• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.5
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• pp.851-856
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• 2016

Wind speed is heavily fluctuated and quite local than other weather elements. It is difficult to improve the accuracy of prediction only in a numerical prediction model. An MOS (Model Output Statistics) technique is used to correct the systematic errors of the model using a statistical data analysis. The Most of previous MOS has used a linear regression model for weather prediction, but it is hard to manage an irregular nature of prediction of wind speed. In order to solve the problem, a nonlinear regression method using SVR (Support Vector Regression) is introduced for a development of MOS for wind speed prediction. Experiments are performed for KLAPS (Korea Local Analysis and Prediction System) re-analysis data from 2007 to 2013 year for Jeju Island and Busan area in South Korea. The MLR and SVR based linear and nonlinear methods are compared to each other for prediction accuracy of wind speed. Also, the comparison experiments are executed for the variation in the number of UM elements.

• Proceedings of the Korea Concrete Institute Conference
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• 1998.04a
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• pp.347-352
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• 1998

The purpose of this paper is to present and establish a procedure to predict the prestress forces during the service life of the structure. The statistical approach of this procedure is using the in-situ measurement data of the post-tensioning system to develop a nonlinear regression analysis. The method of least squares is used to fit a certain function a set of data. Use of a nonlinear model is achieved by its logarithmic transformation and sunsequent use of linear-regression theory. The regression analysis result can be used to check the prestress force during the service life so that the remaining prestress force is equal to or exceeds the design requirement. Results from the measurement data of PSC box girder bridge structure were used to demonstrate the procedures.

• Computers and Concrete
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• v.16 no.4
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• pp.569-585
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• 2015

Various computational tools are available for modeling highly nonlinear structural engineering problems that lack a precise analytical theory or understanding of the phenomena involved. This paper adopts a fairly simple nonparametric adaptive regression algorithm known as multivariate adaptive regression splines (MARS) to model the nonlinear interactions between variables. The MARS method makes no specific assumptions about the underlying functional relationship between the input variables and the response. Details of MARS methodology and its associated procedures are introduced first, followed by a number of examples including three practical structural engineering problems. These examples indicate that accuracy of the MARS prediction approach. Additionally, MARS is able to assess the relative importance of the designed variables. As MARS explicitly defines the intervals for the input variables, the model enables engineers to have an insight and understanding of where significant changes in the data may occur. An example is also presented to demonstrate how the MARS developed model can be used to carry out structural reliability analysis.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.5
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• pp.129-132
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• 2009

Frame erasure is one of the most difficult problems in voice over IP (VoIP) networks and is a major source of speech quality degradation. In this paper, a frame erasure concealment algorithm based on nonlinear regression analysis is presented to minimize speech quality deterioration in code-excited linear prediction (CELP) based coders. We applied the proposed scheme to the ITU-T G.729 standard and obtained improved perceptual evaluation of speech quality (PESQ) scores compared to the conventional methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.5
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• pp.477-482
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• 2015

Linear regressions and evolutionary nonlinear regression based compensation techniques for the short-range prediction of wind speed are investigated. Development of an efficient MOS(Model Output Statistics) is necessary to correct systematic errors of the model, but a linear regression based MOS is hard to manage an irregular nature of weather prediction. In order to solve the problem, a nonlinear and symbolic regression method using GP(Genetic Programming) is suggested for a development of MOS for wind speed prediction. The proposed method is compared to various linear regression methods for prediction of wind speed. Also, statistical analysis of distribution for UM elements for each method is executed. experiments are performed for KLAPS(Korea Local Analysis and Prediction System) re-analysis data from 2007 to 2013 year for Jeju Island and Busan area in South Korea.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1992.10a
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• pp.23-28
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• 1992

The ultimate capacity of end-plate connection is investigated through nonlinear finite element analysis. The example models are divided into stiffened case and unstiffened one. The refined finite element models are analyzed by utilizing a general purpose structural analysis computer program ADINA and the moment-rotation relationships of the connection are determined. The results are compared with the regression equation deduced by Krishnamurthy. It is planned to deduce a bilinear regression equation through a parametric study on various dimensions of the connection.

• Journal of the Korean Data Analysis Society
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• v.20 no.6
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• pp.2721-2731
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• 2018

This study purposes to derive the predictive model for the cold tolerance of Brassica napus, using the data collected in the Tree Breeding Lab of Gyeongsang National University during July and August of 2016. Three Brassica napus samples were treated at each of low temperatures from $4^{\circ}C$ to $-12^{\circ}C$ by decrement of $4^{\circ}C$, step by step, and electrolyte leakage levels were measured at each stage. Electrolyte leakages were observed tangibly from $-4^{\circ}C$. We tried to fit the six nonlinear regression models to the electrolyte leakage data of Brassica napus: 3-parameter logistic model, baseline logistic model, 4-parameter logistic model, (4-1)-parameter logistic model, 3-parameter Gompertz model, and (3-1)-parameter Gompertz model. The baseline levels of the electrolyte leakage estimated by these models were 4.81%, 4.07%, 4.19%, 4.07%, 4.55%, and 0%, respectively. The estimated median lethal temperature, LT50, were $-5.87^{\circ}C$, $-6.31^{\circ}C$, $-6.05^{\circ}C$, $-6.35^{\circ}C$, $-4.98^{\circ}C$, and $-5.15^{\circ}C$, respectively. We compared and discussed the measures of goodness of fit to select the appropriate nonlinear regression model.

• The Korean Journal of Applied Statistics
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• v.6 no.1
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• pp.163-172
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• 1993

A computer software for nonlinear regression analysis, NLIN90, was developed to provide easy access and useful information for more precise analysis which can be obtained from the newly developed theory. Together with the elementary statistics, it provides statistics for curvature analysis of model function and of each parameter, for curvaure analysis of transformed parameters, for experimental design analysis, and for residual analysis. Easy access is obtained by utilizing a database of nonlinear models.

• Journal of the Korean Society of Safety
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• v.24 no.2
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• pp.94-99
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• 2009

This study deals with the traffic accidents at the 3-legged signalized intersections in Cheongu. The goals are to analyze the geometric, traffic and operational conditions of intersections and to develop a various functional forms that predict the accidents. The models are developed through the correlation analysis, the multiple linear, the multiple nonlinear, Poisson and negative binomial regression analysis. In this study, two multiple linear, two multiple nonlinear and two negative binomial regression models were calibrated. These models were all analyzed to be statistically significant. All the models include 2 common variables(traffic volume and lane width) and model-specific variables. These variables are, therefore, evaluated to be critical to the accident reduction of Cheongju.

• Journal of the Korean Geosynthetics Society
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• v.15 no.2
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• pp.65-75
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• 2016

In this study, we constructed a database by collecting field pullout test data of the soil nailing using pressurized grouting, and suggested a method to estimate the ultimate pullout resistance using nonlinear regression analysis to overcome the problems of ultimate pullout resistance estimation using graphical methods. The load-displacement curve estimated by nonlinear regression showed a very high correlation with the field pullout test data. Estimated ultimate pullout load by nonlinear regression method was average 29% higher than estimated ultimate pullout load using previous graphical method. A sigmoidal growth model was found to be the best-fitting nonlinear regression model against rapid pullout failure. Further, an asymptotic regression model was found to be the best fit against progressive nail pullout. The unit ultimate skin friction suggested in this research reflected in the domestic geotechnical characteristics and the specifications of the pressurized grouting method. This research is expected to contribute towards establishing an independent design standard for the soil nailing by providing solutions to the problems that occur when using design charts based on foreign research.