• 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.

• 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 Society of Agricultural Engineers
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• v.59 no.1
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• pp.11-20
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• 2017

This study is to estimate the spatial soil moisture using multiple linear regression model (MLRM) and 15 minutes interval Land Surface Temperature (LST) data of Communication, Ocean and Meteorological Satellite (COMS). For the modeling, the input data of COMS LST, Terra MODIS Normalized Difference Vegetation Index (NDVI), daily rainfall and sunshine hour were considered and prepared. Using the observed soil moisture data at 9 stations of Automated Agriculture Observing System (AAOS) from January 2013 to May 2015, the MLRMs were developed by twelve scenarios of input components combination. The model results showed that the correlation between observed and modelled soil moisture increased when using antecedent rainfalls before the soil moisture simulation day. In addition, the correlation increased more when the model coefficients were evaluated by seasonal base. This was from the reverse correlation between MODIS NDVI and soil moisture in spring and autumn season.

• Communications for Statistical Applications and Methods
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• v.26 no.6
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• pp.539-556
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• 2019

This paper derive a method to solve change point regression problems via a process for obtaining consequential results using properties of a difference-based intercept estimator first introduced by Park and Kim (Communications in Statistics - Theory Methods, 2019) for outlier detection in multiple linear regression models. We describe the statistical properties of the difference-based regression model in a piecewise simple linear regression model and then propose an efficient algorithm for change point detection. We illustrate the merits of our proposed method in the light of comparison with several existing methods under simulation studies and real data analysis. This methodology is quite valuable, "no matter what regression lines" and "no matter what the number of change points".

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2008.11a
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• pp.922-925
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• 2008

Many highway projects are under way in Korea. However, owners frequently find that the project cost exceeds the budget and they are unable to identify the underlining reasons. The main purpose of this research is to develop cost models for transportation projects in Korea using the multiple linear regression (MLR). The data consist of 27 completed transportation projects, built from 1991 to 2001, The technique of multiple regression analysis is used to develop the parametric cost estimating model for total budget cost per highway square meter (TBC/$m^2$). Findings of the study indicated that MLR car be applied to highway projects in Korea. There are twf) major contributions of this research. (1) the identification of transportation parameters as a significant cost driver for transportation costs and (2) the successful development of the parametric cost estimating models for transportation projects in Korea.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.11 no.4
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• pp.89-96
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• 2002

This paper aims to develop an intelligent model for predicting top-bead width for the robotic GMA(Gas Metal Arc) welding process using BP(Back-propagation) neural network and multiple regression analysis. Firstly, based on experimental data, the basic factors affecting top-bead width are identified. Then BP neural network model and multiple regression models of top-bead width are established. The modeling methods and procedure are explained. The developed models are then verified by data obtained from the additional experiment and the predictive behaviors of the two kind of models are compared and analysed. Finally the modeling methods, predictive behaviors md the advantages of each models are discussed.

• Journal of Satellite, Information and Communications
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• v.9 no.4
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• pp.13-15
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• 2014

At frequencies above 10 GHz, rain is a dominant propagation phenomenon on satellite link attenuation. The prediction of rain attenuation is based on the point rainfall rate for 0.01 % of an average year with one minute integration time. Most of available rain data have been measured with 60 minutes integration time, and many researchers have been studying on converting the rainfall rate data from various integration times to one minute integration time. This paper proposes a new Multiple Regression model for the conversion, and the proposed schemes show better performance than the existing schemes.

• Journal of the Korean Data and Information Science Society
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• v.15 no.2
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• pp.507-513
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• 2004

Least-squares support vector machine (LS-SVM) has been very successful in pattern recognition and function estimation problems for crisp data. In this paper, we propose LS-SVM approach to evaluating fuzzy regression model with multiple crisp inputs and a Gaussian fuzzy output. The proposed algorithm here is model-free method in the sense that we do not need assume the underlying model function. Experimental result is then presented which indicate the performance of this algorithm.

• 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.

• Asian-Australasian Journal of Animal Sciences
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• v.33 no.9
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• pp.1387-1399
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• 2020

Objective: The objective of this study was to estimate the genetic parameters and trends for milk, fat, and protein yields in the first three lactations of Thai dairy cattle using a 3-trait,-3-lactation random regression test-day model. Methods: Data included 168,996, 63,388, and 27,145 test-day records from the first, second, and third lactations, respectively. Records were from 19,068 cows calving from 1993 to 2013 in 124 herds. (Co) variance components were estimated by Bayesian methods. Gibbs sampling was used to obtain posterior distributions. The model included herd-year-month of testing, breed group-season of calving-month in tested milk group, linear and quadratic age at calving as fixed effects, and random regression coefficients for additive genetic and permanent environmental effects, which were defined as modified constant, linear, quadratic, cubic and quartic Legendre coefficients. Results: Average daily heritabilities ranged from 0.36 to 0.48 for milk, 0.33 to 0.44 for fat and 0.37 to 0.48 for protein yields; they were higher in the third lactation for all traits. Heritabilities of test-day milk and protein yields for selected days in milk were higher in the middle than at the beginning or end of lactation, whereas those for test-day fat yields were high at the beginning and end of lactation. Genetics correlations (305-d yield) among production yields within lactations (0.44 to 0.69) were higher than those across lactations (0.36 to 0.68). The largest genetic correlation was observed between the first and second lactation. The genetic trends of 305-d milk, fat and protein yields were 230 to 250, 25 to 29, and 30 to 35 kg per year, respectively. Conclusion: A random regression model seems to be a flexible and reliable procedure for the genetic evaluation of production yields. It can be used to perform breeding value estimation for national genetic evaluation in the Thai dairy cattle population.