• Journal of Korean Society of Coastal and Ocean Engineers
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• v.12 no.4
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• pp.181-189
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• 2000

A weakly nonlinear mild-slope equation has been derived directly from the continuity equation with the aid of the Galerkin's method. The equation is combined with the momentum equations defined at the mean water level. A single component model has also been obtained in terms of the surface displacement. The linearized form is completely identical with the time-dependent mild-slope equation proposed by Smith and Sprinks(1975). For the verification purposes of the present nonlinear model, the degenerate forms were compared with Airy(1845)'s non-dispersive nonlinear wave equation, classical Boussinesq equation, andsecond¬order permanent Stokes waves. In this study, the present nonlinear wave equations are discretized by the approximate factorization techniques so that a tridiagonal matrix solver is used for each direction. Through the comparison with physical experiments, nonlinear wave model capacity was examined and the overall agreement was obtained.

• Journal of the Institute of Convergence Signal Processing
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• v.10 no.1
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• pp.49-59
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• 2009

In this paper a watermarking algorithm is proposed to optimize the capacity of the digital watermark insertion in an experimental threshold using the characteristics of human visual system(HVS), adaptive scale factors, and weight functions based on discrete wavelet transform. After the original image is decomposed by a 3-level discrete wavelet transform, the watermarks for capacity optimization are inserted into all subbands except the baseband, by applying the important coefficients from the experimental threshold in the wavelet region. The adaptive scale factors and weight functions based on HVS are considered for the capacity optimization of the digital watermark insertion in order to enhance the robustness and invisibility. The watermarks are consisted of gaussian random sequences and detected by correlation. The experimental results showed that this algorithm can preserve a fine image quality against various attacks such as the JPEG lossy compression, noise addition, cropping, blurring, sharpening, linear and non-linear filtering, etc.

• Journal of the Korean Data and Information Science Society
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• v.9 no.2
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• pp.95-103
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• 1998

Azzalini and Bowman proposed the pseudo-likelihood ratio test for checking the linear relationship using kernel regression estimator when the error of the regression model follows the normal distribution. We modify their method with the bootstrap technique to construct a new test, and examine the power of our test through simulation. Our method can be applied to the case where the distribution of the error is not normal.

• The Korean Journal of Applied Statistics
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• v.34 no.6
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• pp.937-944
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• 2021

The problem of selecting variables is addressed in partial linear regression. Model selection for partial linear models is not easy since it involves nonparametric estimation such as smoothing parameter selection and estimation for linear explanatory variables. In this work, several approaches for variable selection are proposed using a fast forward selection algorithm, least angle regression (LARS). The proposed procedures use t-test, all possible regressions comparisons or stepwise selection process with variables selected by LARS. An example based on real data and a simulation study on the performance of the suggested procedures are presented.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.6
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• pp.1413-1422
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• 2012

Adaptive back stepping control technique may provide robust control characteristics under parameter perturbation caused by changing external condition. In order to synthesize a high-precision velocity controller for IPMSM(Interior Permanent Magnet Synchronous Motor) using this method, the period of control loop should be very small. However, because of the resolution of the encoder for speed measurement, control cycle is limited, which makes it difficult to improve the performance of the controller. This paper proposes a velocity controller design method based on nonlinear adaptive back-stepping method to accomplish fast and accurate performance. Here, an EKF(Extended Kalman Filter) method is incorporated for the estimation of the motor speed into the design of a speed controller using adapted back-stepping control technique. The performance of the proposed controller is demonstrated through simulation using PSIM.

• Information and Communications Magazine
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• v.26 no.6
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• pp.30-36
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• 2009

본고에서는 위성을 이용하여 WiBro(Wide Broadband)서비스를 제공하는데 있어서 WiBro 신호를 위성중계기를 통해 송수신할 경우, OFDM(Orthogonal Frequency Division Multiplexing) 신호가 갖고 있는 높은 PAPR(Peak-to-Average Power Ratio) 특성으로 인해 발생할 수 있는 전력증폭기의 비선형왜곡 현상에 대해서 기술하였다. 그리고 이를 효과적으로 극복할 수 있는 여러 방법들을 소개하고 모의실험을 통해 그 유효성을 확인하였다.

• Journal of IKEEE
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• v.17 no.1
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• pp.83-88
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• 2013

This paper presents the output feedback control design for feedforward nonlinear systems with input and output delay. The proposed output feedback controller is composed of a linear observer and a linear controller. It is shown that by using Lyapunov-Krasovskii theorem, the proposed controller ensures a global asymptotic stability for arbitrarily large delay. Finally, an illustrative example is given in order to show the effectiveness of our design method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.9A
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• pp.1299-1305
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• 2000

In B-ISDN, to realize ATM, the optimum control method of multi-media traffic must be proposed. Because there is not the traffic model of multi-media to make clear, the realization of optimum ATM congestion control is very difficult. In this paper, the traffic model is assumed to be slowly time-variable non-linear function and for real-time prediction of it, new model which is composed with parallel triple neural networks is proposed. And the simulation to predict assumed ATM traffic is executed. From the result, it's capability is shown that the proposed neural network model can be used in ATM congestion control.

• 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.

• Journal of the Korean Data and Information Science Society
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• v.24 no.1
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• pp.33-39
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• 2013

In the transformation of response variable in partial linear models outliers can cause a bad effect on estimating the transformation parameter, just as in the linear models. To solve this problem the processes of estimating transformation parameter and detecting outliers are needed, but have difficulties to be performed due to the arbitrariness of the nonparametric function included in the partial linear model. In this study, through the estimation of nonparametric function and outlier detection methods such as a sequential test and a maximum trimmed likelihood estimation, processes for transforming response variable robust to outliers in partial linear models are suggested. The proposed methods are verified and compared their effectiveness by simulation study and examples.