• Proceedings of the KIEE Conference
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• 1997.07c
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• pp.1012-1014
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• 1997

This paper introduces wavelets and shows that they may be efficient and useful for the detection of general faults in power system. The wavelet transform of a signal consists in measuring the "similarity" between the signal and a set of translated and scaled versions of a "mother wavelet". The "mother wavelet" is a chosen fast decaying oscillation function. A number of mother wavelet for signal analysis have been proposed and some of them are in use in fault detection. However, the performance of fault detection depend on used mother wavelet. In the present paper a comparative evaluation of different mother wavelets for low impedance fault detection is performed. The discussion is focused in well-known mother wavelet based wavelet transform. Several families of wavelets are used to analyse transient earth fault signals in a 345kV model system as generated by EMTP.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.724-726
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• 2003

Currently available image fusion methods are not efficient for fusing the Landsat 7 images. Significant color distortion is one of the major problems. In this paper, using the well-known wavelet based method for data fusion between high-resolution panchromatic and low-resolution multispectral satellite images, we performed Landsat 7 image fusion. Based on the experimental results obtained from this study, we analyzed some reasons for color distortion. A new approach using the biorthogonal wavelets based method for data fusion is presented. This new method has reached an optimum fusion result - with the same spectral resolution as the multispectral image and the same spatial resolution as the panchromatic image with minimum artifacts.

• Kyungpook Mathematical Journal
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• v.61 no.2
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• pp.371-381
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• 2021

In this paper, we attempt to obtain sufficient conditions for the existence of tight wavelet frame sets in locally compact abelian groups. The condition is generated by modulating a collection of characteristic functions that correspond to a generalized shift-invariant system via the Fourier transform. We present two approaches (for stationary and non-stationary wavelets) to construct the scaling function for L²(G) and, using the scaling function, we construct an orthonormal wavelet basis for L²(G). We propose an open problem related to the extension principle for Riesz wavelets in locally compact abelian groups.

• Communications of the Korean Mathematical Society
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• v.12 no.2
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• pp.427-456
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• 1997

This paper concerns constructing wavelet bases on the unit interval, where a new boundary treatment is provided to overcome certain drawbacks of eariler constructions. Wavelet expansions on the unit interval usually suffer from artificial boundary effects and poor convergence at the boundaries. Many researchers have suggested a solution to the drawbacks. From a practical point of view, their solutions also have a common disadvantage. This paper provides a new solution using biorthogonality near the boundaries, that avoids the disadvantage while preserving their advantages.

• Communications of the Korean Mathematical Society
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• v.26 no.1
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• pp.89-98
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• 2011

The Fourier series has a rapid oscillation near end points at jump discontinuity which is called the Gibbs phenomenon. There is an overshoot (or undershoot) of approximately 9% at jump discontinuity. In this paper, we prove that a bunch of series representations (certain nonharmonic Fourier series) give good approximations vanishing Gibbs phenomenon. Also we have an application for approximating some shape of upper part of a vehicle in a different way from the method of cubic splines and wavelets.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.589-592
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• 1998

One of the main techniques for diagnosing heart disease is by examining the electrocardiogram(ECG). The earlier noise reduction techniques can not effectively cancellation complex noise from the noisy ECG such powrline interference, baseline drift, muscle artifact. In this paper, we performed the extrude noise from and recovering the ECG signal using wavelets transform that has recently been applying to various fields.

• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1360-1362
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• 1999

This paper describes the fault location technique using wavelets in transmisson line. Estimation of fault location is performed using synchronized data sampled at two ends of line and travelling wave. The similar current wave modeled in PSCAD/EMTDC and MATLAB was applied to evaluate the accuracy of theory proposed in this paper. The results of fault location shown in this paper will be evaluated as an effective suggestion for fault location in real transmisson line

• Journal of applied mathematics & informatics
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• v.16 no.1_2
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• pp.621-632
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• 2004

Multiresoluton analysis(MRA) of space of square integrable functions defined on whole entire line has been well-known. But for many applications, MRA on bounded interval was required and studied. In this paper we give a MRA for $L^2$(0, 1) by means of periodic wavelets based on regular MRA for $L^2$(R) and give the convergence of partial sums.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2933-2935
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• 2000

본 논문에서는, $L^2$ 메트릭(Metric)을 사용한 신호간의 거리 측정 방법의 약점을 보완하기 위해 연구된 인테그라-노말라이저(Integra-Normalizeer)를 사용함에 있어, 웨이브렛(Wavelets)의 멀타이레졸루션(Multiresolution)의 특성을 이용, 신호간의 거리 측정뿐만이 아니라, 신호간의 다른 점이 어느 주파수 영역에 존재하나 하는 정보도 획득할 수 있게 하였다.

• Korean Journal of Cognitive Science
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• v.11 no.2
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• pp.53-58
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• 2000

This paper reconstructs local region of a facial expression image from extracted feature points of facial expression image using FCM(Fuzzy C-Meang) clustering algorithm with Gabor wavelets. The feature extraction in a face is two steps. In the first step, we accomplish the edge extraction of main components of face using average value of 2-D Gabor wavelets coefficient histogram of image and in the next step, extract final feature points from the extracted edge information using FCM clustering algorithm. This study presents that the principal components of facial expression images can be reconstructed with only a few feature points extracted from FCM clustering algorithm. It can also be applied to objects recognition as well as facial expressions recognition.