scholarly journals Application of Mathematical Morphological Filtering to Improve the Resolution of Chang'e-3 Lunar Penetrating Radar Data

2019 ◽  
Vol 11 (5) ◽  
pp. 524 ◽  
Author(s):  
Jianmin Zhang ◽  
Zhaofa Zeng ◽  
Ling Zhang ◽  
Qi Lu ◽  
Kun Wang
Keyword(s):  
High Frequency ◽  
Noise Suppression ◽  
Low Frequency ◽  
Radar Data ◽  
Structural Elements ◽  
Geological Structures ◽  
Mode Decomposition ◽  
Morphological Filtering ◽  
Scale Range ◽  
Band Pass

As one of the important scientific instruments of lunar exploration, the Lunar Penetrating Radar (LPR) onboard China’s Chang'E-3 (CE-3) provides a unique opportunity to image the lunar subsurface structure. Due to the low-frequency and high-frequency noises of the data, only a few geological structures are visible. In order to better improve the resolution of the data, band-pass filtering and empirical mode decomposition filtering (EMD) methods are usually used, but in this paper, we present a mathematical morphological filtering (MMF) method to reduce the noise. The MMF method uses two structural elements with different scales to extract certain scale-range information from the original signal, at the same time, the noise beyond the scale range of the two different structural elements is suppressed. The application on synthetic signals demonstrates that the morphological filtering method has a better performance in noise suppression compared with band-pass filtering and EMD methods. Then, we apply band-pass filtering, EMD, and MMF methods to the LPR data, and the MMF method also achieves a better result. Furthermore, according to the result by MMF method, three stratigraphic zones are revealed along the rover's route.

Frequency Modulated Empirical Mode Decomposition Method

2012 ◽  
Vol 433-440 ◽  
pp. 4776-4781
Author(s):  
Xin Zhang ◽  
Xiu Li Du
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method ◽  
Low Pass ◽  
Frequency Components ◽  
Band Pass ◽  
Stiffness And Damping ◽  
Mixing Problem

Frequency modulation procedure is proposed to overcome the mode-mixing problem associated with the EMD method when processing signals with closely spaced frequencies. This procedure also provides the flexibility to start the realization of IMFs either from the high frequency end as does the original EMD or from the low frequency end when the signal contains unwanted high frequency components. The EMD procedure, under the circumstances, may behave as high pass, low pass or band pass/stop filters. The proposed method, assisted by the Hilbert-Huang transform on the governing equations, identifies the instantaneous stiffness and damping coefficients as functions of vibration amplitude of a nonlinear system. The effectiveness of the proposed method is verified by numerical simulation.

scholarly journals Fusion of GNSS and Speedometer Based on VMD and Its Application in Bridge Deformation Monitoring

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 694 ◽  
Author(s):  
Ruicheng Zhang ◽  
Chengfa Gao ◽  
Shuguo Pan ◽  
Rui Shang
Keyword(s):  
Real Time ◽  
High Precision ◽  
High Frequency ◽  
Spectral Response ◽  
Low Frequency ◽  
Deformation Monitoring ◽  
Full Time ◽  
Dynamic Displacement ◽  
Mode Decomposition ◽  
Acceleration Sensors

Real-time dynamic displacement and spectral response on the midspan of Jiangyin Bridge were calculated using Global Navigation Satellite System (GNSS) and a speedometer for the purpose of understanding the dynamic behavior and the temporal evolution of the bridge structure. Considering that the GNSS measurement noise is large and the velocity/acceleration sensors cannot measure the low-frequency displacement, the Variational Mode Decomposition (VMD) algorithm was used to extract the low-frequency displacement of GNSS. Then, the low-frequency displacement extracted from the GNSS time series and the high-frequency vibration calculated by speedometer were combined in this paper in order to obtain the high precision three-dimensional dynamic displacement of the bridge in real time. Simulation experiment and measured data show that the VMD algorithm could effectively resist the modal aliasing caused by noise and discontinuous signals compared with the commonly used Empirical Mode Decomposition (EMD) algorithm, which is guaranteed to get high-precision fusion data. Finally, the fused displacement results can identify high-frequency vibrations and low-frequency displacements of a mm level, which can be used to calculate the spectral characteristics of the bridge and provide reference to evaluate the dynamic and static loads, and the health status of the bridge in the full frequency domain and the full time domain.

scholarly journals Load Prediction Based on Hybrid Model of VMD-mRMR-BPNN-LSSVM

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Gang Zhang ◽  
Hongchi Liu ◽  
Pingli Li ◽  
Meng Li ◽  
Qiang He ◽  
...  
Keyword(s):  
Power System ◽  
High Frequency ◽  
Prediction Accuracy ◽  
Load Forecasting ◽  
Low Frequency ◽  
Load Prediction ◽  
System Load ◽  
Mode Decomposition ◽  
Frequency Components ◽  
Feature Information

Power system load forecasting is an important part of power system scheduling. Since the power system load is easily affected by environmental factors such as weather and time, it has high volatility and multi-frequency. In order to improve the prediction accuracy, this paper proposes a load forecasting method based on variational mode decomposition (VMD) and feature correlation analysis. Firstly, the original load sequence is decomposed using VMD to obtain a series of intrinsic mode function (IMF), it is referred to below as a modal component, and they are divided into high frequency, intermediate frequency, and low frequency signals according to their fluctuation characteristics. Then, the feature information related to the power system load change is collected, and the correlation between each IMF and each feature information is analyzed using the maximum relevance minimum redundancy (mRMR) based on the mutual information to obtain the best feature set of each IMF. Finally, each component is input into the prediction model together with its feature set, in which back propagation neural network (BPNN) is used to predict high-frequency components, least square-support vector machine (LS-SVM) is used to predict intermediate and low frequency components, and BPNN is also used to integrate the prediction results to obtain the final load prediction value, and compare the prediction results of method in this paper with that of the prediction models such as autoregressive moving average model (ARMA), LS-SVM, BPNN, empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and VMD. This paper carries out an example analysis based on the data of Xi’an Power Grid Corporation, and the results show that the prediction accuracy of method in this paper is higher.

Geomagnetic signal de-noising method based on improved empirical mode decomposition and morphological filtering

2020 ◽  
pp. 095441002095102
Author(s):  
Hongqi Zhai ◽  
Lihui Wang ◽  
Qingya Liu ◽  
Nan Qiao
Keyword(s):  
Empirical Mode Decomposition ◽  
Cross Correlation ◽  
Random Noise ◽  
Pulse Signal ◽  
Structural Elements ◽  
Intrinsic Mode Functions ◽  
Pulse Interference ◽  
Mode Decomposition ◽  
Morphological Filtering ◽  
Mean Square Errors

To solve the problem that geomagnetic signals are susceptible to random noise and instantaneous pulse interference in geomagnetic navigation, a geomagnetic signal de-noising method based on improved empirical mode decomposition (IEMD) and morphological filtering (MF) is proposed. The instantaneous pulse interference is eliminated by designing different structural elements according to the characteristics of the pulse signal. The signal after filtering the instantaneous pulse interference is decomposed by EMD, and the intrinsic mode functions (IMFs) obtained from the decomposition are determined as two modes (i.e. noise IMFs and mixed IMFs) by the cross-correlation coefficient criterion. The noise IMFs are removed directly, and a normalized least means square filter (NLMS) is designed to remove noise from mixed IMFs, which can adaptively adjust the filtering parameters according to the noise level of different IMF components. The noise-reduced mixed IMFs and residual are reconstructed to obtain the final geomagnetic signal. Experiment results illustrate that the proposed MF-IEMD method can effectively achieve noise reduction. Comparing with the traditional EMD and MF-EMD de-noising methods, the root mean square errors(RMSE) decreased by 49.27% and 24.79%, respectively.

Random Noise Reduction Based on Ensemble Empirical Mode Decomposition and Wavelet Threshold Filtering

2012 ◽  
Vol 518-523 ◽  
pp. 3887-3890 ◽  
Author(s):  
Wei Chen ◽  
Shang Xu Wang ◽  
Xiao Yu Chuai ◽  
Zhen Zhang
Keyword(s):  
Noise Reduction ◽  
Empirical Mode Decomposition ◽  
High Frequency ◽  
Reduction Method ◽  
Random Noise ◽  
Low Frequency ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Eemd Method

This paper presents a random noise reduction method based on ensemble empirical mode decomposition (EEMD) and wavelet threshold filtering. Firstly, we have conducted spectrum analysis and analyzed the frequency band range of effective signals and noise. Secondly, we make use of EEMD method on seismic signals to obtain intrinsic mode functions (IMFs) of each trace. Then, wavelet threshold noise reduction method is used on the high frequency IMFs of each trace to obtain new high frequency IMFs. Finally, reconstruct the desired signal by adding the new high frequency IMFs on the low frequency IMFs and the trend item together. When applying our method on synthetic seismic record and field data we can get good results.

scholarly journals High-G Calibration Denoising Method for High-G MEMS Accelerometer Based on EMD and Wavelet Threshold

Micromachines ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 134 ◽  
Author(s):  
Qing Lu ◽  
Lixin Pang ◽  
Haoqian Huang ◽  
Chong Shen ◽  
Huiliang Cao ◽  
...  
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Original Signal ◽  
Denoising Method ◽  
Mems Accelerometer ◽  
Mode Decomposition ◽  
Impact Peak ◽  
Reconstructed Signal ◽  
Mems Accelerometers ◽  
The Impact

High-G MEMS accelerometers have been widely used in monitoring natural disasters and other fields. In order to improve the performance of High-G MEMS accelerometers, a denoising method based on the combination of empirical mode decomposition (EMD) and wavelet threshold is proposed. Firstly, EMD decomposition is performed on the output of the main accelerometer to obtain the intrinsic mode function (IMF). Then, the continuous mean square error rule is used to find energy cut-off point, and then the corresponding high frequency IMF component is denoised by wavelet threshold. Finally, the processed high-frequency IMF component is superposed with the low-frequency IMF component, and the reconstructed signal is denoised signal. Experimental results show that this method integrates the advantages of EMD and wavelet threshold and can retain useful signals to the maximum extent. The impact peak and vibration characteristics are 0.003% and 0.135% of the original signal, respectively, and it reduces the noise of the original signal by 96%.

scholarly journals Lagrangian Drifter to Identify Ocean Eddy Characteristics

2019 ◽  
Author(s):  
Peter C. Chu ◽  
Chenwu Fan
Keyword(s):  
Time Series ◽  
High Frequency ◽  
Low Frequency ◽  
Rossby Number ◽  
Naval Postgraduate School ◽  
Mode Decomposition ◽  
Eddy Characteristics ◽  
Velocity Scale ◽  
Lagrangian Drifter

Deterministic-stochastic empirical mode decomposition (EMD) is used to obtain low-frequency (non-diffusive, i.e., background velocity) and high-frequency (diffusive, i.e., eddies) components from a Lagrangian drifter‘s trajectory. Eddy characteristics are determined from the time series of eddy trajectories from individual Lagrangian drifter such as the eddy radial scale, eddy velocity scale, eddy Rossby number, and eddy-background kinetic energy ratio. A long-term dataset of the SOund Fixing And Ranging float time series obtained near the California coast by the Naval Postgraduate School from 1992 to 2004 at depth between 150 and 600 m (http://www.oc.nps.edu/npsRAFOS/) is used as an example to demonstrate the capability of the deterministic-stochastic EMD.

DETERMINATION OF THE INFLUENCE OF STRUCTURAL ELEMENTS ON ACOUSTIC EFFICIENCY AND BACK PRESSURE IN EXHAUST AND SUCTION NOISE SILENCERS

Akustika ◽  
2021 ◽  
pp. 210-216
Author(s):  
Nickolay Ivanov ◽  
Aleksandr Shashurin ◽  
Aleksandr Burakov
Keyword(s):  
Structural Design ◽  
High Frequency ◽  
Experimental Studies ◽  
Low Frequency ◽  
Back Pressure ◽  
Noise Generation ◽  
Structural Elements ◽  
The Relationship

The features of noise generation processes in exhaust and suction noise silencers are shown. A method for testing silencers has been developed. The classification of the main structural elements of exhaust and suction noise silencers, depending on the purpose, is proposed. Experimental studies of the relationship between the acoustic efficiency and the back pressure of silencers from the structural design of the elements are performed. The factors influencing the efficiency in the low-frequency and high-frequency regions of the spectrum are determined: the volume of silencers, the number of chambers, perforation, sound absorption, flow ejection, etc. Recommendations for the design of noise silencers are proposed.

On a Design of Narrowband FIR Low-Pass Filters

2011 ◽  
pp. 2882-2887
Author(s):  
Gordana Jovanovic Dolecek ◽  
Javier Diaz Carmona
Keyword(s):  
Impulse Response ◽  
High Frequency ◽  
Digital Filters ◽  
Stop Band ◽  
Low Frequency ◽  
Pass Band ◽  
Low Pass ◽  
Frequency Components ◽  
Band Pass ◽  
High Pass

Stearns and David (1996) states that “for many diverse applications, information is now most conveniently recorded, transmitted, and stored in digital form, and as a result, digital signal processing (DSP) has become an exceptionally important modern tool.” Typical operation in DSP is digital filtering. Frequency selective digital filter is used to pass desired frequency components in a signal without distortion and to attenuate other frequency components (Smith, 2002; White, 2000). The pass-band is defined as the frequency range allowed to pass through the filter. The frequency band that lies within the filter stop-band is blocked by the filter and therefore eliminated from the output signal. The range of frequencies between the pass-band and the stop-band is called the transition band and for this region no filter specification is given. Digital filters can be characterized either in terms of the frequency response or the impulse response (Diniz, da Silva & Netto, 2002). Depending on its frequency characteristic, a digital filter is either low-pass, high-pass, band-pass, or band-stop filters. A low-pass (LP) filter passes low frequency components to the output, while eliminating high-frequency components. Conversely, the high-pass (HP) filter passes all high-frequency components and rejects all low-frequency components. The band-pass (BP) filter blocks both low- and high-frequency components while passing the intermediate range. The band-stop (BS) filter eliminates the intermediate band of frequencies while passing both low- and high-frequency components. In terms of their impulse responses digital filters are either infinite impulse response (IIR) or finite impulse response (FIR) digital filters. Each of four types of filters (LP, HP, BP, and BS) can be designed as an FIR or an IIR filter (Ifeachor & Jervis, 2001; Mitra, 2005; Oppenheim & Schafer, 1999).

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