• Journal of Korea Water Resources Association
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• v.33 no.1
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• pp.31-38
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• 2000

Relationships between hydrologic variables are often nonlinear. Usually the functional form of such a relationship is not known a priori. A multivariate, nonparametric regression methodology is provided here for approximating the underlying regression function using locally weighted polynomials. Locally weighted polynomials consider the approximation of the target function through a Taylor series expansion of the function in the neighborhood of the point of estimate. The utility of this nonparametric regression approach is demonstrated through an application to nonparametric short term forecasts of the biweekly Great Salt Lake volume.volume.

• Communications for Statistical Applications and Methods
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• v.4 no.1
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• pp.119-128
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• 1997

Added variable plot and component-plus-residual plot are very useful for studying the role of a predictor in classical regression analysis. The former is usually used to check the effect of adding a new variable to existing model. The latter has been suggested as computationally convenient substitutes for the added variable plots, however, this plot is found to be better in detecting nonlinear relationships of a new predictor. By combining these two plots dynamically, we can take advantages of two plots simultaneously. And even further, we can get some knowledge of collinearity between a new predictor and predictors already in the model, and more accurate information about the possible outliers.

• Communications for Statistical Applications and Methods
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• v.16 no.1
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• pp.163-168
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• 2009

Given the specific mean shift outlier model, the standard approaches to obtaining test statistics for outliers are discussed. Accuracy of outlier tests is investigated using subset curvatures. These subset curvatures appear to be reliable indicators of the adequacy of the linearization based test. Also, we consider obtaining graphical summaries of uncertainty in estimating parameters through confidence curves. The results are applied to the problem of assessing the accuracy of outlier tests.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2001.04a
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• pp.275-282
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• 2001

In this paper, nonlinear analyses of RC (Reinforced Concrete) columns are conducted, and an improved criterion to estimate the design load carrying capacity of slender RC columns is proposed. To simulate the material nonlinearty including the cracking of concrete, the layer model is adopted, and the initial stress matrix is considered for the simulation of P- effect. After correlation studies with previous numerical results to verify the efficiency of the developed numerical model, many parameter studies are followed, and a regression formula which can give more exact resisting capacity of slender RC columns is introduced on the basis of the obtained numerical results.

• Journal of the Korean Data and Information Science Society
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• v.28 no.3
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• pp.533-545
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• 2017

Quantile regression models provide a variety of useful statistical information by estimating the conditional quantile function of the response variable. However, the traditional linear quantile regression model can lead to the distorted and incorrect results when analysing real data having a nonlinear relationship between the explanatory variables and the response variables. Furthermore, as the complexity of the data increases, it is required to analyse multiple response variables simultaneously with more sophisticated interpretations. For such reasons, we propose a multivariate quantile regression tree model. In this paper, a new split variable selection algorithm is suggested for a multivariate regression tree model. This algorithm can select the split variable more accurately than the previous method without significant selection bias. We investigate the performance of our proposed method with both simulation and real data studies.

• Nuclear Engineering and Technology
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• v.42 no.2
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• pp.163-174
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• 2010

As pointed out in the OECD BEMUSE Program, when a high computation time is taken to obtain the relevant output values of a complex physical model (or code), the number of statistical samples that must be evaluated through it is a critical factor for the sampling-based uncertainty analysis. Two alternative methods have been utilized to avoid the problem associated with the size of these statistical samples: one is based on Wilks' formula, which is based on simple random sampling, and the other is based on the conventional nonlinear regression approach. While both approaches provide a useful means for drawing conclusions on the resultant uncertainty with a limited number of code runs, there are also some unique corresponding limitations. For example, a conclusion based on the Wilks' formula can be highly affected by the sampled values themselves, while the conventional regression approach requires an a priori estimate on the functional forms of a regression model. The main objective of this paper is to assess the feasibility of the ACE-RSM approach as a complementary method to the Wilks' formula and the conventional regression-based uncertainty analysis. This feasibility was assessed through a practical application of the ACE-RSM approach to the LOFT L2-5 LBLOCA PCT uncertainty analysis, which was implemented as a part of the OECD BEMUSE Phase III program.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.4887-4907
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• 2017

Accurate traffic flow forecasting is critical to the development and implementation of city intelligent transportation systems. Therefore, it is one of the most important components in the research of urban traffic scheduling. However, traffic flow forecasting involves a rather complex nonlinear data pattern, particularly during workday peak periods, and a lot of research has shown that traffic flow data reveals a seasonal trend. This paper proposes a new traffic flow forecasting model that combines seasonal relevance vector regression with the hybrid chaotic simulated annealing method (SRVRCSA). Additionally, a numerical example of traffic flow data from The Transportation Data Research Laboratory is used to elucidate the forecasting performance of the proposed SRVRCSA model. The forecasting results indicate that the proposed model yields more accurate forecasting results than the seasonal auto regressive integrated moving average (SARIMA), the double seasonal Holt-Winters exponential smoothing (DSHWES), and the relevance vector regression with hybrid Chaotic Simulated Annealing method (RVRCSA) models. The forecasting performance of RVRCSA with different kernel functions is also studied.

• Journal of Biosystems Engineering
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• v.26 no.1
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• pp.39-46
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• 2001

This research conducted to investigate the variation of the moisture content, crack ratio, and hardness of the whole and cracked brown rice after conditioning at the initial moisture content of 13, 14, and 15% with time lapse. The conditioning was conducted by increasing the moisture content of the sample to 0.4 and 0.8%. For basic information and conditioning characteristics for the development of a conditioning machine for the brown rice, predicted models of above three properties were developed using a nonlinear regression analysis of SAS with Gauss-Newton, Gradient, and DUD methods. Results of this research could be summarized as follows. 1. No moisture variation occurred after 0.5 hour conditioning. 2. The increasement of the crack ratio was 7.6 and 17.5% with the sample increased the moisture content of 0.4 and 0.8%, respectively, after 8 hours conditioning. 3. The hardness of the conditioned whole grain of the brown rice decreased 0.82 and 1,000kg$\_$f/ with the sample increased moisture content 0.4 and 0.8%, respectively, after 8 hours conditioning with respect to the non-conditioned sample. 4. The hardness of the conditioned cracked grain of the brown rice decreased 0.54 and 0.81kg$\_$f/ with the sample increased moisture content 0.4 and 0.8%, respectively, after 8 hours conditioning with respect to the non-conditioned sample. The hardness of the broken grain was about 0.81∼1.88kg$\_$f/ lower than whole grain. 5. The moisture content variation, increasing rate of the crack ratio, and hardness of the cracked and whole grain was predicted as a negative exponential function. 6. Each predicted model with the nonlinear regression analysis, which was very accurate and had a very small amount of sum of square of error between experimental value and predicted value, which could be used for predicting the physical variation after conditioning.

• International journal of steel structures
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• v.18 no.4
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• pp.1177-1190
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• 2018

This study presents an analytical framework for estimating the thermo-mechanical behavior of a composite beam exposed to fire. The framework involves: a fire simulation from which the evolution of temperature on the structure surface is obtained; data transfer by an interface model, whereby the surface temperature is assigned to the finite element model of the structure for thermo-mechanical analysis; and nonlinear thermo-mechanical analysis for predicting the structural response under high temperatures. We use a plastic-damage model for calculating the response of concrete slabs, and propose a method to determine the stiffness degradation parameter of the plastic-damage model by a nonlinear regression of concrete cylinder test data. To validate simulation results, structural fire experiments have been performed on a real-scale steel-concrete composite beam using the fire load prescribed by ASTM E119 standard fire curve. The calculated evolution of deflection at the center of the beam shows good agreement with experimental results. The local test results as well as the effective plastic strain distribution and section rotation of the composite beam at elevated temperatures are also investigated.

• Journal of the Korean earth science society
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• v.28 no.2
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• pp.214-226
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• 2007

Because precipitation is influenced by various atmospheric variables, it is highly nonlinear. Although precipitation predicted by a dynamic model can be corrected by using a nonlinear Artificial Neural Network, this approach has limits such as choices of the initial weight, local minima and the number of neurons, etc. In the present paper, we correct simulated precipitation by using a multiple linear regression (MLR) method, which is simple and widely used. First of all, Ensemble hindcast is conducted by the PNU/CME Coupled General Circulation Model (CGCM) (Park and Ahn, 2004) for the period from April to August in 1979-2005. MLR is applied to precipitation simulated by PNU/CME CGCM for the months of June (lead 2), July (lead 3), August (lead 4) and seasonal mean JJA (from June to August) of the Northeast Asian region including the Korean Peninsula $(110^{\circ}-145^{\circ}E,\;25-55^{\circ}N)$. We build the MLR model using a linear relationship between observed precipitation and the hindcasted results from the PNU/CME CGCM. The predictor variables selected from CGCM are precipitation, 500 hPa vertical velocity, 200 hPa divergence, surface air temperature and others. After performing a leave-oneout cross validation, the results are compared with the PNU/CME CGCM's. The results including Heidke skill scores demonstrate that the MLR corrected results have better forecasts than the direct CGCM result for rainfall.