Journal of Professor & SPaC member

If you click the title of the journal, you can get more information about the journal.
Last Updated : 03/2016
all           2017           2016           2015           2014           2013           2012           ~2011          
  • Non-periodic-partial-update affine projection algorithm with data-selective updating

    NW Kong, JW Shin, SY Lee, J Song, H Choi, P Park

    Signal Processing Letters, IEEE 20 (2), 173-176

    Abstract This paper presents a new variable step-size diffusion affine projection algorithm (VSS-DAPA) to advance the filter performance of the diffusion affine projection algorithm (DAPA). The proposed VSS strategy is developed for the DAPA, which can solve the distributed estimation problem over diffusion networks well. To obtain the optimal step size reasonably, we seek the update recursion of mean-square deviation (MSD) that is suitable for the DAPA. The step size is optimally given through the minimization for the MSD of the DAPA at each iteration. The derived step size through the MSD minimization improves the filter performance with respect to the convergence and the estimation error in steady state. The results based on simulations demonstrate that the proposed VSS-DAPA performs better than the existing algorithms with a system-identification scenario in diffusion network. Copyright © 2015 John Wiley & Sons, Ltd.
  • An Affine Projection Algorithm with Evolving Order Using Variable Step-Size.

    JW Yoo, JW Shin, HT Choi, PG Park

    International Journal of Computer & Electrical Engineering 5 (1)

    Abstract This paper proposes an affine projection algorithm (APA) with evolving order using variable step-size. The proposed algorithm has not only a variable projection order with an evolutionary method but also changeable step-size in a certain condition. The step-size varies only when the projection order is not changed by the evolutionary method. The experimental results show that the proposed algorithm achieves faster convergence rate and smaller steady-state estimation errors than the existing algorithms.
  • Variable step-size sign subband adaptive filter

    JW Shin, JW Yoo, PG Park

    Signal Processing Letters, IEEE 20 (2), 173-176

    Abstract This letter proposes a variable step-size sign subband adaptive filter (SSAF) based on the minimization of mean-square deviation (MSD). In the process of minimizing the MSD, because it is not feasible to know the exact value of the MSD, the step size is derived by minimizing the upper bound of the MSD in each iteration. The proposed algorithm uses this step size in the SSAF update equation so as to improve the filter performance in terms of the convergence rate and the steady-state estimation error. The proposed algorithm is tested in a system-identification scenario that includes impulsive noise. Simulation results show that the proposed algorithm performs better than the previous algorithms.
  • An evolving update interval algorithm for the optimal step-size affine projection algorithm

    J Song, SY Lee, HT Choi, PG Park

    Intelligent Signal Processing and Communications Systems (ISPACS), 2013

    Abstract This paper introduces an evolving update interval algorithm for the optimal step-size affine projection algorithm. The optimal step-size affine projection algorithm is one of numerous approaches to get better performance for the affine projection algorithm. It is suggested by analyzing the mean square deviation of fixed step-size affine projection algorithm. With the optimal step-size affine projection algorithm, in this paper, by evolving the update interval, it is able to show much better performance. From the MSD analysis, the learning curve is dived into two stage: the transient stage and the steady-state. By finding the cross point of affine projection algorithm’s learning curve, the update interval is modified. By updating the weight vector for updated interval, the proposed algorithm reduces the computational complexity. With the proposed algorithm from simulations, it shows higher convergence rate and lower steady-state error.
  • 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.