• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.6
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• pp.687-695
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• 2021

With the recent occurrence of large-scale earthquakes in Korea, the importance of seismic design has greatly increased. Seismic design standards stipulate that dynamic time history analysis be performed for important or special structures. In the seismic analysis and design of such structures, determining a rational design input seismic wave is a very important factor in ensuring the reliability of the analysis and design. In the seismic design standards, rational design seismic waves must reflect the characteristics of the area (fault) and satisfy the design response spectrum for each seismic performance level. This requirement can be partially satisfied by modifying the actual seismic wave measured in the area (fault) according to the design response spectrum. In this study, a method of correcting and generating seismic wave time histories according to the design response spectrum based on actual measured seismic waves using the harmonic wavelet transform was proposed. To examine the applicability of the proposed technique, the technique was applied to earthquakes of magnitude 5.8 and 5.4, respectively, that occurred in Gyeongju (2016) and Pohang (2017), and the seismic wave time histories corresponding to the design response spectrum were modified and generated.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.9
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• pp.1731-1733
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• 2015

Since electroencephalogram(EEG) has non-linear and non-stationary properties, it is effective to analyze the characteristic of EEG with time-frequency method rather than spectrum method. In this letter, we propose the modified drowsiness detection system using discrete wavelet transform combined with errors-in-variables and multilayer perceptron methods. For the comparison of the proposed scheme with the previous one, the state 'others' is added to the previous states of drivers: 'alertness,' 'transition,' and 'drowsiness.' From the computer simulation using machine learning, we confirm that the proposed scheme outperforms the previous one for some conditions.

• Structural Engineering and Mechanics
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• v.53 no.3
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• pp.607-623
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• 2015

The wind-induced dynamic response of large-span flexible structures includes two important components-background response and resonant response. However, it is difficult to separate the two components in time-domain. To solve the problem, a relational expression of wavelet packet coefficients and power spectrum is derived based on the principles of digital signal processing and the theories of wavelet packet analysis. Further, a new approach is proposed for separation of the background response from the resonant response. Then a numerical example of frequency detection is provided to test the accuracy and the spectral resolution of the proposed approach. In the engineering example, the approach is applied to compute the power spectra of the wind-induced response of a large-span roof structure, and the accuracy of spectral estimation for stochastic signals is verified. The numerical results indicate that the proposed approach is efficient and accurate with high spectral resolution, so it is applicable for power spectral computation of various response signals of structures induced by the wind. Moreover, the background and the resonant response time histories are separated successfully using the proposed approach, which is sufficiently proved by detailed verifications. Therefore, the proposed approach is a powerful tool for the verification of the existing frequency-domain formulations.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2004.11a
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• pp.359-364
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• 2004

This paper deals with the comparison of signal processing techniques of ultrasonic data. The goal of signal processing is the ultrasonic speckle suppression and the visibility enhancement of flaw-reflected ultrasonic echo. The performance of conventional SSP(split spectrum processing) method and the wavelet denoising method are compared and discussed for tested ultrasonic data. Tested ultrasonic data obtained from the weld area of centrifugal-casted stainless steel material and safe-ending material with holes and notch of variable depths are presented. In experimental results, the outputs of wavelet-based denoising method show the clear and sharp peaks at the positions of flaw-reflected echos comparing with those of SSP method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2000.10a
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• pp.459-462
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• 2000

The analysis of distribution line faults is essential to the proper protection of power system. A high impedance fault(HIF) dose not make enough current to cause conventional protective device operating. so it is well hon that undesirable operating conditions and certain types of faults on electric distribution feeders cannot be detected by using conventional protection system. In this paper, we prove that the nature of the high impedance faults is indeed a deterministic chaos, not a random motion Algorithms for estimating Lyapunov spectrum and the largest Lyapunov exponent are applied to various fault currents detections in order to evaluate the orbital instability peculiar to deterministic chaos dynamically, and fractal dimensions of fault currents which represent geometrical self-similarity are calculated. Wavelet transform analysis is applied the time-scale information to fault signal. Time-scale representation of high impedance faults can detect easily and localize correctly the fault waveform.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2011.04a
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• pp.582-587
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• 2011

Acoustic Emission(AE) is widely used for early detection of faults for rotating machinery in these days because of its high sensitivity. AE signal has to need for transferring to low frequency range for the spectrum analysis included the fault mechanism. In transferring process, we lose a lot of fault information caused by unusable signal processing method. Discrete Wavelet Transform(DWT) is a method of signal processing for AE signatures, but the pattern of its mother function is not optimized with AE signals. So, we can lose the fault information when we want to use the DWT for AE signal. Therefore, in this paper, we will propose a novel pattern for DWT mother function, which is optimized with AE signals. And it will be applied to compare the results of DWT by daubechie and novel pattern.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2002.11a
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• pp.173-176
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• 2002

This paper proposes a new approach for digital watermarking and secure copyright protection of video. the principal aim being to discourage illicit copying and distribution of copyrighted material. The method presented here is based on the three dimensional discrete wavelet transform of video scene. The watermark is a copyright information encoded in the form of a spread spectrum signal to ensure the system security and is embedded in the 3D DWT magnitude of video chunks. The performance of the presented technique is evaluated experimentally.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2525-2527
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• 2000

The analysis of distribution line faults is essential to the proper protection of power system. A high impedance fault(HIF) dose not make enough current to cause conventional protective device operating, so it is well known that undesirable operating conditions and certain types of faults on electric distribution feeders cannot be detected by using conventional protection system. In this paper, we prove that the nature of the high impedance faults is indeed a deterministic chaos, not a random motion. Algorithms for estimating Lyapunov spectrum and the largest Lyapunov exponent are applied to various fault currents detections in order to evaluate the orbital instability peculiar to deterministic chaos dynamically, and fractal dimensions of fault currents which represent geometrical self-similarity are calculated. Wavelet transform analysis is applied the time-scale information to fault signal. Time-scale representation of high impedance faults can detect easily and localize correctly the fault waveform.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.539-542
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• 2003

A motor is the workhorse of our industry. The issues of preventive and condition-based maintenance, online monitoring, system fault detection, diagnosis, and prognosis are of increasing importance. Different internal motor faults (e.g., inter-turn short circuits, broken bearings, broken rotor bars) along with external motor faults (e.g., phase failure, mechanical overload, blocked rotor) are expected to happen sooner or later. This paper introduces the fault detection technique of induction motors based upon the stator current. The fault motors have rotor bar broken or rotor unbalance defect, respectively. The stator currents are measured by the current meters and stored by the time domain. The time domain is not suitable to represent the current signals, so the frequency domain is applied to display the signals. The Fourier Transformer is used for the conversion of the signal. After the conversion of the signals, the features of the signals have to be extracted by the signal processing methods like a wavelet analysis, a spectrum analysis, etc. The discovered features are entered to the pattern classification model such as a neural network model, a polynomial neural network, a fuzzy inference model, etc. This paper describes the fault detection results that use wavelet decomposition. The wavelet analysis is very useful method for the time and frequency domain each. Also it is powerful method to detect the features in the signals.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.05a
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• pp.153-157
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• 2002

자기 진단 센서는 자신의 상태를 스스로 진단하는 기능을 갖는 센서를 말한다. 이러한 기능을 갖기 위해서 자신의 상태를 판단 할 수 있는 정보를 얻는 것이 가장 중요하다. 본 연구에서는 자신의 노이즈 신호만으로 상태를 판단할 수 있는 자기 진단센서의 설계하는 방법을 제안하였다. 웨이브렛 및 ICA 분석기법을 이용하여 자신의 출력 신호로부터 대상목표의 측정물리량을 나타내는 신호성분을 제외한, 센서 자신으로부터 발생하는 특징 노이즈 신호만을 분류한 다음에, 이 신호를 PDS로 정량화하여 특징 데이터를 생성하였다. 실험을 통해 그 타당성을 입증하였다.