• Journal of Mechanical Science and Technology
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• 제20권12호
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• pp.2147-2158
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• 2006

The continuous wavelet transform (CWT) has a frequency-adaptive time-frequency tiling property, which makes it popular for the analysis of dispersive elastic wave signals. However, because the time-frequency tiling of CWT is not signal-dependent, it still has some limitations in the analysis of elastic waves with spectral components that are dispersed rapidly in time. The objective of this paper is to introduce an advanced time-frequency analysis method, called the dispersion-based continuous wavelet transform (D-CWT) whose time-frequency tiling is adaptively varied according to the dispersion relation of the waves to be analyzed. In the D-CWT method, time-frequency tiling can have frequency-adaptive characteristics like CWT and adaptively rotate in the time-frequency plane depending on the local wave dispersion. Therefore, D-CWT provides higher time-frequency localization than the conventional CWT. In this work, D-CWT method is applied to the analysis of dispersive elastic waves measured in waveguide experiments and an efficient procedure to extract information on the dispersion relation hidden in a wave signal is presented. In addition, the ridge property of the present transform is investigated theoretically to show its effectiveness in analyzing highly time-varying signals. Numerical simulations and experimental results are presented to show the effectiveness of the present method.

• 한국구조물진단유지관리공학회 논문집
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• 제12권6호
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• pp.140-146
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• 2008

본 논문에서는 연속웨이블렛 변환(Continuous Wavelet Transform; CWT)을 이용하여 구조물의 손상도를 평가 하였다. 지진하중을 받은 프레임 구조물의 응답 가속도를 CWT를 이용하여 분해한 후 각각의 스케일에 관해서 손상전과 손상후의 정규화된 에너지 곡률(Normalized Energy Curvature; NEC)을 계산하였다. 손상전과 손상후의 NEC 값은 손상된 부재에서 크게 변화 하여 손상된 부재를 쉽게 나타내었고 또한 손상도가 심할 수 록 그 값의 차이가 컸다. 이 논문에서는 CWT로부터 계산된 NEC값이 구조물의 손상위치와 손상도를 평가하는 효과적인 지표임을 나타내었다.

• 한국음향학회지
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• 제20권8호
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• pp.81-87
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• 2001

The human auditory system is appropriate for the "constant Q"system. The STFT (Short Time Fourier Transform) is not suitable for the auditory perception model since it has constant bandwidth. In this paper, the CWT (continuous wavelet transform) is employed for the auditory filter model. In the CWT, the frequency resolution can be adjusted for auditory sensation models. The proposed CWT is applied to the modeling of the JNVF. In addition, other signal processing methods such as STFT, VER-FFT and VFR-STFT are discussed. Among these methods, the model of JNVF (Just Noticeable Variation in Frequency) by using the CWT fits in with the JNVF of auditory model although it requires quite a long time.

• 한국자기공명학회논문지
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• 제4권2호
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• pp.103-115
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• 2000

Wavelet transforms are introduced as a new tool to distinguish real peaks from the noise contaminated NMR data in this paper. New algorithms of two wavelet transforms including Daubechies wavelet transform as a discrete and orthogonal wavelet transform (DWT) and Morlet wavelet transform as a continuous and nonorthogonal wavelet transform(CWT) were developed fer noise elimination. DWT and CWT method were successfully applied to the noise reduction in spectrum. The inevitable distortion of NMR spectral baseline and the imperfection in noise elimination were observed in DWT method while CWT method gives a better baseline ahape and a well noise suppressed spectrum.

• 한국지구과학회지
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• 제28권7호
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• pp.903-913
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• 2007

천부지반에서 레일리파의 군속도와 위상속도 분산곡선을 역산하여 S파 속도구조를 구하는 방법을 비교하였다. 위상속도를 구하기 위해서 ${\tau}-p$ stacking 방법을 이용하였고, 군속도를 구하기 위해서 두 가지 방법, multiple filtering technique(MFT) continuous wavelet transform(CWT) 방법을 사용하였다. 고차모드가 존재하는 경우, 위상속도에서 기본모드와 고차모드를 분리하기가 용이하지 않았고, 군속도에서는 continuous wavelet transform 방법이 multiple filtering technique보다 효과적이었다. S파 속도 역산 결과, 위상속도와 군속도의 기본 모드만 역산할 경우, 신뢰구간의 깊이가 아주 얕았다. Continuous wavelet transform으로 구한 기본 모드와 1차 모드를 동시 역산할 경우, 신뢰구간의 깊이가 2배 이상 증가함을 알 수 있었다. 이는 1차 모드의 에너지가 더 깊은 층을 통과함으로 깊은 층에 대한 5파 속도 정보를 지니고 있기 때문으로 보인다. 위의 방법을 서해안 동호항의 조간대에 적용하여, continuous wavelet transform으로 구한 군속도의 기본 모드와 1차 모드를 동시 역산하여 S파 속도구조를 구하고, 해당 지역의 시추조사 결과와 비교하였다.

• 산업기술연구
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• 제25권B호
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• pp.89-97
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• 2005

Application of proper orthogonal decomposition (POD) and continuous wavelet transform (CWT) is introduced to study wind speed and building roof pressures of flow separation region. In this study, a detailed analysis of the approach wind flow, wind-induced building pressure and the relation between the two fields was carried out using the POD technique and CWT analysis. The results show potential of the application of POD and CWT in characterization of spatio-temporal and spectral properties of the approach wind and its induced dynamic pressure events. Some of findings resulting from the application of this analysis can be summarized as follows: (1) The POD first principal coordinate of the roof pressure in the separated shear layer is closely correlated with the longitudinal component of oncoming flow. (2) The CWT analysis suggests that the extreme peak pressure in the separated shear layer is due to condensed large-scale eddy motions.

• Journal of Welding and Joining
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• 제21권6호
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• pp.33-39
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• 2003

Wavelet transform analysis results show a spectrum energy distribution of CWT along scale factors distinguish the partial, full and over penetration in a electron beam welding by analyzing the curve of spectrum energy at small scale, middle and large scale range, respectively. Two types of signals collected by Ion collector and x-ray sensors and analyzed. The acquired signals from sensors are very complicated since these signals are very closely related the dynamics of keyhole which interact the very high density energy with materials during welding. The results show the wavelet transform is more effective to diagnosis than Fourier Transform, further for the general welding defects which are not a periodic based, but a transient, non-stationary and time-varying phenomena.

• 대한의용생체공학회:의공학회지
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• 제29권4호
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• pp.278-285
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• 2008

In this study the complex-valued continuous wavelet transform (CWT) has been applied in detection of Electrocardiograms (ECG) as response to various signal classification methods such as Fourier transforms and other tools of time frequency analysis. Experiments have shown that CWT may serve as a detector of non-stationary signal changes as ECG. The tested signal is corrupted by short time events. We applied CWT to detect short-time event and the result image representation of the signal has showed us that one can easily find the discontinuity at the time scale representation. Analysis of ECG signal using complex-valued continuous wavelet transform is the first step to detect possible changes and alternans. In the second step, modulus and phase must be thoroughly examined. Thus, short time events in the ECG signal, and other important characteristic points such as frequency overlapping, wave onsets/offsets extrema and discontinuities even inflection points are found to be detectable. We have proved that the complex-valued CWT can be used as a powerful detector in ECG signal analysis.

• Structural Engineering and Mechanics
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• 제30권6호
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• pp.699-712
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• 2008

The effectiveness of wavelet transform in detecting delamination damages in multilayered composite beams and plates is studied here. The damaged composite beams and plates are modeled in finite element software ABAQUS and the first few mode shapes are obtained. The mode shapes of the damaged structures are then wavelet transformed. It is observed that the distribution of wavelet coefficients can identify the damage location of beams and plates by showing higher values of wavelet coefficients at the position of damage. The effectiveness of the method is studied for different boundary conditions, damage location and size for single as well as multiple delaminations in composite beams and plates. It is observed that both discrete wavelet transform (DWT) and continuous wavelet transform (CWT) can detect the presence and location of the damaged region from the mode shapes of the structures. DWT may be used to approximately evaluate the size of the delamination area, whereas, CWT is efficient to detect smaller delamination areas in composites.

• Geomechanics and Engineering
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• 제36권1호
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• pp.1-8
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• 2024

We propose a new method for detecting voids behind tunnel concrete linings using the impact-echo method that is based on continuous wavelet transform (CWT) and a convolutional neural network (CNN). We first collect experimental data using the impact-echo method and then convert them into time-frequency images via CWT. We provide a CNN model trained using the converted images and experimentally confirm that our proposed model is robust. Moreover, it exhibits outstanding performance in detecting backfill voids and their status.