Wavelet Threshold Denoising Research Articles

Roll eccentricity extraction and compensation based on MPSO-WTD and ITD.

To meet the high thickness accuracy requirements in cold-rolling processes, a roll eccentricity signal extraction method based on modified particle swarm optimization and wavelet threshold denoising (MPSO-WTD) with intrinsic time-scale decomposition (ITD) is proposed. The strong denoising ability of the wavelet is combined with the decomposition and recognition attributes of ITD for non-stationary signals. Periodic disturbances in strip thickness caused by roll eccentricity are actively compensated. First, the wavelet is used to denoise the signal and the MPSO algorithm is applied to determine a rational threshold and improve the calculation efficiency. Then, the denoised signal is decomposed into proper rotational components (PRCs) using the ITD method, and an appropriate PRC component representing the eccentricity signal is extracted. Finally, the eccentricity compensation signal is applied in the automatic gauge control (AGC) system of the cold rolling mill. During the rolling process, the rolling speed is not constant and will directly affect the frequency of the roll eccentricity signal. To solve this problem, an encoder is installed at the end of the roll and the compensation frequency of the roller eccentricity signal is determined in the roller eccentricity compensation system according to the pulse number output. The results of simulations and experiments show that roll eccentricity signals extracted using the proposed method can effectively remove the influence of interference signals. An average improvement of 62.3% in the roll eccentricity compensation effect was achieved under the stable rolling condition in the finishing rolling stage.

Application of Smart Fiber Optic Sensor Technology in Feature Extraction, Recognition, and Detection

To improve the signal recognition effect of the security system, this paper studies the security system based on intelligent Fiber Optic Sensor (FOS) technology. Firstly, the research background of intelligent FOS is introduced, and its current situation in feature extraction, recognition, and detection is introduced. Secondly, the double Mach–Zehnder (M-Z) Optical Fiber- (OF-) based interferometer model is implemented, and the Wavelet Analysis (WA) theory is introduced to analyze the characteristic threshold and Frequency (F) characteristics of intrusion signal. Finally, the distributed intelligent FOS-based perimeter security system is constructed, and an empirical study is conducted to verify its performance. The results show that the intruder knocking-induced signal F, intruder climbing-induced signal F, noiseless environment-induced signal F, and rainy environment-induced signal F are 0–250 kHz, 0–25 kHz, 0–1.5 kHz, and 0–3.5 kHz, respectively; in all the four cases, excellent results have been obtained after wavelet threshold denoising. Meanwhile, the received signal is decomposed into seven layers through multiscale WA theory. The signal feature classification is based on WA and takes variance as the representation, and the clear classification results are obtained; when the False Alarm Rate (FAR) = 1%, the detection probability of the proposed system is about 99%, while the detection probability of the traditional system is about 3%. The reference arm and sensing arm of the distributed OF-based perimeter security system can be laid in the same optical cable. Therefore, the designed wavelet threshold filtering method is feasible, and the detection probability of the designed WA-based system is higher than that of the traditional security system. The research content provides a reference for the development of intelligent FOS technology in the field of security.

Diagnosis of Lung Cancer by FTIR Spectroscopy Combined With Raman Spectroscopy Based on Data Fusion and Wavelet Transform.

Lung cancer is a fatal tumor threatening human health. It is of great significance to explore a diagnostic method with wide application range, high specificity, and high sensitivity for the detection of lung cancer. In this study, data fusion and wavelet transform were used in combination with Fourier transform infrared (FTIR) spectroscopy and Raman spectroscopy to study the serum samples of patients with lung cancer and healthy people. The Raman spectra of serum samples can provide more biological information than the FTIR spectra of serum samples. After selecting the optimal wavelet parameters for wavelet threshold denoising (WTD) of spectral data, the partial least squares–discriminant analysis (PLS-DA) model showed 93.41% accuracy, 96.08% specificity, and 90% sensitivity for the fusion data processed by WTD in the prediction set. The results showed that the combination of FTIR spectroscopy and Raman spectroscopy based on data fusion and wavelet transform can effectively diagnose patients with lung cancer, and it is expected to be applied to clinical screening and diagnosis in the future.

Development of Sliding Mode Controller Based on Internal Model Controller for Higher Precision Electro-Optical Tracking System

The electro-optical tracking system (ETS) on moving platforms is affected by the vibration of the moving carrier, the wind resistance torque in motion, the uncertainty of mechanisms and the nonlinear friction between frames and other disturbances, which may lead to the instability of the electro-optical tracking platform. Sliding mode control (SMC) has strong robustness to system disturbances and unknown dynamic external signals, which can enhance the disturbance suppression ability of ETSs. However, the strong robustness of SMC requires greater switching gain, which causes serious chattering. At the same time, the tracking accuracy of SMC has room for further improvement. Therefore, in order to solve the chattering problem of SMC and improve the tracking accuracy of SMC, an SMC controller based on internal model control (IMC) is proposed. Compared with traditional SMC, the proposed method can be used to suppress the strongest disturbance with the smallest switching gain, effectively solving the chattering problem of the SMC, while improving the tracking accuracy of the system. In addition, to reduce the adverse influence of sensor noise on the control effect, lifting wavelet threshold de-noising is introduced into the control structure to further improve the tracking accuracy of the system. The simulation and experimental results verify the superiority of the proposed control method.

Recognition of Key Information in Non-Stationary Signals Based on Wavelet Threshold Denoising and Back Propagation Neural Network Optimized by Manta Ray Foraging Optimization Algorithm

The identification of key information hidden in non-stationary signals is challenging in various fields such as logistics and transportation, biomedicine, and fault diagnosis. To facilitate this identification, we propose a back propagation neural network (BPNN) classification and recognition algorithm based on wavelet threshold denoising (WTD) and manta ray foraging optimization (MRFO) algorithm for the first time. The algorithm first performs WTD on the original signals to obtain denoised signals. Subsequently, the MRFO algorithm is utilized to optimize the initial weights and thresholds of the BPNN. On the base of this, the optimization model is finally obtained to classify and recognize the key information in the non-stationary signals. The comparative experimental results indicate that the proposed WTD-MRFO-BPNN algorithm can be utilized to availably recognize the key information hidden in non-stationary signals. The recognition accuracy reaches 97.25%.

Study of Denoising in the Electricity Anti-Stealing Detection Based on VMD-WTD Combination

In order to solve the failure of electricity anti-stealing detection device triggered by the noise mixed in high-frequency electricity stealing signals, a denoising method based on variational mode decomposition (VMD) and wavelet threshold denoising (WTD) was applied to extract the effective high-frequency electricity stealing signals. First, the signal polluted by noise was pre-decomposed using the VMD algorithm, the instantaneous frequency means of each pre-decomposed components was analyzed, so as to determine the optimal K value. The optimal K value was used to decompose the polluted signal into K intrinsic mode components, and the sensitive mode components were determined through the cross-correlation function. Next, each sensitive mode was reconstructed. Finally, the reconstructed signal denoised using the wavelet threshold to obtain the denoised signal. The simulation analysis and experimental results show that the proposed method is superior to the traditional VMD method, FFT method and EMD method, as it can effectively eliminate the noise and enhance the reliability of high-frequency electricity stealing signal detection.

Weak Signal Detection Based on Lifting Wavelet Threshold Denoising and Multi-Layer Autocorrelation Method

To solve the problem that the weak signal is difficult to detect under a strong background noise, a detection method based on lifting wavelet threshold denoising and multi-layer autocorrelation method is proposed. Firstly, the original signal is denoised by lifting wavelet threshold to improve the signal-to-noise ratio. Secondly, the multi-layer autocorrelation function of the noise-reconstructed signal is calculated, and its time-frequency signature are analyzed. Finally, the combined algorithm is used on weak signals with low signal-to-noise ratio to extract weak signal features. Simulation and experimental results demonstrate that the proposed method can detect weak signal features buried in the heavy noise effectively. The proposed method is compared with the traditional noise reduction method, which reflects its effectiveness and superiority.

A novel adaptive moving average method for signal denoising in strong noise background

The moving average (MA) method has been widely used in signal processing, but it has problems of the dead zone and the fixed window. In this paper, an adaptive moving average (AMA) filtering method is proposed, which can sniff the inherent characteristics of the signal and assign time-varying optimal parameters to signal processing, hence solve dead zone and the fixed window problems. Firstly, this paper builds the theoretical framework of AMA, including the trial steps and optimization of the necessary parameters. To verify the effectiveness of the AMA, three signal processing methods are taken as comparison methods to process the noisy simulation and experimental signals. Comparison methods includes the MA, variational mode decomposition (VMD), and wavelet threshold denoising (WTD). Signals processed include linear frequency modulation (LFM) simulation signal, aperiodic square wave (ASW) simulation signal, LFM experimental signal produced by a signal generator, and nondestructive test signal of wire rope. Also, the output is analyzed qualitatively and quantitatively with signal-to-noise ratio (SNR), cross-correlation coefficient, amplitude error, and a newly defined local coincidence index. Compared to MA, VMD and WTD, the proposed AMA can solve the dead zone problem, recover noisy signal with higher SNR, cross-correlation coefficient, and lower amplitude error. These results indicate that AMA is a promising method in signal processing.

Experimental research on the muzzle response characteristics of small unmanned ground vehicles with small arms

The muzzle response is the main feature affecting the firing accuracy of weapons. To research the muzzle response characteristics of small unmanned ground vehicles with small arms (SUGVsSA) during shooting, this paper designs a test method that combines an inertial measurement system (IMS) with a high-speed photogrammetric system (HSPS) to measure the muzzle response. That is, an inertial measurement unit (IMU) is fixed onto the gun body to record the three-dimensional angular motion of the barrel; meanwhile, a high-speed camera is used to capture the characteristic markers of the unmanned ground vehicle from the side. After data processing, the muzzle response curves during four consecutive firings when the vehicle is running at different speeds and firing angles are obtained. Considering the presence of noise in muzzle response signals, the wavelet threshold de-noising (WTD) algorithm based on a novel variable threshold function is used to de-noise the test signal. The processing results demonstrate that the WTD algorithm based on the novel variable threshold function can not only suppress noise in the muzzle response signal but also retain the local details of the signal. The combination of the IMS and HSPS complements the muzzle response data and can comprehensively and accurately reflect the muzzle response characteristics of SUGVsSA. As the vehicle speed and firing angle increase, the muzzle vibration intensifies, only when the vehicle speed is 0.3 m/s, and the muzzle maximum elevation angle displacement after each firing decreases when it is stationary. The results presented in this paper may provide a workable reference for understanding the muzzle response characteristics of SUGVsSA and evaluating the firearm compatibility of other unmanned systems.

Enhancement method of weak Lidar signal based on adaptive variational modal decomposition and wavelet threshold denoising

Aiming to identify and detect the weak signal of lidar, a cascade method of adaptive variational modal decomposition (VMD) and wavelet threshold (WT) denoising is proposed in this paper. Firstly, VMD is used to decompose the echo signal, and the criterion of adaptive mode selection is improved. Furthermore, the “db4” wavelet and soft threshold denoising method are used to process the denoised signal by AVMD. The optimal threshold value is obtained by minimizing Stein's unbiased risk estimator. In order to verify the superity of the proposed method, Bumps signal is employed in simulation. When the input signal-to-noise ratio (SNR) is −3dB, the SNR of proposed method can reach 7.8587 dB. Compared with three methods, the SNR of proposed method is the highest under different input SNRs. The real Lidar signal is recorded to be denoised. The root mean square errors of WT-db4, EMD-DT, SVD-WT and the proposed method are 0.0046, 0.0044, 0.0044 and 0.0043. Compared with three state-of-art denoising methods, the proposed denoising method can effectively eliminate the spike noise and fully retain the echo signal.

Identification of Ship Hydrodynamic Derivatives Based on LS-SVM with Wavelet Threshold Denoising

Nowadays, system-based simulation is one of the main methods for ship manoeuvring prediction. Great efforts are usually devoted to the determination of hydrodynamic derivatives as required for the mathematical models used for such methods. System identification methods can be applied to determine hydrodynamic derivatives. The purpose of this work is to present a parameter identification study based on least-squares support-vector machines (LS-SVMs) to obtain hydrodynamic derivatives for an Abkowitz-type model. An approach for constructing training data is used to reduce parameter drift. In addition, wavelet threshold denoising is applied to filter out the noise from the sample data during data pre-processing. Most of the resulting derivatives are very close to the original ones—especially for linear derivatives. Although the errors of high-order derivatives seem large, the final predicted results of the turning circle and zigzag manoeuvres agree pretty well with the reference ones. This indicates that the used methods are effective in obtaining manoeuvring hydrodynamic derivatives.

A Hybrid Model for Short-Term Traffic Flow Prediction Based on Variational Mode Decomposition, Wavelet Threshold Denoising, and Long Short-Term Memory Neural Network

Traffic flow prediction plays an important role in intelligent transportation system (ITS). However, due to the randomness and complex periodicity of traffic flow data, traditional prediction models often fail to achieve good results. On the other hand, external disturbances or abnormal detectors will cause the collected traffic flow data to contain noise components, resulting in a decrease in prediction accuracy. In order to improve the accuracy of traffic flow prediction, this study proposes a mixed traffic flow prediction model VMD-WD-LSTM using variational mode decomposition (VMD), wavelet threshold denoising (WD), and long short-term memory (LSTM) network. Firstly, we decompose the original traffic flow sequence into K components through VMD and determine the number of components K according to the sample entropy of different K values. Then, each component is denoised by wavelet threshold to obtain the denoised subsequence. Finally, LSTM is used to predict each subsequence, and the predicted values of each subsequence are combined into the final prediction results. In addition, the performance of the proposed model and the latest traffic flow prediction model is compared on the several well-known public datasets. The empirical analysis shows that the proposed model not only has good prediction accuracy but also has superior robustness.

Power quality disturbance classification under noisy conditions using adaptive wavelet threshold and DBN-ELM hybrid model

To solve the problems of noise interference and artificial feature extraction in power quality disturbance (PQD) classification, a new method combining adaptive wavelet threshold denoising and deep belief network fusion extreme learning machine (DBN-ELM) is proposed. Firstly, the noise content of the layer is determined by calculating the energy ratio of the wavelet coefficients of each layer, and an adaptive wavelet threshold is constructed based on the energy ratio to denoise the PQD signals. Secondly, the feature extraction capability of DBN is used to extract the feature from the PQD signals after denoising. Finally, a novel PQD classifier called DBN-ELM is constructed by integrating an ELM into a DBN, which avoids global fine-tuning of DBN and improves PQD classification efficiency. The simulation result and experimental verification show that the proposed method can effectively suppress PQD noise and performs well on DBN-ELM classification.

Crack Growth Signal Processing Approach Combining Wavelet Threshold Denoising and Variable Amplitude DCPD Technique

The direct current potential drop (DCPD) method is widely used in laboratory environments to monitor the crack initiation and propagation of specimens. In this study, an anti-interference signal processing approach, combining wavelet threshold denoising and a variable current amplitude DCPD signal synthesis technique, was proposed. Adaptive wavelet threshold denoising using Stein’s unbiased risk estimate was applied to the main potential drop signal and the reference potential signal under two different current amplitudes to reduce the interference caused by noise. Thereafter, noise-reduced signals were synthesized to eliminate the time-varying thermal electromotive force. The multiplicative interference signal was eliminated by normalizing the main potential drop signal and the reference potential drop signal. This signal processing approach was applied to the crack growth monitoring data of 316 L stainless steel compact tension specimens in a laboratory environment, and the signal processing results of static cracks and propagation cracks under different load conditions were analyzed. The results showed that the proposed approach can significantly improve the signal-to-noise ratio as well as the accuracy and resolution of the crack growth measurement.

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

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.

A novel wavelet threshold denoising and deep belief network fault detection algorithm

For oil pipeline leakage fault detection problems, a novel negative pressure wave (NPW) leak detection method based on wavelet threshold denoising and deep belief network (Wavelet-DBN) is proposed. Firstly, the wavelet threshold denoising method is used to deal with the sample pressure signal, and the results of wavelet denoising with different wavelet basis functions and different decomposition levels are compared. The optimal parameters are selected for wavelet denoising and the characteristic information of a pipeline pressure signal is extracted. Secondly, in order to improve the accuracy of the pipeline leakage monitoring method based on NPW, the deep belief network algorithms are proposed to classify and identify the NPW sample signals. Finally, the sample data are collected from the industrial oil pipeline leakage experiment. The simulation experimental results show that the proposed method has a higher accuracy rate than other traditional machine learning methods, such as support vector machines, and reduces the false alarm rate of oil pipeline leakage monitoring.

Pipeline Damage Detection Based on Metal Magnetic Memory

The metal magnetic memory detection technology can detect early stress concentration and invisible damage of the pipeline. It can be detected under the action of the geomagnetic field, without the need to magnetize the pipeline in advance, which has the advantage of non-destructive testing. Since the magnetic memory signal is relatively weak, the actual detected signal will be affected by environmental noise, sensor jitter, and pipeline surface deposits. Therefore, the magnetic memory signal needs to be denoised. In this article, the translation invariant wavelet denoising method, which is improved based on the wavelet threshold denoising method, is used to denoise the collected pipeline magnetic memory signals. The experimental results show that the signal-to-noise ratio (SNR) obtained by this method is 1.15% higher than the unmodified wavelet threshold denoising, and the signal obtained is more stable. To solve the problem of fewer data samples for magnetic memory detection, three methods including support vector regression machine, back propagation (BP) neural network, and particle swarm optimization multiple output least-squares support vector regression (MLS-SVR) are compared and analyzed to inverse the overall defect size of pipeline defects. The experiments show that the MLS-SVR inversion result based on particle swarm optimization is the best, and the overall mean square error reaches 0.27 mm. In the end, a pipeline damage detection system is built to detect the magnetic memory signals of pipelines with different sizes of defects, and the defect depth and radius are inverted.

Daily natural gas load forecasting based on the combination of long short term memory, local mean decomposition, and wavelet threshold denoising algorithm

With the opening of the third energy revolution, the booming development of the natural gas industry in China has led to the existing pipeline network facilities no longer meeting the gas demand of its residents. How to adjust pipeline network layout and design peak-regulating gas storage facilities to meet the gas demand of residents has become a challenge. However, accurate load forecasting can help solve this problem. To that end, this paper proposes a novel combined model that integrates local mean decomposition (LMD), wavelet threshold denoising (WTD), and long short term memory (LSTM) methods to predict short-term gas consumption, or “LMD-WTD-LSTM”. To begin with, based on the above three basic methods, this paper adopts two different combination schemes, schemeⅠand schemeⅡ, to predict the gas consumption of London city. The results show that schemeⅡ is better than schemeⅠ. Further, this paper compares the predicted results with four other forecasting methods: such as back propagation neural network (BPNN) model, the gated recurrent unit (GRU) model, the multiple linear regression (MLR) model, and the LSTM model. Finally, this paper discusses the influence of different temperatures and different forecasting time lengths (FTL) on the prediction accuracy of the LMD-WTD-LSTM model. The results show that when the forecasting time length is 20 days, the mean absolute percentage error (MAPE) of schemeⅡ is 11.63 %, which is the lowest in comparison to the other four methods. According to this study, the LMD-WTD-LSTM method has an excellent performance in daily natural gas prediction.

Application of multi-layer denoising based on ensemble empirical mode decomposition in extraction of fault feature of rotating machinery

Aiming at the problem that the weak features of non-stationary vibration signals are difficult to extract under strong background noise, a multi-layer noise reduction method based on ensemble empirical mode decomposition (EEMD) is proposed. First, the original vibration signal is decomposed by EEMD, and the main intrinsic modal components (IMF) are selected using comprehensive evaluation indicators; the second layer of filtering uses wavelet threshold denoising (WTD) to process the main IMF components. Finally, the virtual noise channel is introduced, and FastICA is used to de-noise and unmix the IMF components processed by the WTD. Next, perform spectral analysis on the separated useful signals to highlight the fault frequency. The feasibility of the proposed method is verified by simulation, and it is applied to the extraction of weak signals of faulty bearings and worn polycrystalline diamond compact bits. The analysis of vibration signals shows that this method can efficiently extract weak fault characteristic information of rotating machinery.

Error Correction of Weak Current Measurement System Based on Wavelet Denoising and Generalized Regression Neural Network

Aiming at the problems that the weak current signal circuit is susceptible to noise interference and leakage current at the input terminal affects the measurement accuracy, a weak current measurement error correction scheme based on the combination of wavelet threshold denoising and generalized regression neural network (GRNN) was proposed. The scheme was applied to the error correction of multi-channel weak current measurement system based on the ADAS1134 chip: the wavelet threshold denoising was used to preprocess the original current data measured by the system and the current measurement value was corrected after the system measurement error correction model established with GRNN was constructed. Compared with the correction method based on least square method and back propagation neural network (BPNN), this method has many advantages such as high accuracy, anti-interference ability and strong generalization ability. The experimental results showed that RMSE=0.0911 nA, MAE=0.0354 nA, and MAPE=0.0078%, without increasing the complexity of the measurement circuit, which achieved the purpose of correcting the measurement error of the weak current measurement system.