• Journal of Electrical Engineering and Technology
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• v.12 no.5
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• pp.1697-1708
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• 2017

This paper proposes an algorithm for the differential protection of an Indirect Symmetrical Phase Shift Transformer (ISPST) by considering the different behaviors of the compensated differential current under internal fault and magnetizing inrush conditions. In this algorithm, a criterion function is defined which is based on the difference of amplitude of the wavelet transformation over a specific frequency band. The function has been used for the discrimination between three phase magnetizing inrush and internal fault condition and requires less than a quarter cycle after disturbance. This method is independent of any coefficient or threshold values of wavelet transformation. The merit of this algorithm is demonstrated by the simulation of different faults in series and excitation unit and magnetizing inrush with varying switching conditions on ISPST using PSCAD/EMTDC. Due to unavailability of in-field large interconnected transformers for such a large number of destructive tests, the results are further verified by Real Time Digital Simulator (RSCAD/RTDS). The proposed algorithm has been compared with the conventional harmonic restraint based method that justifies the application of wavelet transform for differential protection of ISPST. The proposed algorithm has also been verified for different rating of ISPSTs and satisfactory results were obtained.

• Communications for Statistical Applications and Methods
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• v.8 no.1
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• pp.109-115
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• 2001

Wavelet coefficients are known to have decorrelating properties, since wavelet is orthonormal transformation. but empirically, those wavelet coefficients of images, like edges, are not statistically independent. Jansen and Bultheel(1999) developed the empirical Bayes approach to improve the classical threshold algorithm using local characterization in Markov random field. They consider the clustering of significant wavelet coefficients with uniform distribution. In this paper, we developed wavelet thresholding algorithm using Laplacian distribution which is more realistic model.

• Proceedings of the Korea Contents Association Conference
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• 2003.05a
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• pp.376-382
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• 2003

Recently, there have been many types of wavelet transformations proposed to remove the noise from an signal and image data By using feature of seperating the noise from the original image the Wavelet transformations can retain the edges of the images The wavelet analysis is complete when the basis function is coded into the wavelet This Thesis describes a method of using wavelet transformation to remove the noise from an image signal. Although the wavelet transformation proposed by Donoho and Johnstone works, it does not reliably remove all the noise from the images. So this thesis propose an algorithm that selected Wavelet Shrinkgae and threshold according to the features of bands and amplitude of noise.

• Journal of the Korean Society of Safety
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• v.20 no.3 s.71
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• pp.64-70
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• 2005

In this literature, fault detection and classification of faulty induction motors are carried out through Z-index and frequency analysis. Above frequency analysis refer Fourier transformation and Wavelet transformation. Z-index is defined as the similar form of energy function, also the faulty and healthy conditions are classified through Z-index. For the detection and classification feature extraction for the fault detection of an induction motor is carried out using the information from stator current. Fourier and Wavelet transforms are applied to detect the characteristics under the healthy and various faulty conditions. We can obtain feature vectors from two transformations, and the results illustrate that the feature vectors are complementary each other.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.08a
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• pp.269-272
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• 2000

It is need to develop and apply a human pulse diagnosis system providing a quantitative and automatic analysis in the the oriental medicine. In order to analyze quantitatively the characteristic of pulsation, each of points had to be recognized accurately notifying the existence and the position of feature point in the wave form. And getting the period of human pulse. Thus, in this paper, it is proposed the preprocessing method of human pulse and the detection method of period by Wavelet Transformation. The human pulse is seprated from each band through Wavelet Transformation and feature points can be recognized through over the fact, and then the parameter of proposed Mac-Jin parameter is measured. Commonly, Human pulse signal has often various noises which are baseline drift, high frequency noise and so on. So it is significant to remove that noises. Thus, in this paper, the one period of human pulse is deciede and the feature points are detected after doing the preprocessing by wavelet transformation. As a result, it could be confirmed that this method is effective as a real program for the auto-diagnosis of human pulse.

• Transactions of the Korean Society of Automotive Engineers
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• v.16 no.2
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• pp.18-28
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• 2008

In inverse acoustic problem with nearfield sources, it is important to separate multiple acoustic sources and to measure the position of each target. This paper proposes a new algorithm by applying MUSIC(Multiple Signal Classification) to the outputs of discrete wavelet transformation with sub-band selection based on the entropy threshold, Some numerical experiments show that the proposed method can estimate the more precise positions than a conventional MUSIC algorithm under moderately correlated signal and relatively low signal-to-noise ratio case.

• Journal of Korea Society of Digital Industry and Information Management
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• v.4 no.2
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• pp.75-80
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• 2008

The impulse noise is very common and typical noise in the digital image. Many methods are invented in order to remove the impulse noise since the development of Digital Image Processing. For example, the median filter has been used for removing the impulse noise. In this paper, we try to remove the impulse noise using wavelet transformation in the wavelet-transformed detail areas. We also compare the algorithm with median filter with the visual and numerical methods. The result using the algorithm in this paper was much better than the median filter in both removing the noise and keeping the edges. The proposed algorithm needs more calculating time but has more advantages than the median filter has.

• 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).

• Journal of radiological science and technology
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• v.27 no.2
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• pp.13-19
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• 2004

This study proposes how to insert a strong watermark creating a big change in the areas of edge and texture. While conversion by existing Fourier transformation can acquire information for all ranges of frequency domain from the image, Wavelet transformation can manipulate edge and texture area selectively. Therefore, through wavelet transformation concerned area may be selected and watermarks in copyright formation are inserted. Our proposed algorithm was compared to Xia's watermarking technique using wavelet transformation. Its fidelity and robustness were tested with attack methods used in existing papers and it turns out that the proposed algorithm using HVS properties is more superior to Xia's techniques.

• The KIPS Transactions:PartD
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• v.13D no.2 s.105
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• pp.287-292
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• 2006

There are two problems associated with the existing principal component analysis, which is regarded as the most effective in object recognition technology. First, it brings about an increase in the volume of calculations in proportion to the square of image size. Second, it gives rise to a decrease in accuracy according to illumination changes. In order to solve these problems, this paper proposes wavelet transformation and histogram equalization. Wavelet transformation solves the first problem by using the images of low resolution. To solve the second problem the histogram equalization enlarges the contrast of images and widens the distribution of brightness values. The proposed technology improves recognition rate by minimizing the effect of illumination change. It also speeds up the processing and reduces its area by wavelet transformation.