• Journal of Welding and Joining
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• v.21 no.6
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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.

• Journal of Mechanical Science and Technology
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• v.15 no.5
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• pp.535-543
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• 2001

This paper presents the ride comfort analysis of a vehicle based on wavelet transform. Traditionally, the objective evaluation of impact harshness is based on the vibration dose value (VDV) and frequency weighting method. These methods do not consider the damping effect of the suspension system of a vehicle. In this paper, the damping is estimated using wavelet transform based on Morlet mother wavelet and its effect is considered for the subjective evaluation of impact harshness of a car.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.3 no.2
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• pp.32-38
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• 2004

The chatter behaviour in endmilling is a complex and nonlinear phenomenon, so it is very difficult to detect and diagnose this chatter phenomenon, This paper presents new method for the detection of chatter in endmilling operation based on the wavelet transform. In this paper, the fundamental property of the wavelet transform is reviewed by comparing the spectrum of other algorithm such as FFT. This result using wavelet transform shows the possibiling of the chatter detection in endmilling operation.

• Earthquakes and Structures
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• v.6 no.4
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• pp.393-408
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• 2014

In this paper, a direct identification of modal parameters using the continuous wavelet transform is proposed. The purpose of this method is to transform the differential equations of motion into a system of algebraic linear equations whose unknown coefficients are modal parameters. The efficiency of the present method is confirmed by numerical data, without and with noise contamination, simulated from a discrete forced system with four degrees-of-freedom (4DOF) proportionally damped.

• Journal of the Korean earth science society
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• v.28 no.7
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• pp.903-913
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• 2007

In this study, the researchers compared the S-wave velocity structures obtained by two kinds of dispersion curves: phase and group dispersions from a tidal flat located in the SW coast of the Korean peninsula. The ${\tau}-p$ stacking method was used for the phase velocity and two different methods (multiple filtering technique: MFT and continuous wavelet transform: CWT) for the phase velocity. It was difficult to separate higher modes from the fundamental mode phase velocities using the ${\tau}-p$ method, whereas the separation of different modes of group velocity were easily achieved by both MFT and CWT. Of the two methods, CWT was found to be more efficient than MFT. The spatial resolutions for the inversion results of the fundamental mode for both phase and group velocities were good for only a very shallow depth of ${\sim}1.5m$. On the other hand, the spatial resolutions were good up to ${\sim}4m$ when both the fundamental and the 1st higher mode poop velocities obtained by CWT were used for S-wave inversion. This implies that the 1st higher mode Rayleigh waves contain more information on the S-wave velocity in deeper subsurface. The researchers applied the CWT method to obtain the fundamental and the 1st higher mode poop velocities of the S-wave velocity structure of a tidal flat located in SW coast of the Korean peninsula. Thea the S-wave velocity structures were compared with the borehole description of the study area.

• Journal of the Korea institute for structural maintenance and inspection
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• v.12 no.6
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• pp.140-146
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• 2008

The damage evaluation method for framed structures using continuous wavelet transform (CWT) is proposed. CWT is applied to the response acceleration of a structure subjected to earthquake load to decompose the response acceleration corresponding to each scale, then the normalized energy value for each scale is calculated. The difference between the normalized energy curvature (NEC) in each node before and after damage indicates a damaged element, which makes it possible to assess the soundness of structural elements. As damage becomes more severe the difference in normalized energy curvature becomes larger. The NEC calculated from the signal corresponding to high scale in CWT analysis is found to be a good index that shows the location and severity of damage.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.18 no.4
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• pp.381-387
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• 2009

The end-milling force behaviour is very complex and it is related to a de-noising phenomenon, so it is very difficult to detect and diagnose this static cutting force phenomenon. This paper presents a new method of filtering of end-milling force in end-milling operation using filter bank technique, based on the wavelet transform. In this paper by comparing the history of end-milling force using wavelet filtering the fundamental end-milling property of the wavelet transform is well reviewed and analyzed. This result of wavelet transform using filter bank shows the possible static prediction of end-milling force with severe dynamic properties such as chatter in end-milling operation.

• Smart Structures and Systems
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• v.1 no.2
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• pp.185-215
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• 2005

Advanced signal processing techniques have been long introduced and widely used in structural health monitoring (SHM) and nondestructive evaluation (NDE). In our research, we applied several signal processing approaches for our embedded ultrasonic structural radar (EUSR) system to obtain improved damage detection results. The EUSR algorithm was developed to detect defects within a large area of a thin-plate specimen using a piezoelectric wafer active sensor (PWAS) array. In the EUSR, the discrete wavelet transform (DWT) was first applied for signal de-noising. Secondly, after constructing the EUSR data, the short-time Fourier transform (STFT) and continuous wavelet transform (CWT) were used for the time-frequency analysis. Then the results were compared thereafter. We eventually chose continuous wavelet transform to filter out from the original signal the component with the excitation signal's frequency. Third, cross correlation method and Hilbert transform were applied to A-scan signals to extract the time of flight (TOF) of the wave packets from the crack. Finally, the Hilbert transform was again applied to the EUSR data to extract the envelopes for final inspection result visualization. The EUSR system was implemented in LabVIEW. Several laboratory experiments have been conducted and have verified that, with the advanced signal processing approaches, the EUSR has enhanced damage detection ability.

• The Journal of the Acoustical Society of Korea
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• v.20 no.8
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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.

• Journal of Korea Society of Digital Industry and Information Management
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• v.10 no.3
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• pp.237-247
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• 2014

This paper describes a 2D discrete-time wavelet transform for which the Q-factor is easily specified. Hence, the transform can be tuned according to the oscillatory behavior of the image signal to which it is applied. The tunable Q-factor wavelet transform (TQWT) is a fully-discrete wavelet transform for which the Q-factor, Q, of the underlying wavelet and the asymptotic redundancy (over-sampling rate), r, of the transform are easily and independently specified. In particular, the specified parameters Q and r can be real-valued. Therefore, by tuning Q, the oscillatory behavior of the wavelet can be chosen to match the oscillatory behavior of the signal of interest, so as to enhance the sparsity of a sparse signal representation. The TQWT is well suited to fast algorithms for sparsity-based inverse problems because it is a Parseval frame, easily invertible, and can be efficiently implemented. The TQWT can also be used as an easily-invertible discrete approximation of the continuous wavelet transform. The transform is based on a real valued scaling factor (dilation-factor) and is implemented using a perfect reconstruction over-sampled filter bank with real-valued sampling factors. The transform is parameterized by its Q-factor and its oversampling rate (redundancy), with modest oversampling rates (e. g. 3-4 times overcomplete) being sufficient for the analysis/synthesis functions to be well localized. Therefore, This method services good performance in image processing fields.