Noise Components Research Articles

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.

ICEEMDAN-Informer-GWO: a hybrid model for accurate wind speed prediction.

Extensive research has been diligently conducted on wind energy technologies in response to pressing global environmental challenges and the growing demand for energy. Accurate wind speed predictions are crucial for the effective integration of large wind power systems. This study presents a novel and hybrid framework called ICEEMDAN-Informer-GWO, which combines three components to enhance the accuracy of wind speed predictions. The improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) component improves the decomposition of wind speed data, the Informer model provides computationally efficient wind speed predictions, and the grey wolf optimisation (GWO) algorithm optimises the parameters of the Informer model to achieve superior performance. Three different sets of wind speed prediction (WSP) models and wind farm data from Block Island, Gulf Coast, and Garden City are used to thoroughly assess the proposed hybrid framework. This evaluation focusses on WSP for three specific time horizons: 5minutes, 30minutes, and 1hour ahead. The results obtained from the three conducted experiments conclusively demonstrate that the proposed hybrid framework exhibits superior performance, leading to statistically significant improvements across all three time horizons.

Low-Frequency Intensity Modulation of High-Frequency Rotor Noise

Acoustic spectra of rotor noise yield frequency distributions of energy within pressure time series. However, they are unable to reveal phase relations between different frequency components while these play a role in the fundamental understanding of low-frequency intensity modulation of higher-frequency rotor noise. A methodology to quantify interfrequency modulation is applied to a comprehensive acoustic dataset of a fixed-pitch benchmark rotor, operating at a low Reynolds number and at advance ratios ranging from J=0 to 0.61. Our findings strengthen earlier observations in case of a hovering rotor, in which the modulation of the high-frequency noise is strongest around an elevation angle of θ=−20° (below the rotor plane). For the nonzero advance ratios, modulation becomes dominant in the sector −45°≲θ≲0° and is most pronounced at the highest advance ratio tested (J=0.61). Intensity modulation of high-frequency noise is primarily the consequence of a far-field observer experiencing a cyclic sweep through the noise directivity pattern of relatively directive trailing-edge noise. This noise component becomes more intense with increasing J and is associated with broadband features of the partially separated flow over the rotor blades.

PDenoiser: A Personalized Speech Enhancement Neural Network for Pre-hospital Emergency Medical Services.

Pre-hospital emergency medical service (EMS) tasks often come with complex and diverse noise interferences, posing challenges in implementing ASR-based medical technologies and hindering efficient and accurate telephonic communication. Among the different types of noise distortion, interfering speech is especially annoying. To address these issues, our aim is to develop a technology capable of extracting the intended speech content of the target physician from noisy and mixed audio during EMS tasks. In this work, we propose a monoaural personalized speech enhancement (PSE) method called pDenoiser, which is a real-time neural network that operates in the time domain. By leveraging the prior vocalization cues of emergency physicians, pDenoiser selectively enhances target speech components while suppressing noise and nontarget speech components, thereby improving speech quality and speech recognition accuracy under noisy conditions. We demonstrate the potential value of our approach through evaluations on both public general-domain test sets and our self-collected real-world EMS test sets. The experimental results are promising, as our model effectively promotes both speech quality and ASR performance under various conditions and outperforms related methods across multiple evaluation metrics. Our methodology will hopefully elevate EMS efficiency and fortify security against nontarget speech during EMS tasks.

Weak faults diagnosis of traction motor bearings based on SR-MED-EEMD method

Rolling bearings play a crucial role in ensuring the security and reliable operation of high-speed trains (HST) as a vital component of their traction system. In response to the difficulty in extracting early fault features of high-speed train traction motor bearings under strong background noise, a method based on sparse representation (SR), minimum entropy deconvolution (MED), and ensemble empirical mode decomposition (EEMD) is proposed. The method reduces the dimensionality of complex signals through SR and then filters the sparse signals through MED to improve the fault signal pulse amplitude. The processed signals are decomposed by using EEMD to remove noise components, further improving the peak signal-to-noise ratio of fault characteristic frequency. The method’s efficacy in detecting faults within rolling bearings has been confirmed through simulation experiments and demonstrated its high precision.

Ultrasonic cavitation noise extraction based on fence sampling

The composition of the ultrasonic cavitation sound field is complex, including fundamental, harmonic, subharmonic, super-harmonic, and broadband transient noise. Fundamental waves, harmonics, etc., may interfere with the analysis and research of broadband transient cavitation noise, so it is necessary to filter out these interferences in the sound field. This article conducts measurement experiments on the ultrasonic cavitation sound field generated by high-power ultrasonic cleaning transducers. By utilizing the characteristics of the fence effect, the acoustic signal in the ultrasonic cavitation sound field is resampled through fence sampling so that the fundamental and harmonic components are fused at 0 Hz and filtered out. The ultrasonic cavitation noise is successfully extracted, and its signal structure is the same as the noise component structure in the original signal.

Denoising multispectral images using non-local rank tensor decomposition and bilateral filtering based on sunflower optimization

ABSTRACT Image denoising is an important pre-processing process in the fields of computer vision and image processing. Traditional denoising techniques blur edges excessively and degrade image quality by removing noise components but failing to maintain clarity. To overcome these problems, this paper proposes a multispectral image denoising strategy combining non-local rank tensor decomposition (NLRTD) and bilateral filtering. To extract patches from noisy images, single-level discrete wavelet transform (DWT) is utilized. Then, similar patches from the extracted images are grouped using spectral clustering. After that, mixed noise is reduced by separating clean images from each clustered group using NLRTD. An optimized bilateral filter using Sunflower optimization (SFO) is used for denoising by preserving edge details and is reconstructed using its constituent parts. The effectiveness of the proposed denoising method is assessed using performance matrices, such as BER, PSNR, MSE, RMSE, SNR and SSIM were 0.8544%, 53.21%, 2.41%, 2.41%, 25.06% and 0.90%, respectively.

D‐CDA: A Denoise and Change Detection Approach for Flood Disaster Location From SAR Images

AbstractFloods are among the most devastating natural disasters worldwide. Such disasters are often accompanied by strong precipitation and other weather factors, making it more difficult to identify affected areas. Moreover, synthetic aperture radar (SAR) technology can capture images in a 24‐hr window and penetrate clouds and fog. Change detection (CD) technology based on SAR images is generally utilized to locate disaster‐stricken areas by analyzing the differences between pre‐ and post‐disaster images. However, this method faces two main challenges: the presence of speckle noise, which reduces the difference detection accuracy, and the lack of a suitable SAR data set for flood disaster CD. Therefore, this study proposes a novel two‐stage approach for locating flood disaster areas, known as the denoising‐change detection approach (D‐CDA). The first stage comprises a nine‐layer denoising network with an encoder‐decoder structure known as the SAR denoising network (SDNet). It utilizes a multiresidual block and a parallel convolutional block attention module to extract features during the encoding process to suppress the noise component. In the second stage, a novel convolution neural network is proposed to detect the changes between bitemporal SAR images, namely, the coordinate attention fused network, which combines the siamese network and UNet++ as the backbone, and fuses coordinate attention modules to enhance the change features. Moreover, a CD data set (Zhengzhou flood data set) was constructed using Sentinel‐1 SAR images based on the 2021 flood disaster in Zhengzhou, China. Simulations verify the effectiveness of the proposed method. The experimental results indicate that D‐CDA achieves favorable detection performance in locating flood disaster areas.

DETECTING NOISE IN FRACTAL TIME SERIES USING MACHINE LEARNING

This study concentrates on devising a method to evaluate the level of noise in fractal Brownian motion through machine learning methods. A method for classifying trajectories of fractal Brownian motion with varying levels of additive noise using a convolutional neural network has been proposed. Modeled fractal time series with additive noise were utilized as the input dataset. The noise component was generated with different dispersion values, allowing the investigation of the noise level's influence on the system and its environment. The results provide insights into the effectiveness and trustworthiness of employing these machine learning techniques for assessing noise within fractal systems.

Selection of noise models for GNSS coordinate time series based on model averaging algorithm

In the field of global navigation satellite system (GNSS) time series noise analysis, appropriately modeling the noise components plays an important role in determining the velocity of GNSS sites and quantifying the uncertainty associated with the velocity estimation. Over the years, researchers have focused on only one optimal noise model, while other noise models that show similar performance to the optimal model have been ignored. We investigated whether these ignored noise models can be made use of to describe the noise in the GNSS time series after applying a model averaging algorithm. The experimental data were derived from 28 International GNSS Service (IGS) sites in the California region of the United States and 110 IGS sites worldwide. The results showed that for the GNSS time series of 28 IGS sites in the California, 79%, 68%, and 75% of the site components can be applied the model averaging algorithm in the east/north/up (E/N/U) directions, respectively. Based on it, the east direction showed the best performance, with 50% of the site components obtaining more conservative velocity uncertainty after applying the model averaging algorithm compared to the optimal noise model. For GNSS time series of 110 IGS stations worldwide, the model averaging algorithm demonstrates excellent performance in all the E/N/U directions. In the E/N/U directions, 86%, 94%, and 57% of the site components can apply the model averaging algorithm. Building upon this, 77%, 65%, and 62% of the site components achieve more conservative velocity uncertainty in the E/N/U directions compared to the optimal noise model. To fully validate the feasibility of the model averaging algorithm, we also tested GNSS time series of varying lengths and different thresholds of the model averaging algorithm. In summary, the model averaging algorithm performs exceptionally well in the noise analysis of GNSS time series. It helps prevent overly optimistic estimation results.

Noise phenomena in distress cries of term and very preterm infants at term-equivalent age

Background and aim To examine whether the noise components in distress cries of term infants differed from very preterm infants whose cries were collected at a comparable “corrected” gestational age. Methods Distress cries were collected from 20 term and 20 preterm infants. The cries were acoustically examined for the occurrence of aperiodic phonatory behavior within and across moments of crying. Results The findings indicated no significant differences between term and preterm infants at term age in the occurrence of noise. Conclusions Distress cries of both term and term-equivalent preterm infants appear to contain high instances of phonatory noise. The high arousal associated with distress crying and associated increase in subglottal pressure appeared to influence both term and term-equivalent preterm infants similarly.

Influence of the boundary-layer thickness on the generation of tonal noise components by subsonic impinging jets

The influence of the boundary layer (BL) thickness on the tonal noise generated by subsonic impinging jets is investigated. For that, initially laminar jets at Mach numbers $0.6$ and $0.9$ with BL thicknesses $0.05r_0$ , $0.1r_0$ and $0.2r_0$ , where $r_0$ is the pipe-nozzle radius, impinging on a plate at $6r_0$ from the nozzle, are simulated. For Mach number $0.9$ , acoustic tones due to feedback loops of axisymmetric nature between the nozzle and the plate are generated at frequencies that do not vary with the BL thickness. The two dominant tones are, however, $17$ and $26\ \mathrm {dB}$ stronger for the thickest BL compared with the thinnest one. For Mach number $0.6$ , for the thinnest BL no acoustic peaks appear, as observed in the experiments of the literature, but narrow peaks resulting from axisymmetric feedback loops emerge for thicker BLs. Therefore, low subsonic impinging jets can be resonant for specific nozzle-exit conditions. The increase in tone amplitude for Mach number $0.9$ , and the establishment of feedback loops for Mach number $0.6$ with increasing BL thickness, are found to result from two changes in the jet flow. The first change is that the shear-layer laminar–turbulent transition occurs farther downstream for a thicker BL, leading to a greater predominance of the axisymmetric aerodynamic fluctuations near the plate. The second change is that the amplification of the flow fluctuations between the nozzle and the plate at the tone frequencies is stronger for thicker BLs.

Advancements in wideband source localization with an acoustic vector sensor line array

A method is presented for localizing wideband acoustic sources using an array of acoustic vector sensors (AVS). The method focuses on leveraging the lack of correlation between acoustic pressure and particle velocity in the underwater environmental noise field. Coherent processing is introduced in the signal processing stage to suppress noise components in the observed data. By employing a focused transform that maps various frequency components of the wideband signal into the same signal subspace, an asymptotically unbiased Direction of Arrival (DOA) estimation is achieved using a multidimensional subspace method. A concise expression for the Cramér-Rao Bound (CRB) on the estimation errors is provided within the framework of the wideband multi-vector-sensor model. The performance of the proposed method for localizing sources is assessed through simulation and experimental data analysis. The research indicates that coherent processing effectively suppresses isotropic components in background noise, the proposed method achieves a lower estimation error, closer to the CRB, compared to the scalar array counterpart under identical signal-to-noise ratio (SNR) conditions.

Wasserstein-GAN-based noise reduction for preserving anatomical structures in low-dose CT

The purpose of low-dose computed tomography (LDCT) is to reduce patient's radiation dose. However, LDCT imaging often suffers from low image quality due to computed tomography (CT) noise patterns. While conventional methods such as iterative reconstruction and image processing have been studied to address this issue, their clinical application remains limited due to the degradation of critical anatomical structures. Recently, deep-learning-based noise reduction approaches have shown promising results in improving the image quality of LDCT. Particularly, generative adversarial network (GAN)-based noise reduction methods have produced outstanding results, approaching the quality of normal-dose CT (NDCT) images. In this study, we propose a hybrid framework based on Wasserstein-GAN with the non-subsampled contourlet transform for enhanced noise reduction in LDCT. The aim is to meticulously address CT noise patterns and generate CT images similar to NDCT. We implemented the proposed algorithm and conducted an experiment using clinical abdominal CT datasets to demonstrate its feasibility. According to our results, the proposed method effectively reduced noise components in LDCT images while preserving the anatomical structures of NDCT. This was confirmed through both qualitative and quantitative evaluations. The implications of our findings for clinical practice are significant, as the proposed method can improve the accuracy of LDCT and reduce radiation dose to patients, ultimately enhancing patient safety.

Spacer effects on the performance of MoTe2/MoSe2 and MoTe2/WSe2 heterostructure double gate MOSFET

In this work, the spacer effects (both low and high-k) on the dc and noise characteristics of heterostructure molybdenum ditelluride (MoTe2/MoSe2 and MoTe2/WSe2) double gate (DG) MOSFET is analyzed. To study the device characteristics, a hybrid methodology that uses both QuantumWise ATK and Sentaurus TCAD tool is used. The performance metrics of the device such as on-current (Ion), Ion/Ioff ratio, subthreshold swing (SS), threshold voltage are obtained. The noise performance of the device is also studied (with/without spacer) using impedance field method. Noise parameters such as noise power spectral density (SID) and noise figure (function of both frequency and bias) has also been simulated and noise components such as G-R noise, flicker noise and white noise are obtained. The simulation results shows that the introduction of spacer material enhances the dc performance of the device and influences its noise characteristics.

Developing hybrid machine learning models to assign health score to railcar fleets for optimal decision making

A large amount of data is generated during the operation of a railcar fleet, which can easily lead to dimensional disaster and reduce the resiliency of the railcar network. To solve these issues and offer predictive maintenance, this research introduces a hybrid fault diagnosis expert system method that combines density-based spatial clustering of applications with noise (DBSCAN) and principal component analysis (PCA). Firstly, the DBSCAN method is used to cluster categorical data that are similar to one another within the same group. Secondly, PCA algorithm is applied to reduce the dimensionality of the data and eliminate redundancy in order to improve the accuracy of fault diagnosis. Finally, we explain the engineered features and evaluate the selected models by using the Gain Chart and Area Under Curve (AUC) metrics. We use the hybrid expert system model to enhance maintenance planning decisions by assigning a health score to the railcar system of the North American Railcar Owner (NARO). The model is based on data from one specific railcar type. We utilize the results of data-driven models to assign a health rate to each railcar. Finally, we use data from NARO to evaluate and verify our proposed framework.

A speech enhancement model based on noise component decomposition: Inspired by human cognitive behavior

In multimedia intelligent systems, speech enhancement is commonly employed to improve the quality of speech signals, making them clearer and more natural. Current deep learning-based speech enhancement models typically treat noise as a unified entity and aim to separate it from the target speech. In this paper, inspired by the cognitive behavior of the human brain when observing noisy speech spectrograms, we decompose the spectral energy of noise into regular and random components. We propose an auxiliary-model-based speech enhancement framework that better suppresses noise components closely resembling speech features. Firstly, we introduce a voiceprint segmentation network (VSnet) that partitions noisy speech into voiceprint and non-voiceprint regions. Subsequently, we present a noise reconstruction network (NRnet) that utilizes noise information from non-voiceprint regions to reconstruct and suppress the regular noise components within the voiceprint region. Finally, we construct a combination of a model dedicated to suppressing random components (RANnet) and a speech enhancement model (SEnet), and train them synchronously. By sharing encoder parameters, SEnet is compelled to reduce the extraction of regular noise features from the original noisy speech, contributing to improving speech quality generated through the decoder. Experimental results on public Voickbank-DEMAND and DNS-challenge 2020 datasets demonstrate that our approach achieves state-of-the-art performance.

Denoising Seismic Waveforms Using a Wavelet-Transform-Based Machine-Learning Method

ABSTRACT Seismic waveform data recorded at stations can be thought of as a superposition of the signal from a source of interest and noise from other sources. Frequency-based filtering methods for waveform denoising do not result in desired outcomes when the targeted signal and noise occupy similar frequency bands. Recently, denoising techniques based on deep-learning convolutional neural networks (CNNs), in which a recorded waveform is decomposed into signal and noise components, have led to improved results. These CNN methods, which use short-time Fourier transform representations of the time series, provide signal and noise masks for the input waveform. These masks are used to create denoised signal and designaled noise waveforms, respectively. However, advancements in the field of image denoising have shown the benefits of incorporating discrete wavelet transforms (DWTs) into CNN architectures to create multilevel wavelet CNN (MWCNN) models. The MWCNN model preserves the details of the input due to the good time–frequency localization of the DWT. Here, we use a data set of over 382,000 constructed seismograms recorded by the University of Utah Seismograph Stations network to compare the performance of CNN and MWCNN-based denoising models. Evaluation of both models on constructed test data shows that the MWCNN model outperforms the CNN model in the ability to recover the ground-truth signal component in terms of both waveform similarity and preservation of amplitude information. Model evaluation of real-world data shows that both the CNN and MWCNN models outperform standard band-pass filtering (BPF; average improvement in signal-to-noise ratio of 9.6 and 19.7 dB, respectively, with respect to BPF). Evaluation of continuous data suggests the MWCNN denoiser can improve both signal detection capabilities and phase arrival time estimates.

Automatic Detection of Nuclear Spins at Arbitrary Magnetic Fields via Signal-to-Image AI Model.

Quantum sensors leverage matter's quantum properties to enable measurements with unprecedented spatial and spectral resolution. Among these sensors, those utilizing nitrogen-vacancy (NV) centers in diamond offer the distinct advantage of operating at room temperature. Nevertheless, signals received from NV centers are often complex, making interpretation challenging. This is especially relevant in low magnetic field scenarios, where standard approximations for modeling the system fail. Additionally, NV signals feature a prominent noise component. In this Letter, we present a signal-to-image deep learning model capable of automatically inferring the number of nuclear spins surrounding a NV sensor and the hyperfine couplings between the sensor and the nuclear spins. Our model is trained to operate effectively across various magnetic field scenarios, requires no prior knowledge of the involved nuclei, and is designed to handle noisy signals, leading to fast characterization of nuclear environments in real experimental conditions. With detailed numerical simulations, we test the performance of our model in scenarios involving varying numbers of nuclei, achieving an average error of less than 2kHz in the estimated hyperfine constants.

Digital IIR filters: Effective in edge preservation?

Infinite Impulse Response (IIR) filtering stands as a fundamental technique in various signal processing applications. Despite its widespread use, the scientific community widely acknowledges the suboptimal nature of IIR filters in preserving edges or abrupt transitions in piecewise signals. In situations where meticulous preservation of abrupt signal transitions is crucial, signal processing practitioners often explore alternative methodologies tailored to specific application demands. Consequently, the effectiveness of digital IIR filters in preserving signal edges while filtering out undesirable noise components continues to be a topic of active debate.In response to this limitation (or answering the question posed by the above title), the paper demonstrates that the suboptimal nature of IIR filters in preserving edges arises from the conventional methodology used to compute the output of zero-phase IIR filters. The standard method of output computation equates to solving an unconstrained optimization problem with an ℓ2-norm penalty term. Following this analysis, the paper introduces a refined approach to compute the output of digital IIR filters, aiming to overcome the challenge of preserving abrupt signal transitions. This innovative proposal replaces the ℓ2-norm with an ℓ1-norm, resulting in the development of an IIR filter that dynamically adjusts its coefficients based on the input signal. As a result, this novel approach to output computation effectively preserves essential signal features while simultaneously reducing noise, highlighting the efficacy of digital IIR filters in edge preservation. Therefore, the answer to the aforementioned question greatly depends on the chosen method for computing the output. By employing the suggested approach, the answer is unequivocally yes.