• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2012.07a
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• pp.176-177
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• 2012

사람의 음성을 압축하는 방법으로 Code Excited Linear Prediction (CELP) 코더가 주로 사용되어 왔다. CELP 코더의 수신단에서는 양자화 된 여기신호를 LPC 필터로 합성하여 신호를 복원한다. LPC 합성필터의 영향으로 양자화 된 여기신호의 보로노이 셀 모양이 변형되는 문제점이 있기 때문에 이런 문제점을 해결하기 위해서 Karhunen-Loeve-Transform based Classify vector Quantization (KLT-CVQ) 코더가 제안되었다. 기존 KLT-CVQ 코더는 KLT 변환과 class 선택을 위해서 Eigen Value Decomposition (EVD)을 이용해서 eigen vector와 eigen value를 계산한다. 본 논문에서는 EVD 대신에 UTV Decomposition (UTVD)을 이용하여 KLT-CVQ의 계산량 문제점을 개선하는 방법을 제안한다.

• Journal of computational fluids engineering
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• v.22 no.1
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• pp.59-66
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• 2017

The guideline of selecting the number of snapshot dataset, $N_s$ in proper orthogonal decomposition(POD) was presented via the analysis of Eigen values based on the singular value decomposition(SVD). In POD, snapshot datasets from the solutions of Euler or Navier-Stokes equations are utilized to SVD and a reduced order model(ROM) is constructed as the combination of Eigen vectors. The ROM is subsequently applied to reconstruct the flowfield data with new set of flow conditions, thereby enhancing the computational efficiency. The overall computational efficiency and accuracy of POD is dependent on the number of snapshot dataset; however, there is no reliable guideline of determining $N_s$. In order to resolve this problem, the order of maximum to minimum Eigen value ratio, O(R) from SVD was analyzed and presented for the decision of $N_s$; in case of steady flow, $N_s$ should be determined to make O(R) be $10^9$. For unsteady flow, $N_s$ should be increased to make O(R) be $10^{11\sim12}$. This strategy of selecting the snapshot dataset was applied to two dimensional NACA0012 airfoil and vortex flow problems including steady and unsteady cases and the numerical accuracies according to $N_s$ and O(R) were discussed.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.5
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• pp.39-49
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• 2015

Some characters and construction theorems of Pseudo Jacket Matrix which is generalized from Jacket Matrix introduced by Jacket Matrices: Construction and Its Application for Fast Cooperative Wireless signal Processing[27] was announced. In this paper, we proposed some examples of Pseudo inverse Jacket matrix, such as $2{\times}4$, $3{\times}6$ non-square matrix for the MIMO channel. Furthermore we derived MIMO singular value decomposition (SVD) pseudo inverse channel and developed application to utilize SVD based on channel estimation of partitioned antenna arrays. This can be also used in MIMO channel and eigen value decomposition (EVD).

• Journal of the Korean Statistical Society
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• v.28 no.3
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• pp.337-346
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• 1999

Simple correspondence analysis is a technique for giving a joint display of points representing both the rows and columns of an n$\times$p two-way contigency table. In simple correspondence analysis, the singular value decomposition is the main algebraic tool. But, Choi and Huh(1996) pointed out the singular value decomposition is not robust. Instead, they developed a robust singular value decomposition and provided applications in principal component analysis and biplots. In this article, by using the analogous procedures of Choi and Huh(1996), we derive a robust version of simple correspondence analysis.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.20 no.3
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• pp.248-256
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• 2009

EB(Eigen-Beamforming) has widely been applied to MIMO(Multiple-Input Multiple-Output) systems to form beams which maximize the effective signal-to-interference plus noise ratio(SINR) of the receiver using the singular value decomposition(SVD) of the MIMO channel. However, the signal detection performance for the mobile station near the cell boundary is severely degraded and the transmission efficiency decreases due to the influence of the interference signal from the adjacent cells. In this paper, we propose an adaptive interference mitigation method for the EB transmission, and evaluate the reception performance. In particular, a reception strategy which adaptively utilizes optimal combining(OC) and minimum mean-squared error for Intercell spatial demultiplexing(MMSE-lSD) is proposed, and the reception performance is investigated in terms of the effective SINR and system capacity. For the average system capacity, the proposed adaptive reception demonstrates the performance enhancement compared to the conventional EB reception using the receiver beamforming vector, and up to 2 bps/Hz performance gain is achieved for mobile station located at the cell edge.

• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.1271-1275
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• 1987

With the stochastic process which consists of the harmonic sinusoid and the white nosie, the power spectrum of background EEG is estimated by the Pisarenko Harmonic Decomposition. The estimating results are examined and compared with the results from the maximum entropy spectral estimation, and the optimal order of this model can be determined from the eigen value's fluctuation of autocorrelation of background EEG. From the comparing results, this paper ensures that this method is possible to analyze the power spectrum of background EEG.

• Journal of Biomedical Engineering Research
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• v.8 no.1
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• pp.69-74
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• 1987

The power spectrum of background EEG is estimated by the Plsarenko Harmonic Decomposition with the stochastic process whlch consists of the nonhamonic sinus Bid and the white nosie. The estimation results are examined and compared with the results from the maximum entropy spectral extimation, and the optimal order of this from the maximum entropy spectral extimation, and the optimal order of this model can be determined from the eigen value's fluctuation of autocorrelation of background EEG. From the comparing results, this method is possible to estimate the power spectrum of background EEG.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.3
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• pp.165-170
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• 2013

In spatial, desired target direction of arrival estimation is to find a incidental signal direction on receive antennas. In this paper, we were an estimation a desired target direction of arrival using maximum likelihood method. Direction of arrival estimation method estimated a desired target calculating the maximum likelihood sensitivity using singular value decomposition above threshold signals among receive signals in maximum likelihood method. Through simulation, we were analysis a performance to compare existing method and proposal method. In direction of arrival estimation, proposed method is effectivity to decrease processing time because it is not doing an eigen decomposition in direction of arrival estimation, and desired target correctly estimated. We showed that proposal method improve more target estimation than general method.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.687-690
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• 1999

Many high-resolution algorithms based on the eigen-decomposition analysis of observed covariance matrix, such as MVE, MUSIC, and EVM, have been proposed. However, the resolution of spectral estimates for these algorithms is severely degraded when Signal-to-Noise Ratio (SNR) is low and arrival angles of signal are close to each other. And EVM and MUSIC is powerful for the characteristic of SNR. But have the limitation that the number of signals presented is known. While MVE is bad the characteristic of SNR. In this study, we propose a modified MVE to enhance the resolution for Direction-Of-Arrival (DOA) estimation of underwater acoustic signal. This is to remove the limitation that existing algorithms should know the information for the number of signals. Because the algorithms founded on the eigen value estimate DOA with only the noise subspace, they have the high-resolution characteristic. And then, with the method reducing the effect of the signal subspace, we are to reduce the degradation because of complementary relationship between the signal subspace and the noise subspace. This paper, with using the simulation data, we have estimated the proposed algorithms, compared it with other high-resolution algorithms. The simulation results show that the modified MVE proposed is accurate and has a better resolution even though SNR is low, under the same condition.

• Korean Journal of Remote Sensing
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• v.23 no.2
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• pp.89-101
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• 2007

This paper investigates the effects of the fusion of multifrequency (C and L bands) polarimetric SAR data in land-cover classification. NASA JPL AIRSAR C and L bands data were used to supervised classification in an agricultural area to simulate the integration of ALOS PALSAR and Radarsat-2 SAR data to be available. Several scattering features derived from target decomposition based on eigen value/vector analysis were used as input for a support vector machines classifier and then the posteriori probabilities for each frequency SAR data were integrated by applying a probabilistic ratio model as a decision level fusion methodology. From the case study results, L band data had the proper amount of penetration power and showed better classification accuracy improvement (about 22%) over C band data which did not have enough penetration. When all frequency data were fused for the classification, a significant improvement of about 10% in overall classification accuracy was achieved thanks to an increase of discrimination capability for each class, compared with the case of L band Shh data.