• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.4
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• pp.65-76
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• 2017

The purpose of this study was to present a prediction model that reflects crime risk area analysis, including factors and spatial characteristics, as a precursor to preparing an alternative plan for crime prevention and design. This analysis of criminal cases in high-risk areas revealed clusters in which approximately 25% of the cases within the study area occurred, distributed evenly throughout the region. This means that using a multiple linear regression model might overestimate the crime rate in some regions and underestimate in others. It also suggests that the number of deserted houses in an analyzed region has a negative relationship with the dependent variable, based on the multiple linear regression model results, and can also have different influences depending on the region. These results reveal that closure signs in a study area affect the dependent variable differently, depending on the region, rather than a simple or direct relationship with the dependent variable, as indicated by the results of the multiple linear regression model.

• Journal of Korean Society for Atmospheric Environment
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• v.10 no.4
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• pp.224-232
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• 1994

A new neural network model has been developed to predict short-term air pollution concentration. In addition, a multiple regression model widely used in statistical analysis was tested. These models were applied for prediction of daily maximum ozone concentration in Seoul during the summer season of 1991. The time periods between May and September 1989 and 1990 were utilized to train set of learning patterns in neural network model, and to estimate multiple regression model. To evaluate the results of the different models, several Performance indices were used. The results indicated that the multiple regression model tended to underpredict the daily maximum ozone concentration with small r$^{2}$(0.38). Also, large errors were found in this model; 21.1 ppb for RMSE, 0.324 for NMSE, and -0.164 for MRE. On the other hand, the results obtained from the neural network model were very promising. Thus, we can know that this model has a prominent efficiency in the adaptive control for the non-linear multi- variable systems such as photochemical oxidants. Also, when the recent new information was added in the neural network model, prediction accuracy was increased. From the new model, the values of RMSE, NMSE and r$^{2}$ were 13.2ppb, 0.089, 0.003 and 0.55 respectively.

• Steel and Composite Structures
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• v.49 no.4
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• pp.419-430
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• 2023

This paper is aimed at investigating the effect of multiple longitudinal stiffeners on the patch loading resistance of slender steel plate girders. Firstly, a numerical study is conducted through geometrically and materially nonlinear analysis with imperfections included (GMNIA), the model is validated with experimental results taken from the literature. The structural responses of girders with multiple longitudinal stiffeners are compared to the one of girders with a single longitudinal stiffener. Thereafter, a patch loading resistance model is developed through machine learning (ML) using symbolic regression (SR). An extensive numerical dataset covering a wide range of bridge girder geometries is employed to fit the resistance model using SR. Finally, the performance of the SR prediction model is evaluated by comparison of the resistances predicted using available formulae from the literature.

• Journal of the East Asian Society of Dietary Life
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• v.17 no.6
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• pp.963-971
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• 2007

This research aimed to determine the relationship between hotel restaurants' SCM activities and their results. The samples are included exclusive high-end hotels located in the seoul area. To analyze the data, frequency analysis, reliability analysis, factor analysis, and regression analysis were applied. Multiple regression analysis showed that SCM activities (${\beta}$=.342, p<.000), information sharing (${\beta}$=.136, p<.006), and cooperative activities (${\beta}$=.120, p<.015) had a significant impact on financial performance. The explanatory power of this model was 14%, and there was statistical significance in the regression model. SCM activities(${\beta}$=.221, p<.000), information sharing (${\beta}$=.475, p<.000), and cooperative activities (${\beta}$=.172, p<.000) also had a significant impact on non-financial performance, and the explanatory power of this model was 29%, with statistical significance in the regression model.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.2
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• pp.137-145
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• 2020

Various elements of Fabrication (FAB), mass production of existing products, new product development and process improvement evaluation might increase the complexity of production process when products are produced at the same time. As a result, complex production operation makes it difficult to predict production capacity of facilities. In this environment, production forecasting is the basic information used for production plan, preventive maintenance, yield management, and new product development. In this paper, we tried to develop a multiple linear regression analysis model in order to improve the existing production capacity forecasting method, which is to estimate production capacity by using a simple trend analysis during short time periods. Specifically, we defined overall equipment effectiveness of facility as a performance measure to represent production capacity. Then, we considered the production capacities of interrelated facilities in the FAB production process during past several weeks as independent regression variables in order to reflect the impact of facility maintenance cycles and production sequences. By applying variable selection methods and selecting only some significant variables, we developed a multiple linear regression forecasting model. Through a numerical experiment, we showed the superiority of the proposed method by obtaining the mean residual error of 3.98%, and improving the previous one by 7.9%.

• Journal of Convergence for Information Technology
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• v.9 no.6
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• pp.1-6
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• 2019

In this study, the development of a weight estimation model of electronic scale with nonlinear characteristics is presented using polynomial regression analysis. The output voltage of the load cell was measured directly using the reference mass. And a polynomial regression model was obtained using the matrix and curve fitting function of MS Office Excel. The weight was measured in 100g units using a load cell electronic scale measuring up to 5kg and the polynomial regression model was obtained. The error was calculated for simple($1^{st}$), $2^{nd}$ and $3^{rd}$ order polynomial regression. To analyze the suitability of the regression function for each model, the coefficient of determination was presented to indicate the correlation between the estimated mass and the measured data. Using the third order polynomial model proposed here, a very accurate model was obtained with a standard deviation of 10g and the determinant coefficient of 1.0. Based on the theory of multi regression model presented here, it can be used in various statistical researches such as weather forecast, new drug development and economic indicators analysis using logistic regression analysis, which has been widely used in artificial intelligence fields.

• Journal of Welding and Joining
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• v.17 no.4
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• pp.85-92
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• 1999

In GMAW, the spatter is generated because of the variation of the arc state. If the arc state is quantitatively assessed, the control method to make the spatter be reduced is able to develop. This study was attempted to develop the optimal model that could estimate the arc state quantitatively. To do this, the generated spatters was captured under the limited welding conditions, and the waveforms of the arc voltage and of the welding current were collected. From the collected waveforms, the waveform factors and their standard deviations were produced, and the linear and non-linear regression models constituted using the factors and their standard deviations are proposed to estimate the arc state. the performance test to the proposed models was practiced. Obtained results are as follow. From the results of correlation analysis between the factors and the amount of the generated spatters, the standard deviations of the waveform factors have more the multiple regression coefficients than the waveform factors. Because the correlation coefficient between T and {TEX}$T_{a}${/TEX}, and s[T] and s[{TEX}$T_{a}${/TEX}] was nearly one, it was found that these factors have the same effect to the spatter generation. In the regression models to estimate the arc state, it was fond that the linear and the non linear models were also consisted of similar factors. In addition, the linear regression model was assessed the optimal model for estimating the arc state because the variance of data was narrow and multiple regression coefficient was highest among the models. But in the welding conditions which the amount of the generated spatters were small, it was found that the non linear regression model had better the estimation performance for the spatter generation than the linear.

• Journal of the Korean Statistical Society
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• v.16 no.2
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• pp.55-70
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• 1987

The principal components regression is one of the substitues for least squares method when there exists multicollinearity in the multiple linear regression model. It is observed graphically that the performance of the principal components regression is strongly dependent upon the values of the parameters. Accordingly, a new deletion criterion which determines proper principal components to be deleted from the analysis is developed and its usefulness is checked by simulations.

• 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.