Noise Characteristics Research Articles

Experimental Investigation and Validation of Measured Wind Turbine Noise Characteristics using Standardized Technology

This paper aims in affirming the accuracy and consistency of acoustic noise assessment at an experimental and Grid Connected wind turbine. The experimental turbine is a three-bladed, upwind turbine located in the Tuticorin District of Tamil Nadu State in India. The measurement campaign was carried out during the noise measurement equipment by Brüel & Kjær (B&K) PULSE Wind Turbine Sound Power Determination system strictly in accordance with the IEC 61400-11 2012-11 AMDI 2018 Ed.3.1. The Experimental study deals in the measurement of total noise emitted from various wind turbine component and sub-systems, when the turbine is in ON condition. The background noise was obtained while the turbine is in OFF condition. The measurement data was obtained as per the above referred standards for wind speeds ranging from 7m/s to 13.5 m/s at 50m hub height. The measurement was also obtained simultaneously at 10m height above ground level for the wind speed ranging 6m/s to 8m/s during the windy season of September 2022. The measurement data both at turbine hub height and 10m level were obtained at the same atmospheric condition 26o C and 0.996atm and in a down wind direction. The other parameters like Aweighted sound pressure level, 1/3 octave band spectrum, electric power, and wind speed were also measured at both hub heights at 10m level. The measured parameters were compared with the PULSE Software output and in the process; it is concluded that the noise characteristic of the experimental wind turbine is less than 3dB at a distance of 66m in the down wind direction from the test turbine. The measured results were also validated from the PULSE software and the results show a close match.

Research on Region Noise Reduction and Feature Analysis of Total Focus Method Ultrasound Image Based on Branch Pipe Fillet Weld

Research on Region Noise Reduction and Feature Analysis of Total Focus Method Ultrasound Image Based on Branch Pipe Fillet Weld

LLpowershap: logistic loss-based automated Shapley values feature selection method

BackgroundShapley values have been used extensively in machine learning, not only to explain black box machine learning models, but among other tasks, also to conduct model debugging, sensitivity and fairness analyses and to select important features for robust modelling and for further follow-up analyses. Shapley values satisfy certain axioms that promote fairness in distributing contributions of features toward prediction or reducing error, after accounting for non-linear relationships and interactions when complex machine learning models are employed. Recently, feature selection methods using predictive Shapley values and p-values have been introduced, including powershap.MethodsWe present a novel feature selection method, LLpowershap, that takes forward these recent advances by employing loss-based Shapley values to identify informative features with minimal noise among the selected sets of features. We also enhance the calculation of p-values and power to identify informative features and to estimate number of iterations of model development and testing.ResultsOur simulation results show that LLpowershap not only identifies higher number of informative features but outputs fewer noise features compared to other state-of-the-art feature selection methods. Benchmarking results on four real-world datasets demonstrate higher or comparable predictive performance of LLpowershap compared to other Shapley based wrapper methods, or filter methods. LLpowershap is also ranked the best in mean ranking among the seven feature selection methods tested on the benchmark datasets.ConclusionOur results demonstrate that LLpowershap is a viable wrapper feature selection method that can be used for feature selection in large biomedical datasets and other settings.

Noise-assisted hybrid attention networks for low-dose PET and CT denoising.

Positron emission tomography (PET) and computed tomography (CT) play a vital role in tumor-related medical diagnosis, assessment, and treatment planning. However, full-dose PET and CT pose the risk of excessive radiation exposure to patients, whereas low-dose images compromise image quality, impacting subsequent tumor recognition and diseasediagnosis. To solve such problems, we propose a Noise-Assisted Hybrid Attention Network (NAHANet) to reconstruct full-dose PET and CT images from low-dose PET (LDPET) and CT (LDCT) images to reduce patient radiation risks while ensuring the performance of subsequent tumor recognition. NAHANet contains two branches: the noise feature prediction branch (NFPB) and the cascaded reconstruction branch. Among them, NFPB providing noise features for the cascade reconstruction branch. The cascaded reconstruction branch comprises a shallow feature extraction module and a reconstruction module which contains a series of cascaded noise feature fusion blocks (NFFBs). Among these, the NFFB fuses the features extracted from low-dose images with the noise features obtained by NFPB to improve the feature extraction capability. To validate the effectiveness of the NAHANet method, we performed experiments using two public available datasets: the Ultra-low Dose PET Imaging Challenge dataset and Low Dose CT Grand Challengedataset. As a result, the proposed NAHANet achieved higher performance on common indicators. For example, on the CT dataset, the PSNR and SSIM indicators were improved by 4.1dB and 0.06 respectively, and the rMSE indicator was reduced by 5.46 compared with the LDCT; on the PET dataset, the PSNR and SSIM was improved by 3.37dB and 0.02, and the rMSE was reduced by 9.04 compared with theLDPET. This paper proposes a transformer-based denoising algorithm, which utilizes hybrid attention to extract high-level features of low dose images and fuses noise features to optimize the denoising performance of the network, achieving good performance improvements on low-dose CT and PETdatasets.

An Enhanced Mobile Localization Algorithm Integrating Multiple AUKF Models for Mixed Indoor Environments

Abstract The positioning technology based on ultra-wideband ranging has been widely applied in the field of indoor positioning due to its excellent localization capabilities. However, mixed line-of-sight (LOS) and non-line-of-sight (NLOS) indoor environments severely constrain positioning accuracy. To address this issue, we propose an innovative algorithm based on the adaptive unscented Kalman filter (AUKF) and interactive multiple model (IMM), designed to significantly enhance positioning accuracy in mixed indoor environments by mitigating the impact of NLOS errors and inaccurate process noise. Firstly, recognizing the distinct characteristics of ranging errors in indoor environments, we develop LOS and NLOS ranging models separately. Based on these models, the unscented Kalman filters are constructed for LOS and NLOS environments to accurately simulate the mixed LOS/NLOS indoor environments. Secondly, determining the statistical characteristics of process noise is challenging, often leading to degraded filter performance. We address this issue by proposing an environment-based AUKF algorithm, which significantly enhances the robustness and accuracy of the positioning system. Finally, the environment-based AUKFs are integrated into the IMM framework to constrain NLOS errors and achieve precise positioning effectively. Simulation, open-source dataset validation and experimental results demonstrate that the proposed algorithm significantly improves the accuracy and stability of mobile target positioning in mixed LOS/NLOS indoor environments.

Effective low-exposure color image enhancement based on histogram equalization with spatial contextual information

Abstract Color image enhancement refers to improving the visual quality of color images. This enhancement aims to make the images more visually appealing, precise, and easier to interpret, often by accentuating essential features or details while minimizing noise or distortion. Effective image enhancement is crucial in numerous fields like medical imaging; enhancing the medical image is vital for accurate diagnosis , and remote sensing . In security and surveillance, enhanced clarity in footage from surveillance cameras, especially in low-light scenarios, is crucial for identifying subjects and activities. However, image enhancement faces several challenges, like noise amplification and Over-enhancement, leading to unnatural-looking images with exaggerated or distorted features. In this paper, low-exposed or night color images are considered for enhancement, and this paper introduces exposure-based recursive histogram equalization techniques along with an Energy Curve instead of the conventional histogram, the energy curve is similar to a histogram based on spatial Contextual Information of an image. The proposed methods are Recursive Exposure-based Sub-image Histogram Equalization, Recursively Separated Exposure-based Sub-image Histogram Equalization, and Exposure-based Sub-image Histogram Equalization techniques considering spatial contextual information of images using an Energy Curve to improve results. These methods were tested on several publicly available datasets with low-contrast color images and compared with HE, BBHE, DSHE, CLAHE, ESIHE, R-ESIHE, and RS-ESIHE. The proposed techniques are evaluated using parameters like AMBE, PSNR, MSE, Entropy, SSIM, and FSIM. The average PSNR values for eight images using the aforementioned techniques were 9.10817, 20.80568, 12.82645, 10.39347, 16.27458, 15.19979, and 14.59595, respectively. In contrast, the proposed ESIHE with Energy Curve achieved a PSNR of 22.31585, outperforming other methods across multiple metrics, particularly excelling in noise reduction (PSNR), error minimization (MSE: 193.44), structural similarity (SSIM: 0.733319), and feature retention (FSIM: 0.917678). The comparison demonstrates that enhancement methods can be significantly improved by considering spatial contextual information.

Identification of wheel surface cracks under high noise environment based on texture segmentation and combination descriptor

ABSTRACT The wheel is easy to produce defects after a long time of use. Train maintenance technicians must overhaul and maintain train wheels on time and regularly to ensure the safe operation of trains. The existing manual detection has disadvantages of low efficiency and strong subjectivity. Compared with other defect forms, the identification accuracy of wheel surface crack is low due to the high noise and weak features, this paper proposes a feature enhancement and identification method for wheel surface crack based on texture segmentation and combination descriptor. Firstly, a morphology-based texture segmentation method is proposed to separate cracks and noise. Then, the crack characteristics are enhanced by filtering. Finally, a crack diagnosis method based on combination descriptor is proposed, which is evaluated from two dimensions of crack morphology and distribution characteristics. In order to verify the effectiveness of the proposed method, the images of the wheel collected from the freight car workshop were used for verification, and the detection rate reached 93.78%. The experimental results show that the proposed method is more effective in the case of weak crack features and surface dirt, and can better adapt to the identification of surface cracks on wheel in complex engineering applications.

Rolling bearing fault diagnosis method based on SSA-IWT-EMD

Aiming at the problem of insufficient wavelet threshold noise reduction and unclear extraction of characteristic frequency of EMD decomposition, a rolling bearing fault diagnosis method based on SSA-IWT-EMD is proposed. Two adjustment factors are introduced to propose an improved threshold function (IWT), which overcomes the shortcomings of traditional soft and hard thresholds, and the sparrow search optimization algorithm (SSA) is used to globally optimize its parameters to achieve rolling bearing signal noise reduction. A comprehensive index P is proposed to select and reconstruct the components generated by empirical mode decomposition (EMD) to highlight the fault characteristic information of the signal. Envelope spectrum analysis is used to realize bearing fault diagnosis. Simulation and measurement results verify the effectiveness of the proposed method; at the same time, compared with the method of selecting components with a single index and the literature method, it is shown that the comprehensive index P and the proposed method have stronger noise reduction and feature extraction capabilities, and the envelope spectrum amplitude and frequency doubling components are more obvious, which can better realize the fault diagnosis of rolling bearings.

Noise Study Auralization of an Open-Rotor Engine

Based on the performance and acoustic data files of reduced-size open-rotor engines in low-speed wind tunnels, the static sound pressure level was derived by converting the 1-foot lossless spectral density into sound-pressure-level data, the background noise was removed, and the results were corrected according to the environmental parameters of the low-speed wind tunnels. In accordance with the requirements of Annex 16 of the Convention on International Civil Aviation Organization and Part 36 of the Civil Aviation Regulations of China on noise measurement procedures, the takeoff trajectory was physically modeled; the static noise source was mapped onto the takeoff trajectory to simulate the propagation process of the noise during takeoff; and the 24 one-third-octave center frequencies that corresponded to the SPL data were corrected for geometrical dispersion, atmospheric absorption, and Doppler effects, so that the takeoff noise could be corrected to represent a real environment. In addition, the audible processing of noise data with a 110° source pointing angle was achieved, which can be useful for enabling practical observers to analyze the noise characteristics.

Applications of deep learning-based denoising methodologies for scanning electron microscope images

Abstract In this paper, we use five types of deep-learning algorithms for denoising scanning electron microscope (SEM) measurement data. Denoising of SEM images is an important task since the images often suffer from noise, which can make it difficult to accurately interpret the data. We also investigate realistic SEM denoising characteristics using a variety of metrics to assess the quality of denoised images. Overall, we find that the trained generative models provide superior denoising performance and that it is crucial to objectively quantify the performance, just like in the scanning process itself. It is anticipated that the deep-learning based technique can accelerate image measurements, which can be utilized for very fast analytical investigations. We also demonstrate that the success of a generative model may depend on the appropriate assessment of noise characteristics in the specific image data analysis of interest. Moreover, it is addressed that denoising performance can be properly evaluated when a relevant metrics that aligns well with human visual systems.

MFCA-MICNN: a convolutional neural network with multiscale fast channel attention and multibranch irregular convolution for noise removal in dMRI

Diffusion magnetic resonance imaging (dMRI) currently stands as the foremost noninvasive method for quantifying brain tissue microstructure and reconstructing white matter fiber pathways. However, the inherent free diffusion motion of water molecules in dMRI results in signal decay, diminishing the signal-to-noise ratio (SNR) and adversely affecting the accuracy and precision of microstructural data. In response to this challenge, we propose a novel method known as the Multiscale Fast Attention-Multibranch Irregular Convolutional Neural Network for dMRI image denoising. In this work, we introduce Multiscale Fast Channel Attention, a novel approach for efficient multiscale feature extraction with attention weight computation across feature channels. This enhances the model's capability to capture complex features and improves overall performance. Furthermore, we propose a multi-branch irregular convolutional architecture that effectively disrupts spatial noise correlation and captures noise features, thereby further enhancing the denoising performance of the model. Lastly, we design a novel loss function, which ensures excellent performance in both edge and flat regions. Experimental results demonstrate that the proposed method outperforms other state-of-the-art deep learning denoising methods in both quantitative and qualitative aspects for dMRI image denoising with fewer parameters and faster operational speed.

Adaptive stochastic resonance enhanced weak linear frequency modulated signal perception in low signal-to-noise ratio environments

Abstract The linear frequency modulated (LFM) signal has gained extensive utilization in radar, sonar and covert communication system due to its large time-bandwidth product, high-precision distance measurement and low detection probability. The perception of weak LMF signals holds significant importance yet encounters substantial challenges within the increasing complexity of the electromagnetic environments. Consequently, this paper proposes an adaptive stochastic resonance (ASR) enhanced approach for perceiving weak LFM signals in low signal-to-noise ratio (SNR) conditions. This approach initially commences a pioneering study on the quantitative synergistic resonance mechanism among LFM signals, random noise, and nonlinear stochastic resonance (SR) systems. Subsequently, the ASR system’s implementation becomes straightforward through the adaptive adjustment of SR system parameters according to the LFM signal and noise characteristics. This implementation leverages the inherent property of noise energy transfer to signal energy, facilitating the enhancement of ordered weak signals via random noise. Following the enhancement of the output signal by the ASR system, the Wigner–Ville distribution (WVD) transform’s time-frequency concentration property is employed to extract the time-frequency characteristics. Building on the WVD transform, the Radon transform with linear integral projection is applied to further harness the time-frequency domain energy concentration, thereby achieving the effective perception of weak LFM signals. Finally, the effectiveness of the proposed method in improving the perception performance of weak LFM signal in very low SNR conditions is verified through numerical simulations. It is anticipated that this method will have potential application value in fields such as radar, sonar, and communication under complex electromagnetic environments.

Transient flow and noise characteristics of vortex-turbulence-noise interaction in centrifugal pump

Centrifugal pump operation noise is an avoidable common phenomenon in engineering practices, and its characteristics are closely influenced by the flow characteristics of the fluid. In this paper, a numerical simulation of centrifugal pump operation with different flow rates is carried out using the detached eddy simulation turbulence model and compared with the experimental results. Then the noise source and far-field radiated noise characteristics of the centrifugal pump are investigated using Lighthill’s analogy. The results show that the noise of centrifugal pump is closely related to the flow characteristics of impeller, especially the generation and development of vortex. The relationship between turbulent kinetic energy (TKE) and vortex is complex, involving the generation, development and shedding of vortex. The influence of flow rate variation on vortex structure in impeller is studied by vortex transport equation. It is found that the relative vortex stretching (RVS) term is the main driving force of vortex distribution. The distribution of the Coriolis force (CORF) term is mainly concentrated in the inlet and outlet areas of the flow channel, which is related to the rotational motion of the impeller and the dynamic characteristic of the fluid. The power spectral density inside the impeller is mainly concentrated in the low frequency region, indicating that the low frequency noise component is dominant in energy. The directivity curve of far-field noise pressure level is influenced by hydrodynamic effect, impeller rotation direction and pump design structure, and will have a certain deviation. The spectral curve analysis of velocity, vorticity, pressure and sound pressure level shows that these physical quantities have strong correlation on the spectrum, in which the low frequency component is large, and the high frequency component is complex and volatile.

Study on the Robust Filter Method of SINS/DVL Integrated Navigation Systems in a Complex Underwater Environment

This paper proposes an improved adaptive filtering algorithm based on the Sage–Husa adaptive Kalman filtering algorithm to address the issue of measurement noise characteristics impacting the navigation accuracy in strapdown inertial navigation system (SINS)/Doppler Velocity Log (DVL) integrated navigation systems. Addressing the non-positive definite matrix problem prevalent in traditional adaptive filtering algorithms and aiming to enhance measurement noise estimation accuracy, this method incorporates upper and lower thresholds determined by a discrimination factor. In the presence of abnormal measurement data, these thresholds are utilized to adjust the covariance of the innovation, subsequently re-estimating the system’s measurement noise through a decision factor based on the innovation. Simulation and experiment results demonstrate that the proposed improved adaptive filtering algorithm outperforms the classical Kalman filter (KF) in terms of navigation accuracy and stability. Furthermore, the filtering performance surpasses that of the Sage–Husa algorithm. The simulation results in this paper show that the relative position positioning error of the improved method is reduced by 49.44% compared with the Sage–Husa filtering method.

Event Stream Denoising Method Based on Spatio-Temporal Density and Time Sequence Analysis.

An event camera is a neuromimetic sensor inspired by the human retinal imaging principle, which has the advantages of high dynamic range, high temporal resolution, and low power consumption. Due to the interference of hardware and software and other factors, the event stream output from the event camera usually contains a large amount of noise, and traditional denoising algorithms cannot be applied to the event stream. To better deal with different kinds of noise and enhance the robustness of the denoising algorithm, based on the spatio-temporal distribution characteristics of effective events and noise, an event stream noise reduction and visualization algorithm is proposed. The event stream enters fine filtering after filtering the BA noise based on spatio-temporal density. The fine filtering performs time sequence analysis on the event pixels and the neighboring pixels to filter out hot noise. The proposed visualization algorithm adaptively overlaps the events of the previous frame according to the event density difference to obtain clear and coherent event frames. We conducted denoising and visualization experiments on real scenes and public datasets, respectively, and the experiments show that our algorithm is effective in filtering noise and obtaining clear and coherent event frames under different event stream densities and noise backgrounds.

A Review of Linear Compressor Vibration Isolation Methods

Linear compressors exhibit high compression efficiency and low noise characteristics, showcasing broad application prospects in various fields such as aerospace, medicine, household appliances, and more. However, due to the complexity of their structures and operation, the issue of vibration isolation in linear compressors has long been a research challenge within the industry. Addressing this challenge, this paper provides an overview of vibration isolation optimization methods for linear compressors. It delves into the discussion of different vibration sources in linear compressors and their respective measurement techniques. By integrating both single degree of freedom (SDOF) and multiple degree of freedom (MDOF) vibration isolation models, this paper describes both active and passive vibration isolation methods tailored to linear compressors. Furthermore, a feasible optimization approach is proposed. Finally, the paper offers insights into the developmental potential and feasibility of vibration energy recovery strategies.

Vehicle noise characteristics in magnetotelluric data and vehicle noise removal using waveform fitting

Magnetic field fluctuations due to vehicle noise were observed in magnetotelluric (MT) time-series data measured near roads. The observed vehicle noise had magnitudes ranging from tens to thousands of μA/m, whereas the observed weak natural MT signal magnitudes were approximately tens of μA/m. A small signal-to-noise ratio made it difficult to apply robust processing for removing vehicle noise. In addition, vehicle noise severely distorts the MT response in the MT deadband from 0.01 Hz to 0.3 Hz, where the MT signal is very weak, and methods to remove it are required for deep structure imaging. In this study, magnetic field fluctuations due to moving vehicles were simulated with a magnetic dipole and attempted to be removed using a waveform fitting method. A total of 378 vehicle noises were extracted from the near-road MT data and synthesized with the remote MT data without vehicle noises to investigate the effect of vehicle noise on the MT response. Removal of vehicle noise from synthesized remote MT data resulted in substantial restoration of the apparent resistivity and phase curves around the MT deadband and below 0.001 Hz. In the MT field data, the vehicle noise was simulated and removed with two moving dipoles; the magnitude of the remaining vehicle noise was reduced by approximately half compared to a single dipole, and very stable apparent resistivity and phase curves were obtained. Although electromagnetic noise distortion remains after vehicle noise removal, the waveform fitting method significantly improves the apparent resistivity and phase curve response in the 0.01–0.3 Hz frequency band.

Modeling and characterization of low frequency noise in self-aligned top-gate coplanar IGZO thin-film transistors

Abstract A modified low-frequency noise (LFN) model was proposed to accurately estimate the quality of the gate dielectric in self-aligned top-gate (SA TG) coplanar structure indium–gallium–zinc oxide (IGZO) thin-film transistors (TFTs). The proposed LFN model was derived by modifying the conventional carrier number with correlated mobility fluctuation model considering the peculiar characteristics of SA TG coplanar IGZO TFTs such as the channel length reduction due to the diffusion of hydrogen atoms or oxygen vacancies from the source/drain to the channel, as well as the relatively large source/drain parasitic resistance. The proposed model was validated by demonstrating that the measured LFN values were in good agreement with the predicted values from the proposed model for all SA TG coplanar IGZO TFTs with SiO2 gate dielectrics deposited under different plasma-enhanced chemical vapor deposition (PECVD) power densities. The near-interface gate dielectric trap densities extracted from each TFT using the proposed LFN model revealed a clear increase as the PECVD power increased, which is considered a major cause of poor positive-bias-temperature-stress stability of the SA TG coplanar IGZO TFT with SiO2 gate dielectric deposited under high PECVD power conditions.

Advancements in Optical Resonator Stability: Principles, Technologies, and Applications

This paper provides an overview of the study of optical resonant cavity stability, focusing on the relevant principles, key technological advances, and applications of optical resonant cavities in a variety of high-precision measurement techniques and modern science and technology. Firstly, the vibration characteristics, thermal noise, and temperature characteristics of the reference cavity are presented. Subsequently, the report extensively discusses the advances in key technologies such as mechanical vibration isolation, thermal noise control, and resistance to temperature fluctuations. These advances not only contribute to the development of theory but also provide innovative solutions for practical applications. Typical applications of optical cavities in areas such as laser gyroscopes, high-precision measurements, and gravitational wave detection are also discussed. Future research directions are envisioned, emphasising the importance of novel material applications, advanced vibration isolation technologies, intelligent temperature control systems, multifunctional integrated optical resonator design, and deepening theoretical models and numerical simulations.

Cross-attention mechanism-based spectrum sensing in generalized Gaussian noise

Spectrum sensing (SS) technology is essential for cognitive radio (CR) networks to effectively identify and utilize idle spectrum resources. Due to the influence of noise characteristics in the channel, providing accurate sensing results is challenging. In order to improve the performance of SS under non-Gaussian noise and overcome the limitations of existing methods that are mostly based on a single feature, we propose a novel time-frequency cross fusion network (TFCFN). Specifically, we utilize gated recurrent units (GRU) to capture long-term dependencies in the time domain on the original signals, meanwhile, we perform a fast Fourier transform (FFT) on the original signals to obtain the frequency domain information, and subsequently use convolutional neural networks (CNN) to extract the local spatial features in the frequency domain. Ultimately, these time-domain and frequency-domain features are dynamically fused through a cross-attention mechanism to construct more comprehensive and robust features for signal classification. We use generalized Gaussian distribution (GGD) as the noise model and reconstruct the RadioML2016.10a dataset to explore the performance under various noise conditions. The experimental results show that compared with the baseline methods, TFCFN exhibits better detection ability and maintains lower complexity in both Gaussian and non-Gaussian noise environments. Notably, when the shape parameter of GGD is set to 0.5 and the signal-to-noise ratio (SNR) of the received signal is -16dB, it can maintain the probability of false alarm (Pf\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$P_f$$\\end{document}) of 10% while still ensuring the probability of detection (Pd\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$P_d$$\\end{document}) of over 90%.