• Proceedings of the KIPE Conference
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• 2002.11a
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• pp.49-52
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• 2002

This paper presents neural load torque observer that used to deadbeat load torque observer and regulation of the compensation gain by parameter estimator. As a result, the response of PMSM follows that of the nominal plant. The load torque compensation method is compose of a neural deadbeat observer. To reduce of the noise effect, the post-filter, which is implemented by MA 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 torque observer to resolve the problems. As a result, the proposed control system becomes a robust and precise system against the load torque and the parameter variation. A stability and usefulness, through the verified computer simulation, are shown in this paper.

• The Journal of the Acoustical Society of Korea
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• v.18 no.7
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• pp.45-55
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• 1999

In this paper, we used the WLMS(Wavelet domain Least Mean Square) adaptive filter based on the wavelet transform to cancel grain noise. Usually, grain noise occurs in changes of the crystalline structure of metals in high temperature environment. It makes the detection of flaw difficult. The WLMS adaptive filtering algorithm establishes the faster convergence rate by orthogonalizaing the input vector of adaptive filter as compared with that of LMS adaptive filtering algorithm in time domain. We implemented the WLMS adaptive filter by using the delayed version of the primary input vector as the reference input vector and then implemented the CA-CFAR(Cell Averaging- Constant False Alarm Rate) threshold estimator. CA-CFAR threshold estimator enables to detect the flaw and back echo signals automatically. Here, we used the output signals of adaptive filter as its input signal. To Cow the statistical characteristic of ultrasonic signals corrupted by grain noise, we performed run test. The results showed that ultrasonic signals are nonstationary signal, that is, signals whose statistical properties vary with time. The performance of each filter is appreciated by the signal-to-noise ratio. After LMS adaptive filtering in time domain, SNR improves to about 2-3㏈ but after WLMS adaptive filtering in wavelet domain, SNR improves to about 4-6㏈.

• Communications for Statistical Applications and Methods
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• v.25 no.6
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• pp.673-685
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• 2018

Procrustes analysis is a useful technique useful to measure, compare shape differences and estimate a mean shape for objects; however it is based on a least squares criterion and is affected by some outliers. Therefore, we propose two generalized Procrustes analysis methods based on M-estimation and least median of squares estimation that are resistant to object outliers. In addition, two algorithms are given for practical implementation. A simulation study and some examples are used to examine and compared the performances of the algorithms with the least square method. Moreover since these resistant GPA methods are available for higher dimensions, we need some methods to visualize the objects and mean shape effectively. Also since we have concentrated on resistant fitting methods without considering shape distributions, we wish to shape analysis not be sensitive to particular model.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.239-240
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• 2008

본 논문에서는 Subtractive clustering 알고리즘을 이용한 Fuzzy Radial Basis Function Neural Network (FRBFNN)의 규칙 수를 자동적으로 생성하는 방법을 제시한다. FRBFNN은 멤버쉽 함수로써 기존 RBFNN에서 가우시안이나 타원형 형태의 특정 RBF를 사용하는 구조와 달리 Fuzzy C-Means clustering 알고리즘에서 사용하는 거리에 기한 멤버쉽 함수를 사용하여 전반부의 공간 분할 및 활성화 레벨을 결정하는 구조이다. 본 논문에서는 데이터의 밀집도에 기반을 두어 클러스터링을 하는 Subtractive clustering 알고리즘을 사용하여 퍼지 규칙의 수와 같은 의미를 갖는 분할할 입력공간의 수와 분할된 입력공간의 중심값을 동정하며, Least Square Estimator (LSE) 알고리즘을 사용하여 후반부 다항식의 계수를 추정 한다.

• The Korean Journal of Applied Statistics
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• v.25 no.3
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• pp.403-413
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• 2012

The modifications suggested in Uhm et al. (2011) are studied using a partly parametric version of Aalen's additive risk model. A follow-up time period is partitioned into intervals, and hazard functions are estimated as a piecewise constant in each interval. A maximum likelihood estimator by iteratively reweighted least squares and variance estimates are suggested based on the model as well as evaluated by simulations using mean square error and a coverage probability, respectively. In conclusion the modifications are needed when there are a small number of uncensored deaths in an interval to estimate the piecewise constant hazard function.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.2
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• pp.311-316
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• 2007

This paper proposes the algorithm which estimates moment of the inertia and friction coefficient of friction for high performance speed control of electric motor. The proposed algorithm finds the moment of inertia and friction coefficient of friction by observing the speed error signal generated by the speed observer and using Recursive Least-Mean-Square method(RLS). By feedbacking the estimated inertia and estimated coefficient of friction to speed controller and full order speed observer, then the errors of the inertia and coefficient of friction and speed due to the inaccurate initial value are decreased. Inertia and coefficient of friction converge to the actual value within several times of speed changing. Simulation and actual experiment results are given to demonstrate the effectiveness of the proposed parameter estimator.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.7
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• pp.1287-1294
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• 2006

In many current wireless packet data system, the short-burst transmissions are used, and training overhead is very significant for such short burst formats. So, the availability of the short training sequence and the fast converging algorithm is essential in the adaptive equalizer. In this paper, the new equalizer algorithm is proposed to improve the performance of a MTLMS (multiple-training least mean square) based DFE (decision feedback equalizer)using the short training sequence. In the proposed method, the output of the DFE is fed back to the LMS (least mean square) based adaptive DEF loop iteratively and used as an extended training sequence. Instead of the block operation using ML (maximum likelihood) estimator, the low-complexity adaptive LMS operation is used for overall processing. Simulation results show that the perfonnance of the proposed equalizer is improved with a linear computational increase as the iterations parameter in creases and can give the more robustness to the time-varying fading.

• Journal of the Korean Data and Information Science Society
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• v.6 no.1
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• pp.1-11
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• 1995

The parameters in the multiplicative model $Y_{1}={\alpha}_{0}{\prod}^{p}_{k=1}X_{kj}^{{\beta}_K}v_{j}$ are usually estimated by the least squares method after logarithmic transformation, and the least square Estimator of ${\alpha}_{0}$ is known to be biased, i.e., $E(e xp(\hat{\beta}_{0})){\neq}{\alpha}_{0})$. In the present study the unbaised estimators of ${\alpha}_{0}$ are examined(1) by modifying the least squares estimator and (2) by applying the Finney's results. The variances are also compared. In addition it has been observed that multiplicative model can be used to express the relationship beetween rice yield and yield components.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.350-355
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• 1997

The problem of mathematical model for an unknown system by measureing its input-output data pairs is generally referred to as state estimates. The state estimation problem is often of importance in its own right since we may want to know the value of the states. For instance, in navigation, we may take noisy positional fixes using satelite or radar navigation, and the estimator can use these measurements to provide accurate estimates of current position, hedaing, and velocity. And the state estimates can also be used for control purposes. Then it is very important to know the state of plant. In this paper, the theory of the minimization of a loss function was used to design the fuzzy system. Here, the used teory is Least Square Esimation method. This parametrization has the Linear in the parameters charcteristic that allows standard parameter estimation technique to be used to estimate the parameters of the fuzzy system. The combination of the fuzzy system and the estimation m thod then performs as a nonlinear estimator. If several fuzzy label are defined for the input variables at the antecedent part, the fuzzy system then behaves as a collection of nonlinear estimators where different regions of rules have different parameters. In simulation results, the fuzzy model controlled a difference in the structure between the actual plant and the fuzzy estimator. It is also proved that the fuzzy system is equivalent to its transformed system. therefore we was able to get the state space equation of system with the estimated paramater.

• The Transactions of the Korean Institute of Power Electronics
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• v.9 no.1
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• pp.42-49
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• 2004

This paper presents a new method of external load disturbance compensation using deadbeat load torque observer and gain compensation by parameter estimator. The response of the permanent magnet synchronous motor(PMSM) follows the nominal plant. The load torque compensation method is composed of a deadbeat observer. To reduce the noise effect, the post-filter implemented by moving average(MA) process is adopted. The parameter compensator with recursive least square method(RLSM) parameter estimator is suggested to make the new system work as same as the name plate system which in used to take gains. The proposed estimator is combined with a high performance load torque observer to resolve the problems. 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.