• Kyungpook Mathematical Journal
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• v.52 no.3
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• pp.327-346
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• 2012

In this paper, we present a simple explicit type numerical method for discretizations in time for solving one dimensional Burgers' equations. The proposed method does not need an iteration process that may be required in most implicit methods and have good convergence and efficiency in computational sense compared to other known numerical methods. For evidences, several numerical demonstrations are also provided.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.2
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• pp.63-69
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• 2019

In this study, bispectrum analysis method introduced to reduce variability of SEF(spectral edge frequency) and MF(median frequency), which are the anesthetic depth indexes extracted by EEG spectral analysis. Bispectrum analysis is an analytical method that can confirm the nonlinearity of EEG. Signal measurement and analysis in the surgical environment should take into consideration various external artifact factors. Bispectrum analysis can confirm the presence of externally introduced artifacts, thereby effectively eliminating artifacts that affect the EEG signal. By applying bispectrum parameters, real-time variability of the anesthetic depth parameters SEF, MF could be reduced. Elimination of variability makes it possible to use SEF, MF as a real-time index during surgery.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.4
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• pp.959-966
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• 2010

Quantitative ultrasound analysis provides fundamental information of various ultrasound parameters using spectral information of the short-gated radiofrequency(RF) data. Therefore, accurate extraction of spectral information from backscattered RF signal is crucial for further analysis of medical ultrasound parameters. In this paper, we propose two techniques for calculating a more accurate power spectrum which are based on the phase-compensation using the normalized cross-correlation to minimize estimation errors due to phase variations, and the weighted averaging technique to maximize the signal-to-noise ratio(SNR). The simulation results demonstrate that the proposed method estimates better results with 10% smaller estimation variances compared to the conventional methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.1
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• pp.48-52
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• 2005

In this paper, the spectral shape invariant real-time voice change method is proposed to change one's voice to mechanical voice. For this purpose, LPC analysis and synthesis is used to maintain the spectraum of voice and the pitch of synthesis speech can be changed freely. In the proposed method, gain matching method is applied to excitation signal generator to make the changed voice natural to hear. In order to evaluate the performance of the proposed method, voice change experiments were conducted. Experimental results showed that original speech signal is changed to the mechanical voice signal in which context of the speaker's voice is conveyed correctly in spite of drastic change of pitch. The system is implemented using TI TMS320C6711DSK board to verify the system runs in real time.

• Communications for Statistical Applications and Methods
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• v.30 no.4
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• pp.369-388
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• 2023

In this paper, we develop the two-step procedure that detects and estimates the position of structural changes for multivariate nonstationary time series, either on mean parameters or second-order structures. We first investigate the presence of mean structural change by monitoring data through the aggregated cumulative sum (CUSUM) type statistic, a sequential procedure identifying the likely position of the change point on its trend. If no mean change point is detected, the proposed method proceeds to scan the second-order structural change by modeling the multivariate nonstationary time series with a multivariate locally stationary Wavelet process, allowing the time-localized auto-correlation and cross-dependence. Under this framework, the estimated dynamic spectral matrices derived from the local wavelet periodogram capture the time-evolving scale-specific auto- and cross-dependence features of data. We then monitor the change point from the lower-dimensional approximated space of the spectral matrices over time by applying the dynamic principal component analysis. Different from existing methods requiring prior information on the type of changes between mean and covariance structures as an input for the implementation, the proposed algorithm provides the output indicating the type of change and the estimated location of its occurrence. The performance of the proposed method is demonstrated in simulations and the analysis of two real finance datasets.

• Journal of Ocean Engineering and Technology
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• v.34 no.6
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• pp.406-418
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• 2020

In this study, a nonlinear wave simulation code is developed using a higher-order spectral (HOS) method. The HOS method is very efficient because it can determine the solution of the boundary value problem using fast Fourier transform (FFT) without matrix operation. Based on the HOS order, the vertical velocity of the free surface boundary was estimated and applied to the nonlinear free surface boundary condition. Time integration was carried out using the fourth order Runge-Kutta method, which is known to be stable for nonlinear free-surface problems. Numerical stability against the aliasing effect was guaranteed by using the zero-padding method. In addition to simulating the initial wave field distribution, a nonlinear adjusted region for wave generation and a damping region for wave absorption were introduced for wave generation simulation. To validate the developed simulation code, the adjusted simulation was carried out and its results were compared to the eighth order Stokes theory. Long-time simulations were carried out on the irregular wave field distribution, and nonlinear wave propagation characteristics were observed from the results of the simulations. Nonlinear adjusted and damping regions were introduced to implement a numerical wave tank that successfully generated nonlinear regular waves. According to the variation in the mean wave steepness, irregular wave simulations were carried out in the numerical wave tank. The simulation results indicated an increase in the nonlinear interaction between the wave components, which was numerically verified as the mean wave steepness. The results of this study demonstrate that the HOS method is an accurate and efficient method for predicting the nonlinear interaction between waves, which increases with wave steepness.

• Journal of Biomedical Engineering Research
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• v.22 no.3
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• pp.259-267
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• 2001

The visual evoked potentials(VEPs) is used to assist in the diagnosis of specific disorders associated with involvement of the sensory visual pathways. The P100 latency is an important parameter which is diagnosis of optic nerve disorders. There are characteristics of latency delay, wave distortion, amplitude deduction in abnormal subjects. It is difficult to diagnose in the case of producing peak at the P100 latency. In this paper, difference of pattern between normal VEPs and abnormal VEPs using the Choi-Williams distribution method is studied. We observed the relationship about time and spectrum. The result shown that normal VEPs had maximum spectral value at 20Hz~26.7Hz and abnormal VEPs had maximum spectral value at 16.7Hz~20Hz. Also normal VEPs spectrum is higher than abnormal VEPs spectrum.

• Journal of Korea Multimedia Society
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• v.4 no.1
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• pp.46-55
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• 2001

In this paper, a new voice activity detection method estimating SNR of enhanced speech with extended spectral subtraction (ESS) is proposed. Voice activity detection is performed by putting an second Wiener filter behind an Wiener filter used in the ESS to estimate speech and noise power of output signal of first Wiener filter. The proposed voice activity detection method does not require many computational loads and performs well under severe input SNR. Boll's spectral substraction algorithm with proposed voice activity detection was compared to ESS under several noise environment having different time-frequency distributions. During speech and non-speech activity, performance of Boll's spectral substraction algorithm with proposed voice activity detection is superior to that of ESS.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1983.04a
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• pp.16-17
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• 1983

In Speech signal processing, ARMA spectral estimation method is used. It has been demonstrated that the ARMA model provides better spectral estimation then the more specialized AR model and MA model. Dynamic program is used to achieve time algnment. Speech sound similarity is defined to be proportional to the distance seperating to sound in a vector space defined by ARMA model. AS a result, the recognition rate of 97.3% for three speaker is obtained.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.427-430
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• 1989

For on-line ARMA spectral estimation, the fast transversal filter algorithm of extended least squares method(ETS FTF) is presented. The projection operator, a key tool for geometric approach, is used in the derivation of the algorithm. ELS FTF is a fast time update recursion which is based on the fact that the correlation matrix of ARMA model satisfies the shift invariance property in each block, and thus it takes 10N+31 MADPR.