• Journal of the Korean Society for Nondestructive Testing
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• v.20 no.6
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• pp.501-510
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• 2000

Recently, advanced signal analysis which is called "time-frequency analysis" has been used widely in nondestructive evaluation applications. Wavelet transform(WT) and Wigner Distribution are the most advanced techniques for processing signals with time-varying spectra. Wavelet analysis method is an attractive technique for evaluation of material characterization nondestructively. Wavelet transform is applied to the time-frequency analysis of ultrasonic echo waveform obtained by an ultrasonic pulse-echo technique. In this study, the feasibility of noise suppression of ultrasonic flaw signal and frequency-dependent ultrasonic group velocity and attenuation coefficient using wavelet analysis of ultrasonic echo waveform have been verified experimentally. The Gabor function is adopted the analyzing wavelet. The wavelet analysis shows that the variations of ultrasonic group velocity and attenuation coefficient due to the change of material characterization can be evaluated at each frequency. Furthermore, to assure the enhancement of detectability and naw sizing performance, both computer simulated results and experimental measurements using wavelet signal processing are used to demonstrate the effectiveness of the noise suppression of ultrasonic flaw signal obtained from austenitic stainless steel weld including EDM notch.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.08a
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• pp.313-316
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• 2000

Wavelet transform has specific properties for image coding. The property used at this Paper is clustering of significant coefficients across subband. These coefficients are classified in significant coefficient and insignificant coefficient on a threshold value, and symbolized EZW decreases symbol-position information using zero-trees, but threshold value fall for raising resolution, then coding cost of significant coefficients is expensive. To avoid this fact, this paper uses quad-tree representing coefficient-position information. a magnitude of significant coefficient is represented on matrix used at EZW. the proposed algorithm is hoped for raising a coding cost.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.449-451
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• 2003

The estimation of time-series data is fundamental process in many data analysis cases. However, the unwanted measurement error is usually added to true data, so that the exact estimation depends on efficient method to eliminate the error components. The wavelet transform method nowadays is expected to improve the accuracy of estimation, because it is able to decompose and analyze the data in various resolutions. Therefore, the wavelet based Kalman filter method for the estimation of time-series data is proposed in this paper. The wavelet transform separates the data in accordance with frequency bandwidth, and the detail wavelet coefficient reflects the stochastic process of error components. This property makes it possible to obtain the covariance of measurement error. We attempt the estimation of true data through recursive Kalman filtering algorithm with the obtained covariance value. The procedure is verified with the fundamental example of Brownian walk process.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.49-54
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• 2013

WPM(Wavelet Packet Modulation) is multicarrier system which is suitable for high speed transmission. The main advantage of using the WPM system is the fact that the flexible system effectively can be realized by the combination of filter coefficient. On the other hand, the major problem of multicarrier system is known to be the high PAPR. In this paper we propose WPM system that has a minimum PAPR by using filter coefficient adjustment method. We first derive the filter coefficient equation and then select the coefficient that has a minimum PAPR. we conduct WPM system by using a selected coefficient and we demonstrate the system performance by using computer simulation.

• Journal of the Institute of Convergence Signal Processing
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• v.2 no.1
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• pp.63-70
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• 2001

EZW(Embedded coding using Zero-trees of Wavelet coefficients) decreases symbol-position information using zero-trees, but threshold value fall lot raising resolution, then coding cost of significant coefficients is expensive. To avoide this fact, this paper uses quad-tree representing coefficient-position information. a magnitude of significant coefficient is represented on matrix used at EZW. the proposed algorithm is hoped for raising a coding cost.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.361-364
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• 1995

Wavelet Transform is a new tools for signal processing, such as data compressing extraction of parameter for Reconition and Diagnostics. This transform has an advandage of a good resolution compared to Fast Fourier Transform (FFT) In this study, we employ the wavelet transform for analysis of Acoustic Emission raw signal generated form rotary compressor. In abnormal condition of rotary compressor, the state of operating condition can be classified by analizing coefficient of wavelet transformed signal.

• Journal of the Institute of Convergence Signal Processing
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• v.8 no.4
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• pp.255-265
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• 2007

In case of wavelet coefficients have correlation, in image signal denoising using wavelet shrinkage denoising method, the denoising effect for the image signal is reduced when the wavelet shrinkage denoising method is used. The coefficients of multiwavelet transform have correlation by pre-filters. To solve the degradation problem in multiwavelet transform, V Sterela suggested a new pre-filter for the Universal threshold or weighting factors to the threshold. In this paper, to improve the denoising effect in the multiwavelet transform, the coefficient normalizing method that the coefficient are divided by estimated noise deviation is adopted to the transformed multiwavelet coefficients in the course of wavelet shrinkage technique. And the thresholds of universal, SURE and GCV are estimated using normalized coefficients and tried to denoise by the wavelet shrinkage technique. We compared PSNRs of denoised images for each thresholds and confirmed the efficiency of the proposed method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.2
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• pp.155-163
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• 2013

As the increasing HIF(High Impedance Fault) with the arc cannot be easily detected for the low fault current magnitude compared to actual load in distribution line. However, the arcing current shows that the magnitude varies with time and the signal is asymmetric. In addition, discontinuous changes occur at starting point of arc. Considering these characteristics, wavelet transformation of actual current data shows difference between before and after the fault. Althogh raw data(detail coefficient) of wavelet transform may not be directly applied to HIF detection logic in a device, there are several developing methods of HIF monitoring data using the original wavelet coefficients. In this paper, a simple and effective developing methods of HIF monitoring data were analized by using the signal data through an actual HIF experiment to apply them to economic devices. The methods using the sumation of the wavelet coefficient squares in one cycle of the fundamental frequency as the energies of the wavelet coefficeits and the sumation of the absolute values were compared. Besides, the improved method which less occupies H/W resouces and can be applied to field detection devices was proposed. and also Verification of this HIF detection method through field test on distribution system in KEPCO power testing center was performed.

• The Transactions of the Korea Information Processing Society
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• v.7 no.6
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• pp.1980-1987
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• 2000

In this paper, an efficient watermarking technique in wavelet transform domain is proposed. Watermarking is embedding a digital signal called as 'watermark' into images to claim the ownership. In the proposed method, the image is 1-level wavelet transformed, and then the watermark with a binary stamp is embedded into the baseband. The watermark is embedded by inverting the polarities of he selected coefficient paris. In the inverting process, we can increase image quality by finding means and differences of the selected neighboring coefficient paris, and then adding values, which are inversely proportional to the differences, to th means. The experimental results show that the proposed method has good quality and is robust to various attacks such as the JPEG lossy comparison, noise addition, clipping, blurring, etc.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.28.5-28
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• 2001

This paper investigates a classification method of the electrocardiogram (ECG) into different disease categories. The features for the classification of the ECG are the coefficients of the discrete wavelet transform (DWT) of ECG signals. The coefficients are calculated with Haar wavelet, and after DWT we can get 64 coefficients. Each coefficient has morphological information and they may be good features when conventional time-domain features are not available. Since all of them are not meaningful, it is needed to reduce the size of meaningful coefficients set. The distributions of each coefficient can be the rules to classify ECG signal. The optimally reduced feature set is obtained by fuzzy c-means algorithm and rough set theory. First, the each coefficient is clustered by fuzzy c-means algorithm and the clustered ...