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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2020.1
- 수록면
- 234 - 242 (9page)
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초록· 키워드
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In this paper, a two-stage denoising algorithm is proposed. Complementary ensemble empirical mode decomposition based on permutation entropy (CEEMD-PE) is carried out for the noisy monitoring signal in the first stage. Several denoising models are established according to the intrinsic mode function obtained by CEEMD-PE. An objective function considers both the smoothness of the denoising model and the similarity between the denoising model and the noisy monitoring signal is established, and the second stage denoising is realized by solving the objective function. The denoising model corresponding to the optimal solution of the objective function is the smooth denoising model. In order to verify the correctness of the two-stage denoising algorithm, the mixed simulation signal with noise is denoised, and based on the definition of signal-to-noise ratio, the effect of two-stage denoising is calculated. Finally, the algorithm is applied to the actual blasting seismic signal denoising processing. It is found that the proposed algorithm can not only reduce the noise interference but also retain the real part of the original signal while filtering the noise.
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등록된 정보가 없습니다.
참고문헌 (18)
참고문헌 신청
Bilich, A. / 2007 / Mapping the GPS multipath environment using the signal-to-noise ratio(SNR) / Radio Science 42(6):1~16
Chen, S. J. / 2019 / Multivariate multiscale fractional order weighted permutation entropy of nonlinear time series / Physica A : Statistical Mechanics and Its Applications 515:217~231
Cheng, J. S. / 2018 / An improved EEMD method and its application in rolling bearing fault diagnosis / Journal of Vibration and Shock 37(16):51~56
Gaci, S. / 2016 / A new Ensemble Empirical Mode Decomposition(EEMD)denoising method for seismic signals / Energy Procedia 97:84~91
Han, J. J. / 2015 / Microseismic and seismic denoising via ensemble empirical mode decomposition and adaptive thresholding / Geophysics 80(6):69~80
Chen, S. J. / 2019 / Multivariate multiscale fractional order weighted permutation entropy of nonlinear time series / Physica A : Statistical Mechanics and Its Applications 515:217~231
Cheng, J. S. / 2018 / An improved EEMD method and its application in rolling bearing fault diagnosis / Journal of Vibration and Shock 37(16):51~56
Gaci, S. / 2016 / A new Ensemble Empirical Mode Decomposition(EEMD)denoising method for seismic signals / Energy Procedia 97:84~91
Han, J. J. / 2015 / Microseismic and seismic denoising via ensemble empirical mode decomposition and adaptive thresholding / Geophysics 80(6):69~80
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