Journal of Professor & SPaC member

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Last Updated : 03/2016
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  • Multicore and Mesh Network-based Parallel Performance Evaluation using Intra Prediction Algorithms

    Yungho Choi, Neungsoo Park

    International Journal of Control and Automation

    Abstract So far, many parallel algorithms have been developed under the assumption that a high performance multicore processor uses a bus for inter-core communications. However, this assumption begins to change as the number of processing cores is increased and thus, higher connectivity among cores is required. So, in this paper, three HEVC intra prediction algorithms are ported into a mesh network-based multicore system by using a wavefront-style parallelization. By analyzing parallel performance, this paper shows that UDIP best fits in the mesh network-based multicore system (almost 2 times faster than other algorithms).
  • A Consistent Binormalized Data-Reusing LMS Algorithm for Noisy FIR Models

    Byung Hoon Kang, Nam Kyu Kwon, Hyon-Taek Choi, Poo Gyeon Park

    International Journal of Computer and Electrical Engineering

    Abstract This paper proposes a consistent binormalized data-reusing least mean square (LMS) algorithm for identifying finite impulse response models whose input and output are corrupted by additive white noise. The proposed algorithm exploits the stochastic properties of the noisy input to compensate a bias of estimation which is occurred by input noise. Furthermore, by reusing the input signal, the algorithm overcomes a decline of convergence performance with highly correlated input signal. The experimental results show that the proposed algorithm achieves consistent estimation with noisy input signal. Furthermore, the proposed algorithm gets faster convergence rate and smaller steady-state estimation errors than the ordinary consistent LMS algorithms when the input signal is highly correlated.
  • A normalized least-mean-square algorithm based on variable-step-size recursion with innovative input data

    Insun Song, PooGyeon Park

    Signal Processing Letters, IEEE

    Abstract This letter presents a variable-step-size normalized least-mean-square algorithm, where the step size is updated only when the current input vector is innovative from the last updated input vector. The instant innovativeness of the two input vectors is investigated through the relation between the angle of the two input vectors and the condition number of the input covariance matrix. Once the condition number is obtained, the resulting algorithm performs an excellent transient and steady-state behavior with different correlations in inputs. To reduce the computational burden of obtaining the condition number, this letter also presents a simple method to determine the condition number based on the power method.
  • An Improved Least Mean Kurtosis (LMK) Algorithm for Sparse System Identification

    Jin Woo Yoo, PooGyeon Park

    International Journal of Information and Electronics Engineering

    Abstract This paper proposes an improved least mean kurtosis (LMK) algorithm based on l0-norm cost for enhancing the filter performance in a sparse system. The LMK adaptive filtering algorithm uses a kurtosis of an estimated error signal to improve the filter performance when the noise contamination is serious. Due to the influence of l0-norm cost, the proposed LMK algorithm ensures a fast convergence rate and a small steady-state error in sparse system environment. Simulation results verify that the proposed algorithm improves the filter performance for sparse system identification.
  • A Robust Variable Step-Size NLMS Algorithm Through A Combination of Robust Cost Functions

    Insun Song, Won Il Lee, Nam Kyu Kwon, PooGyeon Park

    International Journal of Information and Electronics Engineering

    Abstract This letter introduces a new gradient-based adaptive filtering algorithm based on a cost function that is constructed by combining two robust cost functions, which are a new tanh-type cost function and Vega’s cost function. Through the approach to combine robust cost functions, the robustness of the proposed algorithm outperforms that of other adaptive algorithms.Since the proposed algorithm is derived by combining two robust cost functions, it leads to an excellent transient and steady-state behavior in high probability of impulsive measurement noise. The proposed algorithm is tested in different probability of impulsive measurement noise.
  • Mura Region Detection by Using 2D FFT with Exponential Kernel for Black Resin-Coated Steel

    Nam Kyu Kwon, Jong Seok Lee, PooGyeon Park

    International Journal of Information and Electronics Engineering

    Abstract This paper proposes mura region detection algorithm by using two-dimensional fast Fourier transform (2D FFT) with exponential kernel for black resin-coated steel. If the mura exists in the black resin-coated steel image, the image has large low-frequency component. To improve accuracy, multiply exponential kernel to low frequency region. The simulation results show improved performance.
  • New Guaranteed {\ mathcal {H}} _\ infty Performance State Estimation for Delayed Neural Networks

    Won Il Lee, PooGyeon Park

    International Journal of Information and Electronics Engineering

    Abstract In this paper, a new guaranteed performance state estimation problem for static neural networks with time varying delay is investigated. A new Lyapunov-Krasovskii functional is introduced to improve the performance. Moreover, with the help of lower bound lemma, an upper-bound of a linear combination of positive functions weighted by the inverses of convex parameters is obtained. Two simulation examples are given to prove the effectiveness of the proposed theorem.
  • Further improvement of delay-dependent stability criteria for linear systems with time-varying delays

    Won Il Lee, Changki Jeong, PooGyeon Park

    Control, Automation and Systems (ICCAS), 2012 12th International Conference on

    Abstract This paper provides an improved delay-dependent stability criterion for linear systems with interval time-varying delays. Some quadruple-integral terms are introduced in a new Lyapunov-Krasovskii functional to reduce conservatism in the stability analysis of time-delayed system. Moreover, applying lower bounds lemma [8], an upper-bound of a linear combination of positive functions weighted by the inverses of convex parameters that can induce less conservative stability criterion is derived. Numerical examples are given to illustrate the improvement of the proposed stability result.
  • State-Feedback Switching Control for Discrete-Time Takagi-Sugeno Fuzzy Systems Based on Partitioning The Range of Fuzzy Weights

    Won Ill Lee, Jeong Wan Ko, PooGyeon Park

    International Journal of Computer and Electrical Engineering

    Abstract In this paper, we propose an efficient relaxation method of the parameterized linear matrix inequalities (PLMIs) in the framework of the state-feedback stabilization problem for discrete-time Takagi-Sugeno (T?S) fuzzy systems. The matrix elimination method plays a key role in deriving the criterion, which reduces the order of the fuzzy weights by eliminating the quadratic fuzzy weights in the original PLMIs and then transformed to a more tractable one. A partition on the range of the fuzzy weights is introduced, through which a linearly weight-dependent condition can be developed by fixing some decision variables piecewisely. By utilizing the extreme points of each partition, the negativity of the condition can be guaranteed and the corresponding controller is represented in the form of a switching control law based on the partition. Some example shows that finer subdivision in the partition leads to a better performance behavior.
  • Set invariance approach to {\ mathcal {H}} _\ infty control for input-saturated systems with disturbances

    Bum Yong Park, Sung Wook Yun, PooGyeon Park

    ICCAS 2012

    Abstract For input-saturated systems with disturbances, the states cannot converge to the origin, but only to their neighborhood. Aiming to design the smallest possible target invariant set, this paper introduces a set invariance approach to H∞ control for achieving a smaller target invariant set within a given H∞ performance level. A numerical example shows that the proposed methods afford better performance than conventional methods