• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2005년도 춘계학술대회 논문집
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• pp.911-914
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• 2005

Soft tissue characterization and modeling based on living tissues has been investigated in order to provide a more realistic behavior in a virtual reality based surgical simulation. In this paper, we characterize the nonlinear viscoelastic properties of intra-abdominal organs using the data from in vivo animal experiments and inverse FE parameter estimation algorithm. In the assumptions of quasi-linear-viscoelastic theory, we estimated the nonlinear material parameters to provide a physically based simulation of tissue deformations. To calibrate the parameters to the experimental results, we developed a three dimensional FE model to simulate the forces at the indenter and an optimization program that updates new parameters and runs the simulation iteratively. The comparison between simulation and experimental behavior of pig intra abdominal soft tissue are presented to provide a validness of the tissue model using our approach.

• Food Science and Biotechnology
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• 제17권6호
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• pp.1352-1356
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• 2008

Bootstrap method, a computer-intensive statistical technique to estimate the distribution of a statistic was applied to deal with uncertainty and variability of the experimental data in stochastic prediction modeling of microbial growth on a chill-stored food. Three different bootstrapping methods for the curve-fitting to the microbial count data were compared in determining the parameters of Baranyi and Roberts growth model: nonlinear regression to static version function with resampling residuals onto all the experimental microbial count data; static version regression onto mean counts at sampling times; dynamic version fitting of differential equations onto the bootstrapped mean counts. All the methods outputted almost same mean values of the parameters with difference in their distribution. Parameter search according to the dynamic form of differential equations resulted in the largest distribution of the model parameters but produced the confidence interval of the predicted microbial count close to those of nonlinear regression of static equation.

• Steel and Composite Structures
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• 제41권6호
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• pp.821-829
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• 2021

Nonlinear dynamic response of a sandwich beam considering porous core and nano-composite face sheet on nonlinear viscoelastic foundation with temperature-variable material properties is investigated in this research. The Hamilton's principle and beam theory are used to drive the equations of motion. The nonlinear differential equations of sandwich beam respect to time are obtained to solve nonlinear differential equations by Homotopy perturbation method (HPM). The effects of various parameters such as linear and nonlinear damping coefficient, linear and nonlinear spring constant, shear constant of Pasternak type for elastic foundation, temperature variation, volume fraction of carbon nanotube, porosity distribution and porosity coefficient on nonlinear dynamic response of sandwich beam are presented. The results of this paper could be used to analysis of dynamic modeling for a flexible structure in many industries such as automobiles, Shipbuilding, aircrafts and spacecraft with solar easured at current time step and the velocity and displacement were estimated through linear integration.

• Earthquakes and Structures
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• 제7권5호
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• pp.817-842
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• 2014

ASCE/SEI 41-13 provides modeling parameters and numerical acceptance criteria for various types of members that are useful for evaluating the seismic performance of reinforced concrete (RC) building structures. To accurately evaluate the global performance of a coupled wall system, it is crucial to first properly define the component behaviors (i.e., force-displacement relationships of shear walls and coupling beams). However, only a few studies have investigated on the modeling of RC coupling beams subjected to earthquake loading to date. The main objective of this study is to assess the reliability of ASCE 41-13 modeling parameters specified for RC coupling beams with various design details, based on a database compiling almost all coupling beam tests available worldwide. Several recently developed coupling beam models are also reviewed. Finally, a rational method is proposed for determining the chord yield rotation of RC coupling beams.

• 한국정보통신학회논문지
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• 제10권1호
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• pp.88-95
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• 2006

PID 제어기는 구조가 간단하고 적용이 용이하다는 장점으로 인하여 널리 사용되고 있는 제어방식이다. 이러한 선형 PID 제어기는 시스템의 파라메터가 변화가 있거나 부하 특성이 비 선형적으로 변화할 때에 적절한 이득과 성능을 얻기 어려워 고성능 제어 특성을 기대하기 어렵다. 본 연구에서는 세포성 면역 반응과 경사감소학습에 기초하여 비선형 PID 제어기를 설계하고, 설계된 제어기의이득과 비선형 함수의 파라메터들을 실시간 적응적으로 학습할 수 있는 학습 알고리즘을 개발하고, 이를 제어시스템에 적용하였다. 제안된 비선형 PID 제어기는 비선형 직류 모터 시스템의 파라메터들이 변화하거나 주파수가 다른 추종 명령에 대하여, 적응적으로 이득을 변화 시키며 추종함을 보였다.

• 한국해양공학회지
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• 제37권1호
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• pp.20-30
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• 2023

Modeling a nonlinear ocean wave is one of the primary concerns in ocean engineering and naval architecture to perform an accurate numerical study of wave-structure interactions. The high-order spectral (HOS) method, which can simulate nonlinear waves accurately and efficiently, was investigated to see its capability for nonlinear wave generation. An open-source (distributed under the terms of GPLv3) project named "HOS-ocean" was used in the present study. A parametric study on the "HOS-ocean" was performed with three-hour simulations of long-crested ocean waves. The considered sea conditions ranged from sea state 3 to sea state 7. One hundred simulations with fixed computational parameters but different random seeds were conducted to obtain representative results. The influences of HOS computational parameters were investigated using spectral analysis and the distribution of wave crests. The probability distributions of the wave crest were compared with the Rayleigh (first-order), Forristall (second-order), and Huang (empirical formula) distributions. The results verified that the HOS method could simulate the nonlinearity of ocean waves. A set of HOS computational parameters was suggested for the long-crested irregular wave simulation in sea states 3 to 7.

• Computers and Concrete
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• 제22권6호
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• pp.539-550
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• 2018

Concrete is highly non-linear material which is originating from the transition zone in the form of micro-cracks, governs material response under various loadings. In this paper, the constitutive models published by many researchers have been used to generate novel stiffness parameters and constitutive curves for concrete. Following such linear material formulations, where the energy is conservative during the curvature, and a nonlinear contribution to the concrete has been made and investigated. In which, nonlinear concrete elastic modulus modeling has been developed that is capable-of representing concrete elasticity for grades ranging from 10 to 140 MPa. Thus, covering the grades range of concrete up to the ultra-high strength concrete, and replacing many concrete models that are valid for narrow ranges of concrete strength grades. This has been followed by the introduction of the nonlinear Hooke's law for the concrete material through the replacement of the Young constant modulus with the nonlinear modulus. In addition, the concept of concrete elasticity index (${\varphi}$) has been proposed and this factor has been introduced to account for the degradation of concrete stiffness in compression under increased loading as well as the multi-stages micro-cracking behavior of concrete under uniaxial compression. Finally, a sub-routine artificial neural network model has been developed to capture the concrete behavior that has been introduced to facilitate the prediction of concrete properties under increased loading.

• 한국시뮬레이션학회논문지
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• 제14권4호
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• pp.55-68
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• 2005

The advancement in semiconductor technology is leading toward smaller critical dimension designs and larger wafer manufactures. Due to such phenomena, semiconductor industry is in need of an accurate control of the process. Photolithography is one of the key processes where the pattern of each layer is formed. In this process, precise superposition of the current layer to the previous layer is critical. Therefore overlay parameters of the semiconductor photolithography process is targeted for this research. The complex relationship among the input parameters and the output metrologies is difficult to understand and harder yet to model. Because of the superiority in modeling multi-nonlinear relationships, neural networks is used for the simulator modeling. For training the neural networks, conjugate gradient method is employed. An experiment is performed to evaluate the performance among the proposed neural network simulator, stepwise regression model, and the currently practiced prediction model from the test site.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.635-639
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• 2004

In this paper, we present a novel approach for the structure of Fuzzy Neural Network(FNN) based on wavelet function and apply this network structure to the modeling of chaotic nonlinear systems. Generally, the wavelet fuzzy model(WFM) has the advantage of the wavelet transform by constituting the fuzzy basis function(FBF) and the conclusion part to equalize the linear combination of FBF with the linear combination of wavelet functions. However, it is very difficult to identify the fuzzy rules and to tune the membership functions of the fuzzy reasoning mechanism. Neural networks, on the other hand, utilize their learning capability for automatic identification and tuning. Therefore, we design a wavelet based FNN structure(WFNN) that merges these advantages of neural network, fuzzy model and wavelet transform. The basic idea of our wavelet based FNN is to realize the process of fuzzy reasoning of wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. And our network can automatically identify the fuzzy rules by modifying the connection weights of the networks via the gradient descent scheme. To verify the efficiency of our network structure, we evaluate the modeling performance for chaotic nonlinear systems and compare it with those of the FNN and the WFM.

• 한국지능시스템학회논문지
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• 제9권5호
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• pp.555-565
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• 1999

복잡하고 비선형적인 시스템의 규칙베이스 퍼지모델링을 위하여 퍼지시스템의 최적 동정알고리즘을 연구한다. 비선형 시스템은 퍼지모델의 입력변수와 퍼지 입력공간 분할에 의한 구조동정과 파라미터 동정을 통해 표현된다. 본 논문에서 규칙베이스 퍼지모델링은 비선형 시스템을 위해 퍼지추론방법과 두 종류의 최적화 이론의 결합에 의한 하이브리드 구졸를 이용하여 시스템 구조와 파라미터동정을 수행한다. 퍼지모델의 추론방법은 간략추론 및 선형추론에 의한다. 제안된 하이브리드 최적 동정 알고리즘은 유전자 알고리즘과 개선된 콤플렉스 방법을 이용한다. 여기서 유전자 알고리즘은 전반부 퍼지규칙의 멤버쉽함수의 초기 파라미터들을 결정하기 위해 사용되고 강력한 자동동조 알고리즘인 개선된 콤플렉스 방법은 정교한 파라미터들을 얻기 위해 수행된다. 따라서 최적 퍼지모델을 위해 전반부 파라미터 동정에는 하이브리드형의 최적 알고리즘을 이용하고 후반부 동정에는 최소자승법을 이용한다. 또한 학습과 테스트 데이터에 의해 생성된 퍼지모델의 성능결과 사이의 상호균형을 얻기 위해 하중계수를 가지는 합성 성능지수를 제안한다. 제안된 모델의 성능평가를 위해 두가지 수치적 예를이용한다.