• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.948-949
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• 2015

일반적으로 IPMSM의 전압방정식은 d축과 q축이 90도의 위상차를 가지고 있기 때문에 d-q축 간의 상호 인덕턴스를 고려하지 않는다. 하지만 실제로는 d축의 인덕턴스는 q축 전류에 영향을 받으며, 반대로 q축의 인덕턴스도 d축 전류에 영향을 받는다. 따라서 비선형 모델링을 통해 실제 전동기의 형태에 더 가깝게 묘사 하였다. 또한 일반적인 수학식으로 계산하여 Ldq, Lqd를 구해 LPF 필터를 사용하였고 이산적인 최소자승법을 이용한 Gain값을 통해 과도상태에서 더 적합한 LPF와 최소자승법을 비교하는 논문이다.

• Proceedings of the KIPE Conference
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• 1998.07a
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• pp.384-387
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• 1998

This paper presents a newly developed speed sensorless drive using RLS(Recursive Least Squares) based on Neural Network Training Algorithm. The proposed algorithm based on the RLS has just the time-varying learning rate, while the well-known back-propagation (or generalized delta rule) algorithm based on gradient descent has a constant learning rate. The number of iterations required by the new algorithm to converge is less than that of the back-propagation algorithm. The RLS based on NN is used to adjust the motor speed so that the neural model output follows the desired trajectory. This mechanism forces the estimated speed to follow precisely the actual motor speed. In this paper, a flux estimation strategy using filter concept is discussed. The theoretical analysis and experimental results to verify the effectiveness of the proposed analysis and the proposed control strategy are described.

• The Journal of Korea Robotics Society
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• v.3 no.1
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• pp.33-41
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• 2008

Based on object recognition technology, we present a new global localization method for robot navigation. For doing this, we model any indoor environment using the following visual cues with a stereo camera; view-based image features for object recognition and those 3D positions for object pose estimation. Also, we use the depth information at the horizontal centerline in image where optical axis passes through, which is similar to the data of the 2D laser range finder. Therefore, we can build a hybrid local node for a topological map that is composed of an indoor environment metric map and an object location map. Based on such modeling, we suggest a coarse-to-fine strategy for estimating the global localization of a mobile robot. The coarse pose is obtained by means of object recognition and SVD based least-squares fitting, and then its refined pose is estimated with a particle filtering algorithm. With real experiments, we show that the proposed method can be an effective vision- based global localization algorithm.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.8
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• pp.16-22
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• 1999

In this paper, an efficient algorithm for estimating real edge locations to subpixel values is described. Digital images are acquired by projection into image plane and sampling process. However, most of real edge locations are lost in this process, which causes low measurement accuracy. For accurate measurement, we propose an algorithm which estimates the real boundary between two adjacent pixels in digital imagery, with subpixel accuracy. We first define 1D edge operator based on the moment invariant. To extend it to 2D data, the edge orientation of each pixel is estimated by the LSE(Least Squares Error)line/circle fitting of a set of pixels around edge boundary. Then, using the pixels along the line perpendicular to the estimated edge orientation the real boundary is calculated with subpixel accuracy. Experimental results using real images show that the proposed method is robust in local noise, while maintaining low measurement error.

• The Korean Journal of Applied Statistics
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• v.4 no.1
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• pp.13-24
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• 1991

This paper presents a Monte Carlo evaluation of estimators for nonlinear consored simultaneous equations models. We examine the performance of the maximum likelihood estimator (MLE), the two-step quasi maximum likelihood estimator (2QMLE) proposed by Lee and Hurd (1989), and another quasi MLe using least squares at the first step (LSAE) under varying degrees of freedom and underlying distributions, Although QMLE's are not necessarily consistent, the Monte Carlo results show that the 2QMLE may be used as an alternative to MLE when MLE is not applicable in practice.

• Journal of the Korean Statistical Society
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• v.24 no.2
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• pp.481-493
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• 1995

An ARIMA signal observed with measurement error is shown to have another ARIMA representation with nonlinear restrictions on parameters. For this model, the restricted Newton-Raphson estimator(RNRE) of the unit root is shown to have the same limiting distribution as the ordinary least squares estimator of the unit root in an AR(1) model tabulated by Dickey and Fuller (1979). The RNRE of parameters of the ARIMA(p,1,k) process and unit root tests base on the RNRE are developed.

• Proceedings of the KIEE Conference
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• 1987.11a
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• pp.25-28
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• 1987

Design procedure of adaptive controller with variable load condition is present and applied to velocity control of small, permanent magnet DC servo motor. The state feedback control scheme is adopted and Recursive Least Squares algorithm is used for parameter estimation. In order to reduce the time consuming. In the procedure of adaptation-gain tuning of state feedback controller, approximate curve fitting technique is applied to the relations between load condition and poles of the system, load condition and feedback gains. With this method, fast adaptation can be accomplished. It is shown that this procedure can be applied not only to variable load condition but also to variation of other system constants, for example variation of resistance and inductance etc.. Simulation results is present for both cases - variable inertia load, variable motor resistance to verify performance improvements. This design procedure produces an adaptive con troller which is feasible for implementation with microprocessor by reducing calculation time.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.409-413
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• 1997

In linear motor positioning systems, nonlinearitys such as friction and cogging exist. These inner system with compliance may cause the steady state error and oscillation. So it is necessary to consider these elements for precision positioning control. In this paper, a nonlinear model of a linear motor positioning system including the friction, cogging and compliance is proposed. The parameters of the proposed model are identified by recursive least-squares method. The validity of the proposed model is confirmed by computer simulation.

• Nuclear Engineering and Technology
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• v.30 no.3
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• pp.222-234
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• 1998

The Westinghouse OT$\Delta$T DNB protection logic heavily restricts the operation region by applying the same logic for a full range of operating pressure in order to maintain its simplicity. In this work, a fuzzy neural network method is used to estimate the DNB protection limit using the measured average temperature and pressure of a reactor core. Fuzzy system parameters are optimized by a hybrid learning method. This algorithm uses a gradient descent algorithm to optimize the antecedent parameters and a least-squares algorithm to solve the consequent parameters. The proposed method is applied to Yonggwang 3&4 nuclear power plants and the proposed method has 5.99 percent larger thermal margin than the conventional OT$\Delta$T trip logic. This simple algorithm provides a good information for the nuclear power plant operation and diagnosis by estimating the DNB protection limit each time step.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.52 no.8
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• pp.450-456
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• 2003

This paper proposes a multiple time-series model with dummy variables for one-hour ahead load forecasting. We used 11 dummy variables that were classified by day characteristics such as day of the week, holiday, and special holiday. Also, model specification and selection of input variables including dummy variables were made by test statistics such as AIC(Akaike Information Criterion) and t-test statistics of each coefficient. OLS (Ordinary Least Squares) method was used for estimation and forecasting. We found out that model specifications for each hour are not identical usually at 30％ of optimal significance level, and dummy variables reduce the forecasting error if they are classified properly. The proposed model has much more accurate estimates in forecasting with less MAPE (Mean Absolute Percentage Error).