• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.2
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• pp.75-81
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• 2006

The purpose of this paper is to recognize the feature points of ECG and human pulse -which signal shows the electric and physical characteristics of heart respectively- using wavelet transform. Wavelet transform is proper method to analyze a signal in time-frequency domain. In the process of wavelet decomposition and reconstruction of ECG and human pulse signal, we removed the noises of signal and recognized the feature points of signal using some of decomposed component of signal. We obtained the result of recognition rate that is estimated about 95.45$\%$ in case of QRS complex, 98.08$\%$ in case of S point and P point and 92.81$\%$ in case of C point. And we computed diagnosis parameters such as RRI, U-time and E-time.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.435-438
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• 1997

One of the main techniques or diagnosing heart disease is by examining the electrocardiogram(ECG). Many studies on detecting the QRS complex, P, and T waves have been performed because meaningful information is contained in these parameters. However, the earlier detecting techniques can not effectively extract those parameters from the ECG that is severely contaminated by noise source such 60Hz powerline interference, motion artifact and baseline drift. in this paper, we performed the extracting parameters from and recovering the ECG signal using wavelet transform that has recently been applying to various fields.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.59-62
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• 1997

In this paper, we used Wavelet Transform to analyze EMG signals. Wavelet transform has an advantage of dividing the nonstationary signals into the high frequency and low frequency band effectively. For determining the characterized value of EMG signals, it was wavelet-transformed, absoluted, and integral-calculated. As the result, we acquired characterized value of each signals, and acknowledged the differences among them. It was concluded that the results of this study using wavelet transform could be used to powerful tool or analysis of EMG signals.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.05a
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• pp.1077-1080
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• 2002

In building acoustics, reverberation time is an important acoustic parameter. However, it is often difficult to measure short reverberation times at low frequencies using the traditional band pass filter bank if the product of bandwidth (B) and reverberation time (T) is small. It is well known that the minimum permissible product of bandwidth and reverberation time of the traditional band pass filter is at least 16 ［F. Jacobsen, J. Sound Vib. 115, 163-170 (1987)]. This strict requirement makes it difficult to measure short reverberation times of an acoustic room at low frequencies exactly. In order to reduce this strict requirement, recently, the wavelet filter bank is developed and the minimum permissible product of bandwidth and reverberation time is replaced with 4 [S. K. Lee, J, Sound Vib. 252, 141-153 (2002)]. In the present paper, it is demonstrated how the short reverberation times at low frequencies are successfully measured by using the wavelet filter bank. In order to present this job, two synthetic signals and one measured signal are used for impulse responses of an acoustic room.

• 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.

• Speech Sciences
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• v.7 no.3
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• pp.153-165
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• 2000

During the phonation of voiced sounds, instants exist where the glottis is opened or closed, due to the periodic vibration of the vocal cord. When closed, this is called the glottal closure instant(GCI) or epoch.. The correct detection of the GCI is one of the important problems in speech processing for pitch detection, pitch synchronous analysis, and so on. Recently, it has been shown that the local maxima points of the wavelet transformed speech signal correspond to the GCIs of speech signal. In this paper, we investigate the accuracy of Gels estimated from this wavelet transformed speech signal. For this purpose we compare them with the negative peak points of the differentiated EGG signal that represents the actual GCIs of speech signal.

• Speech Sciences
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• v.4 no.2
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• pp.41-54
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• 1998

Speech signals are, depending on the characteristics of waveform, classified as voiced sound, unvoiced sound, and silence. Voiced sound, produced by an air flow generated by the vibration of the vocal cords, is quasi-periodic, while unvoiced sound, produced by a turbulent air flow passed through some constriction in the vocal tract, is noise-like. Silence represents the ambient noise signal during the absence of speech. The need for deciding whether a given segment of a speech waveform should be classified as voiced, unvoiced, or silence has arisen in many speech analysis systems. In this paper, a voiced/unvoiced/silence classification algorithm using spectral change in the wavelet transformed signal is proposed and then, experimental results are demonstrated with our discussions.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.5
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• pp.242-250
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• 2005

This paper proposes a new protective relaying algorithm using Neuro-Fuzzy and wavelet transform. To organize advanced nuero-fuzzy algorithm, it is important to select target data reflecting various transformer transient states. These data are made of changing-rates of Dl coefficient and RSM value within half cycle after fault occurrence. Subsequently, advanced neuro-fuzzy algorithm is obtained by converging the target data. As a result of applying the advanced neuro-fuzzy algorithm, discrimination between internal fault and inrush is correctly distinguished within 1/2 after fault occurrence. Accordingly, it is evaluated that the proposed algorithm can effectively protect a transformer by correcting discrimination between winding fault and inrushing state.

• Proceedings of the KIEE Conference
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• 2001.07a
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• pp.229-232
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• 2001

This paper presents the protective relaying scheme as a method for discriminating of power transform's transient state associated with magnetizng inrush state, over-exciting state and internal fault using wavelet based neural networs. The simulation of EMTP with respect to different fault, inrush condition and over-exciting condition in transformer have been conducted, and the result prove that the proposed method is able to discriminate between inrush magnetizing current and internal fault.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.717-719
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• 1997

This paper discusses approximate modelling of discrete-time linear time-varying system(LTVS). The wavelet transform is considered as a tool for representing and approximating a LTVS. The joint time-frequency properties of wave analysis are appropriate for describing the LTVS. Simulatly results is included to illustrate the popential application of the technique.