• 제목/요약/키워드: Time-frequency Transform

검색결과 912건 처리시간 0.028초

Wavelet변환을 이용한 초음파 잡음신호의 제거에 관한 연구 (A Study on Suppression of Ultrasonic Background Noise Signal using wavelet Transform)

  • 박익근
    • 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.

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국부열손상을 받은 복합재료의 강도특성 및 비파괴평가 (Strength Characteristics and Non-Destructive Evaluation of Composites with Heat Damage)

  • 남기우;김영운
    • 한국해양공학회:학술대회논문집
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    • 한국해양공학회 2002년도 춘계학술대회 논문집
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    • pp.173-178
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    • 2002
  • Fourier transform has been one of the most common tools to study the frequency characteristics of signals. With the Fourier transform alone, it is difficult to tell whether signal's frequency contents evolve in time or not. Except for a few special cases, the frequency contents of most signals encountered in the real world change with time. Time-frequency methods are developed recently to overcome the drawbacks of Fourier transform, which can represent the information of signals in time and frequency at the same time. In this study, heat damage process of a carbon fiber reinforced plastic(CFRP) and glass fiber reinforced plastic(GFRP) under monotonic tensile loading was characterized by acoustic emission. Different kinds of specimens were used to determine the characteristics of Strength and AE signals. Time-frequency analysis methods were employed for the analysis of fracture mechanism in CFRP such as matrix cracking, debonding and fiber fracture.

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스파이크 웨이블렛 변환을 이용한 기어 시스템의 건전성 감시 (Condition Monitoring in Gear System Using Spike Wavelet Transform)

  • 이상권;심장선
    • 한국음향학회지
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    • 제20권5호
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    • pp.21-27
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    • 2001
  • 기어 시스템의 충격음과 충격 신호는 대개 기어의 결함과 관련이 있다. 그래서 이러한 충격음과 충격 신호는 기어 시스템의 건전성 감시의 주요 요소로 사용되어진다. 본 연구에서는 이런 충격음과 충격 신호를 효율적으로 추출해 내기 위해 스파이크 웨이블렛 변환을 이용하는 방법을 제안한다. 스파이크 웨이블렛 변환은 기존에 제안된 연속 웨이블렛 변환의 한계점인 임의의 영역에서의 시간-주파수 분해능의 스케일 변수에 대한 선형성을 보완하여 비 선형적으로 이것을 조절할 수 있게 하였다. 이로 인해서 스파이크 웨이블렛 변환은 관심 주파수를 기준으로 연속 웨이블렛 변환보다 고주파 영역에서는 시간 분해능이 향상되고 동시에 저주파 영역에서는 주파수 분해능이 향상되어 기어 결함에 대한 정보 손실 없이 기어의 결함 위치를 보다 명확히 판단할 수 있는 장점을 가진다. 또한 본 연구에서는 상단 절손의 결함을 갖는 기어에 대한 실험을 통해 본 연구에서 제안하는 스파이크 웨이블렛 변환의 유용성을 검증하였다.

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웨이블릿 변환을 이용한 유도초음파의 모드 확인 (Guided Wave Mode Identification Using Wavelet Transform)

  • Ik-Keun Park
    • 한국공작기계학회논문집
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    • 제12권5호
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    • pp.94-100
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    • 2003
  • One of unique characteristics of guided waves is a dispersive behavior that guided wave velocity changes with an excitation frequency and mode. In practical applications of guided wave techniques, it is very important to identify propagating modes in a time-domain waveform for determination of detect location and size. Mode identification can be done by measurement of group velocity in a time-domain waveform. Thus, it is preferred to generate a single or less dispersive mode But, in many cases, it is difficult to distinguish a mode clearly in a time-domain waveform because of superposition of multi modes and mode conversion phenomena. Time-frequency analysis is used as efficient methods to identify modes by presenting wave energy distribution in a time-frequency. In this study, experimental guided wave mode identification is carried out in a steel plate using time-frequency analysis methods such as wavelet transform. The results are compared with theoretically calculated group velocity dispersion own. The results are in good agreement with analytical predictions and show the effectiveness of using the wavelet transform method to identify and measure the amplitudes of individual guided wave modes.

이산푸리에변환과 시계열데이터의 고속 파라미터 추정 (A Fast Parameter Estimation of Time Series Data Using Discrete Fourier Transform)

  • 심관식;남해곤
    • 대한전기학회논문지:전력기술부문A
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    • 제55권7호
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    • pp.265-272
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    • 2006
  • This paper describes a method of parameter estimation of time series data using discrete Fourier transform(DFT). DFT have been mainly used to precisely and rapidly obtain the frequency of a signal. In a dynamic system, a real part of a mode used to learn damping characteristics is a more important factor than the frequency of the mode. The parameter estimation method of this paper can directly estimate modes and parameters, indicating the characteristics of a dynamic system, on the basis of the Fourier transform of the time series data. Real part of a mode estimates by subtracting a frequency of the Fourier spectrum corresponding to 0.707 of a magnitude of the peak spectrum from a peak frequency, or subtracting a frequency of the power spectrum corresponding to 0.5 of the peak power spectrum from a peak frequency, or comparing the Fourier(power) spectrum ratio. Also, the residue and phase of time signal calculate by simple equation with the real part of the mode and the power spectrum that have been calculated. Accordingly, the proposed algorithm is advantageous in that it can estimate parameters of the system through a single DFT without repeatedly calculating a DFT, thus shortening the time required to estimate the parameters.

Advanced signal processing for enhanced damage detection with piezoelectric wafer active sensors

  • Yu, Lingyu;Giurgiutiu, Victor
    • 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.

저소음 타이어 설계에 대한 시변주파수 분석 적용 (The Reduction of Tire Pattern Noise Using Time-Frequency Transform)

  • 황성욱;방명제;김선주;조춘택
    • 한국소음진동공학회:학술대회논문집
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    • 한국소음진동공학회 2005년도 추계 학술대회논문집(수송기계편)
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    • pp.144-147
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    • 2005
  • 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.

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웨이브릿 변환을 이용한 마이크로스트립 선로에서의 전파 특성 연구 (A Study on the Propagation Characteristics along the Microstrip Lines using Wavelet Transforms)

  • 이재웅;송용원;김건욱;박한규
    • 대한전자공학회:학술대회논문집
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    • 대한전자공학회 1999년도 하계종합학술대회 논문집
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    • pp.223-226
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    • 1999
  • We study the propagation property of the transient signals along the microstrip using the wavelet transform. Wavelet transform can offer the time-frequency windows. It makes the resolution of time high in high frequency range and the resolution of frequency high in low frequency range. So It is useful to analyze the signals which have both low and high frequency components.

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CHARACTERIZATION OF RATIONAL TIME-FREQUENCY MULTI-WINDOW GABOR FRAMES AND THEIR DUALS

  • Zhang, Yan;Li, Yun-Zhang
    • 대한수학회지
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    • 제51권5호
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    • pp.897-918
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    • 2014
  • This paper addresses multi-window Gabor frames with rational time-frequency product. Such issue was considered by Zibulski and Zeevi (Appl. Comput. Harmonic Anal. 4 (1997), 188-221) in terms of Zak transform matrix (so-called Zibuski-Zeevi matrix), and by many others. In this paper, we introduce of a new Zak transform matrix. It is different from Zibulski-Zeevi matrix, but more direct and convenient for our purpose. Using such Zak transform matrix we characterize rational time-frequency multi-window Gabor frames (Riesz bases and orthonormal bases), and Gabor duals for a Gabor frame. Some examples are also provided, which show that our Zak transform matrix method is efficient.

웨이브렛 변환을 이용한 부분방전신호의 잡음제거 특성 (Noise elimination of PD signal using Wavelet Transform)

  • 이현동;주재현;김기채;박원주;이광식;이동인
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 2001년도 하계학술대회 논문집 C
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    • pp.1679-1681
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    • 2001
  • In this paper, As the wavelet transform has the properties of multi-resolution analysis and time-frequency domain localization, application of wavelet transform is used at partial discharge(PD) signal detected by electromagnetic wave detection method to extract PD signal's various frequency component and its time domain. therefore we can analyzed PD signal's time-frequency domain simultaneously. On the other hand, using wavelet transform denoising process, inclued noise signal in detected PD signal is well elimiated. we can propose the true shape of PD signal.

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