• Journal of the Korean Geotechnical Society
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• v.38 no.12
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• pp.103-112
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• 2022

The existing subsidence prediction method based on the measurement data were confirmed in this study through literature research. It was confirmed that the hyperbolic method and the Asaoka method showed high accuracy, while the other prediction methods showed significantly low accuracy. Based on the analysis results, the limitations of the existing prediction equations were derived, and the improvement method of the settlement prediction equations was suggested. In this study, a weighted nonlinear regression analysis method that gives higher weight to the later data was proposed to improve the existing hyperbolic method.

• Communications for Statistical Applications and Methods
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• v.31 no.2
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• pp.247-262
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• 2024

In this paper, we explore nonlinear sufficient dimension reduction (SDR) methods, with a primary focus on establishing a foundational framework that integrates various nonlinear SDR methods. We illustrate the generalized sliced inverse regression (GSIR) and the generalized sliced average variance estimation (GSAVE) which are fitted by the framework. Further, we delve into nonlinear extensions of inverse moments through the kernel trick, specifically examining the kernel sliced inverse regression (KSIR) and kernel canonical correlation analysis (KCCA), and explore their relationships within the established framework. We also briefly explain the nonlinear SDR for functional data. In addition, we present practical aspects such as algorithmic implementations. This paper concludes with remarks on the dimensionality problem of the target function class.

• Journal of Korean Society of Water and Wastewater
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• v.20 no.5
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• pp.737-745
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• 2006

The goal of this study is to present an external pit corrosion rate($p_{ecr}$) model with considering both the age of pipe and the soil characteristics. The correlation of nonlinear exponential model among conventional empirical models was a little higher than other empirical models in the prediction of $p_{ecr}$ according to the age of pipe. However, there has been a limit to predict Peer with the model by using only a pipe age since installation as a variable. The soil analysis results from sixty nine samples showed that all of the samples were non corrosive in the assessment of ANSI/AWWA scoring system. The correlation of soil corrosion factors and $p_{ecr}$ was also low. The application result of linear and nonlinear regression models that soil characteristics only showed a low correlation with $p_{ecr}$ Proposed nonlinear regression model in this study, with considering both the age of pipe and the soil characteristics, showed a little higher correlation ($R^2=0.46$) than conventional model.

• Communications for Statistical Applications and Methods
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• v.27 no.3
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• pp.365-376
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• 2020

Interval-valued data, a type of symbolic data, is given as an interval in which the observation object is not a single value. It can also occur frequently in the process of aggregating large databases into a form that is easy to manage. Various regression methods for interval-valued data have been proposed relatively recently. In this paper, we introduce a nonparametric regression model using the kernel function and a nonlinear regression model for the interval-valued data. We also propose applying the local linear regression model, one of the nonparametric methods, to the interval-valued data. Simulations based on several distributions of the center point and the range are conducted using each of the methods presented in this paper. Various conditions confirm that the performance of the proposed local linear estimator is better than the others.

• Journal of the Korea Concrete Institute
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• v.18 no.4 s.94
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• pp.507-515
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• 2006

The purpose of the paper is to propose a method to give a more accurate prediction of prestress changes in prestressed concrete(PSC) bridges. The statistical approach of the method is using the measurement data of the structural system to develop a nonlinear regression analysis. Long-term prediction of prestress is achieved using nonlinear regression analysis. The proposed method is applied to the prediction of prestress of an actual prestressed concrete box girder bridge. The present study represents that confidence interval of long-term prediction becomes progressively narrower with the increase of in-situ measurement data. Therefore, the numerical results prove that a more realistic long-term prediction of prestress changes in PSC structures can be achieved by employing the proposed method. The prediction results can be efficiently used to evaluate prestress during the service life of structure so that the remaining prestress exceeds the control criteria.

• Computers and Concrete
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• v.13 no.4
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• pp.531-545
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• 2014

The purpose of this paper is to develop a prediction model for the compressive strength of waste LCD glass applied in concrete by analyzing a series of laboratory test results, which were obtained in our previous study. The hyperbolic function was used to perform the nonlinear-multivariate regression analysis of the compressive strength prediction model with the following parameters: water-binder ratio w/b, curing age t, and waste glass content G. According to the relative regression analysis, the compressive strength prediction model is developed. The calculated results are in accord with the laboratory measured data, which are the concrete compressive strengths of different mix proportions. In addition, a coefficient of determination $R^2$ value between 0.93 and 0.96 and a mean absolute percentage error MAPE between 5.4% and 8.4% were obtained by regression analysis using the predicted compressive analysis value, and the test results are also excellent. Therefore, the predicted results for compressive strength are highly accurate for waste LCD glass applied in concrete. Additionally, this predicted model exhibits a good predictive capacity when employed to calculate the compressive strength of washed glass sand concrete.

• Proceedings of the Korean Printing Society Conference
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• 2000.12a
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• pp.0.2-0
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• 2000

Most important step for the color matching in scanner is the color coordinate transformation from the scanner RGB space to device independent uniform color space. A variety of color calibration technologies have been developed for input device. Linear or nonlinear matrices have been conveniently applied to correct the color filter\`s mismatch with color matching function in scanners. The color matching accuracy is expected to be further improved when the nonlinear matrices are optimized into subdivided smaller color spaces than in single matrix of the entire color space. This article proposed the scanner calibration method using subspace division regression analysis and it were compared with conventional method.

• Journal of the Korean Graphic Arts Communication Society
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• v.19 no.1
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• pp.4-16
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• 2001

Most important step for the color matching in scanner is the color coordinate transformation from the scanner RGB space to device independent uniform color space. A variety of color calibration technologies have been developed for input device. Linear or nonlinear matrices have been conveniently applied to correct the color filter's mismatch with color matching function in scanners. The color matching accuracy is expected to be further improved when the nonlinear matrices are optimized into subdivided smaller color spaces than in single matrix of the entire color space. This article proposed the scanner calibration method using subspace division regression analysis and it were compared with conventional method.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.10a
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• pp.47-52
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• 2002

There are many factors in machine tool error. These are thermal deformation, geometric error, machine's part assembly error, error caused by tool bending. Among them thermal error is 70% of total error of machine tool . Prediction of thermal error is very difficult. because of nonlinear tendency of machine tool deformation. In this study, we tried thermal error prediction by using multi regression analysis.

• Tribology and Lubricants
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• v.26 no.6
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• pp.329-335
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• 2010

Recently, the End-milling processing is needed the high-precise technique to get a good surface roughness and rapid time in manufacturing of precision machine parts and electronic parts. The optimum surface roughness has an effect on end-milling working condition such as, cutting direction, spindle speed, feed rate and depth of cut, and so on. It needs to form the correlation of working conditions and surface roughness. Therefore this study was carried out to presume of surface roughness on end-milling working condition of Al7075 by regression analysis. The results was shown that the coefficient of determination($R^2$) of regression equation had a fine reliability of 87.5% and nonlinear regression equation of surface rough was made by multiple regression analysis.