• Proceedings of the Korean Society of Marine Engineers Conference
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• 2002.05a
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• pp.213-217
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• 2002

Recently the subject of "wavelet analysis" has be drawn by both mathematical and engineering application fields such as Signal Processing, Compression/Decomposition, Wavelet-Neural Network, Statistics and etc. Even though its similar to courier analysis, wavelet is a versatile tool with much mathematical content and great potential for applications. Especially, wavelet transform uses localizable various mother wavelet functions in time-frequency domain. Therefore, wavelet transform has good time-analysis ability for high frequency component, and has good frequency-analysis ability for low frequency component. Using the discriminative ability is more easy method than other conventional techniques. In this paper, Morlet wavelet transform was applied to discriminate the kind of line fault by acquired data from real power transformation network. The experimental result presented that Morlet wavelet transform is easier, and more useful method than the FFW (Fast courier Transform).ransform).

• Current Optics and Photonics
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• v.7 no.5
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• pp.529-536
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• 2023

The Morlet wavelet transform method is proposed to analyze a single interferogram with spatial carrier frequency that is captured by an optical interferometer. The method can retain low frequency components that contain the phase information of a measured optical surface, and remove high frequency disturbances by wavelet decomposition and reconstruction. The key to retrieving the phases from the low-frequency wavelet components is to extract wavelet ridges by calculating the maximum value of the wavelet transform amplitude. Afterwards, the wrapped phases can be accurately solved by multiple iterative calculations on wavelet ridges. Finally, we can reconstruct the wave-front of the measured optical element by applying two-dimensional discrete cosine transform to those wrapped phases. Morlet wavelet transform does not need to remove the spatial carrier frequency components manually in the processing of interferogram analysis, but the step is necessary in the Fourier transform algorithm. So, the Morlet wavelet simplifies the process of the analysis of interference fringe patterns compared to Fourier transform. Consequently, wavelet transform is more suitable for automated programming analysis of interference fringes and avoiding the introduction of additional errors compared with Fourier transform.

• Journal of Korea Water Resources Association
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• v.38 no.6 s.155
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• pp.439-448
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• 2005

This paper introduces the wavelet transform that was improved by the fourier transform to assess periodicities and trends, we assessed propriety with examples of two monthly precipitation data, annual precipitation, SOI index and SST index. The wavelet transform can effectively assess the power spectrum corresponding to frequency as maintaining chronological characteristics. The results of the analysis using the wavelet transform showed that the monthly precipitation have the strongest power spectrum near that of 1 year, and the annual precipitation represent the dominated spectrum in the band of 2-8 years. Also, the SOI index and SST index indicate the strongest power spectrum in the band of 2-8 years.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2002.09a
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• pp.173-180
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• 2002

This paper presents a signal processing procedure to detect damage locations of frame structures by using continuous wavelet transform. Morlet wavelet is used as a mother wavelet in wavelet transform. Wavelet transform has the characteristics that allows the use of long time intervals at more precise low-frequency information, and shorter regions at high-frequency information. By this wavelet transform characteristics, Morlet wavelet may be used to identify the locations of damages in the structures. The numerical case studies show that this method can be applied to detect the damage location under a controlled sweeping load.

• The Journal of the Acoustical Society of Korea
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• v.19 no.5
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• pp.27-34
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• 2000

This paper deals with speech enhancement methods using the wavelet transform. A cycle-spinning scheme and undecimated wavelet transform are used for denoising of speech signals, and then their results are compared with that of the conventional wavelet transform. We apply soft-thresholding technique for removing additive background noise from noisy speech. The symlets 8-tap wavelet and pyramid algorithm are used for the wavelet transform. Performance assessments based on average SNR, cepstral distance and informal subjective listening test are carried out. Experimental results demonstrate that both cycle-spinning denoising(CSD) method and undecimated wavelet denoising(CWD) method outperform conventional wavelet denoising(UWD) method in objective performance measure as welt as subjective listening test. The two methods also show less "clicks" that usually appears in the neighborhood of signal discontinuities.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.52 no.1
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• pp.25-34
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• 2003

Gas insulated switchgear(GIS) is an important equipment in a substation. It is highly desirable to measure a partial discharge(PD) in GIS which is a symptom before insulation breakdown occurs. The issue is that the PD signal is weak and sensitive to external noise. In this paper, the algorithm for detection of PD in GIS using wavelet transform is proposed. The wavelet transform provides a direct quantitative measure of spectral content, "dynamic spectrum", in the time-frequency domain. The recommended mother wavelet is 'Daubechies' wavelet. 'db4', the most commonly applied mother wavelet in the power quality analysis, can be used most properly in disturbance phenomena which occurs rapidly for a short time. Through the procedure of wavelet transform, noise extraction and reconstruction, the signal is Analyzed to determine the magnitude of PD in GIS. In experimental results, we can know that partial discharge is exactly detected in combination of Dl and D2 using wavelet transform.transform.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.361-364
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• 1995

Wavelet Transform is a new tools for signal processing, such as data compressing extraction of parameter for Reconition and Diagnostics. This transform has an advandage of a good resolution compared to Fast Fourier Transform (FFT) In this study, we employ the wavelet transform for analysis of Acoustic Emission raw signal generated form rotary compressor. In abnormal condition of rotary compressor, the state of operating condition can be classified by analizing coefficient of wavelet transformed signal.

• 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.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.21 no.3
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• pp.75-81
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• 2007

Recently the subject of "wavelet analysis" has be drawn by both mathematical and engineering application fields such as Signal Processing, Compression/Decomposition, Wavelet-Neural Network, Statistics and etc. Even though its similar to Fourier analysis, wavelet is a versatile tool with much mathematical content and great potential for applications. Especially, wavelet transform uses localizable various mother wavelet functions in time-frequency domain. Therefore, wavelet transform has good time-analysis ability for high frequency component, and has good frequency-analysis ability for low frequency component. Using the discriminative ability is more easy method than other conventional techniques. In this paper, Morlet wavelet transform was applied to discriminate the kind of line fault by acquired data from real power transformation network. The experimental result presented that Morlet wavelet transform is easier, and more useful method than the Fast Fourier Transform(FFT).

• The Journal of the Acoustical Society of Korea
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• v.17 no.2E
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• pp.31-37
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• 1998

This paper proposes a feature extraction method using wavelet transform for speech recognition. Speech recognition system generally carries out the recognition task based on speech features which are usually obtained via time-frequency representations such as Short-Time Fourier Transform (STFT) and Linear Predictive Coding(LPC). In some respects these methods may not be suitable for representing highly complex speech characteristics. They map the speech features with same may not frequency resolutions at all frequencies. Wavelet transform overcomes some of these limitations. Wavelet transform captures signal with fine time resolutions at high frequencies and fine frequency resolutions at low frequencies, which may present a significant advantage when analyzing highly localized speech events. Based on this motivation, this paper investigates the effectiveness of wavelet transform for feature extraction of wavelet transform for feature extraction focused on enhancing speech recognition. The proposed method is implemented using Sampled Continuous Wavelet Transform (SCWT) and its performance is tested on a speaker-independent isolated word recognizer that discerns 50 Korean words. In particular, the effect of mother wavelet employed and number of voices per octave on the performance of proposed method is investigated. Also the influence on the size of mother wavelet on the performance of proposed method is discussed. Throughout the experiments, the performance of proposed method is discussed. Throughout the experiments, the performance of proposed method is compared with the most prevalent conventional method, MFCC (Mel0frequency Cepstral Coefficient). The experiments show that the recognition performance of the proposed method is better than that of MFCC. But the improvement is marginal while, due to the dimensionality increase, the computational loads of proposed method is substantially greater than that of MFCC.