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          
  • An optimal variable step-size affine projection algorithm for the modified filtered-x active noise control

    Ju-man Song, PooGyeon Park

    Signal Processing

    Abstract This paper introduces an optimal variable step-size affine projection algorithm for the modified filtered-x active noise control systems. First, the recursion form of the error covariance from the tap weight update equation is constructed, not ignoring the dependency between the estimation error and the secondary noise signal. Such consideration has not been concerned previously for the analysis of the modified filtered-x affine projection algorithm. Second, a recursion form of the mean square deviation is derived from that of the error covariance. From the recursion form, an optimal step size is decided to get the fastest convergence rate. Both the recursion forms of the mean square deviation and the optimal step size require scalar additions and multiplications that do not contribute to the overall complexity seriously. The simulation results on the active noise control environments show both fast convergence rate and low steady-state error.
  • Modelling and control for eliminating flux oscillations in generation IV high temperature gas cooled reactor

    KD Badgujar, PG Park

    Electronics Letters

    Abstract The core power distribution in the advanced high temperature prismatic reactor (AHTPR 625) belonging to the high temperature gas cooled reactor family is controlled using the constrained receding-horizon control algorithm. The outputs from each region of the reactor core are used as the inputs of the system. The reactor model is based on the two-point-coupled xenon oscillation model. It utilises the nonlinear xenon and iodine balance equations along with the one-group, one-dimensional neutron diffusion equation including feedback with a power coefficient. Axially, the reactor core is divided into two regions. Each region has one input and one output. These regions are coupled with each other. The proposed control method demonstrates very fast response to input flux change in the form of a step and ramp change without any residual flux oscillations between the regions of the reactor core. The constrained receding-horizon control problem involves solving an optimisation problem for finite future samples and obtaining the control with few samples. Among the few constrained control samples only the first one is applied and the process to solve the optimisation problem is repeated again.
  • State-feedback control for LPV systems with interval uncertain parameters

    PooGyeon Park, Nam Kyu Kwon, Bum Yong Park

    Journal of the Franklin Institute

    Abstract This paper proposes a state-feedback controller design for linear parameter-varying systems with interval uncertain parameters that are interval-type uncertain weight functions for convex combinations of linear subsystems. The proposed controller hires secondary convex parameters generated through the lower and upper boundaries of the interval uncertain parameters. The resulting stabilization condition is expressed in terms of parameterized linear matrix inequalities, which are then converted into linear matrix inequalities using a parameter relaxation technique. The simulation results illustrate the robustness of the proposed controller.
  • Variable step-size non-negative normalised least-mean-square-type algorithm

    Sang Mok Jung, Ji-Hye Seo, PooGyeon Park

    Signal Processing, IET

    Abstract This paper proposes a fast and precise adaptive filtering algorithm for online estimation under a non-negativity constraint. A novel variable step-size (VSS) non-negative normalised least-mean-square (NLMS)-type algorithm based on the mean-square deviation (MSD) analysis with a non-negativity constraint is derived. The NLMS-type algorithm under the non-negativity constraint is derived by using the gradient descent of the given cost function and the fixed-point iteration method. Furthermore, the VSS derived by minimising the MSD yields improvement of the filter performance in the aspects of the convergence rate and the steady-state estimation error. Simulation results show that the proposed algorithm outperforms existing algorithms.
  • Variable step-size sign algorithm against impulsive noises

    JinWoo Yoo, JaeWook Shin, PooGyeon Park

    Signal Processing, IET

    Abstract This paper proposes a new variable step-size sign algorithm through the minimisation of mean-square deviation (MSD). As it is difficult to obtain the MSD accurately, the upper bound of the MSD is derived for calculating the step size at each iteration. The proposed algorithm is not only robust to impulsive noises, but also has improved filter performance in aspects of convergence rate and steady-state estimation error owing to the proposed variable step-size strategy. The simulation results verify that the proposed algorithm has better performance than the existing algorithms in a system-identification scenario in the presence of impulsive noises.
  • An optimal variable step-size affine projection algorithm for the modified filtered-x active noise control

    Ju-man Song, PooGyeon Park

    Signal Processing

    Abstract This paper introduces an optimal variable step-size affine projection algorithm for the modified filtered-x active noise control systems. First, the recursion form of the error covariance from the tap weight update equation is constructed, not ignoring the dependency between the estimation error and the secondary noise signal. Such consideration has not been concerned previously for the analysis of the modified filtered-x affine projection algorithm. Second, a recursion form of the mean square deviation is derived from that of the error covariance. From the recursion form, an optimal step size is decided to get the fastest convergence rate. Both the recursion forms of the mean square deviation and the optimal step size require scalar additions and multiplications that do not contribute to the overall complexity seriously. The simulation results on the active noise control environments show both fast convergence rate and low steady-state error.
  • Normalized Least-Mean-Square Algorithm with a Pseudo-Fractional Number of Orthogonal Correction Factors

    Sang Mok Jung, Ji-Hye Seo, PooGyeon Park

    Journal of Advanced in Computer networks

    Abstract This paper proposes a normalized LMS algorithm(LMS) that automatically deter ines the number of orthogonal correction factors (OCFs) by using a pseudo-fractional method, which relaxes the constraint that the number of OCFs in the NLMS algorithm must be integral and introduces the concept of a pseudo-fractional OCF number in the adaptation rule. The pseudo-fractional OCF number is adjusted by using the difference between the averages of the accumulated squared-output errors. The experimental results show that the proposed algorithm has not only a fast convergence rate but also a small steady-state estimation error with low computational complexity in comparison to existing algorithms with multiple input vectors.
  • Stabilization of Markovian jump systems with incomplete knowledge of transition probabilities and input quantization

    Bum Yong Park, Nam Kyu Kwon, PooGyeon Park

    Journal of the Franklin Institute

    Abstract This paper introduces the stabilization condition for the Markovian jump systems (MJSs) with incomplete knowledge of transition probabilities and input quantization. To obtain the less conservative stabilization condition, an appropriate weighting method is proposed by using all possible slack variables from the relationship of the transition probabilities, which does lead to a form of linear matrix inequalities (LMIs). Further, a proposed controller not only stabilizes the MJS with incomplete knowledge of transition probabilities but also eliminates the effect of input quantization. Simulation examples report the effectiveness of the proposed criterion.

  • A variable step-size diffusion affine projection algorithm

    Jin Woo Yoo, In Sun Song, Jae Wook Shin, PooGyeon Park

    International Journal of Communication Systems

    AbstractThis 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.
  • Proportionate Sign Subband Adpative Filtering Algorithm for Network Echo Cancellers

    Ji-Hye Seo, Sang Mok Jung, PooGyeon Park

    Journal of Advanced in Computer networks

    Abstract This paper proposes a proportionate sign subband adaptive filtering (PSSAF) and an improved PSSAF (IPSSAF) algorithm for network echo cancellers that deal with impulsive interferences and sparse echo paths. Based on a sign subband adaptive filtering (SSAF) algorithm that is robust against impulsive interferences, the proposed algorithm minimizes L1-norm of the subband a posteriori error vector subject to a weighted constraint on the filter coefficients. A positive definite weighting matrix is used for the constraint. In this paper, we adopt a diagonal proportionate matrix for the constraint to achieve fast initial convergence rate when the impulse response is sparse. The components of it is roughly proportional to the absolute value of current estimate of the filter, so the resulting algorithm is called PSSAF algorithm. To achieve fast initial convergence rate even for rather non-sparse impulse responses, an improved proportionate matrix is also used for the constraint, and the resulting algorithm is named as IPSSAF algorithm. Experimental results show that the proposed algorithms are more robust against highly correlated input signals, impulsive interferences, and double-talk than the original normalized subband adaptive filtering algorithm and the class of roportionate subband adaptive filtering algorithms