• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.422-423
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• 2018

In this paper, we implement Frenelet transform for decomposition of the fringe pattern and analyze its characteristics. The implemented wavelet-like basis functions are well suited for reconstruction and processing of optically generated Fresnel holograms. After analyzing the characteristics of the B-spline function, we will discuss the wavelet-like multi-resolution analysis method. Through this process, we implemented a transform tool that can decompose fringe patterns effectively. We have implemented a B-spline function with various decomposition properties and showed the results of decomposing the fringe pattern.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.1
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• pp.41-51
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• 2017

This Paper Describes a Method for the Evaluation of Sleep Apnea, Namely, the Peak Signal-to-noise ratio (PSNR) of Wavelet Transformed Electroencephalography (EEG) Data. The Purpose of this Study was to Investigate EEG Properties with Regard to Differences between Sleep Spindles and K-complexes and to Characterize Obstructive Sleep Apnea According to Sleep Stage. We Examined Non-REM and REM Sleep in 20 Patients with OSA and Established a New Approach for Detecting Sleep Apnea Base on EEG Frequency Changes According to Sleep Stage During Sleep Apnea Events. For Frequency Bands Corresponding to A3 Decomposition with a Sampling Applied to the KC and the Sleep Spindle Signal. In this Paper, the KC and Sleep Spindle are Ccalculated using MSE and PSNR for 4 Types of Mother Wavelets. Wavelet Transform Coefficients Were Obtained Around Sleep Spindles in Order to Identify the Frequency Information that Changed During Obstructive Sleep Apnea. We also Investigated Whether Quantification Analysis of EEG During Sleep Apnea is Valuable for Analyzing Sleep Spindles and The K-complexes in Patients. First, Decomposition of the EEG Signal from Feature Data was Carried out using 4 Different Types of Wavelets, Namely, Daubechies 3, Symlet 4, Biorthogonal 2.8, and Coiflet 3. We Compared the PSNR Accuracy for Each Wavelet Function and Found that Mother Wavelets Daubechies 3 and Biorthogonal 2.8 Surpassed the other Wavelet Functions in Performance. We have Attempted to Improve the Computing Efficiency as it Selects the most Suitable Wavelet Function that can be used for Sleep Spindle, K-complex Signal Processing Efficiently and Accurate Decision with Lesser Computational Time.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.10
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• pp.51-57
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• 1999

In this paper, we deal with the problem of controlling an unknown nonlinear dynamical system, using wavelet network. Accurate control of the nonlinear systems depends critically on the accuracy and efficiency of the function approximator used to approximate the function. Thus, we use wavelet network which shows high capability of approximating the functions and includes the free-selection of basis functions for the control of the nonlinear system. We find the dilation and translation that are wavelet network parameters by analyzing the time-frequency characteristics of the controller's input to construct an initial adaptive wavelet network controller. Then, weights is adjusted by the adaptive law based on the Lyapunov stability theory. We apply this direct adaptive wavelet network controller to control the inverted pendulum system which is an nonlinear system.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.55.4-55
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• 2001

Analyzing the ECG signal, we can find heart disease. Myocardial ischemia is a disorder of cardiac function caused by insufficient blood flow to the muscle tissue of the heart. Myocardial ischemia is inscribed on ST-segment of the ECG during and after patient takes exercise or is under stress, but after long time past, the ECG pattern is return to steady state. Therefore, it is necessary to monitor and analyze the ECG signal continuously for patient or aged people. Our primary purpose is the detection of temporary change of the ST-segment of ECG automatically. In the signal processing, the wavelet transform decomposes the ECG signal into high and low frequency components using wavelet function. Recomposing the high frequency bands including QRS complex, we can detect QRS complex more easily ...

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.294-294
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• 2000

In this paper, we would like to discuss the signal processing and the algorithm for ECG analysis. The ECG gives us information about the condition of the heart muscle, because myocardial abnormality or infarction is inscribed on the ECG during myocardial depolarization and repolarization. Analyzing the ECG signal, we can find heart disease, for example, arrhythmia and myocardial infarction, etc. Particularly, detecting arrhythmia is more important, because serious arrhythmia can take away the life from patients within ten minutes. The wavelet transform decomposes the ECG signal into high and low frequency component using wavelet function. Recomposing high frequency bands including QRS complex, we can detect QRS complex and eliminate the noise from the original ECG signal. To recognize the ECG signal pattern, we adopted the curve-fitting partially and statistical method. The ECG signal is divided into small parts based on QRS complex, and then, each part is approximated to the polynomials. Comparing the approximated ECG pattern with some kinds of heart disease ECG pattern, we can detect and classify the kind of heart disease.

• 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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.5
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• pp.834-840
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• 2007

A generator plays an important role in transferring an electric power to power system networks. The generator protection systems in Korea have been imported and operated through a tum-key from overseas entirely. Therefore, a study of the generator protection field has in urgent need for a stable operation of the imported goods, and for preparation of next generation protection system. The paper describes the fault detection algorithm using WT(Wave!et Transform) of currents for a generator protection. The fault current signals after executing a terminal fault modeling collect using a MA TLAB package, and calculate the wavelet coefficients through the process of a multi -level decomposition (MLD). The proposed algorithm for a fault detection using the Daubechies WT (wavelet transform) was executed with a C language for the command line function and for the real time realization after analyzing MATLAB's graphical interface. The advanced technique had complemented the defects of a DFT by applying a Daubechies WT. and had improved faster a speed and more accurate of fault discriminant than a conventional DFR.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.57 no.2
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• pp.91-95
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• 2008

The large AC generator fault may lead to large impacts or perturbations in power system. The generator protection control systems in Korea have been imported and operated through a turn-key from overseas entirely. Therefore a study of the generator protection field has in urgent need for a stable operation of the imported goods. In present, the algorithm using the current ratio differential relaying based DFT for stator winding protection or a fault detection had been applied that of internal fault protection of a generator. the DFT used for the analysis of transient state signal conventionally had defects losing a time information in the course of transforming a target signal to frequency domain. In this paper, the discrete wavelet transform (DWT) was applied a fault detection of the generator being superior to a transient state signal analysis and being easy to real time realization. The fault signals after executing a terminal fault modeling collect using a MATLAB package, and calculate the wavelet coefficients through the process of a muiti-level decomposition (MLD). The proposed algorithm for a fault detection using the Daubechies WT (wavelet transform) was executed with a C language and the commend line function for the real time realization after analyzing MATLAB's graphical interface. The advanced technique had improved faster a speed of fault discrimination than a conventional DFR based on DFT.

• Proceedings of the KSME Conference
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• 2001.06b
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• pp.42-47
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• 2001

Analyzing the ECG signal, we can find heart disease, for example, arrhythmia and myocardial infarction, etc. Particularly, detecting arrhythmia is more important, because serious arrhythmia can take away the life from patients within ten minutes. In this paper, we would like to introduce the signal processing for ECG analysis and the device made for wireless communication of ECG data. In the signal processing, the wavelet transform decomposes the ECG signal into high and low frequency components using wavelet function. Recomposing the high frequency bands including QRS complex, we can detect QRS complex and eliminate the noise from the original ECG signal. To recognize the ECG signal pattern, we adopted the polynomial approximation partially and statistical method. The ECG signal is divided into small parts based on QRS complex, and then, each part is approximated to the polynomials. Comparing the approximated ECG pattern with the database, we can detect and classify the heart disease. The ECG detection device consists of amplifier, filters, A/D converter and RF module. After amplification and filtering, the ECG signal is fed through the A/D converter to be digitalized. The digital ECG data is transmitted to the personal computer through the RF transceiver module and serial port.

• Journal of the Earthquake Engineering Society of Korea
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• v.27 no.3
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• pp.139-145
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• 2023

It is essential to determine a proper earthquake time history as a seismic load in a seismic design for a critical structure. In the code, a seismic load should satisfy a design response spectrum and include the characteristic of a target fault. The characteristic of a fault can be represented by a definition of a type of possible earthquake time history shape that occurred in a target fault. In this paper, the pseudo-basis function is proposed to be used to construct a specific type of earthquake, including the characteristic of a target fault. The pseudo-basis function is derived from analyzing the earthquake time history of specific fault harmonic wavelet transform. To show the feasibility of this method, the proposed method was applied to the faults causing the Gyeong-Ju ML5.8 and Pohang ML5.3 earthquakes.