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

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.58 no.9
• /
• pp.1769-1774
• /
• 2009

An algorithm for extracting the BJT DC model parameter values for SPICE model is proposed. The nonlinear optimization method for analyzing the device I-V data using the Levenberg-Marquardt algorithm is proposed and the method for calculating initial conditions of model parameters to improve the convergence characteristics is proposed. The base current and collector current obtained from the proposed method shows the root mean square error of 6.04% compared with the measured data of the PNP BJT named 2SA1980.

• Journal of the Korean Society for Precision Engineering
• /
• v.16 no.7
• /
• pp.178-187
• /
• 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
• /
• v.5 no.6
• /
• pp.727-738
• /
• 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.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2018.05a
• /
• pp.448-448
• /
• 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
• /
• 2001.10a
• /
• pp.110-110
• /
• 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.

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

• Journal of Korea Society of Industrial Information Systems
• /
• v.6 no.3
• /
• pp.123-127
• /
• 2001

The prediction method of the parameter C/sub gs/ of CMOS transistor is proposed by calculating the mobil charge in inversion layer of COMS transistor. This parameter C/sub gs/ decided on the cutoff frequency in MOS transistor in RF range and coupled input and output. This parameter C/sub gs/ in RF range is very important parameter in small signal circuit model. This proposed method is contributed to developing software of extracting parameter value in equivalent circuit model. The method provide the important information to construct a RF nonlinear model for multifinger gate MOSFET. This method will be very valuable to develop a large signal MOSFET model for nonlinear RF IC design.

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

• Proceedings of the Korea Water Resources Association Conference
• /
• 2010.05a
• /
• pp.591-595
• /
• 2010

Conventional methods of model evaluation usually rely only on model performance based on a comparison of simulated variables to corresponding observations. However, this type of model evaluation has been criticized because of its insufficient consideration of the various uncertainty sources involved in modeling processes. This study aims to propose an extended model evaluation method using multiple assesment indices (MAIs) that consider not only the model performance but also the model structure and parameter uncertainties in rainfall-runoff modeling. A simple reservoir model (SFM) and distributed kinematic wave models (KWMSS1 and KWMSS2 using topography from 250m, 500m, and 1km digital elevation models) were developed and assessed by three MAIs for model performance, model structural stability, and parameter identifiability. All the models provided acceptable performance in terms of a global response, but the simpler SFM and KWMSS1 could not accurately represent the local behaviors of hydrographs. In addition, SFM and KWMSS1 were structurally unstable; their performance was sensitive to the applied objective functions. On the other hand, the most sophisticated model, KWMSS2, performed well, satisfying both global and local behaviors. KMSS2 also showed good structural stability, reproducing hydrographs regardless of the applied objective functions; however, superior parameter identifiability was not guaranteed. Numerous parameter sets could lead to indistinguishable hydrographs. This result supports that while making a model complex increases its performance accuracy and reduces its structural uncertainty, the model is likely to suffer from parameter uncertainty. The proposed model evaluation process can provide an effective guideline for identifying a reliable hydrologic model.