High Frequency Noise Components Research Articles

Research on a deconvolution algorithm for laser-induced fluorescence diagnosis based on the maximum entropy principle

Laser-induced fluorescence (LIF) spectroscopy is employed for plasma diagnosis, necessitating the utilization of deconvolution algorithms to isolate the Doppler effect from the raw spectral signal. However, direct deconvolution becomes invalid in the presence of noise as it leads to infinite amplification of high-frequency noise components. To address this issue, we propose a deconvolution algorithm based on the maximum entropy principle. We validate the effectiveness of the proposed algorithm by utilizing simulated LIF spectra at various noise levels (signal-to-noise ratio, SNR = 20–80 dB) and measured LIF spectra with Xe as the working fluid. In the typical measured spectrum (SNR = 26.23 dB) experiment, compared with the Gaussian filter and the Richardson–Lucy (R-L) algorithm, the proposed algorithm demonstrates an increase in SNR of 1.39 dB and 4.66 dB, respectively, along with a reduction in the root-mean-square error (RMSE) of 35% and 64%, respectively. Additionally, there is a decrease in the spectral angle (SA) of 0.05 and 0.11, respectively. In the high-quality spectrum (SNR = 43.96 dB) experiment, the results show that the running time of the proposed algorithm is reduced by about 98% compared with the R-L iterative algorithm. Moreover, the maximum entropy algorithm avoids parameter optimization settings and is more suitable for automatic implementation. In conclusion, the proposed algorithm can accurately resolve Doppler spectrum details while effectively suppressing noise, thus highlighting its advantage in LIF spectral deconvolution applications.

Application of hilbert transform for power quality indicators monitoring in general purpose grids

During the operation of power grids, abnormal modes occur that lead to a decrease or increase in the grid voltage beyond permissible values, the appearance of high-frequency noise components and grid signal fading’s, which fading’s the power quality and can lead to the failure of electrical equipment. This requires constant monitoring of power quality indicators. The methods of measuring power quality indicators have been examined and analyzed in the article, their advantages and limitations have been considered, and the inefficiency of using the Fourier transform for estimating of power quality indicators in time domain has been justified. The application of Hilbert transform has been suggested for monitoring the indicators of power quality in general-purpose grids, which include measuring the duration and magnitude of voltage dips, the duration of voltage fading, localization and determination of the duration of high-frequency noise interference in the power grid, determination of phase shift during voltage dip. A simulation of the process of determining the indicators of power quality in time domain and the time intervals during which these indicators exceed the established limits has been performed, which confirmed the effectiveness of using the Hilbert transform in the systems of their monitoring. Keywords: general purpose power grid, power quality indicators, Hilbert transform.

A Novel Ensemble Machine Learning Model for Oil Production Prediction with Two-Stage Data Preprocessing

Petroleum production forecasting involves the anticipation of fluid production from wells based on historical data. Compared to traditional empirical, statistical, or reservoir simulation-based models, machine learning techniques leverage inherent relationships among historical dynamic data to predict future production. These methods are characterized by readily available parameters, fast computational speeds, high precision, and time–cost advantages, making them widely applicable in oilfield production. In this study, time series forecast models utilizing robust and efficient machine learning techniques are formulated for the prediction of production. We have fused the two-stage data preprocessing methods and the attention mechanism into the temporal convolutional network-gated recurrent unit (TCN-GRU) model. Firstly, the random forest (RF) algorithm is employed to extract key dynamic production features that influence output, serving to reduce data dimensionality and mitigate overfitting. Next, the mode decomposition algorithm, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), is introduced. It employs a decomposition–reconstruction approach to segment production data into high-frequency noise components, low-frequency regular components and trend components. These segments are then individually subjected to prediction tasks, facilitating the model’s ability to capture more accurate intrinsic relationships among the data. Finally, the TCN-GRU-MA model, which integrates a multi-head attention (MA) mechanism, is utilized for production forecasting. In this model, the TCN module is employed to capture temporal data features, while the attention mechanism assigns varying weights to highlight the most critical influencing factors. The experimental results indicate that the proposed model achieves outstanding predictive performance. Compared to the best-performing comparative model, it exhibits a reduction in RMSE by 3%, MAE by 1.6%, MAPE by 12.7%, and an increase in R2 by 2.6% in Case 1. Similarly, in Case 2, there is a 7.7% decrease in RMSE, 7.7% in MAE, 11.6% in MAPE, and a 4.7% improvement in R2.

Estimate of the impact of storm microseisms on long-term gravimetric measurements

This study estimates the impact level of storm microseisms on long-term gravimetric measurements. Gravimetric measurements were conducted at the Zapolskoe, Obninsk, and Murmansk sites using CG-5 Autograv relative gravity meters. Seismic measurements were carried out concurrently with gravimetric measurements at Zapolskoe. The analysis of these measurements demonstrates the feasibility of utilizing seismic data as control information to estimate the high-frequency noise component of gravimetric data. Based on the additional seismic information from the services of the Incorporated Research Institutions for Seismology, the correspondence between the attenuation of the noise component in gravimetric measurements and the data from the modeled sensitive element of the gravimeter, which utilizes seismic series as an input, is identified. The first characterization of the storm-induced background noise of gravimetric measurements at the Murmansk site is obtained. Furthermore, the possibility of predicting measurement errors based on meteorological forecasts is refined, which can aid in the planning of gravimetric work.

Noise Reduction of Cable Partial Discharge Vibration Signal based on EEMD-CIIT method

Abstract The cable partial discharge vibration signal is often polluted by noise. It is necessary to denoise the signal, improve the signal-to-noise ratio, and then realize the feature extraction of the vibration signal. Therefore, this paper builds a cable partial discharge test platform, collects the time-domain waveform of vibration signals under different working conditions, analyzes the noise reduction effect of ensemble empirical mode decomposition clearing repetition interval threshold noise reduction method under different threshold functions, and analyzes the difference and approximation degree between the simulated signal and the real signal after noise reduction by using signal-to-noise ratio and correlation, By comparing the difference of statistical characteristics in different defects before and after noise reduction, the wavelet threshold denoising method is established synchronously and compared with the proposed method. The results show that the high-frequency part of the vibration signal has obvious random noise components, and the effective frequency is mainly concentrated in the low-frequency band; The smoothing shear absolute deviation penalty threshold function has the smoothest curve after noise reduction and the highest approximation to the real signal. The high-frequency noise component is eliminated, and the low-frequency band frequency component is effectively retained. The method proposed in this paper can be extended to the noise reduction of vibration signals of other industrial equipment and provide technical support for the signal noise reduction of various industrial equipment.

Method for Denoising the Vibration Signal of Rotating Machinery through VMD and MODWPT.

The vibration signals from rotating machinery are constantly mixed with other noises during the acquisition process, which has a negative impact on the accuracy of signal feature extraction. For vibration signals from rotating machinery, the conventional linear filtering-based denoising method is ineffective. To address this issue, this paper suggests an enhanced signal denoising method based on maximum overlap discrete wavelet packet transform (MODWPT) and variational mode decomposition (VMD). VMD decomposes the vibration signal of rotating machinery to produce a set of intrinsic mode functions (IMFs). By computing the composite weighted entropy (CWE), the phantom IMF component is then removed. In the end, the sensitive component is obtained by computing the value of the degree of difference (DID) after the high-frequency noise component has been decomposed through MODWPT. The denoised signal reconstructs the signal's intrinsic characteristics as well as the denoised high-frequency IMF component. This technique was used to analyze the simulated and real-world signals of gear faults and it was compared to wavelet threshold denoising (WTD), empirical mode decomposition reconstruction denoising (EMD-RD), and ensemble empirical mode decomposition wavelet threshold denoising (EEMD-WTD). The outcomes demonstrate that this method can accurately extract the signal feature information while filtering out the noise components in the signal.

An Optimized Heading De-Noising Method of the Polarization Compass for Improving Orientation Accuracy

In recent years, the polarization compass (PC) has been extensively applied in the field of autonomous navigation due to its strong autonomy and anti-interference characteristics. Nevertheless, the existing research shows that the PC with high extinction ratio polarization sensor still offers poor orientation accuracy since there is a great quantity of noise in the heading data output from the compass. Here, we present a novel and promising scheme to denoise heading data to improve deficiency in heading data denoising of existing research. The purpose of this study aims to solve the problem that the orientation accuracy of PC caused by noise needs to be improved. Concretely, the major points of this research include an effective separation strategy of noise and useful signal based on the optimized variational mode decomposition (OVMD) and an effective heading data denoising method based on an adaptive singular spectrum analysis (AD-SSA). OVMD based on the center frequency is employed to decompose the original noisy heading data into a finite number of band-limited data intrinsic mode functions (DIMFs) so that the best number of decomposed layers for the noising data can be obtained. Subsequently, the above decomposed DIMFs are divided into low-frequency signal components, mixed components, and high-frequency noise components exploiting the maximum similar eigenvalue of the autocorrelation matrix corresponding to each DIMF as a classification method. An AD-SSA uses the correlation coefficient to further suppress the noise in classified DIMFs. These approaches are combined effectively to improve the orientation accuracy of the PC significantly and very contribute to the navigation application of unmanned mobile platforms such as unmanned aerial vehicles (UAVs). Compared to existing prior arts, the effectiveness of the proposed heading denoising scheme for the PC is demonstrated through the results obtained from the heading data sets on two representative sunny and dust days.

Extraction of Strain Characteristic Signals from Wind Turbine Blades Based on EEMD-WT

Analyzing the strain signal of wind turbine blade is the key to studying the load of wind turbine blade, so as to ensure the safe and stable operation of wind turbine in natural environment. The strain signal of the wind turbine blade under continuous crosswind state has typical non-stationary and unsteady characteristics. The strain signal contains a lot of noise, which makes the analysis error. Therefore, it is very important to denoise and extract features of measured signals before signal analysis. In this paper, the joint algorithm of ensemble empirical mode decomposition (EEMD) and wavelet transform (WT) is used for the first time to achieve sufficient noise reduction and effectively extract the feature signals of non-stationary strain signals. The application process of EEMD-WT is optimized. This optimization can avoid the repeated selection of wavelet basis function and the number of decomposition layers due to different crosswind conditions. EEMD adaptively decomposes the strain signal into intrinsic mode functions, to judge the frequency of IMFs, remove the high-frequency noise components, retain the useful components. The useful components are denoised twice by the wavelet transform, the components and residual terms after the secondary denoising are reconstructed to obtain the characteristic signal. The EEMD-WT was applied to process the simulating signals and measured the strain signals. The results were compared with the results of the EEMD. The results showed that the EEMD-WT method has better noise reduction performance, and can effectively extract the characteristics of strain signals, which lays a solid foundation for accurate analysis of wind turbine blade strain signals under crosswind conditions.

BEMD based adaptive Lee filter for despeckling of SAR images

Synthetic aperture radar (SAR) image processing finds application in gathering features to detect the ecological changes in earth observatory. But the speckle noise in the acquired image degrades the quality of detection. This paper explains a new method to minimize speckle noise in SAR images using bidimensional empirical mode decomposition (BEMD) based adaptive Lee filter. The main innovation of this proposed work is the use of the BEMD technique on SAR images. The decomposed levels are called bidimensional intrinsic mode functions (BIMF). By this decomposition algorithm, the high-frequency noise component gets separated in the first level of BIMF, which is further filtered by the proposed adaptive filter and reconstructed. In this paper, we are proposing a BEMD based adaptive Lee filter. The BEMD based Lee filter with different window sizes is used. The designed filter algorithm is validated by calculating the performance parameters to demonstrate its denoising performance.

Research on ultrasonic signal processing algorithm based on CEEMDAN joint wavelet packet thresholding

Considering the large reconstruction error of ensemble empirical mode decomposition (EEMD) and the loss of useful high-frequency signals, a complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) joint wavelet packet threshold processing method is proposed for processing ultrasonic non-destructive testing defect signals. The ultrasonic echo signal is decomposed using CEEMDAN to obtain a group of intrinsic mode functions (IMF). The noisy components are selected using the correlation coefficient method and the high-frequency noise components are processed using the wavelet packet threshold. After threshold processing, the IMF components and reserved IMF components are accumulated and reconstructed. Compared with the EEMD algorithm, the signal-to-noise ratio was increased by 48.03% and the root mean square error and total number of iterations were reduced by 38.77% and 33.34%, respectively. This method reduces the reconstruction error and iteration time and ensures the integrity of the signal.

Investigation of hardware and software techniques to enhance the characteristics of focused ultrasound (FUS) spectra

Objective. Microbubble cavitation generated by focused ultrasound (FUS) can induce safe blood-brain-barrier (BBB) opening allowing therapeutic drug passage. Spectral changes in the hydrophone sensor signal are currently used to distinguish stable cavitation from inertial cavitation that can damage the BBB. Gibbs’ ringing, peak intensity loss and peak width increase are well-known distortions evident when using the discrete Fourier transform (DFT) to transform data containing a few hundred points. We investigate overcoming the fact that FUS time signals (10 ms providing 312 500 points sampled at 32 ns intervals) can generate such sharp spectral peaks that variations in their DFT-related distortions can significantly impact the values of the key metrics used for cavitation characterization. Approach. We introduce low-pass filter hardware to improve how the analogue to digital convertor handles high-frequency noise components and the orders of magnitude differences between FUS harmonic intensities. We investigate the enhanced FUS spectral stability and resolution obtained from a new technique, physical sparsification (PH-SP), customized to the a-priori information that all key FUS components are harmonically related. Results are compared with standard DFT optimizations involving time data windowing and Fourier interpolation. Main results. A new simulation model showed peak intensity, widths and metrics modified by small changes in the transformed signal’s length when removing the noisy starting transient of the FUS hydrophone signal or following minor excitation frequency or sampling rate adjustments. 25%–60% area-under-the-curve changes occurred in phantom studies at different pressure levels. Spectral peak sharpness was best optimized and stabilized with PH-SP. Significance. Special FUS characteristics mean starting transients and minor variations in experimental procedures lead to significant changes in the spectral metrics used to monitor cavitation levels. Customizing PH-SP to these characteristics led to sharper, more stable spectra with the potential to track the impact of microbubble environment changes.

A Novel Bitcoin and Gold Prices Prediction Method Using an LSTM-P Neural Network Model.

As a result of the fast growth of financial technology and artificial intelligence around the world, quantitative algorithms are now being employed in many classic futures and stock trading, as well as hot digital currency trades, among other applications today. Using the historical price series of Bitcoin and gold from 9/11/2016 to 9/10/2021, we investigate an LSTM-P neural network model for predicting the values of Bitcoin and gold in this research. We first employ a noise reduction approach based on the wavelet transform to smooth the fluctuations of the price data, which has been shown to increase the accuracy of subsequent predictions. Second, we apply a wavelet transform to diminish the influence of high-frequency noise components on prices. Third, in the price prediction model, we develop an optimized LSTM prediction model (LSPM-P) and train it using historical price data for gold and Bitcoin to make accurate predictions. As a consequence of our model, we have a high degree of accuracy when projecting future pricing. In addition, our LSTM-P model outperforms both the conventional LSTM models and other time series forecasting models in terms of accuracy and precision.

Despeckling of SAR Images Using BEMD-Based Adaptive Frost Filter

In image processing, removal of speckle noise from a satellite image is a challenging task for the researchers. There are various approaches for speckle noise reduction. Generally, the speckle noises are scattered, in satellite images, medical images and synthetic aperture radar (SAR) images. This paper introduces a bidimensional empirical mode decomposition (BEMD)-based adaptive filtering method for despeckling of SAR image. The noisy SAR image is decomposed into different bidimensional intrinsic mode function (BIMF) levels using BEMD and then filtering is performed on the first BIMF level, as it contains the high-frequency noise component. This adaptation process effectively filters out the noisy image component without destroying the original image component. A BEMD-based adaptive Frost filter is introduced in this paper for despeckling of SAR images. The despeckling performances of our proposed filtering method are further analyzed by visual evaluation and also using performance parameters comparatively. Our reconstructed images show better performance quantitatively and qualitatively compared to other filters.

Ultra-short-term wind-speed bi-forecasting system via artificial intelligence and a double-forecasting scheme

A key issue in the wind power industry is the accuracy and stability of wind-speed forecasting, which has an important impact on the power system management and market economy. Accurate prediction of wind speed points could guarantee regular work of smart grids, while interval prediction could evaluate the uncertainty of wind speed and further supply corresponding wind speed information. However, several studies concentrated on the deterministic point forecast of wind speed and seldom considered the interval forecast of wind speed, or performed the two separately, thereby making wind speed prediction inefficient and not comprehensive. To remedy this, we herein propose a wind speed bi-forecasting system that includes three parts: data preprocessing, combination forecasting, and evaluation, making up for the gaps in existing research and analysis. A decomposition algorithm is used to decompose the actual wind speed into a series of finite components as well as remove high-frequency noise components. The combination forecasting module adopts the weight combination mechanism based on a multi-objective multi-verse optimization algorithm to achieve a double output of prediction results. Finally, an evaluation module comprising hypothesis testing and evaluation standards was presented to comprehensively evaluate the system. The experimental results indicate that the system is excellent for predicting wind speed and analyzing the uncertainty of wind speed and thus could be practically applied as a wind power system programming technology.

Assessment of imaging performance of digital breast tomosynthesis based on systematic simulation

To assess the imaging performance of digital breast tomography (DBT) based on systematic simulation. The raw measurements of physical phantoms at a variety of radiation dose levels and clinical patients at the normal radiation dose were acquired from a clinical DBT system for low-dose simulation and reconstruction using 3 reconstruction algorithms, namely Feldkamp-Davis-Kress (FDK), simultaneous algebraic reconstruction technique (SART) and adaptive steepest-descent projection onto convex sets with total-variation constraint (ASDPOCS-TV) algorithms. The image quality was compared across different radiation dose levels and reconstruction algorithms in terms of signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR), noise-power spectrum (NPS), artifact spread function (ASF) and full width at half maximum (FWHM) of ASF indexes. The reliability of low-dose DBT simulation strategy was verified by the experiment. Within a suitable range of dose levels, increasing the doses resulted in reduced high-frequency noise component and significantly increased SNR (P < 0.05). But when the value of exposure was below 40 mAs, the images acquired at different dose levels had similar representation. The performance of the 3 reconstruction algorithms varied for different anatomical structures, and the image quality of ASDPOCS-TV algorithm was generally superior to SART and FDK algorithms with less through-plane artifacts and noise. The quality of DBT images is significantly affected by both radiation dose and reconstruction algorithms. A tradeoff of the parameters, the overall image quality and the clinical needs for diagnostic purposes should be considered to achieve the optimal imaging performance on a given clinical task.

A parametric study of the effect of self-oscillating trailing-edge flaplets on aerofoil self-noise

This paper presents an acoustic study of a standard NACA 0012 aerofoil with additional self-oscillating passive flaplets deployed from the trailing edge for self-noise reduction, putting special emphasis on the potential reduction of tonal noise generated by the periodic shedding of vortices from the trailing edge. The flaplets protruding out of the trailing edge act as rectangular thin cantilever beams exited by the surrounding flow to oscillate in their dominant flexural bending mode. The noise attenuation performance is studied for different deployment length, width and inter-spacing to find the most dominant contributions at chord based Reynolds numbers Rec from 100,000 to 900,000 and three geometric angles of attack αg=0°,10° and 15°. It was observed that all flaplet configurations oscillate and thereby reduce the tonal noise. The range of the highest noise reduction in the low frequency range scales with the Strouhal number based on chord length, whereby this range can be set specifically by how far the flaplets protrude out of the trailing edge. This determines the free vibrating length and therefore the natural frequency of the oscillators. Laser-based measurements of the flaplet oscillations confirm the occurrence of a lock-in mechanism observed in previous studies, which effectively describes the oscillating flaplets as pacemaker to keep the fundamental instabilities of the flow in their linear state. It is concluded that this contributes mainly to the tonal noise reduction while the more broadband noise reduction stems from the geometry of the flaplets acting as ‘slitted-serrations’. A larger width of the flaplets is most effective in the low-frequency range, while narrower flaplets are best addressing high-frequency noise components. The results further show that a smaller inter-spacing also benefits the noise reduction. The study paves the way for novel morphing techniques to target specific noise ranges during different flight manoeuvres.

A Study on the Frequency Characteristics of Highfrequency Noise Components in Ventricular Late Potentials Measured with a High-resolution Holter Electrocardiograph

A Study on the Frequency Characteristics of Highfrequency Noise Components in Ventricular Late Potentials Measured with a High-resolution Holter Electrocardiograph

Computer-generated moir\xe9 profilometry based on fringe-superposition

A computer-generated moiré profilometry based on algebraic addition instead of algebraic multiplication is proposed. Firstly, the two AC components of the captured fringe patterns on the reference plane with pi /2 phase difference are retrieved and saved in advance. While measuring, two sinusoidal gratings with pi phase difference are projected onto the measured object alternatively, and the corresponding deformed patterns are captured. Then the AC component of the captured deformed pattern can be separated exactly. When the positive and negative AC component of the captured deformed pattern are added to the two prestored AC components respectively, two moiré fringes only reflect sine and cosine of the object’s phase information can be successfully generated via a series of data processing procedures. Finally, the phase distribution of the measured object can be extracted by arctangent of the ratio of these two moiré fringes. Compared with computer-generated moiré profilometry based on algebraic multiplication, this proposed method can reduce the effect of high frequency noise and residual DC component on measurement and improve the measurement accuracy. While compared with pi phase shifting FTP, this method can measure more complex objects with better measurement capability. Experimental results verify the feasibility and validity of the proposed method.

Prognosis of Bearing Degeneration Using Adaptive Quaternion Least Mean Biquadrate Under Framework of Hypercomplex Data

Prognostics of bearing degeneration play a crucial role in implementation of systems maintenance strategies. Although numerous classical prognostic models have been developed, these models are carried out based on channel-wise processing and therefore fail to capture the inherent nonlinear and coupling nature of multi-dimensional or multi-channel data. To address this issue, a novel prognostics method based on adaptive quaternion-valued least-mean biquadrate (AQ-LMB) algorithm is proposed for a unified processing of hypercomplex data by the virtue of quaternion algebra. The cost function of the proposed AQ-LMB algorithm is designed via the biquadrate form of system output error to adapt to the more common nonlinear and non-Gaussian data, and the update weight vector is derived through Hamilton calculus instead of traditional complex gradient calculation. First, the time series of health indicators (e.g., root-mean-square, RMS) derived from historical data are decomposed by a nonconvex sparse regularization (SR) algorithm associated with a nonconvex penalty, that is, the low frequency trend component (LFC) and high frequency noise component (HFC) are obtained. Then both LFC and HFC are respectively predicted by the AQ-LMB algorithm. The final predicted health indicators can be obtained by integrating the correspondingly predicted LFC and HFC. The separate analysis of both sub-components makes it possible to distinguish their respective contributions to the entire degeneration process, thus avoiding false deviation and improving the prediction accuracy. Finally, the effectiveness of the proposed nonconvex SR and AQ-LMB approach in improving prognostic accuracy is illustrated via three-and four-dimensional run-to-failure datasets of the rolling bearings.

Fiber optic gyro signal denoising based on variational mode decomposition and adaptive noise cancellation

The output signal of the fiber optic gyro (FOG) often contains a large amount of noise, which makes it difficult to model and calibrate. Therefore, it must be denoised. In this paper, an adaptive noise cancellation method based on variational mode decomposition (VMD) is proposed. The FOG signal was decomposed into a series of mode components with different center frequencies, and the correlation coefficient method was used to determine the number of decomposition layer. The low-frequency signal and the high-frequency noise components were distinguished by the frequency distribution of each component. They were respectively accumulated to use as the ready denoising signal and the reference noise, then obtaining the denoised signal by adaptive noise cancellation. Using FOG measured data, the proposed method was taken into experiment compared with traditional methods. Allan variance analysis verified that the proposed method effectively suppresses the noise in the FOG signal.