scholarly journals A Novel Noise Reduction Method of UAV Magnetic Survey Data Based on CEEMDAN, Permutation Entropy, Correlation Coefficient and Wavelet Threshold Denoising

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1309
Author(s):  
Yaoxin Zheng ◽  
Shiyan Li ◽  
Kang Xing ◽  
Xiaojuan Zhang
Keyword(s):  
Noise Reduction ◽  
Correlation Coefficient ◽  
Field Experiments ◽  
Reduction Method ◽  
Magnetic Data ◽  
Permutation Entropy ◽  
Magnetic Survey ◽  
Intrinsic Mode Functions ◽  
Real Signal ◽  
The Real

Despite the increased attention that has been given to the unmanned aerial vehicle (UAV)-based magnetic survey systems in the past decade, the processing of UAV magnetic data is still a tough task. In this paper, we propose a novel noise reduction method of UAV magnetic data based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), permutation entropy (PE), correlation coefficient and wavelet threshold denoising. The original signal is first decomposed into several intrinsic mode functions (IMFs) by CEEMDAN, and the PE of each IMF is calculated. Second, IMFs are divided into four categories according to the quartiles of PE, namely, noise IMFs, noise-dominant IMFs, signal-dominant IMFs, and signal IMFs. Then the noise IMFs are removed, and correlation coefficients are used to identify the real signal-dominant IMFs. Finally, the wavelet threshold denoising is applied to the real signal-dominant IMFs, the denoised signal can be obtained by combining the signal IMFs and the denoised IMFs. Both synthetic and field experiments are conducted to verify the effectiveness of the proposed method. The results show that the proposed method can eliminate the interference to a great extent, which lays a foundation for the further interpretation of UAV magnetic data.

The improved noise reduction method for the vibration signal based on variational mode decomposition

Filomat ◽  
2020 ◽  
Vol 34 (15) ◽  
pp. 4975-4983
Author(s):  
Zhiting Liu ◽  
Yuhua Wang ◽  
Wenwei Zheng ◽  
Yuexia Zhou
Keyword(s):  
Noise Reduction ◽  
Correlation Coefficient ◽  
Reduction Method ◽  
Vibration Signal ◽  
Energy Concentration ◽  
Variational Model ◽  
Original Signal ◽  
Intrinsic Mode Functions ◽  
Model Decomposition ◽  
Mode Decomposition

The variational model decomposition (VMD) has a problem that is dificult to determine the number of intrinsic mode functions (IMF).We use the leaked energy to determine the number of IMFs. And we use the energy concentration rate of the IMF?s autocorrelation function and the correlation coefficient between the IMFs and the original signal, define Q as the ratio of the energy concentration and the correlation coefficient, and use Q to determine the noise IMFs in the IMFs. Then, we filter the noise IMFs and use the remaining IMFs to reconstruct signal to achieve noise reduction. Finally, we use the signal-tonoise ratio (SNR) to compare the noise reduction method proposed in this paper and the Empirical Mode Decomposition (EMD) noise reduction method.

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 Noise Reduction Method of Underwater Acoustic Signals Based on Uniform Phase Empirical Mode Decomposition, Amplitude-Aware Permutation Entropy, and Pearson Correlation Coefficient

Entropy ◽  
2018 ◽  
Vol 20 (12) ◽  
pp. 918 ◽  
Author(s):  
Guohui Li ◽  
Zhichao Yang ◽  
Hong Yang
Keyword(s):  
Noise Reduction ◽  
Empirical Mode Decomposition ◽  
High Frequency ◽  
Reduction Method ◽  
Pearson Correlation ◽  
Low Frequency ◽  
Acoustic Signals ◽  
Permutation Entropy ◽  
Underwater Acoustic ◽  
Mode Decomposition

Noise reduction of underwater acoustic signals is of great significance in the fields of military and ocean exploration. Based on the adaptive decomposition characteristic of uniform phase empirical mode decomposition (UPEMD), a noise reduction method for underwater acoustic signals is proposed, which combines amplitude-aware permutation entropy (AAPE) and Pearson correlation coefficient (PCC). UPEMD is a recently proposed improved empirical mode decomposition (EMD) algorithm that alleviates the mode splitting and residual noise effects of EMD. AAPE is a tool to quantify the information content of nonlinear time series. Unlike permutation entropy (PE), AAPE can reflect the amplitude information on time series. Firstly, the original signal is decomposed into a series of intrinsic mode functions (IMFs) by UPEMD. The AAPE of each IMF is calculated. The modes are separated into high-frequency IMFs and low-frequency IMFs, and all low-frequency IMFs are determined as useful IMFs (UIMFs). Then, the PCC between the high-frequency IMF with the smallest AAPE and the original signal is calculated. If PCC is greater than the threshold, the IMF is also determined as a UIMF. Finally, all UIMFs are reconstructed and the denoised signal is obtained. Chaotic signals with different signal-to-noise ratios (SNRs) are used for denoising experiments. Compared with EMD and extreme-point symmetric mode decomposition (ESMD), the proposed method has higher SNR and smaller root mean square error (RMSE). The proposed method is applied to noise reduction of real underwater acoustic signals. The results show that the method can further eliminate noise and the chaotic attractors are smoother and clearer.

scholarly journals Noise Reduction of Welding Crack AE Signal Based on EMD and Wavelet Packet

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 761 ◽  
Author(s):  
Kuanfang He ◽  
Zixiong Xia ◽  
Yin Si ◽  
Qinghua Lu ◽  
Yanfeng Peng
Keyword(s):  
Noise Reduction ◽  
Basis Function ◽  
Crack Detection ◽  
Reduction Method ◽  
Wavelet Packet ◽  
Welding Process ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Ae Signal ◽  
Adaptive Decomposition

The acoustic emission (AE) signal collected by a sensor in the welding process has an overlapping frequency band and weak characteristics under a complex noise background. It is difficult for the wavelet noise reduction method, with single basis function, to effectively match the different characteristic information of the welding crack AE signal. Taking into account the adaptive decomposition characteristics of Empirical Mode Decomposition (EMD), a novel wavelet packet noise reduction method for welding AE signal was proposed. The welding AE signal was adaptively decomposed into several Intrinsic Mode Functions (IMFs) by the EMD. The effective IMFs were selected by the frequency distribution characteristics of the welding crack AE signal. A wavelet packet, with a specific basis function, was subsequently performed on the effective IMFs, which were reconstructed to be the welding crack AE signal. The simulated and experimental results indicated that the proposed method can effectively achieve noise reduction of the welding crack AE signal, which provided a mean for structure crack detection in the welding process.

scholarly journals Noise Reduction Method of Underwater Acoustic Signals Based on CEEMDAN, Effort-To-Compress Complexity, Refined Composite Multiscale Dispersion Entropy and Wavelet Threshold Denoising

Entropy ◽  
2018 ◽  
Vol 21 (1) ◽  
pp. 11 ◽  
Author(s):  
Guohui Li ◽  
Qianru Guan ◽  
Hong Yang
Keyword(s):  
Noise Reduction ◽  
Empirical Mode Decomposition ◽  
Reduction Method ◽  
Signal To Noise Ratio ◽  
Acoustic Signals ◽  
Ensemble Empirical Mode Decomposition ◽  
Underwater Acoustic ◽  
Real Signal ◽  
Mode Decomposition ◽  
Soft Threshold

Owing to the problems that imperfect decomposition process of empirical mode decomposition (EMD) denoising algorithm and poor self-adaptability, it will be extremely difficult to reduce the noise of signal. In this paper, a noise reduction method of underwater acoustic signal denoising based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), effort-to-compress complexity (ETC), refined composite multiscale dispersion entropy (RCMDE) and wavelet threshold denoising is proposed. Firstly, the original signal is decomposed into several IMFs by CEEMDAN and noise IMFs can be identified according to the ETC of IMFs. Then, calculating the RCMDE of remaining IMFs, these IMFs are divided into three kinds of IMFs by RCMDE, namely noise-dominant IMFs, real signal-dominant IMFs, real IMFs. Finally, noise IMFs are removed, wavelet soft threshold denoising is applied to noise-dominant IMFs and real signal-dominant IMFs. The denoised signal can be obtained by combining the real IMFs with the denoised IMFs after wavelet soft threshold denoising. Chaotic signals with different signal-to-noise ratio (SNR) are used for denoising experiments by comparing with EMD_MSE_WSTD and EEMD_DE_WSTD, it shows that the proposed algorithm has higher SNR and smaller root mean square error (RMSE). In order to further verify the effectiveness of the proposed method, which is applied to noise reduction of real underwater acoustic signals. The results show that the denoised underwater acoustic signals not only eliminate noise interference also restore the topological structure of the chaotic attractors more clearly, which lays a foundation for the further processing of underwater acoustic signals.

scholarly journals Research on the Noise Reduction Method of the Vibration Signal of the Hydrogenerator Unit Based on ITD-PE-SVD

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yan Ren ◽  
Pan Liu ◽  
Leiming Hu ◽  
Ruoyu Qiao ◽  
Linlin Zhang ◽  
...  
Keyword(s):  
Noise Reduction ◽  
Electromagnetic Interference ◽  
Reduction Method ◽  
Recognition Rate ◽  
Difference Spectrum ◽  
Singular Value ◽  
Permutation Entropy ◽  
Vibration Signals ◽  
Signal Features ◽  
Reduction Effect

Aiming at the problem that the vibration signals of the hydrogenerator unit are nonlinear and nonstationary and it is difficult to extract the signal features due to strong background noise and complex electromagnetic interference, this paper proposes a dual noise reduction method based on intrinsic time-scale decomposition (ITD) and permutation entropy (PE) combined with singular value decomposition (SVD). Firstly, the vibration signals are decomposed by ITD to obtain a series of PRC components, and the permutation entropy of each component is calculated. Secondly, according to the set permutation entropy threshold, the PRC components are selected for reconstruction to achieve a noise reduction effect. On this basis, SVD is carried out, and the appropriate reconstruction order is selected according to the position of the singular value difference spectrum mutation point for reconstruction, so as to achieve the secondary noise reduction effect. The proposed method is compared with the LMD-PE-SVD and EMD-PE-SVD dual noise reduction method by simulation, taking the correlation coefficient and signal-to-noise ratio to evaluate the noise reduction performance and finding that the ITD-PE-SVD noise reduction has good noise reduction and pulse effect. Furthermore, this method is applied to the analysis of the upper guide swing data in the X-direction and Y-direction of a unit in a hydropower station in China, and it is found that this method can effectively reduce noise and accurately extract signal features, thus determining the vibration cause, which is helpful to improve the turbine fault recognition rate.

scholarly journals A Data-Driven Noise Reduction Method and Its Application for the Enhancement of Stress Wave Signals

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Hai-Lin Feng ◽  
Yi-Ming Fang ◽  
Xuan-Qi Xiang ◽  
Jian Li ◽  
Guan-Hui Li
Keyword(s):  
Noise Reduction ◽  
Stress Wave ◽  
Reduction Method ◽  
Ensemble Empirical Mode Decomposition ◽  
Data Driven ◽  
Intrinsic Mode Functions ◽  
Time Intervals ◽  
Zero Crossings ◽  
Partial Reconstruction ◽  
Mode Decomposition

Ensemble empirical mode decomposition (EEMD) has been recently used to recover a signal from observed noisy data. Typically this is performed by partial reconstruction or thresholding operation. In this paper we describe an efficient noise reduction method. EEMD is used to decompose a signal into several intrinsic mode functions (IMFs). The time intervals between two adjacent zero-crossings within the IMF, called instantaneous half period (IHP), are used as a criterion to detect and classify the noise oscillations. The undesirable waveforms with a larger IHP are set to zero. Furthermore, the optimum threshold in this approach can be derived from the signal itself using the consecutive mean square error (CMSE). The method is fully data driven, and it requires no prior knowledge of the target signals. This method can be verified with the simulative program by using Matlab. The denoising results are proper. In comparison with other EEMD based methods, it is concluded that the means adopted in this paper is suitable to preprocess the stress wave signals in the wood nondestructive testing.

Noise Reduction Method for the Ring LaserGyro Signal Based on Ceemdan and the Savitzky–Golay Algorithm

2021 ◽  
pp. 2250005
Author(s):  
Hao Liang ◽  
Xingfa Zhao ◽  
Yu Guo
Keyword(s):  
Noise Reduction ◽  
Ring Laser ◽  
Measurement Accuracy ◽  
Reduction Method ◽  
Ensemble Empirical Mode Decomposition ◽  
Engineering Problem ◽  
Permutation Entropy ◽  
Laser Gyro ◽  
Mode Decomposition ◽  
Ring Laser Gyroscope

The ring laser gyro signal contains complex noise components, affecting the system’s measurement accuracy. It is an engineering problem worthy of study to find an effective method to reduce the noise in the sampling signal and improve the system’s accuracy. In order to reduce various noises of the ring laser gyroscope and improve its measurement accuracy, a noise reduction method combining complete ensemble empirical mode decomposition with adaptive noise and the Savitzky–Golay algorithm is proposed. First, the measured samples are mode decomposed, and the concept of weighted-permutation entropy is introduced to distinguish the noisy modes. Then, the Savitzky–Golay algorithm is used to process the noisy modes, and finally, the signal is reconstructed. Simulated test signal and actual gyro signal are used to test, and compared with the EMD noise reduction method, each indicator is improved. The paper proposes a new noise reduction method for the laser gyro signal, and introduces weighted-permutation entropy to analyze the dividing point. The test data show the effectiveness of the method.

Airborne geophysical surveys in Greenland in 1998

1999 ◽  
pp. 34-38 ◽  
Author(s):  
Thorkild M. Rasmussen ◽  
Leif Thorning
Keyword(s):  
High Resolution ◽  
Geophysical Data ◽  
Digital Data ◽  
Geological Survey ◽  
Magnetic Data ◽  
Magnetic Survey ◽  
Original Series ◽  
Geophysical Surveys ◽  
The Government

NOTE: This article was published in a former series of GEUS Bulletin. Please use the original series name when citing this article, for example: Rasmussen, T. M., & Thorning, L. (1999). Airborne geophysical surveys in Greenland in 1998. Geology of Greenland Survey Bulletin, 183, 34-38. https://doi.org/10.34194/ggub.v183.5202 _______________ Airborne geophysical surveying in Greenland during 1998 consisted of a magnetic project referred to as ‘Aeromag 1998’ and a combined electromagnetic and magnetic project referred to as ‘AEM Greenland 1998’. The Government of Greenland financed both with administration managed by the Geological Survey of Denmark and Greenland (GEUS). With the completion of the two projects, approximately 305 000 line km of regional high-resolution magnetic data and approximately 75 000 line km of detailed multiparameter data (electromagnetic, magnetic and partly radiometric) are now available from government financed projects. Figure 1 shows the location of the surveyed areas with highresolution geophysical data together with the area selected for a magnetic survey in 1999. Completion of the two projects was marked by the release of data on 1 March, 1999. The data are included in the geoscientific databases at the Survey for public use; digital data and maps may be purchased from the Survey.

scholarly journals Compact Quantum Magnetometer System on an Agile Underwater Glider

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1092
Author(s):  
Brian R. Page ◽  
Reeve Lambert ◽  
Nina Mahmoudian ◽  
David H. Newby ◽  
Elizabeth L. Foley ◽  
...  
Keyword(s):  
Test Procedure ◽  
Reduction Process ◽  
Ground Truth ◽  
Sensor Technology ◽  
Magnetic Data ◽  
Magnetic Survey ◽  
Underwater Glider ◽  
Combined System ◽  
Extended Operation ◽  
Quantum Magnetometer

This paper presents results from the integration of a compact quantum magnetometer system and an agile underwater glider for magnetic survey. A highly maneuverable underwater glider, ROUGHIE, was customized to carry an increased payload and reduce the vehicle’s magnetic signature. A sensor suite composed of a vector and scalar magnetometer was mounted in an external boom at the rear of the vehicle. The combined system was deployed in a constrained pool environment to detect seeded magnetic targets and create a magnetic map of the test area. Presented is a systematic magnetic disturbance reduction process, test procedure for anomaly mapping, and results from constrained operation featuring underwater motion capture system for ground truth localization. Validation in the noisy and constrained pool environment creates a trajectory towards affordable littoral magnetic anomaly mapping infrastructure. Such a marine sensor technology will be capable of extended operation in challenging areas while providing high-resolution, timely magnetic data to operators for automated detection and classification of marine objects.

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