• Bulletin of the Korean Chemical Society
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• v.24 no.7
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• pp.971-974
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• 2003

By utilizing singular value decomposition (SVD) and shift averaged Harr wavelet transform (WT) with a set of Daubechies wavelet coefficients (1/2, -1/2), a method that can simultaneously eliminate an unwanted large solvent peak and noise peaks from NMR data has been developed. Noise elimination was accomplished by shift-averaging the time domain NMR data after a large solvent peak was suppressed by SVD. The algorithms took advantage of the WT, giving excellent results for the noise elimination in the Gaussian type NMR spectral lines of NMR data pretreated with SVD, providing superb results in the adjustment of phase and magnitude of the spectrum. SVD and shift averaged Haar wavelet methods were quantitatively evaluated in terms of threshold values and signal to noise (S/N) ratio values.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.1
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• pp.26-32
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• 2007

This study was carried out fusion-welded butt joints in SWS 490A high strength steel subjected to tensile test that load-deflection curve. The windowed or short-time Fourier transform(WFT or STFT) makes possible for the analysis of non-stationary or transient signals into a joint time-frequency domain and the wavelet transform(WT) is used to decompose the acoustic emission(AE) signal into various discrete series of sequences over different frequency bands. In this paper, for acoustic emission signal analysis to use a continuous wavelet transform, in which the Gabor wavelet base on a Gaussian window function is applied to the time-frequency domain. A wavelet transform is demonstrated and the plots are very powerful in the recognition of the acoustic emission features. As a result, the technique of acoustic emission is ideally suited to study variables which control time and stress dependent fracture or damage process in metallic materials.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2005.05a
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• pp.342-348
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• 2005

This study was carried out fusion-welded butt joints in SWS 490A high strength steel subjected to tensile test that load-deflection curve. The windowed or short-time Fourier transform (WFT or SIFT) makes possible for the analysis of non-stationary or transient signals into a joint time-frequency domain and the wavelet transform (WT) is used to decompose the acoustic emission (AE) signal into various discrete series of sequences over different frequency bands. In this paper, for acoustic emission signal analysis to use a continuous wavelet transform, in which the Gabor wavelet base on a Gaussian window function is applied to the time-frequency domain. A wavelet transform is demonstrated and the plots are very powerful in the recognition of the acoustic emission features. As a result, the technique of acoustic emission is ideally suited to study variables which control time and stress dependent fracture or damage process in metallic materials.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10d
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• pp.302-305
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• 2006

In this paper, we have proposed a novel method using quantization of wavelet coefficients for secret data communication. First, speech signal is partitioned into small time frames and the frames are transformed into frequency domain using a WT(Wavelet Transform). We quantize the wavelet coefficients and embedded secret data into the quantized wavelet coefficients. The destination regard quantization errors of received speech as seceret dat. As most speech watermark techniques have a trade off between noise robustness and speech quality, our method also have. However we solve the problem with a partial quantization and a noise level dependent threshold. In additional, we improve the speech quality with de-noising method using wavelet transform. Since the signal is processed in the wavelet domain, we can easily adapt the de-noising method based on wavelet transform. Simulation results in the various noisy environments show that the proposed method is reliable for secret communication.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.49-50
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• 2011

본 논문에서는 WT(Wavelet Transform)와 SVD(Singular Value Decomposition)를 함께 사용한 WSVD(Wavelet Singular Value Decomposition)를 이용하여 발전기 탈락 시의 전압 변동 특성을 분석하였다. WSVD 특성 분석을 위해 부산 지역의 345kV급 송전계통을 EMTP-RV로 모델링하였으며, 이 계통모델에서 발전기 탈락을 모의하였다. MATLAB을 통해 이 때 측정된 전압의 WSVD를 계산하여 발전기 탈락에 따른 특성을 분석하였다.

• Journal of the Korean Society for Nondestructive Testing
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• v.20 no.6
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• pp.501-510
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• 2000

Recently, advanced signal analysis which is called "time-frequency analysis" has been used widely in nondestructive evaluation applications. Wavelet transform(WT) and Wigner Distribution are the most advanced techniques for processing signals with time-varying spectra. Wavelet analysis method is an attractive technique for evaluation of material characterization nondestructively. Wavelet transform is applied to the time-frequency analysis of ultrasonic echo waveform obtained by an ultrasonic pulse-echo technique. In this study, the feasibility of noise suppression of ultrasonic flaw signal and frequency-dependent ultrasonic group velocity and attenuation coefficient using wavelet analysis of ultrasonic echo waveform have been verified experimentally. The Gabor function is adopted the analyzing wavelet. The wavelet analysis shows that the variations of ultrasonic group velocity and attenuation coefficient due to the change of material characterization can be evaluated at each frequency. Furthermore, to assure the enhancement of detectability and naw sizing performance, both computer simulated results and experimental measurements using wavelet signal processing are used to demonstrate the effectiveness of the noise suppression of ultrasonic flaw signal obtained from austenitic stainless steel weld including EDM notch.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.4
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• pp.37-43
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• 2005

Real time monitoring of exit burr formation is critical in manufacturing automation. In this paper, acoustic emission (AE) was used to detect the burr formation during drilling. By using wavelet transform (WT), AE data were compressed without unnecessary details. Then the transformed data were used as selected features (inputs) of a back-propagation artificial neural net (ANN). In order to validate the in process AE monitoring system, both WT-based ANN and cutting condition (cutting speed, feed, drill diameter, etc.) based ANN outputs were compared with experimental data.

• Structural Engineering and Mechanics
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• v.67 no.5
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• pp.457-464
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• 2018

This paper presents a method for detecting damage in irregular 2D and 3D continuum structures based on combination of wavelet transform (WT) with fuzzy inference system (FIS) and particle swarm optimization (PSO). Many damage detection methods study regular structures. This method studies irregular structures and doesn't need response of healthy structures. First the damaged structure is analyzed with finite element methods, and damage response is obtained at the finite element points that have irregular distance, secondly the FIS, which is optimized by PSO is used to obtain responses at points, having equal distance by response at those points that previously obtained by the finite element methods. Then a 2D (for 2D continuum structures) or a 3D (for 3D continuum structures) matrix is performed by equal distance point response. Thirdly, by applying 2D or 3D wavelet transform on 2D or 3D matrix that previously obtained by FIS detail matrix coefficient of WT is obtained. It is shown that detail matrix coefficient can determine the damage zone of the structure by perturbation in the damaged area. In order to illustrate the capability of proposed method some examples are considered.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.5
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• pp.667-672
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• 2013

In this paper, we propose a multi-impedance changes localization method of on-voltage underground power cable using the wavelet transform based time-frequency domain reflectometry (WTFDR). To localize the impedance change in on-voltage power cable, the TFDR is the most suitable among reflectometries because the inductive coupler is used to inject the reference signal to the live cable. At this time, the actual on-voltage power cable has multi-impedance changes such as the automatic section switches and the auto load transfer switches. However, when the multi-impedance changes are generated in the close range, the conventional TFDR has the cross term interference problem because of the nonlinear characteristics of the Wigner-Ville distribution. To solve the problem, the wavelet transform (WT) is used because it has the linearity. That is, using WTFDR, the cross term interference is not generated in multi-impedance changes due to the linearity of the WT. To confirm the effectiveness and accuracy of the proposed method, the actual experiments are carried out for the on-voltage underground power cable.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.12
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• pp.45-61
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• 1996

This paper presents the ECG data compression using wavelet transform (WT) and adaptive fractal interpolation (AFI). The WT has the subband coding scheme. The fractal compression method represents any range of ECG signal by fractal interpolation parameters. Specially, the AFI used the adaptive range sizes and got good performance for ECG data cmpression. In this algorithm, the AFI is applied into the low frequency part of WT. The MIT/BIH arhythmia data was used for evaluation. The compression rate using WT and AFI algorithm is better than the compression rate using AFI. The WT and AFI algorithm yields compression ratio as high as 21.0 wihtout any entropy coding.