• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.578-580
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• 1998

The paper presents an approach to the structure identification based on the CART (Classification And Regression Tree) algorithm and to the parameter identification by hybrid learning method in neuro-fuzzy system. By using the CART algorithm, the proposed method can roughly estimate the numbers of membership function and fuzzy rule using the centers of decision regions. Then the parameter identification is carried out by the hybrid learning scheme using BP (Back-propagation) and RLSE (Recursive Least Square Estimation) from the numerical data. Finally, we will show it's usefulness for fuzzy modeling to truck backer upper control.

• Proceedings of the KIEE Conference
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• 1998.07f
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• pp.1922-1924
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• 1998

In this paper, the robust vector control method of Induction Motor for the purpose of improving the system performance deterioration caused by parameter variations is proposed. The full order state observer estimates the stator current and the rotor flux by using the state prediction of state variables. And, the motor speed is estimated without speed sensor using the full order state observer. Also, the parameter variation is compensate by the Sliding Observer. By using this method, speed sensorless control and current contol with no affection of the parameter variation can be obtained simultaneously.

• The Transactions of the Korean Institute of Power Electronics
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• v.23 no.5
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• pp.321-327
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• 2018

DC microgrid system composed of multiple converters has a tendency to make the system unstable due to the interaction of converters. To solve this problem, in this paper, the interaction between cascaded converters with LC input filter is analyzed with impedance modeling using g-parameter. The input impedance and the output impedance of the system can be obtained through this technique. The stability of the system can be determined by applying Middlebrook's stability criterion to the impedance. Virtual impedance is added to the controller to enhance stability. The validity of the analysis is verified by the result of several simulations and experiments.

• Journal of the Korean Data and Information Science Society
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• v.21 no.2
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• pp.345-354
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• 2010

The Gamma Distribution is widely used in Engineering and Industrial applications. Estimation of parameters is revisited in the two-parameter Gamma distribution. The parameters are estimated by minimizing the likelihood ratios. A comparative study between the method of moments, the maximum likelihood method, the method of product spacings, and minimization of three different likelihood ratios is performed using simulation. For the scale parameter, the maximum likelihood estimate performs better and for the shape parameter, the product spacings estimate performs better. Among the three likelihood ratio statistics considered, the Anderson-Darling statistic has inferior performance compared to the Cramer-von-Misses statistic and the Kolmogorov-Smirnov statistic.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.748-750
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• 1995

An early detection of cancer is very important for the complete cure of cancer. Therefore, it is considered a diagnosis of cancer via the detection of an abrupt change from the healthy state to the cancerous state. It includes the development of algorithm for the detection of parameter change for conditionally-linear stochastic systems for the cancer diagnosis. The statistical testing is proposed to implement a parameter change algorithm. The detection algorithm studied in this research is based on sequential hypotheses testing in a so-called local asymptotic framework. Here a simple numerical example is provided to highlight some of the concepts and to provide a basis for further investigation. Despite its simplicity this research may have practical application in clinical oncology.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.4
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• pp.410-418
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• 1999

This paper describes the use of residual and parameter identification methods for fault detection in an air handling unit. Faults can be detected by comparing expected condition with the measured faulty data using residuals. Faults can also be detected by examining unmeasurable parameter changes in a model of a controlled system using a system identification technique. In this study, AutoRegressive Moving Average with seXtrnal input(ARMAX) and AutoRegressive with eXternal input(ARX) models with both single-input/single-input and multi-input/single-input structures are examined. Model parameters are determined using the Kalman filter recursive identification method. Regression equations are calculated from normal experimental data and are used to compute expected operating variables. These approaches are tested using experimental data from a laboratory's variable-air-volume air-handling-unit.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2252-2254
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• 2001

This paper proposes a algebraic parameter determination of MRAC (Model Reference Adaptive Control) controller using block Pulse functions and block Pulse function's differential operation. Generally, adaption is performed by solving differential equations which describe adaptive low for updating controller parameter. The proposes algorithm transforms differential equations into algebraic equation, which can be solved much more easily in a recursive manner. We believe that proposes methods are very attractive and proper for parameter estimation of MRAC controller on account of its simplicity and computational convergence.

• IE interfaces
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• v.5 no.1
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• pp.25-34
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• 1992

A study to analyze and solve inherent problems of a pilot cola machine has been presented in this paper. The product considered in this study involves at least 16 variables(factors) which simultaneously affect the product performance. For this multi-variate statistical problem, we first carefully examined their relationships and selected four variables considered as the most important. We have taken Taguchi's parameter design approach, specifically the $L_8\:(2^7)$ orthogonal array, and determined the optimal levels of the selected variables through the analysis of the experimental results. Finally, we conclude this study with providing general comments drawn from the analysis and verification experiments.

• Journal of Electrical Engineering and Technology
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• v.12 no.3
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• pp.1166-1176
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• 2017

Estimation errors of the rotor speed and position in sensorless control systems of Permanent Magnet Synchronous Motors (PMSM) will lead to low efficiency and dynamic-performance degradation. In this paper, a parallel-type extended nonlinear observer incorporating the nominal parameters is constructed in the stator-fixed reference frame, with rotor position, speed, and the load torque simultaneously estimated. The stability of the extended nonlinear observer is analyzed using the indirect Lyapunov's method, and observer gains are selected according to the transfer functions of the speed and position estimators. Taking into account the parameter inaccuracies issue, explicit estimation error equations are derived based on the error dynamics of the closed-loop sensorless control system. An equivalent flux error is defined to represent the back Electromotive Force (EMF) error caused by the inaccurate motor parameters, and a compensation strategy is designed to suppress the estimation errors. The effectiveness of the proposed method has been validated through simulation and experimental results.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2009.05a
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• pp.368-371
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• 2009

Earth parameters me essential to design and analysis of earth. In this study, a algorithm to estimate earth parameter using artificial neural network(ANN) was proposed. Structures of the soil are grouped by using KSOM algorithm before estimation. Earth parameter is obtained by using BP algorithm. The effectiveness of the proposed algorithm was verified in the case study.