• Communications for Statistical Applications and Methods
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• v.20 no.4
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• pp.259-270
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• 2013

This paper considers a penalized composite quantile regression (CQR) that performs a variable selection in the linear model with grouped variables. An adaptive sup-norm penalized CQR (ASCQR) is proposed to select variables in a grouped manner; in addition, the consistency and oracle property of the resulting estimator are also derived under some regularity conditions. To improve the efficiency of estimation and variable selection, this paper suggests the two-stage penalized CQR (TSCQR), which uses the ASCQR to select relevant groups in the first stage and the adaptive lasso penalized CQR to select important variables in the second stage. Simulation studies are conducted to illustrate the finite sample performance of the proposed methods.

• Progress in Superconductivity
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• v.4 no.1
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• pp.48-52
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• 2002

When applying a SQUID system for diagnosing heart disease, it is informative to obtain the source current distributions from the measured MCG (magnetocardiogram) signals since the bioelectric activity in the heart is generally represented by distributed current sources. In order to estimate the Primary current distribution in a heart, the minimum norm estimate was computed, assuming a source plane below the chest surface. In the simulation, current distributions, which were computed for the test dipoles represented well the essential feature of the test-current configurations. Source current reconstruction was performed for MCG signal of a healthy volunteer, which was recorded using a 40-channel SQUID system in a magnetically shielded room. It was found that the obtained current distribution is consistent with the electrical activity in a heart.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.7A
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• pp.705-710
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• 2002

In this paper, we present a method to find motion vectors in frequency domain for video data compression. The proposed algorithm is based on the Format Number Transform (FNT), and it declares the most correlated-block as the best matching block, as opposed to declaring the block with least sum of differences between blocks. We show that the proposed method is equivalent to declaring the block with the minimum L2-norm as the best matching block. Unlike other previous fast algorithms, the time requirement for the proposed algorithm does not defend on the image type for finding the optimum solution.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.10
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• pp.2067-2072
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• 2002

The closed-loop state and input observer is a pole-placement type observer and estimates unknown state and input variables simultaneously. Pole-placement type observers may have poor performances with respect to modeling error and sensing bias error. The effects of these ill-conditioning factors must be minimized for the robust performance in designing observers. In this paper, the steady-state performance of the closed-loop state and input observer is investigated quantitatively and is represented as the estimation error bounds. The performance indices are selected from these error bounds and are related to the robustness with respect to modeling errors and sensing bias. By considering both transient and steady-state performance, the main performance index is determined as the condition number of the eigenvector matrix based on $L_2$-norm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.5
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• pp.1061-1066
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• 2014

Transmission characteristics of the sound propagation is very complicate and sparse in shallow water. To increase the performance of underwater acoustic communication system, lots of channel estimation technique has been proposed. In this paper, we proposed the channel estimation based on LMS(Least Mean Square) algorithm which has faster convergence speed than conventional sparse-aware LMS algorithms. The proposed method combines $L_p$-norm LMS with soft decision process. Simulation was performed by using the sound velocity profile which acquired in real sea trial. As a result, we confirmed that the proposed method shows the improved performance and faster convergence speed than conventional methods.

• The Journal of the Acoustical Society of Korea
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• v.37 no.6
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• pp.499-505
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• 2018

Time delay estimation between two acoustic sensors is widely used in room acoustics and sonar for target position estimation, tracking and synchronization. A cross-correlation based method is representative for the time delay estimation. However, this method does not have enough consideration for the noise added to the receiving acoustic sensors. This paper proposes a new time delay estimation method considering the added noise on the receiver acoustic sensors. From comparing with the existing GCC (Generalized Cross Correlation) method, and adaptive eigen decomposition method, we show that the proposed method outperforms other methods for a colored signal source in the white Gaussian noise condition.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.894-900
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• 2005

A new approach to the straightforward implementation of the unscented filter in a unit quaternion space is proposed for spacecraft attitude estimation. Since the unscented filter is formulated in a vector space and the unit quaternions do not belong to a vector space but lie on a nonlinear manifold, the weighted sum of quaternion samples does not produce a unit quaternion estimate. To overcome this difficulty, a method of weighted mean computation for quaternions is derived in rotational space, leading to a quaternion with unit norm. A quaternion multiplication is used for predicted covariance computation and quaternion update, which makes a quaternion in a filter lie in the unit quaternion space. Since the quaternion process noise increases the uncertainty in attitude orientation, modeling it either as the vector part of a quaternion or as a rotation vector is considered. Simulation results illustrate that the proposed approach successfully estimates spacecraft attitude for large initial errors and high tip-off rates, and modeling the quaternion process noise as a rotation vector is more optimal than handling it as the vector part of a quaternion.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.04a
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• pp.21-26
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• 2003

The well-conditioned observer in a stochastic system is designed so that the observer is less sensitive to the ill-conditioning factors in transient and steady-state observer performance. These factors include not only deterministic issues such as unknown initial estimation error, round-off error, modeling error and sensing bias, but also stochastic issues such as disturbance and sensor noise. In deterministic perspectives, a small value in the L$_2$ norm condition number of the observer eigenvector matrix guarantees robust estimation performance to the deterministic issues and its upper bound can be minimized by reducing the observer gain and increasing the decay rate. Both deterministic and stochastic issues are considered as a weighted sum with a LMI (Linear Matrix Inequality) formulation. The gain in the well-conditioned observer is optimally chosen by the optimization technique. Simulation examples are given to evaluate the estimation performance of the proposed observer.

• ETRI Journal
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• v.45 no.3
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• pp.379-393
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• 2023

Underwater communication (UWC) is widely used in coastal surveillance and early warning systems. Precise channel estimation is vital for efficient and reliable UWC. The sparse direct-adaptive filtering algorithms have become popular in UWC. Herein, we present an improved adaptive convex-combination method for the identification of sparse structures using a reweighted normalized leastmean-square (RNLMS) algorithm. Moreover, to make RNLMS algorithm independent of the reweighted l₁-norm parameter, a modified sparsity-aware adaptive zero-attracting RNLMS (AZA-RNLMS) algorithm is introduced to ensure accurate modeling. In addition, we present a quantitative analysis of this algorithm to evaluate the convergence speed and accuracy. Furthermore, we derive an excess mean-square-error expression that proves that the AZA-RNLMS algorithm performs better for the harsh underwater channel. The measured data from the experimental channel of SPACE08 is used for simulation, and results are presented to verify the performance of the proposed algorithm. The simulation results confirm that the proposed algorithm for underwater channel estimation performs better than the earlier schemes.

• Bulletin of the Korean Mathematical Society
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• v.33 no.3
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• pp.377-383
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• 1996

We consider in this paper the following nonlinear regression model $$ (1.1) y_t = f(x_t, \theta_o) + \in_t, t = 1, \ldots, n, $$ where $y_t$ is the tth response, $x_t$ is m-vector imput variable, $\theta_o$ is a p-vector of unknown parameter belong to a parameter space $\Theta, f:R^m \times \Theta \ to R^1$ is a nonlinear known function, and $\in_t$ are independent unobservable random errors with finite second moment.