• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.15 no.2 s.95
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• pp.192-198
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• 2005

Empirical mode decomposition(EMD) method has been recently proposed to analyze non-linear and non-stationary data. This method allows the decomposition of one-dimensional signals into intrinsic mode functions(IMFs) and is used to calculate a meaningful multi-component instantaneous frequency. In this paper, it is assumed that each mode of damped vibration signal could be well separated in the form of IMF by EMD. In this case, we can have a new powerful method to calculate natural frequencies and dampings from damped vibration signal which usually has multiple modes. This proposed method has been verified by both simulation and experiment. The results by EMD method whichhas used only output vibration data are almost identical to the results by FRF method which has used both input and output data, thereby proving usefulness and accuracy of the proposed method.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.05a
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• pp.109-114
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• 2002

The purpose of this study is to propose the general method for evaluating the equivalent damping ratios of a structure with supplemental response control dampers. We define Lyapunov function of which derivative can be expressed in autoregressive form and evaluate the equivalent damping ratios by using Lyapunov function and its derivative. This Lyapunov function may be called as generalized structural energy. In this study, it is assumed that the response of a structure is stationary random process and control dampers do not affect the modal shapes of a structure, and the structure has proportional damping. Proposed method can be used to get the equivalent damping ratios of a structure with non-linear control dampers such as friction dampers as well as linear control dampers. To show the effectiveness of the proposed method. we evaluate the equivalent damping ratios of a structure with viscous dampers. AMDs. and friction dampers. The equivalent damping ratios from proposed method are compared to those from eigenvalue analysis for linear control dampers. and those from time history analysis for non-linear control dampers. respectively.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.11a
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• pp.699-704
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• 2004

Empirical mode decomposition(EMD) method has been recently proposed to analyze non-linear and non-stationary data. This method allows the decomposition of one-dimensional signals into intrinsic mode functions(IMFs) and is used to calculate a meaningful multi-component instantaneous frequency. In this paper, it is assumed that each mode of damped vibration signal could be well separated in the form of IMF by EMD. In this case, we can have a new powerful method to calculate natural frequencies and dampings from damped vibration signal which usually has multiple modes. This proposed method has been verified by both simulation and experiment. The result by EMD method which has used only output vibration data is almost identical to the result by FRF method which has used both input and output data, thereby proving usefulness and accuracy of the proposed method.

• Journal of Korean Association for Spatial Structures
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• v.19 no.2
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• pp.101-108
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• 2019

In this paper, the characteristic of intrinsic mode function(IMF) and its orthogonalization of ensemble empirical mode decomposition(EEMD), which is often used in the analysis of the non-linear or non-stationary signal, has been studied. In the decomposition process, the orthogonal IMF of EEMD was obtained by applying the Gram-Schmidt(G-S) orthogonalization method, and was compared with the IMF of orthogonal EMD(OEMD). Two signals for comparison analysis are adopted as the analytical test function and El Centro seismic wave. These target signals were compared by calculating the index of orthogonality(IO) and the spectral energy of the IMF. As a result of the analysis, an IMF with a high IO was obtained by GSO method, and the orthogonal EEMD using white noise was decomposed into orthogonal IMF with energy closer to the original signal than conventional OEMD.

• The Journal of the Acoustical Society of Korea
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• v.25 no.7
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• pp.325-332
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• 2006

In this paper. we Propose the noise suppressor with the Perceptual model and wavelet packet transform. The objective is to enhance speech corrupted colored or non-stationary noise. If corrupted noise is colored. subband approach would be more efficient than whole band one. To avoid serious residual noise and speech distortion, we must adjust the Wavelet Coefficient Threshold (WCT). In this Paper. the subband is designed matching with the critical band and WCT is adapted noise masking threshold (NMT) and segmental signal to noise ratio (seg_SNR). Consequently. it has similar Performance with EVRC in PESQ-MOS. But it's better than wavelet packet transform using universal threshold about 0.289 in PESQ-MOS. The important thing is that it's more useful than EVRC in coded speech. In coded speech. PESQ-MOS is higher than EVRC about 0.23.

• Water for future
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• v.11 no.2
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• pp.54-61
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• 1978

An attemption of synthesizing and forecasting of monthly river flow has been made by employing a linear stochastic difference equation model. As one of the linear stochestic difference equation model, an ARIMA Type is tested to find the suitability of the model to the monthly river flows. On the assumption of the stationary covariacne of differenced monthly river flows the model is identrfield and is evaluated so that the residuale have the minimum variance. Finally a test is performed to finld the residerals beings White noise. Monthly river flows at six stations in Han River Basin are applied for case studies. It was found that the difference operator is a good measure of forecasting the monthly river flow.

• The Journal of the Acoustical Society of Korea
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• v.21 no.3
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• pp.323-329
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• 2002

This paper proposes a frame reliability weighting method to compensate for a time-selective noise that occurs at random positions of speech signal contaminating certain parts of the speech signal. Speech frames have different degrees of reliability and the reliability is proportional to SNR (signal-to noise ratio). While it is feasible to estimate frame Sl? by using the noise information from non-speech interval under a stationary noisy situation, it is difficult to obtain noise spectrum for a time-selective noise. Therefore, we used statistical models of clean speech for the estimation of the frame reliability. The proposed MFR (model-based frame reliability) approximates frame SNR values using filterbank energy vectors that are obtained by the inverse transformation of input MFCC (mal-frequency cepstral coefficient) vectors and mean vectors of a reference model. Experiments on various burnt noises revealed that the proposed method could represent the frame reliability effectively. We could improve the recognition performance by using MFR values as weighting factors at the likelihood calculation step.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2012.05a
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• pp.555-561
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• 2012

The Hilbert Huang Transform (HHT) technigue with Empirical Mode Decomposition (EMD) is one of the time-frequency domain analysis methods and it has several advantages such that analyzing non-stationary and nonlinear signal is possible. However, there are shortcomings in detecting near-range of frequencies and added noise signals. In this paper, to analyze characteristics of each method, HHT and Short-Time Fourier Transform (STFT) effective in dealing with stationary signals are compared. And with thermoacoustic instabilities signals from a Rijke tube test, HHT and the improved HHT with Ensemble Empirical Mode Decomposition (EEMD) are compared. The results show that the improved HHT is more appropriate than the original HHT due to the relative insensitivity to noise. Therefore it will result in more accurate analysis.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.7
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• pp.945-951
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• 2007

Noise cancellation is essential for the devices which use speech as an interface. In real environments, speech quality and recognition rates are degraded by the auditive noises coming near the microphone. In this paper, we propose a noise cancellation algorithm using stereo microphones basically. The advantage of the use of multiple microphones is that the direction information of the target source could be applied. The proposed noise canceller is based on the Wiener filter. To estimate the filter, noise and target speech frequency responses should be known and they are estimated by the spectral classification in the frequency domain. The performance of the proposed algorithm is compared with that of the well-known Frost algorithm and the generalized sidelobe canceller (GSC) with an adaptation mode controller (AMC). As performance measures, the perceptual evaluation of speech quality (PESQ), which is the most widely used among various objective speech quality methods, and speech recognition rates are adopted.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.4
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• pp.729-734
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• 2008

Vibroarthrographic(VAG) signals, emitted by human knee joints, are non-stationary and multi-component in nature and time-frequency distributions(TFD) provide powerful means to analyze such signals. The objective of this paper is to classify VAG signals, generated during joint movement, into two groups(normal and patient group) using the characteristic parameters extracted by time-frequency transform, and to evaluate the classification accuracy. Noise within TFD was reduced by singular value decomposition and back-propagation neural network(BPNN) was used for classifying VAG signals. The characteristic parameters consist of the energy parameter, energy spread parameter, frequency parameter, frequency spread parameter by Wigner-Ville distribution and the amplitude of frequency distribution, the mean and the median frequency by fast Fourier transform. Totally 1408 segments(normal 1031, patient 377) were used for training and evaluating BPNN. As a result, the average value of the classification accuracy was 92.3(standard deviation ${\pm}0.9$)%. The proposed method was independent of clinical information, and showed good potential for non-invasive diagnosis and monitoring of joint disorders such as osteoarthritis and chondromalacia patella.