• Korean Journal of Remote Sensing
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• v.25 no.3
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• pp.215-223
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• 2009

The noise removal as a preprocessing was tried with various kinds of wavelet pairs. Wavelet transform for 2D images generally uses the same wavelets as basis functions in horizontal and vertical directions. A method with different wavelets was tried for each direction separately, which gives more precise interpretation of the classification. Total 486 pairs of wavelets from nine basis functions were tried to remove image noises. The classification accuracies before and after the noise removal were compared. Although all kinds of wavelet pairs showed the increased accuracies in classification, there were best and worst wavelet pairs depending on the data sets. Wavelet pairs with low energy percentage of LL band showed the high classification accuracy. A pattern was found in the results that very similar vertical accuracy was distributed for each horizontal ones. Since Haar is the shortest length filter, Haar could be a predictor wavelet to find the good wavelet pairs.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.09a
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• pp.820-821
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• 2005

• 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.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.7
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• pp.328-336
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• 2005

Power quality has become concern both utilities and their customers with wide spread use of electronic and power electronic equipment. The poor quality of electric power causes malfunctions, instabilities and shorter lifetime of the load. In power system operation, power system disturbances such as faults, overvoltage, capacitor switching transients, harmonic distortion and impulses affects power quality. For diagnosing power quality problem, the causes of the disturbances should be understood before appropriate actions can be taken. In this paper we present a new approach to detect, localize, and investigate the feasibility of classifying various types of power quality disturbances. This paper deals with the use of a multi-resolution analysis by a discrete wavelet transform to detect power system disturbances such as interruption, sag, swell, transients, etc. We also proposed do-noising and threshold technique to detect power system disturbances in a noisy environment. To find the better mother wavelet for detecting disturbances, we compared the performance of the disturbance detection with the several mother wavelets such as Daubechies, Symlets, Coiflets and Biorthogonals wavelets. In our analysis, we adopt db4 wavelet as mother wavelet because it shows better results for detecting several disturbances than other mother wavelets. To show the effectiveness of the proposed method, a various case studies are simulated for the example system which is constructed by using PSCAD/EMTDC. From the simulation results. proposed method detects time Points of the start and end time of the disturbances.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.5
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• pp.1095-1100
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• 2003

The wavelet transform pair used simultaneously when two wavelets are designed to form an approximate Hilbert transform pair provide excellent property than present DWT(discrete wavelet transform), especially in field that detect wide-band signals like pulse and increase the bit rate at the same bandwidth. In this paper, the two dyadic wavelet bases which form an approximate Hilbert transform pair were designed, and flat delay filter which has the truncated coefficient vector is used in order that the two filters can form Hilbert transform relation in the process of design.

• KIEE International Transactions on Power Engineering
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• v.3A no.3
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• pp.130-138
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• 2003

This paper presents a discrete wavelet transform approach for determining the beginning and end times of voltage sags. Firstly, investigations in the use of some typical mother wavelets, namely Daubechies, Symlets, Coiflets and Biorthogonal are carried out and the most appropriate mother wavelet is selected. The proposed technique is based on utilizing the maximum value of Dl (at scale 1) coefficients in multiresolution analysis (MRA) based on the discrete wavelet transform. The results are compared with other methods for determining voltage sag duration, such as the Root Mean Square (RMS) voltage and Short Time Fourier Transform (STFT) methods. It is shown that the voltage sag detection technique based on the wavelet transform is a satisfactory and reliable method for detecting voltage sags in power quality disturbance analysis.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2002.05a
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• pp.219-223
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• 2002

Voltage and current signals during impulse tests on transformer are treated as non-stationary signals. A new method incorporating signal-processing method such as Wavelets and courier transform is proposed for failure identification. It is now possible to distinguish failure during impulse tests. The method is experimentally validated on a transformer winding. The wavelet transforms enables the detection of the time of occurrence of switching or failure events. After establishing the time of occurrence, the original waveform is split into two or more sections. The wavelet transform has ability to analysis the failure signal on time domain as well as frequency domain. Therefore, the wavelet transform is superior than courier transform to analysis the failure signal. In this paper, the fact was proved by real data which was achieved.

• Communications for Statistical Applications and Methods
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• v.22 no.2
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• pp.159-172
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• 2015

This paper considers the problem of reconstructing an underlying signal from noisy data. This paper presents a reconstruction method based on synchrosqueezed wavelet transform recently developed for multiscale representation. Synchrosqueezed wavelet transform based on continuous wavelet transform is efficient to estimate the instantaneous frequency of each component that consist of a signal and to reconstruct components. However, an objective selection method for the optimal number of intrinsic mode type functions is required. The proposed method is obtained by coupling the synchrosqueezed wavelet transform with cross-validation scheme. Simulation studies and musical instrument sounds are used to compare the empirical performance of the proposed method with existing methods.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.147-147
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• 2000

Recently, a considerable amount of attention is being given to the use of wavelets and neural network for modulation and equalization. We proposed a new scheme of equalization for constellation using discrete wavelet transform(DWT) and neural network. The DWT is used for noise reduction and the neural network is used to update the equalizer coefficients adaptively.

• The KIPS Transactions:PartC
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• v.11C no.5
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• pp.621-632
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• 2004

Recent measurement studies of real teletraffic data in modern telecommunication networks have shown that self-similar (or fractal) processes may provide better models of teletraffic in modern telecommunication networks than Poisson processes. If this is not taken into account, it can lead to inaccurate conclusions about performance of telecommunication networks. Thus, an important requirement for conducting simulation studies of telecommunication networks is the ability to generate long synthetic stochastic self-similar sequences. A new generator of pseu-do-random self-similar sequences, based on the fractional Gaussian nois and a wavelet transform, is proposed and analysed in this paper. Specifically, this generator uses Daubechies wavelets. The motivation behind this selection of wavelets is that Daubechies wavelets lead to more accurate results by better matching the self-similar structure of long range dependent processes, than other types of wavelets. The statistical accuracy and time required to produce sequences of a given (long) length are experimentally studied. This generator shows a high level of accuracy of the output data (in the sense of the Hurst parameter) and is fast. Its theoretical algorithmic complexity is 0(n).