• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.5 s.305
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• pp.157-164
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• 2005

The conventional adaptive detectors can not maintain sufficient detection performance at the presence of non-stationary clutter with unknown characteristics. This is caused by the lack of a priori information about clutter parameters changing over radar coordinates. To solve this problem, it is necessary to use clutter classifiers which have functions, such as the selection of the applied algorithm and its parameters extraction according to clutter conditions. In this paper, we describe the implementation of a clutter environment classifier for adaptive processing. In the environment classifier implemented on Visual C++, the extraction of the parameters and selection of processing algorithm for the adaptive processing unit are possible, and the result of algorithms can be verified at each stage.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.12
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• pp.1920-1925
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• 2012

In this paper, we propose a novel method to improve the performance of the improved minima controlled recursive averaging (IMCRA) in non-stationary noisy environment. The conventional IMCRA algorithm efficiently estimate the noise power by averaging past spectral power values based on a smoothing parameter that is adjusted by the signal presence probability in frequency subbands. Since the minimum of smoothing parameter is defined as 0.85, it is difficult to obtain the robust estimates of the noise power in non-stationary noisy environments that is rapidly changed the spectral characteristics such as babble noise. For this reason, we proposed the modified IMCRA, which adaptively estimate and updata the noise power according to the noise type classified by the Gaussian mixture model (GMM). The performances of the proposed method are evaluated by perceptual evaluation of speech quality (PESQ) and composite measure under various environments and better results compared with the conventional method are obtained.

• Bulletin of the Korean Chemical Society
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• v.33 no.12
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• pp.4141-4144
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• 2012

This paper introduces a technique for resolving amino acids that combines the advantages of the conventional CSP (chiral stationary phase) method with the CMPA (chiral mobile phase additive) method. A commercially available chiral crown ether column, CROWNPAK CR(+), was used as the CSP and three cyclodextrins (${\beta}$-CD, ${\gamma}$-CD, HP-${\beta}$-CD) were used as the mobile phase additives. Chromatographic resolution was performed at $25^{\circ}C$ and $50^{\circ}C$ with or without sonication. A comparison of the chromatographic results under ultrasonic conditions with those under non-ultrasonic conditions showed that ultrasound decreased the elution time and enantioselectivity at all temperatures. In the case of the ${\beta}$-CD mobile phase additive, the elution time and enantioselectivity under ultrasonic condition were significantly higher than under non-sonic condition at all temperatures. Commercially available Chiralpak AD, Whelk-O2 and Pirkle 1-J columns were used as CSPs to examine more meticulously the effects of ultrasonication and temperature on the optical resolution. The optical resolution of some chiral samples analyzed at $25^{\circ}C$ and $50^{\circ}C$ with or without sonication was compared. As in the previous case, the enantioselectivity was lower at $25^{\circ}C$ but similar enantioselectivity was observed at $50^{\circ}C$.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.9
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• pp.1141-1145
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• 2016

In general, a variable forgetting factor is applied to the RLS algorithm for the time-varying parameter estimation in the non-stationary environments. The introduction of a variable forgetting factor to RLS needs heavy additional calculation complexity. We propose a new Gauss Newton variable forgetting factor RLS algorithm which needs small amount of calculation as well as estimates the better parameters in time-varying nonstationary environment. The algorithm performs as good as the conventional Gauss Newton variable forgetting factor RLS and the required additional calculation complexity reduces from $O(N^2)$ to O(N).

• The Transactions of the Korea Information Processing Society
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• v.4 no.10
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• pp.2629-2639
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• 1997

The video coding for very low bit-rate has recently received considerable attention, but the conventional coding schemes with block based transform suffer from the blocky effect for the constraints of limited bit-rate. In this paper, we present a video coding system based on wavelet transform and multiresolution motion estimation/compensation for very low bit-rate video. The proposed scheme uses the wavelet transform which is flexible to represent non-stationary image signals and adaptable to the human visual characteristics. The wavelet transformed coefficients are coded by various coding modes in accordance with the sum of absolute error after motion estimation/compensation in wavelet decomposed domain. And simple buffer control technique is applied to handle constant image quality. It is shown that the presented scheme has more acceptable image quality without blocky effects than conventional block based transform video coding.

• Smart Structures and Systems
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• v.23 no.1
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• pp.61-69
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• 2019

Offshore structures are generally exposed to harsh environments such as strong tidal currents and wind loadings. Monitoring the structural soundness and integrity of offshore structures is crucial to prevent catastrophic collapses and to prolong their lifetime; however, it is intrinsically challenging because of the difficulties in accessing the critical structural members that are located under water for installing and repairing sensors and data acquisition systems. Virtual sensing technologies have the potential to alleviate such difficulties by estimating the unmeasured structural responses at the desired locations using other measured responses. Despite the usefulness of virtual sensing, its performance and applicability to the structural health monitoring of offshore structures have not been fully studied to date. This study investigates the use of virtual sensing of offshore structures. A Kalman filter based virtual sensing algorithm is developed to estimate responses at the location of interest. Further, this algorithm performs a multi-sensor data fusion to improve the estimation accuracy under non-stationary tidal loading. Numerical analysis and laboratory experiments are conducted to verify the performance of the virtual sensing strategy using a bottom-fixed offshore structural model. Numerical and experimental results show that the unmeasured responses can be reasonably recovered from the measured responses.

• Wind and Structures
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• v.38 no.6
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• pp.461-475
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• 2024

A reliable wind speed forecasting method is crucial for the applications in wind engineering. In this study, the generalized S-transform (GST) is innovatively applied for wind speed forecasting to uncover the time-frequency characteristics in the non-stationary wind speed data. The improved grey wolf optimizer (IGWO) is employed to optimize the adjustable parameters of GST to obtain the best time-frequency resolution. Then a hybrid method based on IGWO-optimized GST is proposed to validate the effectiveness and superiority for multi-step non-stationary wind speed forecasting. The historical wind speed is chosen as the first input feature, while the dynamic time-frequency characteristics obtained by IGWO-optimized GST are chosen as the second input feature. Comparative experiment with six competitors is conducted to demonstrate the best performance of the proposed method in terms of prediction accuracy and stability. The superiority of the GST compared to other time-frequency analysis methods is also discussed by another experiment. It can be concluded that the introduction of IGWO-optimized GST can deeply exploit the time-frequency characteristics and effectively improving the prediction accuracy.

• Proceedings of the Korean Statistical Society Conference
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• 2005.11a
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• pp.55-58
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• 2005

This paper discusses the asymptotic efficiency of estimators for optimal portfolios when returns are vector-valued non-Gaussian stationary processes. We give the asymptotic distribution of portfolio estimators ${\hat{g}}$ for non-Gaussian dependent return processes. Next we address the problem of asymptotic efficiency for the class of estimators ${\hat{g}}$ First, it is shown that there are some cases when the asymptotic variance of ${\hat{g}}$ under non-Gaussianity can be smaller than that under Gaussianity. The result shows that non-Gaussianity of X(t) does not always affect worse. Second, we give a necessary and sufficient condition for ${\hat{g}}$ to be asymptotically efficient when the return process is Gaussian, which shows that ${\hat{g}}$ is not asymptotically efficient generally. From this point of view we propose to use maximum likelihood type estimators for g, which are asymptotically efficient. We examine our approach numerically.

• The Journal of the Acoustical Society of Korea
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• v.28 no.3E
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• pp.93-98
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• 2009

In this paper, we propose a new VSS-NLMS algorithm through a simple modification of the conventional NLMS algorithm, which leads to a low complexity algorithm with enhanced performance. The step size of the proposed algorithm becomes smaller as the error signal is getting orthogonal to the input vector. We also show that the proposed algorithm is an approximated normalized version of the KZ-algorithm and requires less computation than the KZ-algorithm. We carried out a performance comparison of the proposed algorithm with the conventional NLMS and other VSS algorithms using an adaptive channel equalization model. It is shown that the proposed algorithm presents good convergence characteristics under both stationary and non-stationary environments despites its low complexity.

• Proceedings of the Korean Statistical Society Conference
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• 2002.11a
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• pp.265-272
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• 2002

An Integer-valued autoregressive integrated (INARI) model is introduced to eliminate stochastic trend and seasonality from time series of count data. This INARI extends the previous integer-valued ARMA model. We show that it is stationary and ergodic to establish asymptotic normality for conditional least squares estimator. Optimal estimating equations are used to reflect categorical and serial correlations arising from panel count data and variations arising from three random processes for obtaining observation into estimation. Under regularity conditions for martingale sequence, we show asymptotic normality for estimators from the estimating equations. Using cancer mortality data provided by the U.S. National Center for Health Statistics (NCHS), we apply our results to estimate the probability of cells classified by 4 causes of death and 6 age groups and to forecast death count of each cell. We also investigate impact of three random processes on estimation.