• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.11b
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• pp.1260-1265
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• 2001

It is important to model the mechanical structure precisely and reasonably in predicting the dynamic characteristics, controlling the vibration, and designing the structural dynamics. In the finite element modeling, the errors can be contained from the physical parameters, the approximation of the boundary conditions, and the element modeling, From the dynamic test. more precise dynamic characteristics can be obtained. Model updating using parameter modification is appropriate when the design parameter is used to analyze the input parameter like finite element method. Finite element analysis for free-free-free-free(FFFF) and clamped-free-free-free(CFFF) plate with uniform area and shape change are carried out as model updating examples, Mass and stiffness matrices are updated by comparing test and analytical modal frequencies. The result shows that the updated frequencies become closer to the test frequencies.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.7
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• pp.178-187
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• 1999

In order to identify a transfer function model with noise, penalty function method has been widely used. In this method, estimation process for possible model parameters from low to higher order proceeds the model identification process. In this study, based on linear estimation method, a new approach unifying the estimation and the identification of ARMAX model is proposed. For the parameter estimation of a transfer function model with noise, linear estimation method by noise separation is suggested instead of nonlinear estimation method. The feasibility of the proposed model identification and estimation method is verified through simulations, namely by applying the method to time series model. In the case of time series model with noise, the proposed method successfully identifies the transfer function model with noise without going through model parameter identification process in advance. A new algorithm effectively achieving model identification and parameter estimation in unified frame has been proposed. This approach is different from the conventional method used for identification of ARMAX model which needs separate parameter estimation and model identification processes. The consistency and the accuracy of the proposed method has been verified through simulations.

• Journal of Environmental Science International
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• v.5 no.6
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• pp.727-738
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• 1996

An one dimensional atmosphere-canopy-soil interaction model is developed to estimate of the heat budget parameter in the atmospheric boundary layer. The canopy model is composed of the three balance equations of energy, temperature, moisture at ground surface and canopy layer with three independent variables of Tf(foliage temperature), Tg(ground temperature), and qg(ground specific humidity). The model was verilied by comparative study with OSUID(Oregon State University One Dimensional Model) proved in HAPEX-MOBILHY experiment. Also we applied this model in two dimensional land-sea breeze circulation. According to the results of this study, surface characteristics considering canopy acted importantly upon the simulation of meso-scale circulation. The factors which used in the numerical experiment are as follows ; the change for a sort of soil(sand and peat), the change for shielding factor, and the change for a kind of vegetation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.1B
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• pp.17-24
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• 2008

The effect of the variable packet size on the LRD characteristic of the MMPP traffic model is investigated. When we generate packet traffic for the performance evaluation of IP packet network, MMPP model can be used to generate packet interarrival time. And a random length of packet size from a certain distribution can be assigned to each packet. However, there is a possibility that the variable packet size might change the LRD characteristic of the original MMPP model. In this study, we investigate this possibility. For this purpose the 'refined traffic' is defined, where packet arrival time is generated according to the MMPP model and a random packet length from a specific distribution is assigned to each generated packet. Hurst parameter of the refined traffic is estimated and compared with the original Hurst parameter, which is the input parameter of the MMPP model. We also investigate the effect of the packet size distribution on the queueing performance of the MMPP traffic model and the relationship between the Hurst parameter and queueing performance.

• 한국데이터정보과학회:학술대회논문집
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• 2005.10a
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• pp.135-138
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• 2005

In this paper, we consider the problem of testing for parameter change in time series models based on a cusum of squares. Although the test procedure is well-established for the mean and variance in time series models, a general parameter case was not discussed in literatures. Therefore, here we develop the cusum of squares type test for parameter change in a more general framework. As an example, we consider the change of the parameters in an RCA(1) model. Simulation results are reported for illustration.

• Proceedings of the IEEK Conference
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• 2003.07b
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• pp.763-766
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• 2003

A direct parameter extraction method using several two-port parameter equations derived in cutoff and active bias modes has been studied to obtain an accurate Gummel-Poon BJT model. First, dc model parameters were extracted from slopes and y-axis intercepts of I-V curve and Gummel plot. The pad capacitances and junction capacitance parameters were determined by using measured S-parameter sets in the cutoff bias. The resistance and transit time parameters were extracted by using measured S-parameter sets in the active bias.

• Journal of the Korean Society of Hazard Mitigation
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• v.10 no.6
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• pp.119-130
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• 2010

The universal model for the parameter estimation of the Storage Function Model(SFM) was developed through the applications of the distributed model for various hypothetical watersheds and runoff conditions. The existing parameter estimation equations are based on observations and these equations which are derived from the restricted conditions are not sensitive to the variation of physical characteristics of a watershed. This study developed the universal model for the parameter estimation through the runoff simulations of 35,000 times. As the simulation results, we have known that the lag time is related to the longest stream channel characteristics and the storage coefficient is related to the watershed characteristics.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.448-448
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• 2018

In this study, the regression models (Load ESTimator and eight-parameter model) were evaluated to estimate instantaneous pollutant loads under various criteria and optimization methods. As shown in the results, LOADEST commonly used in interpolating pollutant loads could not necessarily provide the best results with the automatic selected regression model. It is inferred that the various regression models in LOADEST need to be considered to find the best solution based on the characteristics of watersheds applied. The recently developed eight-parameter model integrated with Genetic Algorithm (GA) and Gradient Descent Method (GDM) were also compared with LOADEST indicating that the eight-parameter model performed better than LOADEST, but it showed different behaviors in calibration and validation. The eight-parameter model with GDM could reproduce the nitrogen loads properly outside of calibration period (validation). Furthermore, the accuracy and precision of model estimations were evaluated using various criteria (e.g., $R^2$ and gradient and constant of linear regression line). The results showed higher precisions with the $R^2$ values closed to 1.0 in LOADEST and better accuracy with the constants (in linear regression line) closed to 0.0 in the eight-parameter model with GDM. In hence, based on these finding we recommend that users need to evaluate the regression models under various criteria and calibration methods to provide the more accurate and precise results for pollutant load estimations.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.110-110
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• 2001

By the state transformation including independent outputs functions, a nonlinear process model can be decomposed into two subsystems; the one(design model) is described in output variables as new states and used for control system synthesis and the other(disturbance model) is described in the original unavailable states and its couplings with the design model are treated as uncertain time-varying parameters in the design model. Its existence with respect to the design model is ignored. So, the design model is and uncertain time-variant system. Control synthesis based on a reduced design model is a combined form of a time-variant input-output linearization with parameter estimation. The parameter estimation is also based on the design model and it gives the parameter estimates such that the estimated outputs follow the actual outputs in a specified way. The disturbances form disturbance model and as well all the other uncertainties affecting the outputs will be reflected into the estimated parameters used in the linearizing control law.

• Structural Engineering and Mechanics
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• v.70 no.2
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• pp.209-219
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• 2019

By normalizing the internal hysteresis variable and eliminating the redundant parameter, the normalized Bouc-Wen model is considered to be an improved and more reasonable form of the Bouc-Wen model. In order to facilitate application and further research of the normalized Bouc-Wen model, some key aspects of the model need to be uncovered. In this paper, hysteresis characterization of the normalized Bouc-Wen model is first studied with respect to the model parameters, which reveals the influence of each model parameter to the shape of the hysteresis loops. The parameter identification scheme is then proposed based on an improved genetic algorithm (IGA), and verified by experimental test data. It is proved that the proposed method can be an efficacious tool for identification of the model parameters by matching the reconstructed hysteresis loops with the target hysteresis loops. Meanwhile, the IGA is shown to outperform the standard GA. Finally, a simplified identification method is proposed based on parameter sensitivity, which indicates that the efficiency of the identification process can be greatly enhanced while maintaining comparable accuracy if the low-sensitivity parameters are reasonably restricted to narrower ranges.