• Smart Structures and Systems
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• 제1권2호
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• pp.185-215
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• 2005

Advanced signal processing techniques have been long introduced and widely used in structural health monitoring (SHM) and nondestructive evaluation (NDE). In our research, we applied several signal processing approaches for our embedded ultrasonic structural radar (EUSR) system to obtain improved damage detection results. The EUSR algorithm was developed to detect defects within a large area of a thin-plate specimen using a piezoelectric wafer active sensor (PWAS) array. In the EUSR, the discrete wavelet transform (DWT) was first applied for signal de-noising. Secondly, after constructing the EUSR data, the short-time Fourier transform (STFT) and continuous wavelet transform (CWT) were used for the time-frequency analysis. Then the results were compared thereafter. We eventually chose continuous wavelet transform to filter out from the original signal the component with the excitation signal's frequency. Third, cross correlation method and Hilbert transform were applied to A-scan signals to extract the time of flight (TOF) of the wave packets from the crack. Finally, the Hilbert transform was again applied to the EUSR data to extract the envelopes for final inspection result visualization. The EUSR system was implemented in LabVIEW. Several laboratory experiments have been conducted and have verified that, with the advanced signal processing approaches, the EUSR has enhanced damage detection ability.

• 한국공작기계학회논문집
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• 제16권1호
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• pp.26-32
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• 2007

This study was carried out fusion-welded butt joints in SWS 490A high strength steel subjected to tensile test that load-deflection curve. The windowed or short-time Fourier transform(WFT or STFT) makes possible for the analysis of non-stationary or transient signals into a joint time-frequency domain and the wavelet transform(WT) is used to decompose the acoustic emission(AE) signal into various discrete series of sequences over different frequency bands. In this paper, for acoustic emission signal analysis to use a continuous wavelet transform, in which the Gabor wavelet base on a Gaussian window function is applied to the time-frequency domain. A wavelet transform is demonstrated and the plots are very powerful in the recognition of the acoustic emission features. As a result, the technique of acoustic emission is ideally suited to study variables which control time and stress dependent fracture or damage process in metallic materials.

• 한국산학기술학회논문지
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• 제20권7호
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• pp.151-157
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• 2019

최근 비침투 또는 비접촉 방식을 활용한 호흡상태 관찰에 대한 관심이 높아지고 있다. 여러 가지 많은 생체신호들 중 호흡신호를 활용하여 건강상태를 점검하는 것은 비정상적인 건강 상태에 대한 신속한 대응을 가능하게 해 준다. 본 논문에서는 국소 퓨리에 변환을 활용한 실시간 무호흡 상태 검출에 대한 방법을 제시한다. 기존의 고속 퓨리에 변환을 활용한 신호해석과 달리, 본 논문에서는 국소 퓨리에 변환을 사용하여 짧은 신호 구간에서의 주파수 응답을 분석한다. 본 연구에서 호흡 신호는 비접촉 방식을 활용하였으며, 초광대역 레이더 모듈을 활용하여 신호를 획득하였다. 국소 퓨리에 변환을 활용하여 호흡 상태를 검출한 후, 검출 결과에 따라 호흡 상태에 대한 분류가 가능하다. 특히 국소 퓨리에 변환은 실시간으로 호흡 상태에 대한 주파수 분석이 가능하도록 하였다. 호흡신호에 잡음이 존재할 경우를 대비하여 적절한 필터링 알고리즘이 적용되었다. 본 논문에서 제안하는 방법은 직관적으로 구현이 가능하고, 실질적으로 사람의 호흡상태에 대한 분석이 가능하도록 해준다. 제안한 방법을 검증하기 위해 호흡신호를 활용한 실험결과를 제시한다.

• 비파괴검사학회지
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• 제24권2호
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• pp.142-150
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• 2004

발전설비의 보수검사에 적용하기 위한 예비 연구로 comb transducer를 이용한 파이프 내에서의 유도초음파 모드의 거동을 실험적으로 검증하였다. 유도초음파의 모드식별은 STFT와 WT에 의한 시간-주파수해석을 통하여 최적의 모드를 선정하였다. 시간-주파수해석과 이론적 해석 방법인 분산 곡선을 비교한 결과 잘 일치함을 알 수 있었으며, pitch-catch법과 선단부로부터 반사된 신호를 모드 분석한 결과 L(0,1) 모드가 다른 모드에 비해 모드변환에 의한 영향이 적었다. 따라서 L(0,1)을 최적의 모드로 선정하고, 결함위치를 추정한 결과 유용함을 알 수 있었다.

• KIEE International Transactions on Power Engineering
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• 제3A권3호
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• pp.130-138
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• 2003

This paper presents a discrete wavelet transform approach for determining the beginning and end times of voltage sags. Firstly, investigations in the use of some typical mother wavelets, namely Daubechies, Symlets, Coiflets and Biorthogonal are carried out and the most appropriate mother wavelet is selected. The proposed technique is based on utilizing the maximum value of Dl (at scale 1) coefficients in multiresolution analysis (MRA) based on the discrete wavelet transform. The results are compared with other methods for determining voltage sag duration, such as the Root Mean Square (RMS) voltage and Short Time Fourier Transform (STFT) methods. It is shown that the voltage sag detection technique based on the wavelet transform is a satisfactory and reliable method for detecting voltage sags in power quality disturbance analysis.

• 전기학회논문지
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• 제63권8호
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• pp.1092-1097
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• 2014

In this paper, we propose the grid-based output control method for wind farm. To do this, we propose the output control method using the PSO(Particle Swarm Optimization) algorithm. Secondly, we propose the method for detecting the harmonics using STFT(Short-Time Fourier Transform) algorithm. And last, we propose the method for compensating the harmonics using neural network. Finally, we show the effectiveness and feasibility of the proposed method though some simulations.

• 한국음향학회:학술대회논문집
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• 한국음향학회 1997년도 영남지회 학술발표회 논문집 Acoustic Society of Korean Youngnam Chapter Symposium Proceedings
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• pp.27-34
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• 1997

In this study , we tried to test the classification performance of a neural netow and thereby to examine its applicability to the signals distorted by a shallow water einvironment . We conducted an acoustic experiment iin a shallow sea near Pohang, Korea in which water depth is about 60m. The signals, on which the network has been tested, is ilinear frequency modulated ones centered on one of the frequencies, 200, 400, 600 and 800 Hz, each being swept up or down with bandwidth 100Hz. we considered two transforms, STFT(short-time Fourier transform) and PWVD (pseudo Wigner-Ville distribution), form which power spectra were derived. The training signals were simulated using an acoutic model based on the Fourier synthesis scheme. When the network has been trained on the measured signals of center frequency 600Hz,it gave a little better results than that trained onthe simulated . With the center frequencies varied, the overall performance reached over 90% except one case of center frequency 800Hz. With the feature extraction techniques(STFT and PWVD) varied,the network showed performance comparable to each other . In conclusion , the signals which have been simulated with water depth were successully applied to training a neural network, and the trained network performed well in classifying the signals distorted by a surrounding environment and corrupted by noise.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 하계학술대회 논문집 C
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• pp.1784-1786
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• 2004

PD(Partial Discharge) signal emitted from PD sources has their intrinsic features in the region of time and frequency. STFT(Short Time Fourier Transform) shows time-frequency distribution at the same time. 2-Dimensional matrices(33${\times}$77) from STFT for PD pulse signals are a good feature vectors and can be decreased in dimension by wavelet 2D data compression technique. Decreased feature vectors(13${\times}$24) were used as inputs of Back-propagation ANN(Artificial Neural Network) for discrimination of Multi-PD sources(air discharge sources(3), surface discharge(1)). They are a good feature vectors for discriminating Multi-PD sources.

• 한국생산제조학회지
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• 제8권1호
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• pp.135-141
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• 1999

Recently, advance signal analysis which is called "Time-Frequency Analysis" has been developed. Wavelet and Wigner Distribution are used to the method. Wavelet transform(WT) is applied to time-frequency analysis of waveforms obtained by an ultrasonic pulse-echo technique. The Gabor function is adopted as the analyzing wavelet. Wavelet analysis method is an attractive technique for evolution of material characterization evoluation. In this paper, the feasibility of suppression of ultrasonic background noise signal using WT has been presented. These results suggest that ultrasonic background noise ginal can be suppressed and enhanced even for SNR of 20.8 dB. This property of the WT is extremely useful for the detecting flaw echos embedded in background noise.und noise.

• 한국소음진동공학회논문집
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• 제16권6호
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• pp.627-633
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• 2006

The tire is considered as one of the important noise sources having an influence on vehicle's performance. The Pattern noise of a tire is the transmission sound of airborne noise. On smooth asphalt road, Pattern noise is amplified with the velocity. In recent, the study on the reduction of Pattern noise is energetically processed. Pattern noise is strongly related with pitch sequence. To reduce the pattern noise, tire's designer has to randomize the sequence of pitch. The FFT is a traditional method to evaluate the level of the randomization of the pitch sequence, but gives no information on time-varying, instantaneous frequency. In the study, we found that Time-Frequency transform is a useful method to non-stationary signal such as tire noise.