variables of Google Mobility Data. The second dataset is COVID-19 infection status data and only two variables of Google Mobility Data: (1) Retail stores and leisure facilities (2) Grocery stores and pharmacies. The models' performance has been compared using the mean absolute error indicator. We also a correlation analysis of the random forest model and the multiple linear regression model.

• Korean Journal of Materials Research
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• v.29 no.12
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• pp.781-789
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• 2019

The prediction of Jominy hardness curves and the effect of alloying elements on the hardenability of boron steels (19 different steels) are investigated using multiple regression analysis. To evaluate the hardenability of boron steels, Jominy end quenching tests are performed. Regardless of the alloy type, lath martensite structure is observed at the quenching end, and ferrite and pearlite structures are detected in the core. Some bainite microstructure also appears in areas where hardness is sharply reduced. Through multiple regression analysis method, the average multiplying factor (regression coefficient) for each alloying element is derived. As a result, B is found to be 6308.6, C is 71.5, Si is 59.4, Mn is 25.5, Ti is 13.8, and Cr is 24.5. The valid concentration ranges of the main alloying elements are 19 ppm < B < 28 ppm, 0.17 < C < 0.27 wt%, 0.19 < Si < 0.30 wt%, 0.75 < Mn < 1.15 wt%, 0.15 < Cr < 0.82 wt%, and 3 < N < 7 ppm. It is possible to predict changes of hardenability and hardness curves based on the above method. In the validation results of the multiple regression analysis, it is confirmed that the measured hardness values are within the error range of the predicted curves, regardless of alloy type.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.60 no.1
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• pp.57-70
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• 2024

In order to present a predictive drift model, Jeju National University's training ship was tested for about 11 hours and 40 minutes, and 81 samples that selected one of the entire samples at ten-minute intervals were subjected to regression analysis after verifying outliers and influence points. In the outlier and influence point analysis, although there is a part where the wind direction exceeds 1 in the DFBETAS (difference in Betas) value, the CV (cumulative variable) value is 6%, close to 1. Therefore, it was judged that there would be no problem in conducting multiple regression analyses on samples. The standard regression coefficient showed how much current and wind affect the dependent variable. It showed that current speed and direction were the most important variables for drift speed and direction, with values of 47.1% and 58.1%, respectively. The analysis showed that the statistical values indicated the fit of the model at the significance level of 0.05 for multiple regression analysis. The multiple correlation coefficients indicating the degree of influence on the dependent variable were 83.2% and 89.0%, respectively. The determination of coefficients were 69.3% and 79.3%, and the adjusted determination of coefficients were 67.6% and 78.3%, respectively. In this study, a more quantitative prediction model will be presented because it is performed after identifying outliers and influence points of sample data before multiple regression analysis. Therefore, many studies will be active in the future by combining them.

• Applied Biological Chemistry
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• v.45 no.1
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• pp.18-24
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• 2002

This study was conducted to find out optimum conditions for kanjang fermented with barley bran. The correlation between taste components and sensory evaluation score was analyzed with stepwise multiple regression analysis. It was revealed that the taste of kanjang was explained with the mix of free amino acids, free sugars and organic acids. The highest multiple correlation coefficient was obtained from absolute value transformed with logarithm. Thus, stepwise multiple regression analysis was conducted with absolute value transformed with logarithm, for which F-value was highest and standard error of estimation was lowest among the multiple regression models transformed with six variables. The stepwise multiple regression analysis showed that the taste components which most contribute to the quality of taste of kanjang fermented with barley bran was salty taste component followed by palatable taste component, and bitter taste component.

• Journal of Korean Society on Water Environment
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• v.29 no.5
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• pp.625-629
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• 2013

The objective of this study is to develop and evaluate simple and multiple regression models for Total Organic Carbon (TOC) concentration estimation in stream flow. For development (using water quality data in 2012) and evaluation (using water quality data in 2011) of regression models, we used water quality data from downstream of Yeongsan river basin during 2011 and 2012, and correlation analysis between TOC and water quality parameters was conducted. The concentrations of TOC were positively correlated with Chemical Oxygen Demand (COD), Biochemical Oxygen Demand (BOD), TN (Total Nitrogen), Water Temperature (WT) and Electric Conductivity (EC). From these results, simple and multiple regression models for TOC estimation were developed as follows : $TOC=0.5809{\times}BOD+3.1557$, $TOC=0.4365{\times}COD+1.3731$. As a result of the application evaluation of the developed regression models, the multiple regression model was found to estimate TOC better than simple regression models.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.1
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• pp.143-151
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• 2015

In this paper, we investigate how the power consumption of a heat pump dryer depends on various factors in the drying process by analyzing variables that affect the power consumption. Since there are in general many variables that affect the power consumption, for a feasible analysis, we utilize the principal component analysis to reduce the number of variables (or dimensionality) to two or three. We find that the first component is correlated positively to the entrance temperature of various devices such as compressor, expander, evaporator, and the second, negatively to condenser. We then model the power consumption as a multiple regression with two and/or three transformed variables of the selected principal components. We find that fitted value from the multiple regression explains 80~90% of the observed value of the power consumption. This results can be applied to a more elaborate control of the power consumption in the heat pump dryer.

• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.3
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• pp.199-212
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• 2012

This study focuses on the influence of followership on leadership and empowerment, and to verify based on the control variables taken in IT SME's to enhance competitiveness through innovation and improvement plan that have been taken. Because there can be a lot of information to be taken, the laws of Moderated Regression Multiple analysis(MMR) were used. Amos, due to the moderating effect of Structural Equation Modeling(SEM) has been employed to re-verify the results seen with Moderated Regression Multiple analysis. The paper focuses on determining whether transformational leadership or transactional leadership is effective as shown by the levels of empowerment derived from these two types of leadership under study. As a result, both the Moderated Regression Multiple analysis and structural equation model searched information on transformational and followership for empowerment having moderating effects. In the Moderated Regression Multiple analysis, results showed that empowerment for leadership in business in the regulation of followership role appeared not to be seen. However, using the structural equation modeling, moderating effects have been found.

• Journal of The Korean Society of Agricultural Engineers
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• v.65 no.3
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• pp.15-28
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• 2023

The compaction properties of the soil change depending on the physical properties, and are also affected by crushing of the particles. Since the particle size distribution of soil affects the engineering properties of the soil, it is necessary to analyze the material properties to understand the compaction characteristics. In this study, the size of each sieve was classified into four in the particle size analysis as a material property, and the compaction characteristics were evaluated by multiple regression and maximum dry unit weight. As a result of maximum dry unit weight prediction, multiple regression analysis showed R² of 0.70 or more, and DNN analysis showed R² of 0.80 or more. The reliability of the prediction result analyzed by DNN was evaluated higher than that of multiple regression, and the analysis result of DNN-T showed improved prediction results by 1.87% than DNN. The prediction of maximum dry unit weight using particle size distribution seems to be applied to evaluate the compacting state by identifying the material characteristics of roads and embankments. In addition, the particle size distribution can be used as a parameter for predicting maximum dry unit weight, and it is expected to be of great help in terms of time and cost of applying it to the compaction state evaluation.