• Journal of Biomedical Engineering Research
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• v.28 no.2
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• pp.199-204
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• 2007

In digital radiography, to improve the contrast of digital radiography image, the multi-scale nonlinear amplification algorithm based on unsharp masking is one of the major image enhancement algorithms. In this paper, we used the Laplacian pyramid to decompose a digital radiography(DR) image. In our simulation, the DR image was decomposed into seven layers and the coefficients of the each layer was amplified with nonlinear function. We also imported a noise containment algorithm to limit noise amplification. To enhance the contrast of image, we proposed a new adaptive non-linear gain amplification coefficients. As a result of having applied to some clinical data, a detail visibility was improved significantly without unacceptable noise boosting. Images that acquired with the proposed adaptive non-linear gain coefficients have shown superior quality to those that applied similar gain control method and expected to be accepted in the clinical applications.

• Journal of the Korean Statistical Society
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• v.28 no.2
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• pp.225-234
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• 1999

We consider the following semi-parametric non-linear mixed effect regression model : y\ulcorner=f($\chi$\ulcorner;$\beta$)+$\sigma$$\mu$($\chi$\ulcorner)+$\sigma$$\varepsilon$\ulcorner,i=1,…,n,y*=f($\chi$;$\beta$)+$\sigma$$\mu$($\chi$) where y'=(y\ulcorner,…,y\ulcorner) is a vector of n observations, y* is an unobserved new random variable of interest, f($\chi$;$\beta$) represents fixed effect of known functional form containing unknown parameter vector $\beta$\ulcorner=($\beta$$_1$,…,$\beta$\ulcorner), $\mu$($\chi$) is a random function of mean zero and the known covariance function r(.,.), $\varepsilon$'=($\varepsilon$$_1$,…,$\varepsilon$\ulcorner) is the set of uncorrelated measurement errors with zero mean and unit variance and $\sigma$ is an unknown dispersion(scale) parameter. On the basis of finite-sample, small-dispersion asymptotic framework, we derive an absolute lower bound for the asymptotic mean squared errors of prediction(AMSEP) of the regular-consistent non-linear predictors of the new random variable of interest y*. Then we construct an optimal predictor of y* which attains the lower bound irrespective of types of distributions of random effect $\mu$(.) and measurement errors $\varepsilon$.

• Coupled systems mechanics
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• v.11 no.2
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• pp.107-119
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• 2022

The aim of the work presented in this paper is development of numerical model for prediction of temperature distribution in pavement according to the measured meteorological parameters, with introduction of non-linear heat transfer coefficient which is a function of temerature difference between the air and the pavement. Developed model calculates heat radiated from the pavement back in the air, which is an important part of the heat trasfer process in the open air surfaces. Temperature of the pavement surface, heat radiation together with many meteorological parameters were measured in series during two years in order to validate the model and calibrate model parameters. Special finite element method for temperature heat transfer towards the soil together with the time integration scheme are used to solve the governing equation. It is proved that non-linear heat transfer coefficient, which is a function of time and temperature difference between the air and the pavement, is required to decribe this phenomena. Proposed model includes heat tranfer coefficient callibration for specific climate region, through the iterative inverse procedure.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.447-452
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• 1998

A fault is considered as a variation of physical parameters; therefore the design of fault detection and identification(FDI) can be reduced to the parameter identification of a non linear system and to the association of the set of the estimated parameters with the mode of faults. ANFIS(Adaptive Neuro-Fuzzy Inference System) which contains multiple linear models as consequent part is used to model non linear systems. In this paper, we proposes an FDI system for non linear systems using ANFIS. The proposed diagnositc system consists of two ANFISs which operate in two different modes (parallel-and series-parallel mode). It generates the parameter residuals associated with each modes of faults which can be further processed by additional RBF (Radial Basis function) network to identify the faults. The proposed FDI scheme has been tested by simultation on a two-tank system

• Korean Journal of Mathematics
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• v.25 no.1
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• pp.45-60
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• 2017

This paper focuses on estimation of an non-integral quadratic function (NIQF) and integral quadratic function (IQF) of a random signal in dynamic system described by a linear stochastic differential equation. The quadratic form of an unobservable signal indicates useful information of a signal for control. The optimal (in mean square sense) and suboptimal estimates of NIQF and IQF represent a function of the Kalman estimate and its error covariance. The proposed estimation algorithms have a closed-form estimation procedure. The obtained estimates are studied in detail, including derivation of the exact formulas and differential equations for mean square errors. The results we demonstrate on practical example of a power of signal, and comparison analysis between optimal and suboptimal estimators is presented.

• Journal of KSNVE
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• v.7 no.2
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• pp.223-229
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• 1997

Presented is a method to estimate system parameters of a system with polynomial non-linerities from the measured higher order frequency response functions. Higher order FRFs can be measured on some restricted regions by sinusoidally exciting a non-linear system with various input amplitudes and measuring the response component at the excitation frequency. These higher order FRFs can be expressed in terms of system parameter, and the system parameters can be estimated from the measured FRFs. Since the expressions for higher order FRFs are complicated, system parameters can be estimated from them using an optimization technique. The present method has been applied to a simulated single degree of freedom system with non-linear stiffness and damping, and has estimated accurate system parameters.

• International Journal of Aeronautical and Space Sciences
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• v.14 no.3
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• pp.256-263
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• 2013

The saturation of the actuator impairs the response performance of the near space interceptor control system. A control system based on the properties of linear tracking system is designed for this problem. The properties are that the maximum value of the pseudo-Lyapunov function of the linear tracking control system do not present at the initial state but at the steady state, based on which the bounded stability problem is converted into linear tracking problem. The pseudo-Lyapunov function of the linear tracking system contain the input variables; the amplitude and frequency of the input variables affect the stability of the nonlinear control system. Designate expected closed-loop poles area for different input commands and obtain a controller which is function of input variables. The coupling between variables and linear matrices make the control system design problem non-convex. The non-convex problem is converted into a convex LMI according to the Shur complement lemma and iterative algorithm. Finally the simulation shows that the designed optimal control system is quick and accurate; the rationality of the presented design techniques is validated.

• Communications of the Korean Mathematical Society
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• v.35 no.1
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• pp.229-241
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• 2020

For a second order linear differential equation f" + A(z)f' + B(z)f = 0, with A(z) and B(z) being transcendental entire functions under some restrictions, we have established that all non-trivial solutions are of infinite order. In addition, we have proved that these solutions, with a condition, have exponent of convergence of zeros equal to infinity. Also, we have extended these results to higher order linear differential equations.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.10
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• pp.77-83
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• 2016

This paper presents an adaptive retinex algorithm using skewness for contrast enhancement of color images. In order to estimate the degree of low contrast of an image, skewness of luminance of an observed image is used to define a parameter, and a non-linear function is proposed to compensate the reflectance using the parameter and estimated reflectance. In addition, determination of gain and offset of the non-linear function is addressed using statistics of the estimated reflectance. The relation between an observed luminance and the compensated luminance is used to compensate color components with the reduction of computational cost. The experimental results show that the proposed algorithm has the capability to effectively improve the contrast without color distortion.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.4
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• pp.499-505
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• 2006

Regularlization approach to regression can be easily found in Statistics and Information Science literature. The technique of regularlization was introduced as a way of controlling the smoothness properties of regression function. In this paper, we have presented a new method to evaluate linear and non-linear fuzzy regression model based on Tanaka's model using the idea of regularlization technique. Especially this method is a very attractive approach to model non -linear fuzzy data.