• 한국해양공학회:학술대회논문집
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• 한국해양공학회 2000년도 추계학술대회 논문집
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• pp.118-122
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• 2000

In this study, Ringing is investigated by continuous wavelet transform. Ringing is considered to be one of the typical transient phenomena in the field of ocean engineering. The wavelet analysis is adopted to analyze ringing from the point that wavelet analysis is capable of frequency analysis as well as time domain analysis. The use mother wavelet is the Morlet wavelet. The relation between the frequency of the time series and that of wavelet can be clearly defined with Mor1et wavelet. Experimental data obtained by other researchers was used. The wave height time series and acceleration times series of the surface piercing cylinder were analyzed. The results show that the proposed scheme can detect typical frequency region by the time domain analysis which could hardly be detected if one relied on the frequency analysis.

• 한국마린엔지니어링학회:학술대회논문집
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• 한국마린엔지니어링학회 2002년도 춘계학술대회논문집
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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).

• 한국생산제조학회지
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• 제8권1호
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• pp.135-141
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• 1999

Recently, advance signal analysis which is called "Time-Frequency Analysis" has been developed. Wavelet and Wigner Distribution are used to the method. Wavelet transform(WT) is applied to time-frequency analysis of waveforms obtained by an ultrasonic pulse-echo technique. The Gabor function is adopted as the analyzing wavelet. Wavelet analysis method is an attractive technique for evolution of material characterization evoluation. In this paper, the feasibility of suppression of ultrasonic background noise signal using WT has been presented. These results suggest that ultrasonic background noise ginal can be suppressed and enhanced even for SNR of 20.8 dB. This property of the WT is extremely useful for the detecting flaw echos embedded in background noise.und noise.

• Current Optics and Photonics
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• 제7권5호
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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.

• 조명전기설비학회논문지
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• 제21권3호
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• pp.75-81
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• 2007

Wavelet 변환은 신호를 분석하고 해석하는데 효과적인 수학적 도구로 알려져 여러 응용분야에서 다양한 연구가 진행되고 있다. Wavelet 변환은 Fourier 변환과 유사한 측면도 있으나, Fourier 변환과는 달리 다양한 Wavelet 모함수를 사용함으로써 해석 속도가 빠르고, 시간-주파수 영역에서 국재화가 가능하다는 특징을 가지고 있을뿐만 아니라 고주파 성분에 대해선 시간 분해능이 높고, 저주파 성분에 대해선 주파수 분해능이 좋다는 장점을 가지고 있으므로, 전력계통의 다양한 고장 전류의 판별에 적극 이용할 수 있을 것으로 생각된다. 본 논문에서는 고장 전류의 특성을 해석하는데 용이한 복소형의 Morlet Wavelet 모함수를 사용하여 전력계통의 고장기록장치로부터 얻어지는 선로의 전류 데이터를 Wavelet 변환하였고, 이로부터 다양한 고장 모드를 판별할 수 있었다. 실험 결과 Wavelet 변환을 이용하여 선로의 고장 모드를 판별하는 것이 기존의 고속 Fourier 변환을 이용하는 것보다 특징점 고찰에 더욱 유용하다는 것을 확인할 수 있었다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.1209-1211
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• 2003

The wavelet and semivariogram analysis method are used to identify the city landscape and farmland landscape pattern on the 1m resolution IKONOS images. The results prove that wavelet method is a potential way for landscape pattern analysis. Compared to semivariogram analysis, Wavelet analysis can not only detect the overall spatial pattern, but also find multi-scale and direction structures. In this experiment, the wavelet analysis results indicate: (1) the city landscape image is mainly composed of three level structures whose spatial pattern characters appear at 2m, 16m, 128m and 256m accordingly; (2) the farmland landscape is mainly two scale spatial patterns appearing at the 2m, 128m and 256m. IKONOS Remote sensing, with the high spatial and spectral information, is a powerful tool that can use in many ecological systems research and sustainable management.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2002년도 추계학술대회 및 정기총회
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• pp.427-430
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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 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 FFT (Fast Fourier Transform).

• Earthquakes and Structures
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• 제3권6호
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• pp.839-852
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• 2012

Earthquake records are often analyzed in various earthquake engineering problems, making time-frequency analysis for such records of primary concern. The best tool for such analysis appears to be based on wavelet functions; selection of which is not an easy task and is commonly carried through trial and error process. Furthermore, often a particular wavelet is adopted for analysis of various earthquakes irrespective of record's prime characteristics, e.g. wave's magnitude. A wavelet constructed based on records' characteristics may yield a more accurate solution and more efficient solution procedure in time-frequency analysis. In this study, a low-pass reconstruction filter is obtained for each earthquake record based on multi-resolution decomposition technique; the filter is then assigned to be the normalized version of the last approximation component with respect to its magnitude. The scaling and wavelet functions are computed using two-scale relations. The calculated wavelets are highly efficient in decomposing the original records as compared to other commonly used wavelets such as Daubechies2 wavelet. The method is further advantageous since it enables one to decompose the original record in such a way that a clear time-frequency resolution is obtained.

• 한국수자원학회논문집
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• 제38권12호
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• pp.995-1008
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• 2005

본 연구에서는 우리나라 강우량과 광범위한 해수면 온도(SST)와의 공간적인 상관성 분석을 위해서 Wavelet 분석과 독립성분분석을 결합하여 계절강우량의 주요성분을 추출하였다. 계절강우량의 Wavelet분석결과 2년과 8년 사이의 주기에서 강한 스펙트럼을 나타내었으며 모든 계절강우량에 대하여 저빈도 특성을 확인할 수 있었다. Wavelet에서 추출된 Scale Average Wavelet Power(SAWP)를 대상으로 독립성분분석을 실시하였으며 계절에 따라 경년변화와 10년 이상의 주기를 갖는 성분을 확인할 수 있었고 봄과 여름의 주성분에서 증가경향의 성분을 관측할 수 있었다. 추출된 강우량의 주성분과 SST와의 상관관계를 평가한 결과 봄과 여름 강우량에서 인도양과 일부 태평양에서 강한 상관관계를 나타내었다 특히, 인도양의 필리핀 연안과 태평양의 호주 연안에서 통계적으로 유의한 상계관계를 갖는 SST구역을 확인할 수 있었다. 또한 3개월 이전 SST와 강우량에 대해서도 이들 구역에서 큰 상관성을 나타내었으며 이들 3개월 SST를 이용하여 강우량의 예측 가능성을 제시하였다.

• 한국정밀공학회지
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• 제16권10호
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• pp.194-202
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• 1999

A method for pattern recognition based on wavelet transform is proposed in this paper. The boundary of the object to be recognized includes shape information for object of machine parts. The contour is first represented using a one-dimensional signal and normalized about translation, rotation and scale, then is used to build the wavelet transform representation of the object. Wavelets allow us to decompose a function into multi-resolution hierarchy of localized frequency bands. The recognition of 2-dimensional object based on the wavelet is described to analyze the shape of analysis technique; the discrete wavelet transform(DWT). The feature vectors obtained using wavelet analysis is classified using a multi-layer neural network. The results show that, compared with the use of fourier descriptors, recognition using wavelet is more stable and efficient representation. And particularly the performance for objects corrupted with noise is better than that of other method.