• The Journal of the Acoustical Society of Korea
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• v.15 no.1E
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• pp.59-64
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• 1996

When the input-output record is available, the identification of a bilinear system is considered. It is assumed that the input is noise free and the output is contaminated by an additive noise. It is further assumed that the covariance matrix of the noise is known up to a factor of proportionality. The extended generalized total least squares (e-GTLS) method is proposed as one of the consistent estimators of the bilinear system parameters. Considering that the input is noise-free and that bilinear system equation is linear with respect to the system parameters, we extend the GTLS problem. The extended GTLS problem is reduced to an unconstrained minimization problem, and is solved by the Newton-Raphson method. We compare the GTLS method and the e-GTLS method in the point of the accuracy of the estimated system parameters.

• Journal of Electrical Engineering and information Science
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• v.2 no.3
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• pp.113-122
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• 1997

An adaptive input-output linearization technique for a robust speed control of a brushless C(BLDC) motor is presented. By using this technique, the nonlinear moro model can be effectively linearized in Brunovski canonical form, an the desired speed dynamics can be obtained based on the linearized model. This control technique, however, gives an undesirable output performance under the mismatch of the system parameters and load conditions caused by the incomplete linearization. for the robust output response, the controller parameters will be estimated by a model reference adaptive technique where the disturbance torque and flux linkage are estimated. The adaptation laws are derived by the Popov's hyperstability theory nd positivity concept. The proposed control scheme is implemented on a BLDC motor using the software of DSP TMS320C30 and the effectiveness is verified through the comparative simualtions and experiments.

• Journal of the Korean Society of Hazard Mitigation
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• v.10 no.3
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• pp.13-20
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• 2010

To evaluate the performance of safety apparatus without the full scale crash test, the computer simulation is inevitable. But, to improve the accuracy of computer simulation, it is important to reasonably determine the input parameters in which the interaction of vehicle-guardrail-soil should be accounted for. This study is focused on how to enhance the reliance of the dynamic performance of guardrail obtained by computer simulation. Analyses were done on the sensitivity of output variables to the change of input parameters by using BARRIER VII of which the usefulness was proved on the barrier-vehicle impact analysis. Through the analyses important input parameters, which give sensitive effects to output of computer simulation, are found out, and methods to determine such parameters are suggested to improve the accuracy of simulation.

• Proceedings of the KIPE Conference
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• 1997.07a
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• pp.89-96
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• 1997

A robust speed control scheme for a brushless DC(BLDC) motor using an adaptive input-output linearization technique is presented. By using this technique, the nonlinear motor model can be linearized in Brunovski canonical form, and the desired speed dynamics can be obtained based on the linearized model. This control technique, however, gives an undesirable output performance under the mismatch of the system parameters and load conditions. For the robust output response, the controller parameters will be estimated by a model reference adaptive technique where the disturbance torque and flux linkage are estimated. The adaptation laws are derived by the Popov's hyperstability theory and positivity concept. The proposed control scheme is implemented on a BLDC motor using the software of DSP TMS320C30 and the effectiveness is verified through the comparative experiments.

• Journal of Korean Society for Atmospheric Environment
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• v.5 no.1
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• pp.1-10
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• 1989

The sensitivity analysis is a method to quantify to what extent the output of a model changes with the values of input parameters. This will lead to increase model accuracy through measurement validation. Three line source air quality models, HIWAY 2, PAL, and CALINE 3 were selected for this study. The input parameters analysed included wind speed, wind direction, stability, emission rate, mixing height, receptor distance, initial dispersion coefficient, surface roughness, and averaging time. It turned out that PAL model generally showed higher concentration than other two models, and that between CALINE 3 and HIWAY 2, CALINE 3 showed higher concentration than HIWAY 2 model near the line sources, but beyond a certain downwind distances HIWAY 2 model showed higher concentration. The modesl were very sensitive to wind speed especially in the range of 0 $\sim$ 1 m/s and to wind direction near the parallel wind to streets. In case of emission rate, the output concentration was directly proportional to these input parameters. And the sensitivity of the input parameters such as stability, mixing height, initial dispersion coefficient, surface roughness, and averaging time were not very significant.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.273-275
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• 2006

In this paper, we introduce a new design of fuzzy neural networks using input-output data information of target system. The proposed fuzzy neural networks is constructed by input-output data information and used the center of data distance by HCM clustering to obtain the characteristics of data. A membership function is defined by HCM clustering and is applied input-output dat included each rule to conclusion polynomial functions. We use triangular membership functions and simplified fuzzy inference, linear fuzzy inference, and modified quadratic fuzzy inference in conclusion. In the networks learning, back propagation algorithm of network is used to update the parameters of the network. The proposed model is evaluated with benchmark data.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.6
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• pp.767-781
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• 2013

In this paper, we proposes the AC input voltage and current sensorless control scheme to control the input power factor and DC output voltage of the three-phase Z-source PWM rectifier. For DC-link voltage control which is sensitive to the system parameters of the PWM rectifier, fuzzy-PI controller is used. Because the AC input voltage and current are estimated using only the DC-link voltage and current, AC input voltage and current sensors are not required. In addition, the unity input power factor and DC output voltage can be controlled. The phase-angle of the detected AC input voltage and estimated voltage, the response characteristics of the DC output voltage according to the DC voltage references, the FFT results of the estimated voltage and current, efficiency, and the response characteristics of the conventional PI controller and fuzzy-PI controller are verified by PSIM simulation.

• Bulletin of the Korean Chemical Society
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• v.24 no.3
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• pp.279-284
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• 2003

The One Pass Method (OPM) previously presented for the identification of single input single output systems is used to estimate the parameters of a Decentralized Control System (DCS). The OPM is a linear and therefore a simple estimation method. All of the calculations are performed in one pass, and no initial parameter guess, iteration, or powerful search methods are required. These features are of interest especially when the parameters of multi input-output model are estimated. The benefits of the OPM are revealed by comparing its results against those of two recently published methods based on pulse testing. The comparison is performed using two databases from the literature. These databases include single and multi input-output process transfer functions and relevant disturbances. The closed loop responses of these processes are roughly captured by the previous methods, whereas the OPM gives much more accurate results. If the parameters of a DCS are estimated, the OPM yields the same results in multi or single structure implementation. This is a novel feature, which indicates that the OPM is a convenient and practice method for the parameter estimation of multivariable DCSs.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.10
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• pp.697-702
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• 2004

In this paper, we proposed that the problem of controlling induction motor with magnetic saturation, is studied from an input-output feedback linearization with adaptive algorithm. is considered. An adaptive input-output feedback linearizing controller is considered under the assumption of known motor parameters and unknown load torque. In order to achieve the speed regulation with the consideration of improving power efficiency, rotor angular speed and flux amplitude tracking objectives are formulated. Simulation results are provided for illustration.

• Magazine of the Korean Society of Agricultural Engineers
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• v.39 no.1
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• pp.41-47
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• 1997

A geographical resource analysis support system (GRASS) was incorporated to an input and output processor for the agricultural nonpoint source pollution (AGNPS) model. The resulting interface system, GIS-AGNPS was a user-friendly, menu-driven system. GIS-AGNPS was developed to automatically process the input and output data from GIS-based data using GRASS and Motif routines. GIS-AGNPS was consisted of GISAGIN which was an input processor for the AGNPS model, GISAGOUT a output processor for the AGNPS and management submodel. The system defines an input data set for AGNPS from attributes of basic and thematic maps. It also provides with editing modes so that users can adjust and detail the values for selected input parameters, if needed. The post-processor at the system displays graphically the outputs from AGNPS, which may he used to identify areas significantly contributing nonpoint source pollution loads.