• Korean Journal of Acupuncture
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• v.20 no.3
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• pp.49-60
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• 2003

The aim of this study was to examine the effects of electroacupuncture(EA) at the PC6 (Naegwan) on normal humans using KarhunenLoeve decomposition method. Electroencephalogram(EEG) is a multi-scaled signal consisting of several components of time series with different dominant frequency ranges and different origins. EEG KarhunenLoeve decomposition method exibit site-specific and state-related differences in specific frequency bands. In this study, KarhunenLoeve decomposition method was used as a measure(D2) of complexity. 30 channel EEG study was carried out in 10 subjects (10 males; $age=21.4{\pm}0.5$ years). Results : We found that the average values and standard deviations of D2 at FP1, FP2, FTC1, FTC2, TT1, TT2, T4, TCP1, P3, P4, T6, OZ channel (p<0.05) were higher than during the acupuncture treatment, and the average values and standard deviations of D2 at F3, F8 channels(p<0.05) were lowered than during the acupuncture treatment. However, the comparison with that before and after the treatment shows no significant differences in all channels.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2004.10a
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• pp.11-18
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• 2004

In this paper, an efficient model reduction scheme is presented for large scale dynamic systems. The method is founded on a modal analysis in which optimal eigenvalue is extracted from time samples of the given system response. The techniques we discuss are based on classical theory such as the Karhunen-Loeve expansion. Only recently has it been applied to structural dynamics problems. It consists in obtaining a set of orthogonal eigenfunctions where the dynamics is to be projected. Practically, one constructs a spatial autocorrelation tensor and then performs its spectral decomposition. The resulting eigenfunctions will provide the required proper orthogonal modes(POMs) or empirical eigenmodes and the correspondent empirical eigenvalues (or proper orthogonal values, POVs) represent the mean energy contained in that projection. The purpose of this paper is to compare the reduced order model using Karhunen-Loeve expansion with the full model analysis. A cantilever beam and a simply supported plate subjected to sinusoidal force demonstrated the validity and efficiency of the reduced order technique by K-L method.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.11
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• pp.72-82
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• 1996

In this paper, newly developed algorithm for enhancing images corrupted by white gaussian noise is proposed. In the method proposed here, image is subdivided into a number of subblocks, and each block is separated into cimponents corresponding to signal and noise subspaces, respectively through the signal subspace method. A clean signal is then estimated form the signal subspace by the adaptive wiener filtering. The decomposition of noisy signal into noise and signal subspaces in is implemented by eigendecomposition of covariance matrix for noisy image, and by performing blockwise KLT (karhunen loeve transformation) using eigenvector. To reduce the perceptual noise level and distortion, wiener filtering is implementd by adaptively adjusting noise level according to activity characteristics of given block. Simulation results show the effectiveness of proposed method. In particular, edge bluring effects are reduced compared to the previous methods.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.1
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• pp.96-107
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• 1996

A voice personality transformation algorithm using orthogonal vector space conversion is proposed in this paper. Voice personality transformation is the process of changing one person's acoustic features (source) to those of another person (target). In this paper, personality transformation is achieved by changing the LPC cepstrum coefficients, excitation spectrum and pitch contour. An orthogonal vector space conversion technique is proposed to transform the LPC cepstrum coefficients. The LPC cepstrum transformation is implemented by principle component decomposition by applying the Karhunen-Loeve transformation and minimum mean-square error coordinate transformation(MSECT). Additionally, we propose a pitch contour modification method to transform the prosodic characteristics of any speaker. To do this, reference pitch patterns for source and target speaker are firstly built up, and speaker's one. The experimental results show the effectiveness of the proposed algorithm in both subjective and objective evaluations.