• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.1A
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• pp.54-61
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• 2010

We propose a subspace interference alignment by orthogonalization of reference vectors. The proposed method improves the sum-rate capacity degradation due to the channel decomposition error and channel estimation error in the real environment. Using the proposed method, each cell uses the reference vector that is orthogonal to the adjacent cells. Then the residual interference produced by the channel decomposition error and the channel estimation error is decreased. The simulation results demonstrate that the proposed method achieves the enhanced sum-rate capacity.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.5
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• pp.997-1003
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• 2007

In this paper, we propose a global approach for automatic inspection of defects in the polarizer film image. The proposed method does not rely on local feature of the defect. It is based on a global image reconstruction scheme using the singular value decomposition(SVD). SVD is used to decompose the image and then obtain a diagonal matrix of the singular values. Among the singular values, the first singular value is used to reconstruct a image. In reconstructed image, the normal pixels in background region have a different characteristics from the pixels in defect region. It is obtained the ratio of pixels in the reconstructed image to ones in the original image and then the defects are detected based on the the statistical process of the ratio. The experiment results show that the proposed method is efficient for defect inspection of polarizer lam image.

• The Korean Journal of Applied Statistics
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• v.31 no.6
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• pp.811-823
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• 2018

Multivariate control chart based on Hotelling's $T^2$ statistic is a powerful tool in statistical process control for identifying an out-of-control process. It is used to monitor multiple process characteristics simultaneously. Detection of the out-of-control signal with the $T^2$ chart indicates mean vector shifts. However, these multivariate signals make it difficult to interpret the cause of the out-of-control signal. In this paper, we review methods of signal interpretation based on the Mason, Young, and Tracy (MYT) decomposition of the $T^2$ statistic. We also provide an example on how to implement it using R software and demonstrate simulation studies for comparing the performance of these methods.

• The Journal of the Korea Contents Association
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• v.12 no.9
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• pp.357-368
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• 2012

The purpose of this study is to empirically examine the impact of enterprise R&D investment on international trade in Korea under the new Normal Era. In order to test whether the time series data of trade variables are stationary or not, we put in operation unit root test and cointegration test. Based on VECM (Vector Error Correction Model), we also apply impulse response functions and variance decomposition to estimate the dynamic effects in the short-run and long-run. The results show that the relationship between enterprise R&D investment and international trade (export and import) exists in the long-run as well as in the short-run. The results of applying impulse response functions and variance decomposition also indicate that the impact of enterprise R&D investment on international trade is positive, and a significant portion of fluctuations in the trade variable is explained by enterprise R&D investment. Therefore, enterprise R&D investment must be continuously increased to improve economic growth with promoting trading competition power in Korea under the new Normal Era.

• Journal of KIISE
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• v.41 no.12
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• pp.1050-1057
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• 2014

Electroencephalogram (EEG)-based brain-computer interfaces (BCI) can be used for a number of purposes in a variety of industries, such as to replace body parts like hands and feet or to improve user convenience. In this paper, we propose a method to decompose and extract motor imagery EEG signal using Empirical Mode Decomposition (EMD) and Fast Fourier Transforms (FFT). The EEG signal classification consists of the following three steps. First, during signal decomposition, the EMD is used to generate Intrinsic Mode Functions (IMFs) from the EEG signal. Then during feature extraction, the power spectral density (PSD) is used to identify the frequency band of the IMFs generated. The FFT is used to extract the features for motor imagery from an IMF that includes mu rhythm. Finally, during classification, the Support Vector Machine (SVM) is used to classify the features of the motor imagery EEG signal. 10-fold cross-validation was then used to estimate the generalization capability of the given classifier., and the results show that the proposed method has an accuracy of 84.50% which is higher than that of other methods.

• The Journal of the Acoustical Society of Korea
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• v.20 no.8
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• pp.3-11
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• 2001

This paper describes an implementation of inverse filter using SVD in order to recover the input in multi-channel system. The matrix formulation in SISO system is extended to MIMO system. In time and frequency domain we investigates the inversion of minimum phase system and non-minimum phase system. To execute an effective inversion of non-minimum phase system, SVD is introduced. First of all we computes singular values of system matrix and then investigates the phase property of system. In case of overall system is non-minimum phase, system matrix has one (or more) very small singular value (s). The very small singular value (s) carries information about phase properties of system. Using this property, approximate inverse filter of overall system is founded. The numerical simulation shows potentials in use of the inverse filter.

• The KIPS Transactions:PartB
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• v.13B no.6 s.109
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• pp.625-632
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• 2006

Collaborative filtering is a technology that aims at teaming predictive models of user preferences. Collaborative filtering systems have succeeded in Ecommerce market but they have shortcomings of high dimensionality and sparsity. In this paper we propose the nearest neighbor collaborative filtering method using non-negative matrix factorization(NNMF). We replace the missing values in the user-item matrix by using the user variance coefficient method as preprocessing for matrix decomposition and apply non-negative factorization to the matrix. The positive decomposition method using the non-negative decomposition represents users as semantic vectors and classifies the users into groups based on semantic relations. We compute the similarity between users by using vector similarity and selects the nearest neighbors based on the similarity. We predict the missing values of items that didn't rate by a new user based on the values that the nearest neighbors rated items.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.6
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• pp.302-308
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• 2001

SVD(Singular Value Decomposition) filter for the suppression of EMG in multi-lead stress ECG is studied. SVD filter consists of two parts. In the first part, the basis vectors were chosen from the averaged singular vectors obtained from the decomposed noise-free ECG. The singular vector is computed from the stress ECG and is compared itself with basis vectors to know whether the noise exist in stress ECG. In the second part, the existing elimination method is used, when one(or two) channels is(or are) contaminated by noise. But the proposed enhanced SVD filter is used in case of having the noise in the many channels. During signal decomposition and reconstruction, the noise-free channel or the least noisy channel have the weight of 1, the next less noisy channel has the weight of 0.8. In this way, every channel was weighted by decreased of 0.2 in proportion to the amount of the added noise. For the evaluation of the proposed enhanced SVD filter, we compared the SNR computed by the enhanced SVD filter with the standard average filter for the noise-free signal added with artificial noise and the patient data. The proposed SVD filter showed better in the SNR than the standard average filter. In conclusion, we could find that the enhanced SVD filter is more proper in processing multi-lead stress ECG.

• Proceedings of the KOSOMBE Conference
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• v.1995 no.05
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• pp.235-238
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• 1995

In this paper, we propose an effective coding method of the angiography by using the scalable structure in the frequency domain for MPACS(Medical Picture Archiving and Communication System). We employed the subband decomposition method and MPEG-2 system which is the international standard coding method of the general moving picture. After the subband decomposition is applied to split an input image into 4 bands in the spatial frequency domain, the motion compensated DPCM coding method of MPEG-2 is carried out for each subband. As a result, an easily controllable coding Structure is accomplished by composing the compound hit stream for each subband group. Follows are the simulation results of the proposed sheme for the angiography. A scalable structure which can be easily controlled for a loss of transmission or the band limit can be accomplisbed in the MPEG-2 stucture by the subband decomposition minimizing the side information. And by reducing the search area of the motion vector between -4 and 3, the processing speed of a codec is enhanced by more than two times without a loss of the picture quality compare with the conventional DCT coefficients decompositon method. And the processing speed is considerably improved in the case of the parallel construction of each subband in the hardware.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.5
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• pp.45-54
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• 2007

Time/frequency analysis has been extensively used in biomedical signal processing. By extracting some essential features from the electro-physiological signals, these methods are able to determine the clinical pathology mechanisms of some diseases. However, this method assumes that the signal should be stationary, which limits its application in non-stationary system. In this paper, we develop a new signal processing method using Hilbert-Huang Transform to perform analysis of the nonlinear and non-stationary ventricular fibrillation(VF). Hilbert-Huang Transform combines two major analytical theories: Empirical Mode Decomposition(EMD) and the Hilbert Transform. Hilbert-Huang Transform can be used to decompose natural data into independent Intrinsic Mode Functions using the theories of EMD. Furthermore, Hilbert-Huang Transform employs Hilbert Transform to determine instantaneous frequency and amplitude, and therefore can be used to accurately describe the local behavior of signals. This paper studied for Return Of Spontaneous Circulation(ROSC) and non-ROSC prediction performance by Support Vector Machine and three parameters(EMD-IF, EMD-FFT) extracted from ventricular fibrillation ECG waveform using Hilbert-Huang transform. On the average results of sensitivity and specificity were 87.35% and 76.88% respectively. Hilbert-Huang Transform shows that it enables us to predict the ROSC of VF more precisely.