The precise detection of flaw echoes buried in backscattefing noise caused by material microstructure is a problem of great importance in uhrasonic non-destructive testing （NDT）. In this work, empirical mode decompo...The precise detection of flaw echoes buried in backscattefing noise caused by material microstructure is a problem of great importance in uhrasonic non-destructive testing （NDT）. In this work, empirical mode decomposition （EMD） is proposed to deal with ultrasonic signal. A time-frequency filtering method based on EMD is designed to suppress noise and enhance flaw signals. Simulated results are presented, showing that the proposed method has an excellent performance even for a very low signal-to-noise ratio （SNR）. The improvement in flaw detection was experimentally verified using stainless steel pipe sample with artificial flaws.展开更多
Empirical mode decomposition(EMD) is a new signal decomposition method, which could decompose the non-stationary signal into several single-component intrinsic mode functions (IMFs) and each IMF has some physical mean...Empirical mode decomposition(EMD) is a new signal decomposition method, which could decompose the non-stationary signal into several single-component intrinsic mode functions (IMFs) and each IMF has some physical meanings. This paper studies the single trial extraction of visual evoked potential by combining EMD and wavelet threshold filter. Experimental results showed that the EMD based method can separate the noise out of the event related potentials (ERPs) and effectively extract the weak ERPs in strong background noise, which manifested as the waveform characteristics and root mean square error (RMSE).展开更多
The basic signal model of deformation monitoring with GPS was introduced and the main problems of GPS deformation monitoring in mining area were discussed. For the problem of noise signal extraction in GPS deformation...The basic signal model of deformation monitoring with GPS was introduced and the main problems of GPS deformation monitoring in mining area were discussed. For the problem of noise signal extraction in GPS deformation monitoring, the Kalman-EMD method was proposed to obtain the effective deformation signal. The reliability and effectiveness of the methodology were tested and verified by analog signal. The results of experiment in Mongolia show that the accuracy of the proposed GPS deformation monitoring model is equivalent to that of level method.展开更多
Time–frequency electromagnetic data contain frequency and transient electromagnetic information and can be used to determine the apparent resistivity both in the frequency and time domains.The observation data contai...Time–frequency electromagnetic data contain frequency and transient electromagnetic information and can be used to determine the apparent resistivity both in the frequency and time domains.The observation data contains three types of noise:the harmonics interference at 50 Hz,high-frequency random noise,and low-frequency noise.We use frequency-domain bandstop filtering to remove the harmonics interference noise,segmentation and extension median filtering,and fitting of fixed extremes in empirical mode decomposition to remove the high-frequency and low-frequency noise,respectively;furthermore,we base the selection of median filtering window size on the variance and skewness coefficient of the data.We first remove the harmonics interference at 50 Hz,then the high-frequency noise,and finally the low-frequency noise.We test the proposed methodology by using theory and experiments,and we find that the three types of noises are removed,the phase and amplitude information of the signal are maintained,and high-quality waveforms are obtained in the time domain.展开更多
文摘The precise detection of flaw echoes buried in backscattefing noise caused by material microstructure is a problem of great importance in uhrasonic non-destructive testing （NDT）. In this work, empirical mode decomposition （EMD） is proposed to deal with ultrasonic signal. A time-frequency filtering method based on EMD is designed to suppress noise and enhance flaw signals. Simulated results are presented, showing that the proposed method has an excellent performance even for a very low signal-to-noise ratio （SNR）. The improvement in flaw detection was experimentally verified using stainless steel pipe sample with artificial flaws.
基金Natural Science Fund for Colleges and Universities in Jiangsu Province of China grant number: 10 KJB510003+2 种基金Natural Science Fund in Changzhou grant number: CJ20110023 Open Project of the State Key Laboratory of Robotics and System (HIT), and the State Key Laboratory of Cognitive Neurosciences and Learning
文摘Empirical mode decomposition(EMD) is a new signal decomposition method, which could decompose the non-stationary signal into several single-component intrinsic mode functions (IMFs) and each IMF has some physical meanings. This paper studies the single trial extraction of visual evoked potential by combining EMD and wavelet threshold filter. Experimental results showed that the EMD based method can separate the noise out of the event related potentials (ERPs) and effectively extract the weak ERPs in strong background noise, which manifested as the waveform characteristics and root mean square error (RMSE).
基金Project(2014ZDPY29)supported by the Fundamental Research Funds for Central Universities,ChinaProject(CXZZ11-0299)supported by the Postgraduate Innovative Program of Jiangsu Province,China
文摘The basic signal model of deformation monitoring with GPS was introduced and the main problems of GPS deformation monitoring in mining area were discussed. For the problem of noise signal extraction in GPS deformation monitoring, the Kalman-EMD method was proposed to obtain the effective deformation signal. The reliability and effectiveness of the methodology were tested and verified by analog signal. The results of experiment in Mongolia show that the accuracy of the proposed GPS deformation monitoring model is equivalent to that of level method.
基金supported by the National Natural Science Foundation of China(No.41574127 and No.41227803)
文摘Time–frequency electromagnetic data contain frequency and transient electromagnetic information and can be used to determine the apparent resistivity both in the frequency and time domains.The observation data contains three types of noise:the harmonics interference at 50 Hz,high-frequency random noise,and low-frequency noise.We use frequency-domain bandstop filtering to remove the harmonics interference noise,segmentation and extension median filtering,and fitting of fixed extremes in empirical mode decomposition to remove the high-frequency and low-frequency noise,respectively;furthermore,we base the selection of median filtering window size on the variance and skewness coefficient of the data.We first remove the harmonics interference at 50 Hz,then the high-frequency noise,and finally the low-frequency noise.We test the proposed methodology by using theory and experiments,and we find that the three types of noises are removed,the phase and amplitude information of the signal are maintained,and high-quality waveforms are obtained in the time domain.
