• Journal of Wetlands Research
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• v.18 no.2
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• pp.148-153
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• 2016

Hydraulic and hydrological flood routing methods are commonly used to analyze temporal and spatial flood influences of flood wave through a river reach. Hydrological flood routing method has relatively more simple and reasonable performance accuracy compared to the hydraulic method. Storage constant used in Muskingum method widely applied in hydrological flood routing is very similar to the travel time. Focusing on this point, in this study, we estimate the travel time from HEC-RAS results to estimate storage constant, and develop a non-linear regression equation for the travel time using reach length, channel slope, and discharge. The estimated flow by Muskingum model with storage constant of nonlinear equation is compared with the flow calculated by applying the HEC-RAS 1-D unsteady flow simulation. In addition, this study examines the effect on the weighting factor changes and interval reach divisions; peak discharge increases with the bigger weighting factor, and RMSE decreases with the fragmented division.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2009.10a
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• pp.383-386
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• 2009

Contract power conversion factor which is applied to estimate contract power of industrial customers is an important standard to calculate transformer capacity. This paper shows a reasonable contract power conversion factor, that was made by the systematic and statistical way considering actual conditions, such as investigated contract power and peak power for the last 5 years of each customer for industrial customers as to AMR system. In this dissertation, it is necessary to analyze the key features and general trend from the investigated data. It made an analysis of the feature parameters, such as average, standard deviation, median, maximum. minimum and thus it was carried the linear and nonlinear regression analysis. Therefore, this paper compared characteristics for a contract power conversion factor which is applied to calculate contract power with characteristics for a regression model for customers which maximum utilization factor of transformer is more than 60%.

• Proceedings of the KIEE Conference
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• 2000.11d
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• pp.800-802
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• 2000

In this paper, we propose the Self-Organizing Networks(SON) based on competitive Fuzzy Polynomial Neuron(FPN) for the optimal design of nonlinear process system. The SON architectures consist of layers with activation nodes based on fuzzy inference rules. Here each activation node is presented as FPN which includes either the simplified or regression Polynomial fuzzy inference rules. The proposed SON is a network resulting from the fusion of the Polynomial Neural Networks(PNN) and a fuzzy inference system. The conclusion part of the rules, especially the regression polynomial uses several types of high-order polynomials such as liner, quadratic and modified quadratic. As the premise part of the rules, both triangular and Gaussian-like membership functions are studied. Chaotic time series data used to evaluate the performance of our proposed model.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2008.05a
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• pp.293-296
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• 2008

Contract power conversion factor which is applied to estimate contract power of general customers IS an important standard to caculate transformer capacity. This paper shows a reasonable contract power conversion factor, that was made by the systematic and statistical way considering actual conditions, such as investigated contract power and peak power for the last 5 years of each customer for 132 office building customers as to AMR system. In this dissertation, it is necessary to analyze the key features and general trend from the investigated data. It made an analysis of the feature parameters, such as average, standard deviation, median, maximum, minimun and thus it was carried the linear and nonlinear regression analysis. Therefore, this paper compared characteristics for a contract power conversion factor which is applied to calculate contract power with characteristics for a regression model for customers which maximum utilization factor of transformer is more than 60%.

• Journal for History of Mathematics
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• v.11 no.2
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• pp.63-76
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• 1998

When teaching the linear regression analysis in the class, the power transformation must be introduced to fit the linear regression model for nonlinear data. Box and Cox (1964) proposed the attractive power transformation technique which is so called Box-Cox transformation. In this paper, an effective algorithm selecting an appropriate value for Box-Cox transformation is developed which is considered to find a value minimizing error sum of squares. When the proposed algorithm is used to find a value for transformation, the number of iterations needs to be considered. Thus, the number of iterations is examined through simulation study. Since SAS is one of most widely used packages and does not provide the procedure that performs iterative Box-Cox transformation, a SAS program automatically choosing the value for transformation is developed. Hence, the students could learn how the Box-Cox transformation works, moreover, researchers can use this for analysis of data.

• Journal of Biosystems Engineering
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• v.26 no.1
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• pp.57-66
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• 2001

This study was performed to determine adsorption equilibrium moisture contents of rough rice, brown rice, white rice and rice hull grown in Korea. EMC values were measured by static method using saturated salt solutions at three temperature levels of 20$\^{C}$, 30$\^{C}$ and 40$\^{C}$, and eight relative humidity levels in the range from 11.2% to 85.0%. The measured EMC values were fitted to modified Henderson, Chung-Pfost, and modified Oswin models by using nonlinear regression analysis. The results of comparing root mean square errors for three models showed that modified Henderson and Chung-Pfost models could serve as good models, and that modified Oswin model could not be applicable to rough rice, brown rice, white rice and rice hull.

• Journal of applied mathematics & informatics
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• v.42 no.1
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• pp.149-158
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• 2024

A least squares problem without correspondences is expressed as the following optimization: Π∈P^min_{m, x∈ℝn} ║Ax-Πy║, where A ∈ ℝ^m×n and y ∈ ℝ^m are given. In general, solving such an optimization problem is highly challenging. In this paper we use the rearrangement inequalities to find the closed form of solutions for certain cases. Moreover, despite the stringent constraints, we successfully tackle the nonlinear least squares problem without correspondences by leveraging rearrangement inequalities.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.250-255
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• 2003

The position of a 3-dimensional(3D) point can be measured by using calibrated stereo camera. To obtain more accurate measurement ,more accurate camera calibration is required. There are many existing methods to calibrate camera. The simple linear methods are usually not accurate due to nonlinear lens distortion. The nonlinear methods are accurate more than linear method, but it increase computational cost and good initial guess is needed. The multi step methods need to know some camera parameters of used camera. Recent years, these explicit model based camera calibration work with the development of more precise camera models involving correction of lens distortion. But these explicit model based camera calibration have disadvantages. So implicit camera calibration methods have been derived. One of the popular implicit camera calibration method is to use neural network. In this paper, we propose implicit stereo camera calibration method for 3D reconstruction using support vector machine. SVM can learn the relationship between 3D coordinate and image coordinate, and it shows the robust property with the presence of noise and lens distortion, results of simulation are shown in section 4.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.35 no.3
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• pp.216-222
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• 2002

Autocorrelation in time series data can affect statistical inference in correlation or regression analyses. To improve a regression model from which the residuals are autocorrelated, Yule-Walker method, nonlinear least squares estimation, maximum likelihood method and 'prewhitening' method have been used to estimate the parameters in a regression equation. This study reviewed on the estimation methods of preventing spurious correlation in the presence of autocorrelation and applied the former three methods, Yule-Walker, nonlinear least squares and maximum likelihood method, to a 20-year real data set. Monte carlo simulation was used to compare the three parameter estimation methods. However, the simulation results showed that the mean squared error distributions from the three methods simulated do not differ significantly.

• The Korean Journal of Applied Statistics
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• v.32 no.4
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• pp.543-559
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• 2019

Data often include missing values due to various reasons. If the missing data mechanism is not MCAR, analysis based on fully observed cases may an estimation cause bias and decrease the precision of the estimate since partially observed cases are excluded. Especially when data include many variables, missing values cause more serious problems. Many imputation techniques are suggested to overcome this difficulty. However, imputation methods using parametric models may not fit well with real data which do not satisfy model assumptions. In this study, we review imputation methods using nonlinear models such as kernel, resampling, and spline methods which are robust on model assumptions. In addition, we suggest utilizing imputation classes to improve imputation accuracy or adding random errors to correctly estimate the variance of the estimates in nonlinear imputation models. Performances of imputation methods using nonlinear models are compared under various simulated data settings. Simulation results indicate that the performances of imputation methods are different as data settings change. However, imputation based on the kernel regression or the penalized spline performs better in most situations. Utilizing imputation classes or adding random errors improves the performance of imputation methods using nonlinear models.