Low-pass Filtering Method Research Articles

Robust laser phase noise measurement by integration heterodyne for coherent beam combining applications.

Phase noise characteristics are critical for coherent beam combination engineering. Heterodyne with integration method for phase noise measurement has been studied numerically and experimentally, which reveals that the method is not only simple to implement with the least equipment but also capable of phase retrieval using under-sampling data. The integration method is compared with the traditional low-pass filter (LPF) method from both numerical and experimental perspectives. By introducing an evaluation criterion of measurement accuracy, the errors of the integration method are 0.44% and 0.08% for white noise and pink noise cases, respectively, which are smaller than that achieved by LPF one (1.52% and 0.25%). The errors of the integration method are below 1.4% when under-sampling data has been employed, which means that large consumption of data processing can be avoided, and the method is robust. Phase noise measurements in quiet laboratory and disturbed conditions are implemented, and the error between the results of the sampling rate of 250 MHz and 31.25 MHz is less than 0.08%, which is consistent with the simulation and demonstrates the excellent performance of the integration method.

Study on load rejection test of pumped storage units

Abstract The rise rate of rotating speed, the maximum inlet pressure of the spiral casing and the minimum inlet pressure of the draft tube cone of unit 2 in Jinzhai pumped storage power station were analyzed when 50% and 100% of the rated load were rejected in this paper. The low pass filtering method (cut off frequency is 0.2 times of the frequency) was used to process the pressure data at the inlet of the spiral casing and the draft tube cone to retain the trend term. The peak to peak values (97% confidence) of the pressure pulsation term under different data durations were studied, which provided technical support for rational and accurate calculation of the maximum inlet pressure of the spiral casing and the minimum inlet pressure of the draft tube cone, and provided an example reference for the load rejection test analysis of pumped storage units.

Control strategy for three-phase four-switch active power filter based on positive sequence extraction in stationary coordinate fundamental reference frame

Traditional three-phase four-switch active power filter often uses the method of phase-locked loop (PLL) and low-pass filter for harmonic detection, which seriously affects the detection accuracy. In order to improve the detection accuracy, a lock-free three-phase four-switch active power filter based on fundamental positive sequence extraction is proposed. The harmonic detection adopts the fundamental positive sequence extraction method, and the rotation coordinate axis transformation adopts the phase-locked loop (PLL) free method. Compared with the traditional three-phase four-switch active power filter, this method does not require a phase-locked loop and a low-pass filter, and does not need complex coordinate transformation[2].The control method is simpler. Finally, simulation results prove the effectiveness of the control method.

Uncertainties of landslide susceptibility prediction: Influences of random errors in landslide conditioning factors and errors reduction by low pass filter method

In the existing landslide susceptibility prediction (LSP) models, the influences of random errors in landslide conditioning factors on LSP are not considered, instead the original conditioning factors are directly taken as the model inputs, which brings uncertainties to LSP results. This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP uncertainties, and further explore a method which can effectively reduce the random errors in conditioning factors. The original conditioning factors are firstly used to construct original factors-based LSP models, and then different random errors of 5%, 10%, 15% and 20% are added to these original factors for constructing relevant errors-based LSP models. Secondly, low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter method. Thirdly, the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study case. Three typical machine learning models, i.e. multilayer perceptron (MLP), support vector machine (SVM) and random forest (RF), are selected as LSP models. Finally, the LSP uncertainties are discussed and results show that: (1) The low-pass filter can effectively reduce the random errors in conditioning factors to decrease the LSP uncertainties. (2) With the proportions of random errors increasing from 5% to 20%, the LSP uncertainty increases continuously. (3) The original factors-based models are feasible for LSP in the absence of more accurate conditioning factors. (4) The influence degrees of two uncertainty issues, machine learning models and different proportions of random errors, on the LSP modeling are large and basically the same. (5) The Shapley values effectively explain the internal mechanism of machine learning model predicting landslide susceptibility. In conclusion, greater proportion of random errors in conditioning factors results in higher LSP uncertainty, and low-pass filter can effectively reduce these random errors.

Heart sound classification using wavelet scattering transform and support vector machine

OBJECTIVE: A representation of the sound recordings that are associated with the movement of the entire cardiac structure is termed the Phonocardiogram (PCG) signal. In diagnosing such diverse diseases of the heart, PCG signals are helpful. Nevertheless, as recording PCG signals are prone to several surrounding noises and other disturbing signals, it is a complex task. Thus, prior to being wielded for advanced processing, the PCG signal needs to be denoised. This work proposes an improved heart sound classification by utilizing two-stage Low pass filtering and Wavelet Threshold (WT) technique with subsequent Feature Extraction (FE) using Wavelet Scatter Transform and further classification utilizing the Cubic Polynomial Support Vector Machine (SVM) technique for CVD. METHOD: A computer-aided diagnosis system for CVD detection centered on PCG signal analysis is offered in this work. Initially, by heavily filtering the signal, the raw PCG signals obtained using the database were pre-processed. Then, to remove redundant information and noise, it is denoised via the WT technique. From the denoised PCG, wavelet time scattering features were extracted. After that, by employing SVMs, these features were classified for pathology. RESULTS: For the analysis, the PCG signal obtained from the Physionet dataset was considered. Heavy low-pass filtering utilizing a Low-Pass Butterworth Filter (LPBF) is entailed in the pre-processing step. This removed 98% of the noise inherently present in the signal. Further, the signal strength was ameliorated by denoising it utilizing the WT technique. Promising results with maximum noise removal of up to 99% are exhibited by the method. From the PCG, Wavelet Scattering (WS) features were extracted, which were later wielded to categorize the PCG utilizing SVMs with 99.72% accuracy for different sounds. DISCUSSION: The Classification accuracies are analogized with other classification techniques present in the literature. This technique exhibited propitious outcomes with a 3% improvement in the F1 score when weighed against the top-notch techniques. The improvement in the metrics is attributed to the usage of the pre-processing stage comprising of Low-pass filter and WT method, WS Transform (WST), and SVMs. CONCLUSION: The superiority of the proposed technique is advocated by the comparative investigation with prevailing methodologies. The system revealed that Coronary Artery Disease (CAD) can be implemented with superior methods to achieve high accuracy.

Natural gas pipeline leak detection based on acoustic signal analysis and feature reconstruction

The natural gas pipeline leakage detection task based on acoustic signal has some problems such as background noise coverage, lack of effective features, and low fault identification accuracy caused by small sample data. However, only one of these problems was usually studied in previous technologies. Almost no one has attempted to challenge multiple issues at the same time. In this study, a natural gas pipeline leak detection model that integrates acoustic feature processing techniques and feature reconstruction is proposed to resolve the above problems collaboratively. This model consists of two components. The first component is a feature processing technique of the acoustic signal that integrates frequency domain vector denoising and time domain associative function feature enhancement. The second component is a one-dimensional convolutional neural network with an expanded structural feature encoder (FAE) for feature reconstruction (FAE-1D-CNN). In the feature processing stage of the acoustic signal, firstly, the acoustic signal collected by the acoustic sensor is discretized into a digital signal. Secondly, the energy modal function is used to perform high/low energy modal clustering of digital signal features. The feature validity is enhanced by adding association factors to the low-energy modal features matrix. A low-pass filtering method is then used in the high-energy modal features to remove the background noise coverage of the high-frequency components. In the fault feature extraction stage, a feature encoder (FAE) is introduced in the 1D-CNN network to extract effective fault features while performing secondary reconstruction of local spatial features, addressing the problem of small sample leakage signals with few effective fault characteristics. The global average pooling layer is used instead of the fully connected layer, and the Softmax function is adopted as the classifier for fault discrimination. The performance of the proposed method was evaluated on the GPLA-12 dataset, and the fault identification accuracy is up to 95.17%. Compared with other competing methods, the method in this paper exhibits optimal performance and has broad application prospects.

Optimal allocation method of hybrid energy storage capacity of multi-energy system under low-carbon background

Abstract The energy dispatching and distribution ability is improved by optimizing the configuration of hybrid energy storage capacity of multi-energy system in low-carbon background, and an optimal configuration method of hybrid energy storage capacity of multi-energy system in low-carbon background based on equilibrium control and dynamic optimization algorithm is proposed. The data structure model of hybrid energy storage capacity distribution of multi-energy system is constructed. Under the condition that the energy storage optimal allocation model based on cost analysis meets the system performance index, the energy storage optimal allocation model is established with the objective function of minimizing the cost of configuring energy storage system, and with the objective of minimizing the fluctuation of active power of distributed power sources, such as wind and light. Taking the penalty cost of wind energy storage combined output power exceeding the fluctuation limit as the objective function, low-pass filtering method is adopted to stabilize the fluctuation of new energy power, and the optimal configuration capacity of energy storage system is determined according to the allowable frequency deviation and voltage stability of the system. Balanced control and dynamic optimization algorithm are adopted to realize the optimal configuration of hybrid energy storage capacity of multi-energy system under low-carbon background by combining different wind and solar energy combinations, different sampling intervals and different number of power stations. The simulation results show that the hybrid energy storage capacity allocation of multi-energy system has strong adaptability and high environmental adaptability, which effectively improves the transient stability of power grid system and further promotes the safe and stable operation of power grid system.

Demand Response Using Disturbance Estimation-Based Kalman Filtering for the Frequency Control

Demand response (DR) has a great potential for stabilizing the frequency of power systems. However, the performance is limited by the accuracy of the frequency detection, which is affected by measurement disturbances. To overcome this problem, this paper proposes a disturbance estimation-based Kalman filtering method, which is utilized for the frequency control. By using the rate of change of frequency (RoCoF), the Kalman filtering method can estimate the state of the ON/OFF loads well. In this way, the influence of detection error can be reduced, and the DR performance can be improved. Test results show that the proposed disturbance estimation-based Kalman filtering method has a higher accuracy of frequency detection than existing methods (such as the low-pass filter method) and therefore improves the frequency control performance of DR.

Household flow detection using FEAT (flow estimating accelerometer-thermometer) device

The use of IoT devices in water end use disaggregation verification is an emerging field which offers benefits over conventional approaches, in terms of cost, accuracy and scalability. Having reliably disaggregated water appliance consumption data will enable smart water meter data to be used in household water conservation approaches and for understanding water consumption behaviours. The FEAT device provides a low cost, easily applied and scalable solution that is demonstrated to work even for very low flow conditions of 0.03 l/s. The FEAT device is a combination of a battery, Wi-fi board and MPU6050 sensors providing multi-modal accelerometer and thermometer data. The study places 7 of these FEAT devices onto hot and cold water pipes leading to a shower, which is operated 4 times in a high flow situation, 0.13 l/s, and 4 times in a low flow situation, 0.03 l/s. The data is then analysed and compared with a flow logger to determine if the FEAT device can detect when a domestic appliance is using water. There are limiting cases where the level of noise or external interference limits distorts the data, obscuring the distinguishable peaks in the data due to the similarity of the values. By using high and low pass filtering methods it was possible to enhance the peaks but there are still situations where peaks cannot be detected: for example, if a rigid pipe is not able to vibrate easily or if a hot water boiler is not triggered due to the low flow rate. However, the results show it should be possible to overcome these limiting cases, as it is much less likely for both the vibration and temperature data to be adversely affected by noise or external influences simultaneously, therefore decreasing the effect of noise and external influences. In conclusion, this research paper demonstrates that FEAT devices are a low cost, easily applied and scalable solution for detecting flow. By using high and low pass filtering, placing sensors on freely moving pipes and through the use of multi-modal verification, the FEAT device is shown to work on both metal and plastic pipes even in the lowest flow situations of 0.03 l/s. Therefore the FEAT device is a suitable solution for appliance identification in disaggregation verification datasets.

A stable Q reverse time migration method with regularization

Q reverse time migration (Q-RTM) is an effective method to compensate the deep amplitude energy of seismic records and improve the resolution. However, an important problem of the Q-RTM method is that the wavefield extrapolation process will generate high-frequency noise, resulting in unstable imaging results. On the basis of the decoupled viscousacoustic wave equation based on standard linear solid model, a viscoacoustic wave equation with regularization term is derived, and a stable Q-RTM based on this equation is developed. And the detailed derivation process of the viscousacoustic wave equation containing the regularization term is given. Compared with the commonly used low-pass filtering method, the method based on the regularization term avoids the design of the filter and the selection of the cutoff frequency. We test the Q-RTM method on the BP gas layer model, the Marmousi model and real seismic data. The numerical example results show that the Q-RTM method proposed in this paper can effectively solve the instability problem, compensate the amplitude energy, and obtain a higher resolution seismic profile.

Assessing Dynamic Load Allowance of the Negative Bending Moment in Continuous Girder Bridges by Weighted Average Method

Accurate acquisition of dynamic load allowance (DLA) based on measurement data is essential to the safety assessment of a bridge. When static load tests cannot be achieved, and filtering fails, the estimated DLAs from the experimental method vary widely due to the choice of a left or right band. In this paper, the proposed weighted average method (WAM) is used to possibly solve the above problem in continuous gird bridges. Two-span and three-span precast concrete box-gird bridges were selected to optimize intercepted segments of WAM for the first time with the assistance of standard deviation and coefficient of variation in statistics. Then, a DLA measurement case of the negative bending moment was utilized to verify the validity of the WAM. The results show that the intercepted segments of 10/16 to 1 times the span length were suitable for the WAM to calculate the DLA of the negative bending moment due to small offset moments and stable variation coefficients. The WAM had a strong anti-interference ability of outliers filtering in “bad data,” which differed significantly from the experimental method. In three measurements of a field bridge, DLAs obtained by the WAM had less dispersion than the experimental and low-pass filtering methods.

Well-posed UV completion for simulating scalar Galileons

The Galileon scalar field theory is a prototypical example of an effective field theory that exhibits the Vainshtein screening mechanism, which is incorporated into many extensions to Einstein gravity. The Galileon describes the helicity zero mode of gravitational radiation, the presence of which has significant implications for predictions of gravitational waves from orbiting objects, and for tests of gravity sensitive to additional polarizations. Because of the derivative nature of their interactions, Galileons are superficially not well-posed as effective field theories. Although this property is properly understood merely as an artifact of the effective field theory truncation, and is not theoretically worrisome, at the practical level it nevertheless renders numerical simulation highly problematic. Notwithstanding, previous numerical approaches have successfully evolved the system for reasonable initial data by slowly turning on the interactions. We present here two alternative approaches to improving numerical stability in Galileon numerical simulations. One of these is a minor modification of previous approaches, which introduces a low pass filter that amounts to imposing a UV cutoff together with a relaxation method of turning on interactions. The second approach amounts to constructing a (numerical) UV completion for which the dynamics of the high momentum modes is under control, and for which it is unnecessary to slowly turn on nonlinear interactions. We show that numerical simulations of the UV theory successfully reproduce the correct Galileon dynamics at low energies, consistent with the low-pass filter method and with previous numerical simulations.

A heuristic DEM generalization by combining catchments

Multi-scale digital elevation model (DEM) is an important content of digital terrain analysis. To generate multi-scale DEM, this study reports a heuristic DEM generalization method. The key of DEM generalization is to maintain the main topographic features and remove the minor topographic features. Our method takes catchment as generalizing object, not single grid pixel as generalizing object. In this way, DEM simplification can be achieved on the basis of maintaining the main terrain characteristics. Compared with the resample method, VIP method and low pass filter method, the proposed method can preserve the main topographic features well when the minor topographic features are deleted. Our main contribution lies in: taking terrain information rather than raw data as the generalizing object, which improves the rationality of the generalizing results; and the heuristic method proposed in this paper has good operability and high automation. Highlights A heuristic DEM generalization method by combining catchments is developed; The proposed DEM simplification can maintain the main terrain characteristics; The heuristic method proposed in this paper has good operability and high automation;

A Step-by-Step Design for Low-Pass Input Filter of the Single-Stage Converter

Active power factor correction converters are often introduced as the front stage of power electronic equipment to improve the power factor and eliminate higher harmonics. A Boost or Buck-Boost converter operating in discontinuous current mode is always adopted to achieve high power factor correction. In addition, the input current contains a large amount of higher harmonics, and a low-pass input filter is commonly adopted to filter it out. In this paper, a single-stage high-frequency AC/AC converter is taken as an example to demonstrate the design method of a passive low-pass filter. Firstly, the input side of the grid needs to meet the power factor and harmonic requirements. The preset parameters are set to a range to characterize the performance of the LC filter. The quantitative design method of input filter is proposed and summarized. Moreover, the sensitivity of the filter parameters is analyzed, providing a direction in practical applications. Preset parameters are all proved to conform to the preset range through PSIM simulation. Finally, a 130-W prototype is established to verify the correction of proposed design method. The power factor is around 0.935 and harmonic content in the input current is about 26.4%. All requirements can be satisfied.

Low-Pass Filtering Method for Poisson Data Time Series

Problems of digital processing of Poisson-distributed data time series from various counters of radiation particles, photons, slow neutrons etc. are relevant for experimental physics and measuring technology. A low-pass filtering method for normalized Poisson-distributed data time series is proposed. A digital quasi-Gaussian filter is designed, with a finite impulse response and non-negative weights. The quasi-Gaussian filter synthesis is implemented using the technology of stochastic global minimization and modification of the annealing simulation algorithm. The results of testing the filtering method and the quasi-Gaussian filter on model and experimental normalized Poisson data from the URAGAN muon hodoscope, that have confirmed their effectiveness, are presented.

ECG Signal Denoising and Reconstruction Based on Basis Pursuit

The electrocardiogram (ECG) is widely used for the diagnosis of heart diseases. However, ECG signals are easily contaminated by different noises. This paper presents efficient denoising and compressed sensing (CS) schemes for ECG signals based on basis pursuit (BP). In the process of signal denoising and reconstruction, the low-pass filtering method and alternating direction method of multipliers (ADMM) optimization algorithm are used. This method introduces dual variables, adds a secondary penalty term, and reduces constraint conditions through alternate optimization to optimize the original variable and the dual variable at the same time. This algorithm is able to remove both baseline wander and Gaussian white noise. The effectiveness of the algorithm is validated through the records of the MIT-BIH arrhythmia database. The simulations show that the proposed ADMM-based method performs better in ECG denoising. Furthermore, this algorithm keeps the details of the ECG signal in reconstruction and achieves higher signal-to-noise ratio (SNR) and smaller mean square error (MSE).

PENERAPAN METODE FILTER GAUSSIAN DALAM PERBAIKAN KUALITAS CITRA SATELIT MODIS

One technology that uses satellite capabilities that can capture the image of an area is the MODIS (Moderate Resolution Imaging Spectroradiometer) sensor, the resulting image may experience noise (noise) this is caused when sending through the transmission line, the image is too dark or bright, the image less sharp or blurred. For that we need a method of noise reduction such as remote sensing through satellite imagery. The purpose of this study is to apply the Gaussian lowpass filter method in improving the quality of fashionable satellite imagery in order to have good image quality. The results of PSNR show the highest value on the MOD_04 image, with smoother image results, the resulting PSNR value is 19.7791 higher than other images, where the higher the PSNR value, the better the resulting image.

A Comparison of Denoising Methods in Onset Determination in Medial Gastrocnemius Muscle Activations during Stance

One of the most basic pieces of information gained from dynamic electromyography is accurately defining muscle action and phase timing within the gait cycle. The human gait relies on selective timing and the intensity of appropriate muscle activations for stability, loading, and progression over the supporting foot during stance, and further to advance the limb in the swing phase. A common clinical practice is utilizing a low-pass filter to denoise integrated electromyogram (EMG) signals and to determine onset and cessation events using a predefined threshold. However, the accuracy of the defining period of significant muscle activations via EMG varies with the temporal shift involved in filtering the signals; thus, the low-pass filtering method with a fixed order and cut-off frequency will introduce a time delay depending on the frequency of the signal. In order to precisely identify muscle activation and to determine the onset and cessation times of the muscles, we have explored here onset and cessation epochs with denoised EMG signals using different filter banks: the wavelet method, empirical mode decomposition (EMD) method, and ensemble empirical mode decomposition (EEMD) method. In this study, gastrocnemius muscle onset and cessation were determined in sixteen participants within two different age groups and under two different walking conditions. Low-pass filtering of integrated EMG (iEMG) signals resulted in premature onset (28% stance duration) in younger and delayed onset (38% stance duration) in older participants, showing the time-delay problem involved in this filtering method. Comparatively, the wavelet denoising approach detected onset for normal walking events most precisely, whereas the EEMD method showed the smallest onset deviation. In addition, EEMD denoised signals could further detect pre-activation onsets during a fast walking condition. A comprehensive comparison is discussed on denoising EMG signals using EMD, EEMD, and wavelet denoising in order to accurately define an onset of muscle under different walking conditions.

Wavelet-Based Noise Removal from Raman Signal to Study PLD Coated Forsterite–Hydroxyapatite Thin Film on Stainless Steel 316l Substrate

Raman spectroscopy is proposed here for the study of forsterite–hydroxyapatite (FS–HA) composite coating on a stainless-steel substrate. However, in order to analyze the Raman spectrum accurately, noise and background removal is always required. A comparative study has been done for the correction of background. The waveletbased denoising of the signal was done using level 6 decomposition with sym4 wavelet and the thresholding method used was soft thresholding. In the present work, the effectiveness of the wavelet-based denoising method has been compared with Savitsky–Golay smoothing, quadratic regression, and the low-pass filter method. It is found that the wavelet-based denoising method works better as compared to other methods as it is able to smooth the signal and to increase the SNR while maintaining the peak intensity undistorted. Peaks are calculated for the different composition of the FS–HA composite. The variation of peak location in the processed Raman spectra suggests that the variation in concentration of FS and HA in the coating can be studied by using Raman spectroscopy.

Development of groundwater lens for transient recharge in strip islands

Groundwater in surficial aquifers in small islands is stored in the form of freshwater lenses floating on the seawater due to density difference. Recharge from rainfall infiltration is one of the primary mechanisms that control the lens growth and decay. Unlike previous studies either assuming steady-state groundwater lenses or relying on numerical simulations, we derive an approximate analytical solution to describe the transient change of groundwater lenses for a time-dependent, fluctuating recharge rate. The approximate analytical solution is validated by numerical simulations of variable density flow and transport. Based on this analytical solution and dimensional analysis, we investigate the impact of periodic recharge, extended drought events and random time-series recharge on the time-dependent behavior of the lens thickness and volume. Results show that only slowly changing recharge patterns with long periods such as seasonal and yearly changes can cause significant fluctuations of groundwater lenses. In specific, 10%, 50% and 100% fluctuations correspond to dimensionless periods of 200, 500 and 1300, respectively, which are 69, 174 and 451 days for our specified hydrogeological parameters. Thus, assuming an average constant recharge rate may be valid for simulating the transient lens profile and volume for recharge patterns with short periods such as daily fluctuations. We further analyze the approximate analytical solution using the method of low-pass filter. The complex physical process of the lens development is fitted by a second-order system on the frequency domain. Such a filter passes sine-wave recharge components with frequencies smaller than the break frequency and attenuates those higher than the break frequency, yielding smooth profiles of the lens thickness and volume. By analyzing the recharge rate spectrum and comparing the component frequency with the break frequency, we demonstrate how the development of a groundwater lens is affected by the transient recharge in reported laboratory and field observations.