• Journal of Environmental Science International
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• v.2 no.3
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• pp.193-199
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• 1993

This study has two objectives. One is developing the runoff model for Hoe-Dong Reservoir basin located at the upstream of Su-Young River in Pusan. To develop the runoff model, basic hydrological parameters - curve number to find effective rainfall, and storage coefficient, etc. - should be estimated. In this study, the effective rainfall was calculated by the SCS method, and the storage coefficient used in the Clark watershed routing was cited from the report of P.E.B. The other is the derivation of transfer function for Hoe-Dong Reservoir basin. The linear, discrete, input-output model which contained six parameters was selected, and the parameters were estimated by the least square method and the correlation function method, respectively. Throughout this study, rainfall and flood discharge data were based on the field observation in 1981.8.22 - 8.23 (typhoon Gladys). It was observed that the Clark watershed routing regenerated the flood hydrograph of typhoon Gladys very well, and this fact showed that the estimated hydrological parameters were relatively correct. Also, the calculated hydrograph by the linear, discrete, input-output model showed good agreement with the regenerated hydrograph at Hoe-Dong Dam site, so this model can be applicable to other small urban areas. Key Words : runoff, effective rainfall, SCS method, clark watershed iou상ng, hydrological parameters, parameter estimation, least square method, correlation function method, input-output model, typhoon gladys.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.6
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• pp.915-923
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• 2008

This paper focuses on optimal tuning of nonlinear parameters of a dual-input power system stabilizer(dual-input PSS), which can improve the system damping performance immediately following a large disturbance. Until recently, various PSS models have developed to bring stability and reliability to power systems, and some of these models are used in industry applications. However, due to non-smooth nonlinearities from the interaction between linear parameters(gains and time constants of linear controllers) and nonlinear parameters(saturation output limits), the output limit parameters cannot be determined by the conventional tuning methods based on linear analysis. Only ad hoc tuning procedures('trial and error' approach) have been used. Therefore, the steepest descent method is applied to implement the optimal tuning of the nonlinear parameters of the dual-input PSS. The gradient required in this optimization technique can be computed from trajectory sensitivities in hybrid system modeling with the differential-algebraic-impulsive-switched(DAIS) structure. The optimal output limits of the dual-input PSS are evaluated by time-domain simulation in both a single machine infinite bus(SMIB) system and a multi-machine power system in comparison with those of a single-input PSS.

• ETRI Journal
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• v.15 no.2
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• pp.35-51
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• 1993

This paper presents function approximation based on nonparametric estimation. As an estimation model of function approximation, a three layered network composed of input, hidden and output layers is considered. The input and output layers have linear activation units while the hidden layer has nonlinear activation units or kernel functions which have the characteristics of bounds and locality. Using this type of network, a many-to-one function is synthesized over the domain of the input space by a number of kernel functions. In this network, we have to estimate the necessary number of kernel functions as well as the parameters associated with kernel functions. For this purpose, a new method of parameter estimation in which linear learning rule is applied between hidden and output layers while nonlinear (piecewise-linear) learning rule is applied between input and hidden layers, is considered. The linear learning rule updates the output weights between hidden and output layers based on the Linear Minimization of Mean Square Error (LMMSE) sense in the space of kernel functions while the nonlinear learning rule updates the parameters of kernel functions based on the gradient of the actual output of network with respect to the parameters (especially, the shape) of kernel functions. This approach of parameter adaptation provides near optimal values of the parameters associated with kernel functions in the sense of minimizing mean square error. As a result, the suggested nonparametric estimation provides an efficient way of function approximation from the view point of the number of kernel functions as well as learning speed.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.291-294
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• 2006

This paper presents a decentralized input-output feedback linearizing controller for a multi-machine power system. Firstly, the controller is designed using input-output feedback linearization for modified outputs. Then we present a guideline for selecting gains of the controller and parameters in the modified outputs. Simulations illustrate the effectiveness of the proposed control scheme and the selection guideline.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.38 no.2
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• pp.137-143
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• 1989

System modeling from input-output data is generally carried out in two steps. The first step is to determine the form of the model. In the second step, the parameters of the model in an appropriate form are estimated from input-output data. This paper presents a method, via single term Walsh functions, for simultaneous estimation of the order and the parameters of linear systems from input-output data. The estimation of the model order is based on minimizing an error function, which is defined by Desai and Fairman. Unknown system parameters are recursively estimated by the least square method.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.8
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• pp.1997-2004
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• 1994

A model analysis method has been developed in the paper. The method estimates the modal parameters of multiple input-output systems, assesses their quality, and seperates structural modes form computation ones. The modal parameter extraction algorithm is the least squares method with a finite difference model relating input and output time data. The quality of the estimated system model can be assessed in narrow frequency bands by comparing the measured and model predicted responses in time domain with the aid of digital filters. Structural modes can be effectively separated from computational ones using the convergence factor which represents the pole convergence rate. The modal analysis method has been applied to simulated and experimental vibration data to evaluate its utility and limitations.

• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.2
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• pp.70-78
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• 1995

The identification of a fuzzy model using input-output data consists of two parts :Structure identification and parameter identification. In this paper an algorithm to identify those parameters and structures is suggested to solve the problems of the conventional methods. Given a set of input-output data, the consequent parameters are identified by the Hough transform and clustering method, each of which considers the linearity and continuity respectively. The gradient descent algorithm is used to fine-tune parameters of a fuzzy model. Finally, it is shown that this method is useful for the identification of a fuzzy model by simulation, where we only consider a single input and single output system.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1990.04a
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• pp.272-288
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• 1990

The uncertainty quantification process in probabilistic Risk Assessment usually involves a specification of the uncertainty in the input data and the propagation of this uncertainty to the final risk results. The distributional sensitivity analysis is to study the impact of the various assumptions made during the quantification of input parameter uncertainties on the final output uncertainty. The uncertainty importance of input parameters, in this case, should reflect the degree of changes in the whole output distribution and not just in a point estimate value. A measure of the uncertainty importance is proposed in the present paper. The measure is called the distributional sensitivity measure(DSM) and explicitly derived from the definition of the Kullback's discrimination information. The DSM is applied to three typical discrimination information. The DSM is applied to three typical cases of input distributional changes: 1) Uncertainty is completely eliminated, 2) Uncertainty range is increased by a factor of 10, and 3) Type of distribution is changed. For all three cases of application, the DSM-based importance ranking agrees very well with the observed changes of output distribution while other statistical parameters are shown to be insensitive.

• Journal of Power Electronics
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• v.12 no.3
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• pp.439-447
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• 2012

In this paper, an input-series auxiliary power supply scheme is proposed, which is suitable for high input voltage and multiple-output applications. The power supply scheme is based on a two-transistor forward topology, all of the series modules have a common duty ratio, all the switches are turned on and off simultaneously, and the whole circuit has a single power transformer. It does not require an additional controller but still achieves efficient input voltage sharing (IVS) for each series module through its inherent transformer-integration strategy. The IVS process of this power supply scheme is analyzed in detail and the design considerations for the related parameters are given. Finally, a 100W multiple-output auxiliary power supply prototype is built, and the experimental results verify the feasibility of the proposed scheme and the validity of the theoretical analysis.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.1081-1084
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• 1991

A technique of indirect adaptive control based on certainty equivalence for input output linearization of nonlinear system is proven convergent by Teel. It incorporates an adaptive observer for identifying unknown system states and parameters and input-output linearizing controller for robust tracking. In this study, we show that robustness and tracking performances are improved considerably by using its normalized form of Teel's observer-based identifier. Simple examples are presented as illustration.