• 한국자동차공학회논문집
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• 제23권1호
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• pp.56-64
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• 2015

This study presents a model-based gain scheduling strategy for PI-based EGR controllers. The air-to-fuel ratio is used as an indirect measurement of the EGR rate. In order to cope with the nonlinearity and parameter varying characteristics of the EGR system, we proposed a static gain model of the EGR system using a new scheduling parameter. With the 810 steady-state measurements, the static gain model achieved 0.94 of R-squared value. Based on the static gain of the EGR system, the PI gains were robustly designed using quantitative feedback theory. Consequently, the gains of the PI controller are scheduled according to the static gain parameter of the EGR path in runtime. The proposed model-based gain scheduling strategy was validated through various operating conditions of engine experiments such as setpoint step responses and disturbance rejections.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2021년도 학술발표회
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• pp.127-127
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• 2021

In order to estimate parameter uncertainty of hydrological models, the consideration of the likelihood functions which provide reliable parameters of model is necessary. In this study, the Bayesian Markov Chain Monte Carlo (MCMC) method with informal likelihood functions is used to analyze the uncertainty of parameters of the SURR model for estimating the hourly streamflow of Gunnam station of Imjin basin, Korea. Three events were used to calibrate and one event was used to validate the posterior distributions of parameters. Moreover, the performance of four informal likelihood functions (Nash-Sutcliffe efficiency, Normalized absolute error, Index of agreement, and Chiew-McMahon efficiency) on uncertainty of parameter is assessed. The indicators used to assess the uncertainty of the streamflow simulation were P-factor (percentage of observed streamflow included in the uncertainty interval) and R-factor (the average width of the uncertainty interval). The results showed that the sensitivities of parameters strongly depend on the likelihood functions and vary for different likelihood functions. The uncertainty bounds illustrated the slight differences from various likelihood functions. This study confirms the importance of the likelihood function selection in the application of Bayesian MCMC to the uncertainty assessment of the SURR model.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 22-24 Oct. 1991
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• pp.1934-1938
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• 1991

This paper proposes a robust model following control systems with nonlinear time varying plant. which realies good properties such as asymptotic stability, disturbance rejection and model-following with reduced sensitivity for plant parameter variation. The schemes do not incorporate any parameter identification algorithms, but the adaptation is realized through signal synthesis in a fixed parameter structure.

• 대한수학회보
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• 제33권3호
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• pp.377-383
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• 1996

We consider in this paper the following nonlinear regression model $$ (1.1) y_t = f(x_t, \theta_o) + \in_t, t = 1, \ldots, n, $$ where $y_t$ is the tth response, $x_t$ is m-vector imput variable, $\theta_o$ is a p-vector of unknown parameter belong to a parameter space $\Theta, f:R^m \times \Theta \ to R^1$ is a nonlinear known function, and $\in_t$ are independent unobservable random errors with finite second moment.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.2183-2187
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• 2005

In this paper we propose a new fault detection and isolation (FDI) method for those faults of parameter change type. First, we design a residual generator based on the ${\delta}$-operator model of the plant by using the stable pseudo inverse system. Second, the parameter change is estimated by using the property of the block Hankel operator. Third, reliability with respect to stability is quantified. Fourth, the limitations for the meaningful diagnosis in our method are given. The numerical examples demonstrate the effectiveness of the proposed method.

• Journal of the Korean Data and Information Science Society
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• 제16권4호
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• pp.861-870
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• 2005

The Bayesian model selection procedures for the shape parameter of gamma distribution are proposed in order to test that the failure rate of gamma distribution is constant, increasing or decreasing. The encompassing intrinsic Bayes factor by Beger and Pericchi (1996) based on Jeffreys prior for shape parameter is used to investigate the usefulness of the proposed Bayesian model selection procedures via both real data and pseudo data.

• 한국통신학회:학술대회논문집
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• 한국통신학회 1991년도 추계종합학술발표회논문집
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• pp.210-214
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• 1991

We briefly compared GaAs MESFET model and s셔요 on the method of parameter extraction for PSPICE simulation. The parameter determined from above method were substituded into a commercial version of PSPICE which supports the hyperbolic tangentent model. The result of simulation is reasonably good at the lower VGS and is significantly fitted overall by optimization.

• 대한의용생체공학회:의공학회지
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• 제22권3호
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• pp.293-301
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• 2001

A patient-specific musculoskeletal model, whose parameters can be identified noninvasively, was developed for the automatic generation of patient-specific stimulation pattern in FES. The musculotendon system was modeled as a torque-generator and all the passive systems of the musculotendon working at the same joint were included in the skeletal model. Through this, it became possible that the whole model to be identified by using the experimental joint torque or the joint angle trajectories. The model parameters were grouped as recruitment of muscle fibers, passive skeletal system, static and dynamic musculotendon systems, which were identified later in sequence. The parameters in each group were successfully estimated and the maximum normalized RMS errors in all the estimation process was 8%. The model predictions with estimated parameter values were in a good agreement with the experimental results for the sinusoidal, triangular and sawlike stimulation, where the normalized RMS error was less than 17%, Above results show that the suggested musculoskeletal model and its parameter estimation method is reliable.

• 물과 미래
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• 제10권1호
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• pp.79-94
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• 1977

본 연구는 Run-Length와 Run-Sum에 의한 강우량과 하천유량을 분석하여 그 특성을 구명하고, 이로부터 모의발생 모델(Simulation Model)을 설정하여 검토하는데 목적을 두고 있다. 분석에 있어서는 우리나라의 주요도시(서울, 대구 및 부산)의 일강우량과 주요하천(한강, 낙동강 및 금강)의 일류량 자료들을 사용하였다. 또한 해석에 있어서는 Run-Length와 Run-Sum에 대한 각각의 분포형 분석으로부터 Weibull 분포 및 1-변수지수 분포를 하고 있음을 알았으며, 이로부터 Monte Carlo 기법(Monte Carlo technique)을 기초로 하는 Weibull모델(Weibull model)과 1-변수지수 모델(One-Parameter Exponential Model)에 의해서 Run-Length와 Run-Sum을 모의발생 시켰다. 그 결과 기록치(Historical Data)에 근사한 모의발생 자료(Simulation Data)를 얻었다.

• 한국정밀공학회지
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• 제19권11호
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• pp.192-199
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• 2002

This paper demonstrates that hybrid neural-genetic multimodel parameter estimation algorithm can be applied to structured system Identification of electro-hydraulic servo system. This algorithm are consist of a recurrent incremental credit assignment (ICRA) neural network and a genetic algorithm. The ICRA neural network evaluates each member of a generation of model and genetic algorithm produces new generation of model. We manufactured electro-hydraulic servo system and the hybrid neural-genetic multimodel parameter estimation algorithm is applied to the task to find the parameter values(mass, damping coefficient, bulk modulus, spring coefficient) which minimize total square error.