Journal of Professor & SPaC member

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Last Updated : 03/2016
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  • Inspection of defect on LCD panel using local mean algorithm based on similarity (ICCAS 2013)

    J Lee, PG Park

    Control, Automation and Systems (ICCAS), 2013 13th International Conference

    Abstract We introduce a method for detecting defects in TFT-LCD images with periodic patterns. We consider single-patterns with one pattern, and multi-patterns that is classified into a primary pattern region, a secondary pattern region and boundary region. After each region of the patterns is inspected by calculating a new image that removes periodic patterns and highlights defects, we can estimate the boundary region by least squares estimation. Finally we propose a local mean algorithm to inspect the boundary region. The each result of inspection is merged into the final binary image in which the defects are indicated. We focus on increasing speed of simulation to adopt practical system, and results of our methods are promising. We present inspection results on different types of images, the proposed method gives more accurate results than existing methods.
  • Vision based mura detection by using property of line scan camera for black resin-coated steel-Line scan algorithm

    NK Kwon, CH Park, SW Yun, PG Park

    Control, Automation and Systems (ICCAS), 2013 13th International Conference

    Abstract This paper proposes vision based mura detection algorithm for the black resin-coated steel by using property of line scan camera. The proposed algorithm consists of three parts: preprocessing, selection of threshold value, and finally binarization and post processing. Preprocessing consists of moving average filtering, image partitioning and additional weight for black defects. Second, to distinguish between defect and background we must choose proper threshold value. Finally, we binarize original image by using threshold value and use the image opening and closing to eliminate small noise. The simulation results show detection accuracy of the proposed algorithm.
  • Normalised least-mean-square algorithm for adaptive filtering of impulsive measurement noises and noisy inputs

    SM Jung, PG Park

    Electronics Letters 49 (20), 1270-1271

    Abstract A bias-compensated error-modified normalised least-mean-square algorithm is proposed. The proposed algorithm employs nonlinearity to improve robustness against impulsive measurement noise, and introduces an unbiasedness criterion to eliminate the bias due to noisy inputs in an impulsive measurement noise environment. To eliminate the bias properly, a new estimation method for the input noise variance is also derived. Simulations in a system identification context show that the proposed algorithm outperforms the other algorithms because of the improved adaptability to impulsive measurement noise and input noise in the system.
  • An Affine Projection Algorithm With Update-Interval Selection

    JW Shin, CH Lee, N Kong, P Park

    Signal Processing, IEEE Transactions on 61 (18), 4600-4609

    Abstract This paper presents a mean-square deviation (MSD) analysis of the periodic affine projection algorithm (P-APA) and two update-interval selection methods to achieve improved performance in terms of the convergence and the steady-state error. The MSD analysis of the P-APA considers the correlation between the weight error vector and the measurement noise vector. Using this analysis, it is verified that the update interval governs the trade-off between the convergence rate and the steady-state errors in the P-APA. To overcome this drawback, the proposed APAs increase the update interval when the adaptive filter reaches the steady state. Consequently, these algorithms can reduce the overall computational complexity. The simulation results show that the proposed APAs perform better than the previous algorithms.
  • Multistage γ-level\ mathcal {H} _ {\ infty} control for input-saturated systems with disturbances

    BY Park, SW Yun, YJ Choi, PG Park

    Nonlinear Dynamics 73 (3), 1729-1739

    Abstract For input-saturated systems with disturbances, states in the domain of attraction cannot converge to the origin, but only to neighborhood around it. In order to design the smallest possible target invariant set and the largest possible domain of attraction, in this paper, we introduce a multistage γ-level H∞ control for achieving a smaller target invariant set within a given H∞ performance level and a larger domain of attraction than results obtained in previous studies. In particular, for the case in which the disturbances satisfy a matched condition, this paper introduces an H∞ control with an extra control part to perfectly reject these disturbances despite the uncertainties; the introduction of the H∞ control with an extra control part causes the target invariant set to shrink to the origin and the H∞ performance level to become zero.
  • An Efficient Line-Search Algorithm for Unbiased Recursive Least-Squares Filtering With Noisy Inputs

    BH Kang, PG Park

    Signal Processing Letters, IEEE 20 (7), 693-696

    Abstract This letter proposes a new algorithm for efficiently finding an unbiased RLS estimate of FIR models with noisy inputs. The unbiased estimate is obtained without knowing any a priori information via a new cost. Furthermore, to reduce computational complexity, the estimate is updated along the current input-vector direction and the corresponding gain is efficiently computed. In addition, to increase the convergence rate, the algorithm is extended to update the estimate along not only current but also past input-vector directions. Simulation results show that the proposed algorithm exhibits a fast convergence rate and an enhanced tracking performance with noisy correlated inputs.
  • A Normalized Least Mean Squares Algorithm With a Step-Size Scaler Against Impulsive Measurement Noise

    I Song, PG Park, RW Newcomb

    Circuits and Systems II: Express Briefs, IEEE Transactions on 60 (7), 442-445

    Abstract This brief introduces the concept of a step-size scaler by investigating and modifying the tanh cost function for adaptive filtering with impulsive measurement noise. The step-size scaler instantly scales down the step size of gradient-based adaptive algorithms whenever impulsive measurement noise appears, which eliminates a possibility of updating weight vector estimates based on wrong information due to impulsive noise. The most attractive feature of the step-size scaler is that this is easily applicable to various gradient-based adaptive algorithms. Several representative gradient-based adaptive algorithms are performed without or with the step-size scaler in impulsive-noise environments, which shows the improvement of robustness against impulsive noise.
  • A bias-compensated affine projection algorithm for noisy input data

    SM Jung, NK Kwon, P Park

    Control Conference (ASCC), 2013 9th Asian, 1-5

    Abstract This paper proposes a bias-compensated affine projection algorithm (BC-APA) to eliminate bias due to noisy input data and to reduce the performance degradation due to highly correlated input data. A new affine projection algorithm (new APA) using innovative input data is presented for highly correlated input data. We analyze the bias in this innovative new APA under noisy input data and remove it. To remove the bias, an estimation method for the input noise variance is presented and explained. In simulations, the BC-APA provided both fast convergence rate and small mean square deviation. Based on improved precision to estimate a finite impulse response of an unknown system, the BC-APA can be applied extensively in adaptive signal processing areas.
  • LPV controller design with multiple parameters for the nonlinear RTAC system

    NK Kwon, BY Park, SM Jung, P Park

    Control Conference (ASCC), 2013 9th Asian, 1-6

    Abstract This paper proposes linear parameter varying (LPV) model with multiple parameters (LPV-MP) and statefeedback controller for the nonlinear rotational and translational actuator (RTAC) benchmark problem. First, based on LPV-MP, the conditions used for designing the state-feedback controller are formulated in terms of parameterized linear matrix inequalities (PLMIs) and the state-feedback LPV controller using multiple parameters-dependent Lyapunov function (MPDLF) is designed. Then, PLMI conditions are converted into linear matrix inequalities (LMIs) by using a parameter relaxation technique. The proposed method results in the reduced decision variables and simulation results show good performance of the proposed method.
  • Bias-compensated normalised LMS algorithm with noisy input

    B Kang, J Yoo, P Park

    Electronics Letters 49 (8), 538-539

    Abstract A new bias-compensated normalised least mean square (NLMS) algorithm for parameter estimation with a noisy input is proposed. The algorithm is obtained from an approximated cost function based on the statistical properties of the input noise and involves a condition checking constraint to decide whether the weight coefficient vector must be updated. Simulation results show that the proposed algorithm is more robust and accurate than the conventional method.