• Journal of the Optical Society of Korea
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• v.5 no.3
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• pp.93-98
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• 2001

In order to verify the performance of the two-stage iterative Fourier transform algorithm[Hankook Kwanghak Hoeji 11, 47 (2000)], a number of phase-only diffractive pattern elements which produce simple 16x16-pixel intensity patterns useful in the field of optical information processing have been designed and their performance has been compared with that from the nonlinear least-squares algorithm[Appl. Opt. 36, 7297(1977)] which is computationally intensive. for all intensity patterns, elements designed by the former algorithm show better overall signal-to noise ratio and uniformity, although they show essentially the same diffraction efficiency. In the case of continuous phase elements, they show far superior uniformity. Computationally,. the former algorithm is far more efficient than the latter.

• Journal of Convergence for Information Technology
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• v.10 no.9
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• pp.15-21
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• 2020

This paper presents the outlier detection algorithm in the estimation method of a source location and velocity based on two-step weighted least-squares method using time difference of arrival(TDOA) and frequency difference of arrival(FDOA) data. Since the accuracy of the estimated location and velocity of a moving source can be reduced by the outliers of TDOA and FDOA data, it is important to detect and remove the outliers. In this paper, the method to find the minimum inlier data and the method to determine whether TDOA and FDOA data are included in inliers or outliers are presented. The results of numerical simulations show that the accuracy of the estimated location and velocity is improved by removing the outliers of TDOA and FDOA data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3295-3311
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• 2020

With the wide application of hyperspectral images, it becomes more and more important to compress hyperspectral images. Conventional recursive least squares (CRLS) algorithm has great potentiality in lossless compression for hyperspectral images. The prediction accuracy of CRLS is closely related to the correlations between the reference bands and the current band, and the similarity between pixels in prediction context. According to this characteristic, we present an improved CRLS with adaptive band selection and adaptive predictor selection (CRLS-ABS-APS). Firstly, a spectral vector correlation coefficient-based k-means clustering algorithm is employed to generate clustering map. Afterwards, an adaptive band selection strategy based on inter-spectral correlation coefficient is adopted to select the reference bands for each band. Then, an adaptive predictor selection strategy based on clustering map is adopted to select the optimal CRLS predictor for each pixel. In addition, a double snake scan mode is used to further improve the similarity of prediction context, and a recursive average estimation method is used to accelerate the local average calculation. Finally, the prediction residuals are entropy encoded by arithmetic encoder. Experiments on the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) 2006 data set show that the CRLS-ABS-APS achieves average bit rates of 3.28 bpp, 5.55 bpp and 2.39 bpp on the three subsets, respectively. The results indicate that the CRLS-ABS-APS effectively improves the compression effect with lower computation complexity, and outperforms to the current state-of-the-art methods.

• Journal of the Korea Society of Computer and Information
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• v.10 no.2 s.34
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• pp.21-30
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• 2005

In general, the robust estimation method is well known for a good statistical estimator that is insensitive to small departures from the idealized assumptions for which the estimation is optimized. While there are many existing robust estimation techniques that have been proposed in the literature, two main techniques used in computer vision are M-estimators and least-median of squares (LMS). Among these. we utilized the M-estimators since they are known to provide an optimal estimation of affine motion parameters. The M-estimators have higher statistical efficiency but tolerate much lower percentages of outliers unless properly initialized. To resolve these problems, we proposed an adaptive M-estimators algorithm that effectively separates outliers from non-outliers and estimate affine model parameters, using a continuous sigmoid weight function. The experimental results show the superiority of our method.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.24 no.9
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• pp.701-709
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• 2011

In semiconductor wafer fabrication, etching is one of the most critical processes, by which a material layer is selectively removed. Because of difficulty to correct a mistake caused by over etching, it is critical that etch should be performed correctly. This paper proposes a new approach for etch endpoint detection of small open area wafers. The traditional endpoint detection technique uses a few manually selected wavelengths, which are adequate for large open areas. As the integrated circuit devices continue to shrink in geometry and increase in device density, detecting the endpoint for small open areas presents a serious challenge to process engineers. In this work, a high-resolution optical emission spectroscopy (OES) sensor is used to provide the necessary sensitivity for detecting subtle endpoint signal. Partial Least Squares (PLS) method is used to analyze the OES data which reduces dimension of the data and increases gap between classes. Support Vector Machine (SVM) is employed to detect endpoint using the data after PLS. SVM classifies normal etching state and after endpoint state. Two data sets from OES are used in training PLS and SVM. The other data sets are used to test the performance of the model. The results show that the trained PLS and SVM hybrid algorithm model detects endpoint accurately.

• Journal of Power Electronics
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• v.19 no.6
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• pp.1505-1514
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• 2019

The permanent magnet synchronous motor (PMSM) is widely used in various fields and the proportional-integral (PI) controller is popular in PMSM control systems. However, the motor parameters are usually unknown, which can lead to a complicated PI controller design and poor performance. In order to design a PI controller with good performance when the motor parameters are unknown, a control algorithm based on stability margin is proposed in this paper. First of all, based on the mathematical model of the PMSM and the least squares (LS) method, motor parameters are estimated offline. Then based on the estimation values of the motor parameters, natural angular frequency and phase margin, a PI controller is designed. Performance indices including the natural angular frequency and the phase margin are used directly to design the PI controller in this paper. Scalar functions of the d-loop and the q-loop are selected. It can be seen that the designed controller parameters satisfy Lyapunov large scale asymptotic stability theory if the natural angular frequencies of the d-loop and the q-loop are large than 0. Experimental results show that the parameter estimation method has good accuracy and the designed PI controller proposed in this paper has good static and dynamic performances.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.10
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• pp.1326-1337
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• 1995

In this paper, we transformed the bilinear filter into an equivalent linear multichannel filter and derived QR decomposition based recursive least squares algorithms for bilinear lattice filters. We also defined order update relation of the forward and the backward input vectors by using the channel decomposition. The forward and the backward data matrices were defined by using the forward and the backward input vectors and orthogonalized with the QR decomposition. we can obtain the lattice equations of the bilinear filters by using the channel decomposition. we can be derived the lattice equations of the bilinear filters using this decomposition process which are the same as the lattice equations derived by Baik, we can use the coefficient transformation algorithm proposed by Baik. We derived the equation error and the output error algorithm of the QRD based RLS bilinear lattice algorithm. Also, we evaluated the performance of the proposed algorithms through the system identification of the bilinear system.

• Journal of Advanced Marine Engineering and Technology
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• v.24 no.2
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• pp.72-81
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• 2000

In this paper, we design an adaptive neuro-fuzzy inference system(ANFIS) precompensator for load frequency control of 2-area power systems. While proportional integral derivative (PID) controllers are used in power systems, they may have some problems because of high nonlinearities of the power systems. So, a neuro-fuzzy-based precompensation scheme is incorporated with a convectional PID controller to obtain robustness to the nonlinearities. The proposed precompensation technique can be easily implemented by adding a precompensator to an existing PID controller. The applied neruo-fuzzy inference system precompensator uses a hybrid learning algorithm. This algorithm is to use both a gradient descent method to optimize the premise parameters and a least squares method to solve for the consequent parameters. Simulation results show that the proposed control technique is superior to a conventional Ziegler-Nichols PID controller in dynamic responses about load disturbances.

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.488-490
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• 1999

This paper describes the design of self-tuning speed controller for DC motor drive system using RLS(Recursive Least Squares) algorithm and Pole-Placement method. The model parameters, related to inertia and damping coefficient of motor, are estimated on-line by using RLS estimation algorithm. And a control signal is calculated by using pole placement method. Simulation and experimental results show that the proposed controller possesses excellent adaptation capability than a conventional PI/IP controller under parameter change.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.51-55
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• 1995

An adaptive unified predictive control (UPC) algorithm is applied to a batch polymerization reactor for poly(methyl methancrylate) (PMMA) and the effects of controller parameters are investigated. Computational studies are performed for a batch polymerization system model developed in this study. A transfer function in parametric form is estimated by recursive least squares (RLS) method, and the UPC algorithm is implemented to control the reactor temperature on the basis of this transfer function. The adaptive unified predictive controller shows a better performance than the PID controller for tracking set point changes, especially in the latter part of reaction course when gel effect becomes significant. Various performance can be acquired by selecting adequate values for parameters of the adaptive unified predictive controller; in other words, the optimal set of parameters exists for a given set of reaction conditions and control objective.