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          
  • Efficient variable step-size diffusion normalised least-mean-square algorithm

    Sang Mok Jung, Ji-Hye Seo, PooGyeon Park

    Electronics Letters

    Abstract An efficient variable step-size diffusion normalised least-mean-square algorithm is proposed via a mean-square deviation (MSD) analysis for the distributed estimation. The proposed algorithm has two distinguishing features for computational efficiency. In the adaptation step, an intermittent adaptation rule that dynamically adjusts an update interval is proposed to reduce the redundant updates. In the diffusion step, instead of the existing combination rules, a selection rule is proposed to select the intermediate estimate of the most reliable node among its neighbour nodes for the estimate at each node. Moreover, to achieve both fast convergence rate and low steady-state error, a variable step size is obtained by minimising the MSD.
  • An improved stability criterion for discrete-time Lur’e systems with sector-and slope-restrictions

    Bum Young Park, PooGyeon Park, Nam Kyu Kwon

    Automatica

    Abstract This paper introduces an improved stability criterion for discrete-time Lur’e systems with sector- and slope-restrictions. For the stability criterion, a Lur’e Lyapunov functional candidate is constructed to include quadratic terms and integration terms with nonlinearities. To handle the resulting integration terms in the Lyapunov difference, there is proposed a new bound lemma where the integration terms are relaxed into the second-order terms by the geometric point of view. In addition, an appropriate weighting method uses slack variables from the sector- and slope-restrictions. In the overall derivation, the stabilization condition is formulated in terms of a parameterized linear matrix inequality (PLMI), which is then converted into the linear matrix inequality. An example illustrates that the proposed criterion presents a less conservative result than the previous criteria in the literature.

  • A variable step-size diffusion normalized least-mean-square algorithm with a combination method based on mean-square deviation

    Sang Mok Jung, Ji-Hye Seo, PooGyeon Park

    Circuits, Systems, and Signal Processing

    Abstract A novel diffusion normalized least-mean-square algorithm is proposed for distributed network. For the adaptation step, the upper bound of the mean-square deviation (MSD) is derived instead of the exact MSD value, and then, the variable step size is obtained by minimizing it to achieve fast convergence rate and small steady-state error. For the diffusion step, the individual estimate at each node is constructed via the weighted sum of the intermediate estimates at its neighbor nodes, where the weights are designed by using a proposed combination method based on the MSD at each node. The proposed MSD-based combination method provides effective weights by using the MSD at each node as a reliability indicator. Simulations in a system identification context show that the proposed algorithm outperforms other algorithms in the literatures.
  • Normalised least-mean-square algorithm for adaptive filtering of impulsive measurement noises and noisy inputs (vol 49, pg 1270, 2013)

    S Mok, PG Park

    ELECTRONICS LETTERS 50 (3), 233-233

    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.
  • Improved criteria on robust stability and H∞ performance for linear systems with interval time-varying delays via new triple integral functionals

    WI Lee, SY Lee, PG Parkk

    Applied Mathematics and Computation 243, 570-577

    Abstract This paper analyzes delay-dependent robust stability and H∞ performance of linear systems with an interval time-varying delay, based on a new Lyapunov–Krasovskii functional containing new triple integral terms. The time derivative of the Lyapunov–Krasovskii functional produces not only the strictly proper rational functions but also the non-strictly proper rational functions of the time-varying delays with first-order denominators. The combinations of the rational functions are directly handled via the Jensen inequality lemma and the lower bound lemma for reciprocal convexity, whereas such combinations were approximated in the literature. The proposed criteria become less conservative with the significantly smaller number of decision variables than the existing criteria, which will be demonstrated by some numerical examples.
  • Second-order reciprocally convex approach to stability of systems with interval time-varying delays

    WI Lee, PG Park

    Applied Mathematics and Computation 229, 245-253

    Abstract Recently, some triple integral terms in the Lyapunov–Krasovskii functional have been introduced in the literature to reduce conservatism in the stability analysis of systems with interval time-varying delays. When we apply the Jensen inequality to partitioned double integral terms in the derivation of LMI conditions, a new kind of linear combination of positive functions weighted by the inverses of squared convex parameters emerges. This paper proposes an efficient method to manipulate such a combination by extending the lower bound lemma. Some numerical examples are given to demonstrate the improvement of the proposed method.
  • Variable matrix-type step-size affine projection algorithm with orthogonalized input vectors

    PG Park, JH Seo, NW Kong

    Signal Processing 98, 135-142

    Abstract In this paper, we propose a variable matrix-type step-size affine projection algorithm (APA) with orthogonalized input vectors. We generate orthogonalized input vectors using the Gram–Schmidt process to implement the weight update equation of the APA using the sum of normalized least mean squares (NLMS)-like updating equations. This method allows us to use individual step sizes corresponding to each NLMS-like equation, which is equivalent to adopting the step size in the form of a diagonal matrix in the APA. We adopt a variable step-size scheme, in which the individual step sizes are determined to minimize the mean square deviation of the APA in order to achieve the fastest convergence on every iteration. Furthermore, because of the weight vector updated successively only along each innovative one among the reused inputs and effect of the regularization absorbed into the derived step size, the algorithm works well even for badly excited input signals. Experimental results show that our proposed algorithm has almost optimal performance in terms of convergence rate and steady-state estimation error, and these results are remarkable especially for badly excited input signals.
  • Variable Step-Size Affine Projection Sign Algorithm

    JW Yoo, JW Shin, PG Park

    IEEE

    Abstract In this paper, we propose a variable matrix-type step-size affine projection algorithm (APA) with orthogonalized input vectors. We generate orthogonalized input vectors using the Gram–Schmidt process to implement the weight update equation of the APA using the sum of normalized least mean squares (NLMS)-like updating equations. This method allows us to use individual step sizes corresponding to each NLMS-like equation, which is equivalent to adopting the step size in the form of a diagonal matrix in the APA. We adopt a variable step-size scheme, in which the individual step sizes are determined to minimize the mean square deviation of the APA in order to achieve the fastest convergence on every iteration. Furthermore, because of the weight vector updated successively only along each innovative one among the reused inputs and effect of the regularization absorbed into the derived step size, the algorithm works well even for badly excited input signals. Experimental results show that our proposed algorithm has almost optimal performance in terms of convergence rate and steady-state estimation error, and these results are remarkable especially for badly excited input signals.
  • A variable step-size affine projection algorithm with a step-size scaler against impulsive measurement noise

    I Song, PG Park

    Signal Processing 96, 321-324

    Abstract This letter proposes a variable step-size (VSS) affine projection algorithm (APA) associated with a step-size scaler to improve the APA’s robustness against impulsive measurement noise. In the proposed VSS APA, the step-size scaler is applied to the equations for updating the step size, which are developed by interpreting the behavior of the mean square deviation (MSD) of the conventional APA. To reduce the computational complexity, we also propose a simplified version of the step-size scaler, which is suitable for application in the APA. Simulations show that the proposed algorithm leads to an excellent transient and steady-state behavior with colored inputs in impulsive-noise environments.
  • Variable Step-Size Affine Projection Sign Algorithm

    JW Yoo, JW Shin, PG Park

    Signal Processing 104, 407-411

    Abstract This letter proposes a band-dependent variable step-size sign subband adaptive filter using the concept of mean-square deviation (MSD) minimization. Since it is difficult to obtain the value of the MSD accurately, the proposed step size is derived by minimizing the upper bound of the conditional MSD with given input. By assigning the different step size in each band, the filter performance can be improved. Moreover, we suggest the estimation method of the measurement-noise variance in an impulsive-noise environment, because the proposed algorithm needs the measurement-noise variance to calculate the step size. The reset algorithm is also applied for maintaining the filter performance when a system change occurs suddenly. Simulation results demonstrate that the proposed algorithm performs better than the existing algorithms in aspects of the convergence rate and the steady-state estimation error.