• Proceedings of the KOSOMBE Conference
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• v.1996 no.11
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• pp.221-224
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• 1996

This paper presents the ECG data compression using wavelet transform(WT) and adaptive fractal interpolation(AFI). The WT has the subband coding scheme. The fractal compression method represents any range of ECG signal by fractal interpolation parameters. Specially, the AFI used the adaptive range sizes and got good performance for ECG data compression. In this algorithm, the AFI is applied into the low frequency part of WT. The MIT/BIH arrhythmia data was used for evaluation. The compression rate using WT and AFI algorithm is better than the compression rate using AFI. The WT and AFI algorithm yields compression ratio as high as 21.0 without any entroy coding.

• Journal of the Korean Geotechnical Society
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• v.36 no.10
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• pp.41-56
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• 2020

For the rapid and accurate warning, the system requires not only the sufficient number of seismometers but also the appropriate detection technique of sensor data. Instead of installing new seismometers, on-board accelerometers of the train could be utilized as alternatives. However, the data from on-board accelerometers includes train vibrations and the response of embankment site by earthquake, which are different from earthquakes measured from the seismometer. This study suggests signal analysis technique to detect earthquake from the on-board accelerometer data. The virtual on-board accelerometer data including the response of embankment site, obtained from site response analysis method, has been constructed. The constructed data has been analyzed using short time Fourier transform (STFT) and wavelet transform (WT). STFT method provides better performance to detect long-period earthquake whereas WT method is more available to detect short-period earthquake.

• Journal of Digital Convergence
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• v.14 no.4
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• pp.277-283
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• 2016

This study proposed new methods to classify normal and epileptic seizure signals from EEG signals using peaks extracted by wavelet transform(WT) and Hilbert transform(HT) based on a neural network with weighted fuzzy membership functions(NEWFM). This study has the following three steps for extracting inputs for NEWFM. In the first step, the WT was used to remove noise from EEG signals. In the second step, the HT was used to extract peaks from the wavelet coefficients. We also selected the peaks bigger than the average of peaks to extract big peaks. In the third step, statistical methods were used to extract 16 features used as inputs for NEWFM from peaks. The proposed methodology shows that accuracy, specificity, and sensitivity are 99.25%, 99.4%, 99% with 16 features, respectively. Improvement in feature selection method in view to enhancing the accuracy is planned as the future work for selecting good features from 16 features.

• Proceedings of the KIEE Conference
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• 2003.07a
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• pp.265-267
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• 2003

The wavelet transform(WT) has been extensively applied in solving many problems in applied science and engineering following its introduction in early 1980's. The WT analyzes a signal in a changeable frequency range by employing a moving window whereby along time window is used to obtain low frequency information and short time window is used to obtain high frequency information. In this paper, after various fault types in 154 KV transmission system was simulated by using EMTP, and the RMS values by changing Mother wavelet was calculated by applying wavelet transform to the simulated voltage and current signal.

• Journal of the Korean Society for Nondestructive Testing
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• v.21 no.2
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• pp.152-162
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• 2001

Laser-generated ultrasonic technique which is one of the reliable nondestructive evaluation techniques has been applied to evaluate the integrity of structures by analyzing the characteristics of signal obtained from surface crack. Therefore, the signal analysis of the laser-generated ultrasonics is absolutely necessary for the accurate and quantitative estimation of the surface defects. In this study, one-sided measurement by laser-generated ultrasonic has been applied to evaluate the depth of the surface-breaking crack in the materials. However, since the ultrasonic waveform excited by pulse laser is very difficult to distinguish the defect signals, it is necessary to consider the signal analyses of the transient waveform. Wavelet Transform(WT) is a powerful tool for processing transient signals with temporally varying spectra that helps to resolve high and low frequency transient components effectively. In this paper, the analyses of the surface-breaking crack of the ultrasonic signal excited by pulse laser are presented by employing the WT analyses.

• Journal of Electrical Engineering and Technology
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• v.9 no.2
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• pp.695-706
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• 2014

Partial discharge (PD) measurements have emerged as a dominant investigative tool for condition monitoring of insulation in high voltage equipment. But the major problem behind them the PD signal is severely polluted by several noises like White noise, Random noise, Discrete Spectral Interferences (DSI) and the challenge lies with removing these noise from the onsite PD data effectively which leads to preserving the signal for feature extraction. Accordingly the paper is mainly classified into two parts. In first part the PD signal is artificially simulated and mixed with white noise. In second part the PD is measured then it is subjected to the proposed denoising techniques namely Translation Invariant Wavelet Transform (TIWT). The proposed TIWT method remains the edge of the original signal efficiently. Additionally TIWT based denoising is used to suppress Pseudo Gibbs phenomenon. In this paper an attempt has been made to review the methodology of denoising the PD signals and shows that the proposed denoising method results are better when compared to other wavelet-based approaches like Fast Fourier Transform (FFT), Discrete Wavelet Transform (DWT), by evaluating five different parameters like, Signal to noise ratio, Cross-correlation coefficient, Pulse amplitude distortion, Mean square error, Reduction in noise level.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.4C
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• pp.533-540
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• 2004

This paper proposes a new multispectral image data compression algorithm that can efficiently reduce spatial and spectral redundancies by applying classified prediction, a Karhunen-Loeve transform (KLT), and the three-dimensional set partitioning in hierarchical trees (3-D SPIHT) algorithm in the wavelet transform (WT) domain. The classification is performed in the WT domain to exploit the interband classified dependency, while the resulting class information is used for the interband prediction. The residual image data on the prediction errors between the original image data and the predicted image data is decorrelated by a KLT. Finally, the 3-D SPIHT algorithm is used to encode the transformed coefficients listed in a descending order spatially and spectrally as a result of the WT and KLT. Simulation results showed that the reconstructed images after using the proposed algorithm exhibited a better quality and higher compression ratio than those using conventional algorithms.

• Proceedings of the KIEE Conference
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• 2008.11a
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• pp.403-405
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• 2008

전통적인 FT(Fourier Transform)의 대안으로 scale과 shift에 의한 WT(Wavelet Transform)이 제안되어, 전력계통의 각 분야에 적당한 Mother Wavelet 선택에 대한 연구가 시도되고 있다. 본 논문은 발전기 고정자 사고의 경우 고장판별을 위하여 적당한 마더 웨이브릿을 선정할 수 있는 알고리즘에 관한 것이다. ATP 시뮬레이션으로부터 수집한 전류데이터를 이용하여 제안된 적당한 선정기법의 유효성을 증명하였다.

• Smart Structures and Systems
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• v.2 no.2
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• pp.127-140
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• 2006

In this study, a wavelet-based covariance-driven system identification technique is proposed for damage assessment of structures under ambient excitation. Assuming the ambient excitation to be a white-noise process, the covariance computation is shown to be able to separate the effect of random excitation from the response measurement. Wavelet transform (WT) is then used to convert the covariance response in the time domain to the WT magnitude plot in the time-scale plane. The wavelet coefficients along the curves where energy concentrated are extracted and used to estimate the modal properties of the structure. These modal property estimations lead to the calculation of the stiffness matrix when either the spectral density of the random loading or the mass matrix is given. The predicted stiffness matrix hence provides a direct assessment on the possible location and severity of damage which results in stiffness alteration. To demonstrate the proposed wavelet-based damage assessment technique, a numerical example on a 3 degree-of-freedom (DOF) system and an experimental study on a three-story building model, which are all under a broad-band excitation, are presented. Both numerical and experimental results illustrate that the proposed technique can provide an accurate assessment on the damage location. It is however noted that the assessment of damage severity is not as accurate, which might be due to the errors associated with the mode shape estimations as well as the assumption of proportional damping adopted in the formulation.

• Smart Media Journal
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• v.12 no.11
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• pp.36-47
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• 2023

Industrial Control System(ICS), which controls facilities at major industrial sites, is increasingly connected to other systems through networks. With this integration and the development of intelligent attacks that can lead to a single external intrusion as a whole system paralysis, the risk and impact of security on industrial control systems are increasing. As a result, research on how to protect and detect cyber attacks is actively underway, and deep learning models in the form of unsupervised learning have achieved a lot, and many abnormal detection technologies based on deep learning are being introduced. In this study, we emphasize the application of preprocessing methodologies to enhance the anomaly detection performance of deep learning models on time series data. The results demonstrate the effectiveness of a Wavelet Transform (WT)-based noise reduction methodology as a preprocessing technique for deep learning-based anomaly detection. Particularly, by incorporating sensor characteristics through clustering, the differential application of the Dual-Tree Complex Wavelet Transform proves to be the most effective approach in improving the detection performance of cyber attacks.