• Proceedings of the Korea Water Resources Association Conference
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• 2005.09a
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• pp.110-111
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• 2005

• Journal of the Korea society of information convergence
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• v.5 no.2
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• pp.101-108
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• 2012

In the present paper, it removed the low frequency noise under 1Hz which get up base wandering from the various noise which is included in ECG signals. It used wavelet filter, FIR filter and Adaptive FIR filter and compared the efficiency of the filter. The set condition of 3 kind filters which are the comparative object is the next contents. Used wavelet case, used generating functions db7 and after decomposition, the low frequency of 8 phases (cA8) replaced at 0. FIR filter case, filter coefficient set 1400, cutoff frequency (${\omega}$) set 0.002. Adaptive FIR filter case, collecting coefficients (${\mu}$) with 0.005. The comparative result from the output wave shape and FT spectrum, wavelet is excellent in base wandering removals compared FIR filter and Adaptive FIR filter. And SNR comparisons, wavelet filter(44.16) is high compare with other two filters(25.19 and 15.94).

• Journal of the Korean Magnetic Resonance Society
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• v.6 no.1
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• pp.12-19
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• 2002

A shift-averaged Haar wavelet transform was introduced as a new and excellent tool to distinguish real peaks from the noise contaminated NMR signals. It is based on Haar wavelet transform and translation-invariant denoising process. Donoho＇s universal threshold was newly introduced to the shift-averaged Haar wavelet transform for the purpose of automated noise suppression, and was quantitatively compared with the conventional uniform threshold method in terms or threshold and signal to noise ratio (SNR). New algorithm was combined with a routine to suppress a large solvent peak by singular value decomposition (SVD). Combined algorithm was applied to the real spectrum that containing large solvent peak.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.14 no.1
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• pp.84-92
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• 2001

In this papers, we proposed the new method in order to diagnosis aging state of transformers. For wavelet transform, Daubechies filter is used, we can obtain wavelet coefficients which is used to extract feature of statistical parameters (maximum value, average value, dispersion skewness, kurtosis) about each acoustic emission signal. Also, these coefficients are used to identify normal and fault signal of internal partial discharge in transformer. As improved method for classification use neural network. Extracted statistical parameters are input into an back-propagation neural network. The number of neurons of hidden layer are obtained through Result of Cross-Validation. The network, after training, can decide whether the test signal is early aging state, alst aging state or normal state. In quantity analysis, capability of proposed method is superior to compared that of classical method.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.7
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• pp.577-582
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• 2013

In this paper Legendre wavelets are used to approximate the solutions of linear time-invariant system. The Legendre wavelet and its integral operational matrix are presented and an efficient algorithm to solve the Sylvester matrix equation is proposed. The algorithm is based on the decomposition of the Sylvester matrix equation and the preorder traversal algorithm. Using the special structure of the Legendre wavelet's integral operational matrix, the full order Sylvester matrix equation can be solved in terms of the solutions of pure algebraic matrix equations, which reduce the computation time remarkably. Finally a numerical example is illustrated to demonstrate the validity of the proposed algorithm.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.5
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• pp.630-635
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• 1998

The objective of this paper is to propose a new efficient technique for the estimation of time-delay parameters using wavelet theory and third-order cumulants, yielding good performance even in the case of low SNR. In particular, band-limited non-Gaussian signals with non-zero skewness and spatially correlated Gaussian noises are considered here. The approach is based on the fact that the effects of spatially correlated Gaussian noises on time-delay estimation can be reduced by using the projection sequences (based on the redundant wavelet decomposition) of given measurements in the higher-order cumulant domain. Finally, the performance of the proposed approach is demonstrated using simulations.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.11 no.3
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• pp.337-344
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• 2000

The wavelet analysis technique is applied in the spectral domain to efficiently represent the multi-scale features of the impedance matrices. In this scheme, the 2-D quadtree decomposition (applying the wavelet transform to only the part of the matrix) method often used in image processing area is applied for a sparse moment matrix. CG(Conjugate-Gradient) method is also applied for saving memory and computation time of wavelet transformed moment matrix. Numerical examples show that for rectangular cylinder case the non-zero elements of the transformed moment matrix grows only as O($N^{1.6}$).

• Proceedings of the KIEE Conference
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• 1995.11a
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• pp.149-153
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• 1995

In this paper, wavelet transform with multi-resolution property is used to improve the accuracy of pitch estimation of speech signal. Pitch detection of speech signal is based on the local maxima by using wavelet transform. The wavelet transform of a signal is a multiscale decomposition that is well localized in space and frequency. The proposed pitch defection algorithm is suitable for both low-pitched and high-pitched speakers.

• Journal of Information Technology Applications and Management
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• v.10 no.4
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• pp.159-168
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• 2003

This paper is concerned with the compression of EEG signals using wavelet-packet based techniques. EEG data compression is desirable for a number of reasons. Primarily it decreases for transmission time, archival storage space, and in portable systems, it decreases memory requirements or increases channels and bandwidth. Upon wavelet decomposition, inherent redundancies in the signal can be removed through thresholding to achieve data compression. We proposed the energy cumulative function for deciding of the threshold value and it works very innovative of EEG data.

• Proceedings of the KIPE Conference
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• 2005.07a
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• pp.476-478
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• 2005

This paper describes a method for the detection of shorted-turn in the cylindrical synchronous generator rotor windings based on the discrete wavelet transform. Multi-resolution analysis(MRA) based on discrete wavelet transform provides a set of decomposed signals in independent frequency bands. In the proposed method, shorted-turn detection in rotor windings is based on the decomposition of the rotor currents, where wavelet coefficients of these signals have been extracted. Comparing these extracted coefficients is used for diagnosing the healthy machine from faulty machine. Experimental results show the effectiveness of the proposed method for shorted-turn detection in the cylindrical synchronous generator rotor windings.