• International Journal of Fuzzy Logic and Intelligent Systems
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• v.1 no.1
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• pp.24-28
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• 2001

Wavelet transforms are used for implementing digital watermarking methods in the frequency domain. In this paper, we construct the digital watermarking using various wavelet transforms such as the Daubechies transform, Coiflets transform, Symlets transform and the biorthogonal transform, and we compare each digital watermarking method with the others. We investigate the preservation of the watermark after the data compression attack based on the discrete on the discrete cosine transform. We show that the biorthogonal wavelet, denoted by bior3.5, has the best performance among the wavelet types we selected in an experiment.

• Structural Engineering and Mechanics
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• v.60 no.4
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• pp.667-691
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• 2016

This study focuses on the damage detection of defect types in plate structures based on wavelet transform (WT) and curvelet transform (CT). In particular, for damage detection of structures these transforms have been developed since the last few years. In recent years, the CT approach has been also introduced in an attempt to overcome inherent limitations of traditional multi-scale representations such as wavelets. In this study, the performance of CT is compared with WT in order to demonstrate the capability of WT and CT in detection of defect types in plate structures. To achieve this purpose, the damage detection of defect types through defect shape in rectangular plate is investigated. By using the first mode shape of plate structure and the distribution of the coefficients of the transforms, the damage existence, the defect location and the approximate shape of defect are detected. Moreover, the accuracy and performance generality of the transforms are verified through using experimental modal data of a plate.

• International Journal of Ocean Engineering and Technology Speciallssue:Selected Papers
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• v.6 no.1
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• pp.1-6
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• 2003

This study presents the results of series of studies, which are mainly devoted to the application of wavelet transforms to various problems in ocean engineering. Both continuous and discrete wavelet transforms were used. These studies attempted to solve detection of wave directionality, detection of wave profile, and decoupling of the rolling component from free roll decay tests. The results of these analysis, using wavelet transform, demonstrated that the wavelet transform can be a useful tool in analyzing many problems in the filed of ocean engineering.

• Journal of Ocean Engineering and Technology
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• v.17 no.3 s.52
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• pp.1-6
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• 2003

This study presents the results of series of studies, which are mainly devoted to the application of wavelet transforms to various problems in ocean engineering. Both continuous and discrete wavelet transforms were used. These studies attempted to solve detection of wave directionality, detection of wave profile, and decoupling of the rolling component from free roll decay tests. The results of these analysis, using wavelet transform, demonstrated that the wavelet transform can be a useful tool in analyzing many problems in the filed of ocean engineering.

• The Journal of the Acoustical Society of Korea
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• v.18 no.1E
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• pp.13-19
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• 1999

This paper introduces an adaptive wavelet transform based on the lifting scheme, which is applied to signal denoising. The wavelet representation using orthogonal wavelet bases has received widespread attention. Recently the lifting scheme has been developed for the construction of biorthogonal wavelets in the spatial domain. Wavelet transforms are performed through three stages: the first stage or Lazy wavelet splits the data into two subsets, even and odd, the second stage calculates the wavelet coefficients (highpass) as the failure to interpolate or predict the odd set using the even, and the third stage updates the even set using neighboring odd points (wavelet coefficients) to compute the scaling function coefficients (lowpass). In this paper, we adaptively find some of the prediction coefficients for better representation of signals and this customizes wavelet transforms to provide an efficient framework for denoising. Special care has been given to the boundaries, where we design a set of different prediction coefficients to reduce the prediction error.

• Journal of the Korean Magnetic Resonance Society
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• v.4 no.2
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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.

• The Journal of the Korea Contents Association
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• v.9 no.7
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• pp.19-26
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• 2009

This paper presents an approach to classify normal and ventricular tachycardia/fibrillation(VT/VF) from the Creighton University Ventricular Tachyarrhythmia Database(CUDB) using the neural network with weighted fuzzy membership functions(NEWFM) and wavelet transforms. In the first step, wavelet transforms are used to obtain the detail coefficients at levels 3 and 4. In the second step, all of detail coefficients d3 and d4 are classified into four intervals, respectively, and then the standard deviations of the specific intervals are used as eight numbers of input features of NEWFM. NEWFM classifies normal and VT/VF beats using eight numbers of input features, and then the accuracy rate is 90.1%.

• Journal of Korean Ophthalmic Optics Society
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• v.17 no.2
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• pp.203-207
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• 2012

Purpose: Electroretinogram(ERG) signal noise as well as conducting other bio-signal measurement were generated. It was intened to enhance the accuracy of retinal-related diagnosis with removing signal noise. Methods: Sampling signal was made with generating 60 Hz noise and white noise. The noise were removed using wavelet transforms and bandpass filter. De-noising frequency was compared with Fourier transform spectrum. Removed noises were compared numerically using SNR(signal to noise ratio). Results: The result compared Fourier transform spectrum was showed that 60 Hz noise removed completely and most of white noise was removed by wavelet transforms. 60 Hz and the white noise remained using bandpass filters. The result compared SNR showed that wavelet transforms was 22.8638 and bandpass filter was 4.0961. Conclusions: Wavelet transform showed less signal distortion in removing noise. ERG signal is expected to improve the accuracy of retinal-related diagnosis.

• International Journal of Control, Automation, and Systems
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• v.4 no.3
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• pp.365-371
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• 2006

This paper presents an algorithm based on a combination of Discrete Wavelet Transforms and neural networks for detection and classification of internal faults in a two-winding three-phase transformer. Fault conditions of the transformer are simulated using ATP/EMTP in order to obtain current signals. The training process for the neural network and fault diagnosis decision are implemented using toolboxes on MATLAB/Simulink. Various cases and fault types based on Thailand electricity transmission and distribution systems are studied to verify the validity of the algorithm. It is found that the proposed method gives a satisfactory accuracy, and will be particularly useful in a development of a modern differential relay for a transformer protection scheme.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.387-390
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• 2005

Parameter estimation for the probability model of wavelet coefficients is essential to the wavelet-domain interpolation. However, phase uncertainty, one well-known drawback of the orthogonal wavelet transforms, make it difficult to estimate parameters. In this paper, we exploit a phase shifting matrix in order to improve the accuracy of estimation. Nonlinear modeling to capture the interscale characteristics is also described. The experimental results show that the proposed method outperforms the previous wavelet-domain interpolation method as well as the conventional bicubic method.