• Title, Summary, Keyword: Discrete stationary wavelet transform

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Wavelet based multi-step filtering method for bridge health monitoring using GPS and accelerometer

  • Yi, Ting-Hua;Li, Hong-Nan;Gu, Ming
    • Smart Structures and Systems
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    • v.11 no.4
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    • pp.331-348
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    • 2013
  • Effective monitoring, reliable data analysis, and rational data interpretations are challenges for engineers who are specialized in bridge health monitoring. This paper demonstrates how to use the Global Positioning System (GPS) and accelerometer data to accurately extract static and quasi-static displacements of the bridge induced by ambient effects. To eliminate the disadvantages of the two separate units, based on the characteristics of the bias terms derived from the GPS and accelerometer respectively, a wavelet based multi-step filtering method by combining the merits of the continuous wavelet transform (CWT) with the discrete stationary wavelet transform (SWT) is proposed so as to address the GPS deformation monitoring application more efficiently. The field measurements are carried out on an existing suspension bridge under the normal operation without any traffic interference. Experimental results showed that the frequencies and absolute displacements of the bridge can be accurate extracted by the proposed method. The integration of GPS and accelerometer can be used as a reliable tool to characterize the dynamic behavior of large structures such as suspension bridges undergoing environmental loads.

A Study on the Wavelet Transform of Acoustic Emission Signals Generated from Fusion-Welded Butt Joints in Steel during Tensile Test and its Applications (맞대기 용접 이음재 인장시험에서 발생한 음향방출 신호의 웨이블릿 변환과 응용)

  • Rhee Zhang-Kyu;Yoon Joung-Hwi;Woo Chang-Ki;Park Sung-Oan;Kim Bong-Gag;Jo Dae-Hee
    • Proceedings of the Korean Society of Machine Tool Engineers Conference
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    • pp.342-348
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    • 2005
  • 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 SIFT) 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.

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A Study of Data Compression of Power Quality Disturbance Signal (전력품질 왜곡 신호 압축에 관한 연구)

  • Chung Young Sik;Park Chan Woong
    • Proceedings of the KIEE Conference
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    • pp.336-338
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    • 2004
  • This paper introduces a compression algorithm for power quality disturbance signal via the discrete wavelet transform, DWT. Fundamental signal or stationary signal is estimated and then subtracted from a given signal to obtain a difference signal or nonstationary signal. DWT is applied to a difference signal to get coefficients that are thresholded to reduce a number of coefficients. Simulation results show the resonable compression ratio while keep low signal distortion.

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A Study of the Compression for the Power Quality Disturbance Signal by using the Phase Estimation of Stationary Signal (정상신호의 위상 추정을 이용한 전력 품질 왜곡 신호의 압축에 관한 연구)

  • Chung, Young-Sik;Park, Chan-Woong
    • Proceedings of the KIEE Conference
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    • pp.341-343
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    • 2005
  • This paper introduces a compression algorithm for power quality disturbance signal via the discrete wavelet transform, DWT. Algorithm to estimate a time delay from the power quality disturbance signal is proposed. Pseudo-stationary signal is constructed from the estimated time delay. A difference signal or nonstationary signal is obtained by removing a pseudo-stationary signal from a disturbance signal. DWT is applied to a difference signal. The threshold is applied to reduce a number of coefficients. Simulation results show the resonable compression ratio while keep low signal distortion.

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Wavelet Analysis of Visualized Image (가시화 영상의 웨이브렛 해석)

  • Park, Young-Sik;Kim, Okug-Gyu
    • Journal of the Institute of Convergence Signal Processing
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    • v.8 no.3
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    • pp.143-148
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    • 2007
  • The many studies have been proceeding to express accurately the feature of a sudden signal and a uncertain system in the image processing field. It is well know that Fourier Transform is widely used for frequency analysis of any signal. However, The frequency transform domain is not used for expressing the sudden signal change and non-stationary signal at the time-axis by this method. This paper describes of image analysis by discrete wavelet transform. Wavelet modulus maxima on transformed plane gives the Lipschitz exponent expression, which is useful to examine the characteristics of signal or the edge of an image. It is possible to reconstruct the original image only using the few maxima points. The fractal analysis is applied as an examples. The visualized image of oil flow on a ship model is analyzed. The fractal variable is obtained by the maxima analysis and the good results on the exprement is obtained by the visualized image analysis.

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Maxima Analysis from Visualized Image based on Multi-Resolution Analysis (다중해상도 웨이브렛 해석을 기본으로 한 가시화 영상의 극대값 해석)

  • Park, Young-Sik;Kim, Og-Gyu
    • Journal of the Institute of Convergence Signal Processing
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    • v.11 no.2
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    • pp.157-162
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    • 2010
  • In this paper we propose a fractal analysis based on the discrete wavelet transform. It is well known that Fourier Transform is widely used for frequency analysis of random signal. However, the frequency domain is not used for expressing the sudden signal change and non-stationary signal at the time-axis by this method. Maximum value in the wavelet modules can be expressed by the Lipschitz exponent, which is useful to represent the characteristics of signal or the edge of an image. It is possible to reconstruct the original image only by using the few maximum points. The v possible image It iusing oil was acquired to interpret the maximum value. ufter that, it was applied to the v possible image of a ship model. In addition, the fractal dimens by by the conlapse process of the sediment particle was examined. In this paper, the fractal dimens by has been obtained by the maximum value and the experiment obtained from the visualized image also acquired the same result as existing methods.