• Journal of Oral Medicine and Pain
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• v.46 no.4
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• pp.125-130
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• 2021

Purpose: The dental age estimation of children is performed using dental maturity. Postmortem missing of the anterior teeth or the distortion of image of the anterior teeth in panoramic radiographs can make it difficult to analyze the development of the anterior teeth. This pilot study was conducted to derive a new age estimation method based only on the developmental stage of mandibular posterior teeth. Methods: This study was conducted using panoramic radiographs of 650 subjects aged 3 to 15 years old. The dental developmental stages of the lower left first premolar, second premolar, first molar and second molar were evaluated according to the Demirjian's criteria. The intra-/inter-observer reliability was evaluated, and multiple linear regression analyses were performed including the developmental stage of each tooth as an independent variable. Results: The intra-/inter-observer reliability was 0.9626 and 0.8877, respectively, and showed very high reproducibility. Multiple linear regression analyses were performed for males and females, and the age calculation table was derived by obtaining the intercept and the coefficient according to the development stage of each tooth. The coefficient of determination (r²) of the age calculation method was 0.9634 for male and 0.9570 for female subjects, and the mean difference between chronological age and estimated dental age was -0.42 and -0.21, respectively. Conclusions: This pilot study evaluated the developmental stages of four lower posterior teeth in the Korean group according to Demirjian's criteria, and derived age estimation method. The accuracy was lower than when more teeth were used, but it will be useful to estimate age of children when the anterior teeth are difficult to accurately analyze.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.1B
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• pp.9-20
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• 2012

Recently to overcome limitations of conceptual, hydrological and physics based models for flood stage forecasting, multiple linear regression model as one of data-driven models have been widely adopted for forecasting flood streamflow(stage). The objectives of this study are to compare performance of different multiple linear regression models according to regression coefficient estimation methods and determine most effective multiple linear regression flood stage forecasting models. To do this, the time scale was determined through the autocorrelation analysis of input data and different flood stage forecasting models developed using regression coefficient estimation methods such as LS(least square), WLS(weighted least square), SPW(stepwise) was applied to flood events in Jungrang stream. To evaluate performance of established models, fours statistical indices were used, namely; Root mean square error(RMSE), Nash Sutcliffe efficiency coefficient (NSEC), mean absolute error (MAE), adjusted coefficient of determination($R^{*2}$). The results show that the flood stage forecasting model using SPW(stepwise) parameter estimation can carry out the river flood stage prediction better in comparison with others, and the flood stage forecasting model using LS(least square) parameter estimation is also found to be slightly better than the flood stage forecasting model using WLS(weighted least square) parameter estimation.

• 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.

• Geomechanics and Engineering
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• v.13 no.2
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• pp.237-254
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• 2017

Estimation of slope stability is a very important task in geotechnical engineering. However, its estimation using conventional and soft computing methods has several drawbacks. Use of conventional limit equilibrium methods for the evaluation of slope stability is very tedious and time consuming, while the use of soft computing approaches like Artificial Neural Networks and Fuzzy Logic are black box approaches. Multiple Regression (MR) analysis provides an alternative to conventional and soft computing methods, for the evaluation of slope stability. MR models provide a simplified equation, which can be used to calculate critical factor of safety of slopes without adopting any iterative procedure, thereby reducing the time and complexity involved in the evaluation of slope stability. In the present study, a multiple regression model has been developed and tested its accuracy in the estimation of slope stability using real field data. Here, two separate multiple regression models have been developed for dry and wet slopes. Further, the accuracy of these developed models have been compared and validated with respect to conventional limit equilibrium methods in terms of Mean Square Error (MSE) & Coefficient of determination ($R^2$). As the developed MR models here are not based on any region specific data and covers wide range of parametric variations, they can be directly applied to any real slopes.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.13 no.5
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• pp.7-15
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• 2004

The purpose of this study was to present the method to choose the optimum machining condition for the wire EDM. This was completed by examining the ever-changing quality of the material and by improving the function of the wire electric discharge machine. Precision metal mold products and the unmanned wire electric discharge machining system were used and then applied in industrial fields. This experiment uses the wire electric discharge machine with brass wire electrode of 0.25mm. To measure the precision of the machining surface, average values are obtained from 3 samples of measures of center-line average roughness by using a third dimension gauge and a stylus surface roughness gauge.

• Journal of Korean Association for Spatial Structures
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• v.23 no.3
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• pp.87-94
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• 2023

Chloride is one of the most common threats to reinforced concrete (RC) durability. Alkaline environment of concrete makes a passive layer on the surface of reinforcement bars that prevents the bar from corrosion. However, when the chloride concentration amount at the reinforcement bar reaches a certain level, deterioration of the passive protection layer occurs, causing corrosion and ultimately reducing the structure's safety and durability. Therefore, understanding the chloride diffusion and its prediction are important to evaluate the safety and durability of RC structure. In this study, the chloride diffusion coefficient is predicted by machine learning techniques. Various machine learning techniques such as multiple linear regression, decision tree, random forest, support vector machine, artificial neural networks, extreme gradient boosting annd k-nearest neighbor were used and accuracy of there models were compared. In order to evaluate the accuracy, root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R2) were used as prediction performance indices. The k-fold cross-validation procedure was used to estimate the performance of machine learning models when making predictions on data not used during training. Grid search was applied to hyperparameter optimization. It has been shown from numerical simulation that ensemble learning methods such as random forest and extreme gradient boosting successfully predicted the chloride diffusion coefficient and artificial neural networks also provided accurate result.

• Asian-Australasian Journal of Animal Sciences
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• v.31 no.10
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• pp.1542-1549
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• 2018

Objective: Because lactation periods in dairy cows lengthen with increasing total milk production, it is important to predict individual productivities after 305 days in milk (DIM) to determine the optimal lactation period. We therefore examined whether the random regression (RR) coefficient from 306 to 450 DIM (M2) can be predicted from those during the first 305 DIM (M1) by using a RR model. Methods: We analyzed test-day milk records from 85,690 Holstein cows in their first lactations and 131,727 cows in their later (second to fifth) lactations. Data in M1 and M2 were analyzed separately by using different single-trait RR animal models. We then performed a multiple regression analysis of the RR coefficients of M2 on those of M1 during the first and later lactations. Results: The first-order Legendre polynomials were practical covariates of RR for the milk yields of M2. All RR coefficients for the additive genetic (AG) effect and the intercept for the permanent environmental (PE) effect of M2 had moderate to strong correlations with the intercept for the AG effect of M1. The coefficients of determination for multiple regression of the combined intercepts for the AG and PE effects of M2 on the coefficients for the AG effect of M1 were moderate to high. The daily milk yields of M2 predicted by using the RR coefficients for the AG effect of M1 were highly correlated with those obtained by using the coefficients of M2. Conclusion: Milk production after 305 DIM can be predicted by using the RR coefficient estimates of the AG effect during the first 305 DIM.

• 한국방재학회:학술대회논문집
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• 2008.02a
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• pp.711-714
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• 2008

The objective of this study is to analyze the relationship between the watershed characteristics and the critical duration of design rainfall. For estimation of critical duration, adjustment Huff's method and ILLUDAS urban runoff model were applied to urban 21 areas. Watershed characteristics such as area, channel length, channel slope, shape factor, and pipe density were used to simulate correlation analysis. The conclusions of this study are as follows; it is revealed that critical duration is influenced by the watershed characteristics such as pipe density, area and channel length. Also, multiple regression analysis using watershed characteristics is carried out and the determination coefficient of multiple regression equation shows 0.972.

• Journal of the Korean Society of Tobacco Science
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• v.35 no.1
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• pp.1-6
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• 2013

Near InfraRed Spectroscopy(NIRS) is a quick and accurate analytical method to measure multiple components in tobacco manufacturing process. This study was carried out to develop calibration equation of near infrared spectroscopy for the prediction of the amount of chemical components and hot water solubles(HWS) of reconstituted tobacco leaf. Calibration samples of reconstituted tobacco leaf were collected from every lot produced during one year. The calibration equation was formulated as modified partial least square regression method (MPLS) by analyzing laboratory actual values and mathematically pre-treated spectra. The accuracy of the acquired equation was confirmed with the standard error of prediction(SEP) of chemical components in reconstituted tobacco leaf samples, indicated as coefficient of determination($R^2$) and prediction error of sample unacquainted, followed by the verification of model equation of laboratory actual values and these predicted results. As a result of monitoring, the standard error of prediction(SEP) were 0.25 % for total sugar, 0.03 % for nicotine, 0.03 % for chlorine, 0.16 % for nitrate, and 0.38 % for hot water solubles. The coefficient of determination($R^2$) were 0.98 for total sugar, 0.97 for nicotine, 0.96 for chlorine, 0.98 for nitrate and 0.92 for hot water solubles. Therefore, the NIRS calibration equation can be applicable and reliable for determination of chemical components of reconstituted tobacco leaf, and NIRS analytical method could be used as a rapid and accurate quality control method.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.55 no.4
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• pp.357-364
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• 2010

Kjeldahl method used in many materials from various plant parts to determine protein contents, is laborious and time-consuming and utilizes hazardous chemicals. Near-infrared (NIR) reflectance spectroscopy, a rapid and environmentally benign technique, was investigated as a potential method for the prediction of protein content. Near-infrared reflectance spectra(1100-2400 nm) of coarse cereal grains(n=100 for each germplasm) were obtained using a dispersive spectrometer as both of grain itself and flour ground, and total protein contents determined according to Kjeldahl method. Using multivariate analysis, a modified partial least-squares model was developed for prediction of protein contents. The model had a multiple coefficient of determination of 0.99, 0.99, 0.99, 0.96 and 0.99 for foxtail millet, sorghum, millet, adzuki bean and mung bean germplasm, respectively. The model was tested with independent validation samples (n=10 for each germplasm). All samples were predicted with the coefficient of determination of 0.99, 0.99, 0.99, 0.91 and 0.99 for foxtail millet, sorghum, millet, adzuki bean and mung bean germplasm, respectively. The results indicate that NIR reflectance spectroscopy is an accurate and efficient tool for determining protein content of diverse coarse cereal germplasm for nutrition labeling of nutritional value. On the other hands appropriate condition of cereal material to predict protein using NIR was flour condition of grains.