• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1253-1256
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• 1996

In this paper, a new nonlinear feedback linearization control scheme for induction motors is developed. The control scheme employs a fuzzy nonlinear identification scheme based on fuzzy basis function expansion to adoptively compensate the parameter variations, i.e. rotor resistance, mutual and self inductance etc. An important feature of the proposed control scheme is to incorporate the sliding mode controller into the scheme to speed up convergence rate. Simulation tests show the robust behavior of the proposed controller in the presence of the parameter uncertainties of the machine.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.28A no.9
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• pp.692-699
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• 1991

In this paper, graphical representations of parameter variations affected by series or parallel feedback in the load plane were studied. As a result of transformations, all parameters of 2-port was converted to circles, and the step of noise circles and gain circles was 0.1 dB and 1dB, respectively. Compared with conventional commercial CAD softwares, the circuit design procedures could be verified much more systemically. This CAD was programmed in C-language and executed in IBM-PC with VGA graphic adaptor.

• 한국신재생에너지학회:학술대회논문집
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• 2008.05a
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• pp.138-141
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• 2008

The effects of design parameters for small scale hydro power(SSHP) plants due to rainfall condition have been studied. The model to predict hydrologic performance for SSHP plants is used in this study. The results from analysis for rainfall conditions based on Weibull distribution show that the capacity and load factor of SSHP site had large difference between the variation of shape and scale parameter. Especially, the hydrologic performance of SSHP site due to variation of shape parameter varied more sensitive than the case of variation of scale parameter. And also, the methodology represented in this study can be used to decide the primary design specifications of SSHP sites.

• Steel and Composite Structures
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• v.20 no.5
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• pp.1087-1102
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• 2016

The paper proposes to characterize the free vibration behaviour of non-uniform cylindrical shells using spline approximation under first order shear deformation theory. The system of coupled differential equations in terms of displacement and rotational functions are obtained. These functions are approximated by cubic splines. A generalized eigenvalue problem is obtained and solved numerically for an eigenfrequency parameter and an associated eigenvector which are spline coefficients. Four and two layered cylindrical shells consisting of two different lamination materials and plies comprising of same as well as different materials under two different boundary conditions are analyzed. The effect of length parameter, circumferential node number, material properties, ply orientation, number of lay ups, and coefficients of thickness variations on the frequency parameter is investigated.

• The Journal of the Acoustical Society of Korea
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• v.15 no.3E
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• pp.78-85
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• 1996

The set of admissible time-variations in the input signal can be separated into two categories : slow parameter changes and large parameter changes which occur infrequently. A common approach used in the tracking of slowly time-varying parameters is the exponential recursive least-squares(RLS) algorithm. There have been a variety of research works on the error analysis of the exponential RLS algorithm for the slowly time-varying parameters. In this paper, the focus has been given to the error analysis of exponential RLS algorithms for the input data with abrupt property changes. The voiced speech signal is chosen as the principal application. In order to analyze the error performance of the exponential RLS algorithm, deterministic properties of the exponential RLS algorithms is first analyzed for the case of abrupt parameter changes, the impulsive input(or error variance) synchronous to the abrupt change of parameter vectors actually enhances the convergence of the exponential RLS algorithm. The analysis has also been verified through simulations on the synthetic speech signal.

• New & Renewable Energy
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• v.4 no.3
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• pp.15-22
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• 2008

The effects of design parameters for small scale hydro power (SSHP) plants due to rainfall condition have been studied. The model to predict hydrologic performance for SSHP plants is used in this study. The results from analysis for rainfall conditions based on Weibull distribution show that the capacity and load factor of SSHP site had large difference between the variation of shape and scale parameter. Especially, the hydrologic performance of SSHP site due to variation of shape parameter varied more sensitive than the case of variation of scale parameter. And also, the methodology represented in this study can be used to decide the primary design specifications of SSHP sites.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.493-495
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• 1998

Robust design is one method to make manufacturing less sensitive to manufacturing process. Also it is cost effective technique to improve the quality process. This method uses statistically planned experiments to vary settings of important process control parameters. In this paper we apply fuzzy optimization and fuzzy logic system to robust design concept. First a method which uses fuzzy optimization in obtaining optimum settings by measured data from experiments will be presented. Second, fuzzy logic system is made to reduce experiments using experiments results consisted with key control parameter combinations. Then optimum parameter set points are obtained by the descrebed first fuzzy optimization method after prediction the results of each parameter combinations considering each control parameter variations by nonsingleton fuzzy logic system concept.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.53 no.3
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• pp.188-195
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• 2004

This paper presents neural load torque observer that is used to deadbeat load torque observer and gain compensation by parameter estimator As a result, the response of the PMSM(permanent magnet synchronous motor) follows that nominal plant. The load torque compensation method is composed of a neural deadbeat observer To reduce the noise effect, the post-filter implemented by MA(moving average) process, is adopted. The parameter compensator with RLSM (recursive least square method) parameter estimator is adopted to increase the performance of the load torque observer and main controller The parameter estimator is combined with a high performance neural load torque observer to resolve the problems. The neural network is trained in on-line phases and it is composed by a feed forward recall and error back-propagation training. During the normal operation, the input-output response is sampled and the weighting value is trained multi-times by error back-propagation method at each sample period to accommodate the possible variations in the parameters or load torque. As a result, the proposed control system has a robust and precise system against the load torque and the Parameter variation. A stability and usefulness are verified by computer simulation and experiment.

• Journal of Power Electronics
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• v.8 no.4
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• pp.354-362
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• 2008

This paper presents neural load torque compensation method which is composed of a deadbeat load torque observer and gains compensation by a parameter estimator. As a result, the response of the PMSM (permanent magnet synchronous motor) obtains better precision position control. To reduce the noise effect, the post-filter is implemented by a MA (moving average) process. The parameter compensator with an RLSM (recursive least square method) parameter estimator is adopted to increase the performance of the load torque observer and main controller. The parameter estimator is combined with a high performance neural load torque observer to resolve problems. The neural network is trained in online phases and it is composed by a feed forward recall and error back-propagation training. During normal operation, the input-output response is sampled and the weighting value is trained multi-times by the error back-propagation method at each sample period to accommodate the possible variations in the parameters or load torque. As a result, the proposed control system has a robust and precise system against load torque and parameter variation. Stability and usefulness are verified by computer simulation and experiment.

• Proceedings of the ESK Conference
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• 1993.10a
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• pp.124-132
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• 1993

This study compared two prediction methods-regression and artificial neural network (ANN) on the visual search performance when monitoring a multi-parameter screen with different occurrence frequencies. Under the highlighting condition for the highest occurrence frequency parameter as a search cue, it was found from the requression analysis that variations of mean search time (MST) could be expained almost by three factors such as the number of parameters, the target occurrence frequency of a highlighted parameter, and the highlighted parameter size. In this study, prediction performance of ANN was evaluated as an alternative to regression method. Backpropagation method which was commonly used as a pattern associator was employed to learn a search behavior of subjects. For the case of increased number of parameters and incresed target occurrence frequency of a highlighted parameter, ANN predicted MST's moreaccurately than the regression method (p<0.000). Only the MST's predicted by ANN did not statistically differ from the true MST's. For the case of increased highlighted parameter size. both methods failed to predict MST's accurately, but the differences from the true MST were smaller when predicted by ANN than by regression model (p=0.0005). This study shows that ANN is a good predictor of a visual search performance and can substitute the regression method under certain circumstances.