• Journal of Korean Society of Coastal and Ocean Engineers
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• v.6 no.4
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• pp.340-352
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• 1994

A numerical model of nonlinear stream function wave theory evolved from Dean's model (1965) is presented. The stream function theory has been evaluated to be an accurate and useful tool for engineering applications. Effects of damping coefficient employed in a linearized simultaneous equation and number of points in the numerical integration of model on numerical solutions are assessed. Most accurate wave characteristics calculated by the present model are tabulated using revised Dean's Table (Chaplin, 1980) input parameters. Since the well-known feature of nearly breaking waves that with increasing wave steepness the wave length as well as integral properties have a maximum prior to the limiting wave height is represented by the model, the accuracy of model can be proved.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.7
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• pp.1391-1396
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• 2012

It is introduced an approach to model for numerical analysis of a sound absorbing material that has different absorbing coefficient according to frequency. For modeling of a sound absorbing material, we tried to model by a traditional modeling method. But it had large differences on frequency domain, especially a capacitance component due to increasing of frequency. We approach to model a sound absorbing material by the Debye polarization technique with non-linear least square method. At first, we estimated parameters form a polyurethane with thickness 25 mm, then we could model a polyurethane with thickness 50 mm using same parameters. Therefor, we could find that the Debye polarization is an useful way to model sound absorbing materials.

• Journal of Korea Water Resources Association
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• v.39 no.7 s.168
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• pp.593-603
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• 2006

The basin response to storm is regarded as nonlinearity inherently. In addition, the consistent nonlinearity of hydrologic system response to rainfall has been very tough and cumbersome to be treated analytically. The thing is that such nonlinear models have been avoided because of computational difficulties in identifying the model parameters from recorded data. The parameters of nonlinear system considered as dynamic effects in the conceptual model are optimized as the sum of errors between the observed and computed runoff is minimized. For obtaining the optimal parameters of functions, the historical data for the Bocheong watershed in the Geum river basin were tested by applying the numerical methods, such as quasi-linearization technique, Runge-Kutta procedure, and pattern-search method. The estimated runoff carried through from the storage function with dynamic effects was compared with the one of 1st-order differential equation model expressing just nonlinearity, and also done with Nash model. It was found that the 2nd-order model yields a better prediction of the hydrograph from each storm than the 1st-order model. However, the 2nd-order model was shown to be equivalent to Nash model when it comes to results. As a result, the parameters of nonlinear 2nd-order differential equation model performed from the present study provided not only a considerable physical meaning but also a applicability to Korean watersheds.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.5 no.4
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• pp.224-231
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• 2012

In this paper, we introduce a fuzzy inference systems based on fuzzy c-means clustering algorithm for fuzzy modeling of nonlinear process. Typically, the generation of fuzzy rules for nonlinear processes have the problem that the number of fuzzy rules exponentially increases. To solve this problem, the fuzzy rules of fuzzy model are generated by partitioning the input space in the scatter form using FCM clustering algorithm. The premise parameters of the fuzzy rules are determined by membership matrix by means of FCM clustering algorithm. The consequence part of the rules is expressed in the form of polynomial functions and the coefficient parameters of each rule are determined by the standard least-squares method. And lastly, we evaluate the performance and the nonlinear characteristics using the data widely used in nonlinear process.

• Korean Chemical Engineering Research
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• v.45 no.1
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• pp.87-92
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• 2007

Recently, developments of process monitoring system in order to detect and diagnose process abnormalities has got the spotlight in process systems engineering. Normal data obtained from processes provide available information of process characteristics to be used for modeling, monitoring, and control. Since modern chemical and environmental processes have high dimensionality, strong correlation, severe dynamics and nonlinearity, it is not easy to analyze a process through model-based approach. To overcome limitations of model-based approach, lots of system engineers and academic researchers have focused on statistical approach combined with multivariable analysis such as principal component analysis (PCA), partial least squares (PLS), and so on. Several multivariate analysis methods have been modified to apply it to a chemical process with specific characteristics such as dynamics, nonlinearity, and so on.This paper discusses about missing value estimation and sensor fault identification based on process variable reconstruction using dynamic PCA and canonical variate analysis.

• Journal of the Korea Society of Computer and Information
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• v.16 no.8
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• pp.39-47
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• 2011

Ergezinger's method, one of the layer-by-layer algorithms used for multilyer perceptrons, consists of an output node and can make premature saturations in the output's weight because of using linear least squared method in the output layer. These saturations are obstacles to learning time and covergence. Therefore, this paper expands Ergezinger's method to be able to use an output vector instead of an output node and introduces a learning rate to improve learning time and convergence. The learning rate is a variable rate that reflects the correlation coefficient between new weight and previous weight while updating hidden's weight. To compare the proposed method with Ergezinger's method, we tested iris recognition and nonlinear approximation. It was found that the proposed method showed better results than Ergezinger's method in learning convergence. In the CPU time considering correlation coefficient computation, the proposed method saved about 35% time than the previous method.

• Journal of the Korea Computer Graphics Society
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• v.22 no.5
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• pp.1-16
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• 2016

In this paper, we propose and efficient curve reconstruction method based on the classical least-square fitting scheme. Specifically, given planar sample points equipped with normals, we reconstruct the objective curve as the zero set of a hierarchical implicit ZP(Zwart-Powell)-spline that can recover large holes of dataset without loosing the fine details. As regularizers, we adopted two: a Tikhonov regularizer to reduce the singularity of the linear system and a discrete Laplacian operator to smooth out the isocurves. Benchmark tests with quantitative measurements are done and our method shows much better quality than polynomial methods. Compared with the hierarchical bi-quadratic spline for datasets with holes, our method results in compatible quality but with less than 90% computational overhead.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.2
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• pp.185-191
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• 2004

Partial least squares(PLS) is one of multiplicate statistical process methods and has been developed in various algorithms with the characteristics of principal component analysis, dimensionality reduction, and analysis of the relationship between input variables and output variables. But it has been limited somewhat by their dependency on linear mathematics. The algorithm is proposed to classify for the non-linear data using PLS and the residual compensator(RC) based on radial basis function network (RBFN). It compensates for the error of the non-linear data using the RC based on RBFN. The experimental result is given to verify its efficiency compared with those of previous works.

• Journal of Energy Engineering
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• v.9 no.4
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• pp.372-378
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• 2000

확산 모형은 원래 연속적인 확산과정을 개발하기 위하여 도입되었으나, 기본적인 확산 모형은 많은 제약과 가정을 내포하고 있다. 본 논문은 잘못된 추정의 가능성을 줄이기 위해 많은 제약하에서 유용한 데이터가 별로 많지 않은 상태에서의 파라메터 추정을 시도하였으며, DSM 프로그램의 효과를 예측하기 위하여 피이드 백 방법과 비선형 최소자승법에 의한 파라메터를 추정하였다. 또한 DSM에 많은 영향을 끼치는 광고효과를 반영하기 위하여 본 연구에서는 마케팅 변수에 의존하는 수정된 확산모델을 이용하여 수요관리의 모니터링 시스템을 연구하였다. 본 논문에서는 고효율 조명기기에 의한 사례 연구를 통하여 DSM 효과에 의한 전력수요 변화를 추정하였으며, 우리나라 처럼 축적된 자료의 양이 적은 상황에서 초반 추정 오류의 가능성을 줄일 수 있는 방안을 제시하였다.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1865_1866
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• 2009

본 연구에서는 복잡하고 비선형 시스템의 모델을 동정하기 위해 Information Granulation에 기반한 퍼지추론 시스템의 새로운 범주를 소개한다. Information Granulation은 근접성, 유사성 EH는 기능성 등에 인하여 서로 결합되는 대상(특히, 데이터)의 연결된 모임으로 간주된다. HCM클러스터링에 의한 Information Granulation은 퍼지 규칙의 전반부 및 후반부에서 사용되는 멤버쉽 함수의 초기 정점과 다항식함수의 초기 값과 같은 퍼지 모델의 초기 파라미터를 결정하는데 도움을 준다. 그리고 초기 파라미터는 PSO 알고리즘과 최소자승법에 의해 효과적으로 동조된다. 제안된 모델은 Box와 jenkins가 사용한 가스로 공정[6]을 모델링하여 기존 퍼지 모델링 방법과 비교 평가한다.