• 한국지진공학회논문집
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• 제6권6호
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• pp.7-15
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• 2002

본 논문에서는 다자유도계 구조물의 진동대 실험결과 분석에서 효율적인 지진손상도 평가 및 소성모형 추정을 목적으로, 계측결과를 각 부재별 소성이력으로 환산하고 이 소성이력에 대해 비선형 계수 추정법을 적용하는 다단계 방안을 연구하였다. 이때, 추정된 부재별 소성이력은 부재별 지진 손상도를 평가하는 지표로 활용될 수 있으며, 추정된 비선형 모형 계수를 이용하여 구축된 비선형 다자유도계 구조는 다양한 구조재해석의 모형으로 활용될 수 있다. 제시된 방법의 검증을 위해, 해석적 방법과 실험적 방법의 예제해석이 수행되었다. 예제해석 결과는 해석적 방법과 실험적 방법 모두에서 본 논문의 방법이 매우 효과적임을 보여 주고 있다.

• Earthquakes and Structures
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• 제8권4호
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• pp.915-934
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• 2015

Within the context of Structural Health Monitoring (SHM), it is often the case that structural systems are described by uncertainty, both with respect to their parameters and the characteristics of the input loads. For the purposes of system identification, efficient modeling procedures are of the essence for a fast and reliable computation of structural response while taking these uncertainties into account. In this work, a reduced order metamodeling framework is introduced for the challenging case of nonlinear structural systems subjected to earthquake excitation. The introduced metamodeling method is based on Nonlinear AutoRegressive models with eXogenous input (NARX), able to describe nonlinear dynamics, which are moreover characterized by random parameters utilized for the description of the uncertainty propagation. These random parameters, which include characteristics of the input excitation, are expanded onto a suitably defined finite-dimensional Polynomial Chaos (PC) basis and thus the resulting representation is fully described through a small number of deterministic coefficients of projection. The effectiveness of the proposed PC-NARX method is illustrated through its implementation on the metamodeling of a five-storey shear frame model paradigm for response in the region of plasticity, i.e., outside the commonly addressed linear elastic region. The added contribution of the introduced scheme is the ability of the proposed methodology to incorporate uncertainty into the simulation. The results demonstrate the efficiency of the proposed methodology for accurate prediction and simulation of the numerical model dynamics with a vast reduction of the required computational toll.

• 한국염색가공학회지
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• 제34권4호
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• pp.250-260
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• 2022

In this research, a high-elasticity acrylic emulsion binder with core-shell polymerization and self-crosslinking system is mixed with a flame-retardant water-dispersed polyurethane (PUD) binder. In addition, finite element analysis was conducted through virtual engineering software ANSYS by applying three representative nonlinear material models. The most suitable nonlinear material model was selected after the relative comparison between the actual experimental values and the predicted values of the properties derived from simulations. The selected nonlinear material model is intended to be used as a nonlinear material model for computational simulation analysis that simulates the experimental environment of the vibration test (ASTM E1399) and the actual fire safety test (ASTM E1966). When the mass fraction of thermally expandable graphite was 0.7%, the thermal and physical properties were the best. Among the nonlinear material models, the simulation result of the Ogden model showed the closest value to the actual result.

• 한국대기환경학회지
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• 제7권3호
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• pp.156-168
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• 1991

The purpose of this paper is to develop a stochastic model for daily sulphur dioxide $(SO_2)$ concentrations prediction in urban area (Seoul). For this, the influence of the meteorological parameters on the $SO_2$ concentrations is investigated by a statistical analysis of the 24-hr averaged $SO_2$ levels of Seoul area during 1989 $\sim$ 1990. The annual fluctuations of the regression trend, periodicity and dependence of the daily concentration are also analyzed. Based on these, a nonlinear regression transfer function model for the prediction of daily $SO_2$ concentrations is derived. A statistical procedure for using the model to predict the concentration level is also proposed.

• Journal of the Korean Data and Information Science Society
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• 제14권2호
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• pp.337-343
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• 2003

In this paper we present the prediction interval estimation method using bootstrap method for least squares support vector machine(LS-SVM) regression, which allows us to perform even nonlinear regression by constructing a linear regression function in a high dimensional feature space. The bootstrap method is applied to generate the bootstrap sample for estimation of the covariance of the regression parameters consisting of the optimal bias and Lagrange multipliers. Experimental results are then presented which indicate the performance of this algorithm.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2000년도 전력전자학술대회 논문집
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• pp.339-342
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• 2000

Repetitive Load Prediction is proposed for the UPS inverter application of the second order deadbeat controller which is robust against the calculation time delay and the parameter variation and which gets fast response against the load variation. The proposed technique predicts the load current ahead of two sampling time using that the load current is periodic. This is effective under nonlinear load condition. The proposed technique is derived theoretically and verified through simulation and experimental result.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2000년도 춘계학술대회 논문집
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• pp.529-536
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• 2000

This paper describes the analysis of aerodynamics and the prediction of airflow induced noise around simplified pantograph. First, computational fluid dynamics (CFD) is conducted far several model to evaluate linear/nonlinear flow field characteristics due to high speed flow and the CFD results support the computational aeroacoustics. The accurate prediction of the aeroacoustic analysis is necessary for designers to control and reduce the airflow induced noise. We adopt the acoustic analogy based on Ffowcs Williams- Hawkings (FW-H) equation and predict aeroacoustic noise.

• 디지털산업정보학회논문지
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• 제5권3호
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• pp.141-145
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• 2009

In this paper, we propose an efficient lossless compression algorithm using spatial and temporal information. The proposed method obtains higher lossless compression of images than other lossless compression techniques. It is divided into two parts, a motion adaptation based predictor part and a residual error coding part. The proposed nonlinear predictor can reduce prediction error by learning from its past prediction errors. The predictor decides the proper selection of the spatial and temporal prediction values according to each past prediction error. The reduced error is coded by existing context coding method. Experimental results show that the proposed algorithm has better performance than those of existing context modeling methods.

• Industrial Engineering and Management Systems
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• 제14권3호
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• pp.312-317
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• 2015

The dynamic system approach in time series has been used in many real problems. Based on Taken's embedding theorem, we can build the predictive function where input is the time delay coordinates vector which consists of the lagged values of the observed series and output is the future values of the observed series. Although the time delay coordinates vector from multivariate time series brings more information than the one from univariate time series, it can exhibit statistical redundancy which disturbs the performance of the prediction function. We apply dimension reduction techniques to solve this problem and analyze the effect of this approach for prediction. Our experiment uses delayed Lorenz series; least squares support vector regression approximates the predictive function. The result shows that linearly preserving projection improves the prediction performance.

• 한국수자원학회논문집
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• 제39권12호
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• pp.997-1012
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• 2006

본 연구에서는 조건부 핵밀도함수와 CAFPE(Corrected Asymptotic Final Prediction Error) 차수결정 방법에 근거한 비매개변수적 비선형 자기회귀 (Nonlinear AutoRegressive, NAR) 모형을 소개하고 이를 SOI(Southern Oscillation Index)에 적용하였다. SOI 자료에 대해서 선형 AR 모형을 적용하였으나 잔차에 대한 검정결과 이분산성(heteroscedasticity)을 나타내었다. 또한 BDS(Brock-Dechert-Sheinkman) 검정에서 비선형성이 존재함을 확인하였다. 따라서 NAR 모형에 SOI 자료를 적용시켰다. CAFPE를 이용하여 가장 적합한 모형으로 지체 1, 2와 4가 선택되었으며 조건부 평균함수를 추정하여 SOI 자료를 모의한 결과 잔차에 대해서 정규성과 이분산성 가정이 Jarque-Bera 검정과 ARCH-LM 검정에서 각각 기각되었으며 또한 조건부 표준편차함수의 최적 차수로 3, 8과 9가 CAPFE를 통해 선택되었다. 조건부 평균함수와 표준편차함수를 모두 고려한 모형에 대한 잔차 검정 결과 잔차의 I.I.D 가정을 만족하였으며 특히, BDS 검정에서 신뢰구간 95%와 99%에서 모두 만족한 결과를 나타내었다. 마지막으로 전체의 15%에 해당하는 SOI 자료에 대해서 One-Step 예측을 수행하였으며 선형 모형에 비해 평균제곱예측오차가 7% 적게 나타났다. 따라서, NAR 모형은 여타의 매개변수적 방법과 달리 모형 선택에 있어 자유로우며 비선형성을 고려할 수 있는 모형으로서 SOI 자료와 같은 비선형 자료를 위한 모의방법으로 선형 모형에 비해 많은 장점을 가지고 있다.