Large-scale Noise Research Articles

Two-point radiation statistics from large-scale turbulent structures within supersonic jets

The noise from large-scale coherent turbulent structures within jets remains the dominant source. For the purpose of developing future control systems for the large-scale noise source, we investigate the statistics between upstream and downstream radiating waves. We investigate two off-design supersonic jet flows with instability theory and associated noise radiation, large-eddy simulation (LES), and experiments. We compare the auto-correlation, cross-correlation, coherence, and other statistics predicted by aeroacoustic instability theory. As instability waves are closely connected with the formation of large-scale turbulent structures, they yield insight into large-scale noise statistics. We investigate two nozzles at two supersonic off-design conditions. The first is a biconic nozzle operating at an unheated condition, and the second is a NASA nozzle operating at a heated condition. We find that for these jets, the noise from instability waves is coherent between 0.40 to 0.70 at large-scale radiation frequencies between the downstream and upstream radiation directions.

Overall Filtering Algorithm for Multiscale Noise Removal From Point Cloud Data

The multiscale noise in the 3D point cloud data of rock surfaces which collected by 3D scanners has a significant influence on the exploration of rock surface morphology. To this end, this paper proposes a multiscale noise removal overall filtering algorithm. The specific processing procedure of the algorithm is as follows. First, a weighted principal component analysis is performed on point cloud data, i.e., the neighboring point distance is used as a weight in the principal component analysis, the covariance feature matrix of the weighted point is estimated, and the eigenvector corresponding to the lowest eigenvalue is used as the normal vector of the point cloud data. Second, in the weighted principal component analysis, estimating three eigenvalues corresponding to the Eigen matrix of the point cloud data, the ratio of the eigenvalue corresponding to the normal vector to the sum of three eigenvalues is used as the surface change factor. For the sample point, if the surface change factor of one sample point is less than the average value of the surface change factor of all sample points in the neighborhood, the sample point belongs to a flat area; otherwise, it belongs to a mutation area. Finally, in order to achieve multiscale noise removal, statistical filtering algorithm is used to remove large scale noise in flat area, additionally bilateral filtering algorithm is adopted to remove small scale noise in mutation area. In the experiments, the improved principal component analysis is combined with the overall filtering algorithm to accurately estimate the eigenvalues of the point cloud data points. After that, the eigenvalues of the sample points are used to distinguish between flat area and mutation area, so as to consider large scale noise and small scale noise. From the experimental results, it can be seen that overall filtering algorithm can consider both large scale and small scale noise and can remove noise from the point cloud data of rock samples. Visual judgment, normal distribution and fractal distribution tests are employed on filtered rock sample point cloud data to verify the reliability of the filtering results.

Low-Frequency and Large-Scale Hybrid Sound Absorption Using Active Force Control

Effective low-frequency and large-scale noise reduction are crucial in industrial applications. Conventional passive methods lack the effective low-frequency performance, and existing hybrid methods are costly to realize large-scale absorption. Hence, an effective and simply actuated solution for low-frequency and large-scale absorption is urgently needed. In this study, the low-frequency (100–500 Hz) quasi-perfect absorption characteristics of a hybrid structure which adopts the active force control (AFC) strategy are confirmed by experiment and its large-scale properties are analyzed. By using a flexible plate driven by a concentrated force as the AFC component and a passive MPP absorber, the large-scale model is established to absorb the normally incident plane wave. The structural–acoustic coupling characteristics are analyzed in detail and validated both by the experiment and finite element method. It is observed that by the acoustic–structural coupling, the frequency shift of the first structural mode will be inversely proportional to the total depth of the air cavities. Key parameters relating to the control force and other specifications of the hybrid system are analyzed to improve the broadband performance of the hybrid structure. Owing to the optimized control force, the coupled structural modes could be used to realize commendable large-scale (up to ten times larger than conventional hybrid absorber) absorptions. Compared with conventional methods, the designed absorber is able to realize low-frequency broadband quasi-perfect absorptions while dramatically reducing the number of secondary sources.

Analyzing Convergence of Distributed FxLMS Algorithm

The distributed FxLMS algorithm based on acoustic sensor networks can solve the problem of the increased computational complexity caused by a large-scale active noise control (ANC) system. Firstly, the distributed algorithm is deduced theoretically. In the ANC process based on sensor networks, the iteration of partial weight coefficients is replaced by the real-time communication of weight coefficients among nodes. Then the convergence characteristics of the distributed algorithm in its mean and mean square senses are analyzed and verified with simulation. Finally, the noise reduction effects of different algorithms are compared. The results show that the distributed algorithm, with its computational complexity reduced, can achieve the same noise reduction effects as the centralized ANC system.

Experimental study of vibration characteristics of 110 kV oil-immersed transformer

As the voltage level and capacity of power transformer continue to rise, this results in its increasingly prominent vibration and noise problems. In this article, we study the vibration characteristics of the 110-kV oil-immersed transformer by means of experiments. Under different no-load voltages and short-circuit currents, the vibration signals in different positions and directions of the transformer are monitored respectively, and the vibration laws are analyzed. First, the vibration law of transformers is closely related to the operating conditions of transformers, which directly affects the frequency components of transformer vibration and the amplitude corresponding to different frequencies; second, it is found that the vibration law of transformers facing different directions will change under different operating conditions; and third, it summarizes the vibration of transformers in different positions under different operating conditions. These results can not only be used as the experimental basis for the theoretical research on large-scale transformer noise but also provides experimental support for the research on vibration reduction and noise reduction methods of large-scale transformer in engineering.

플랜트 대형기계의 소음 발생 실태에 관한 실험적 연구

The purpose of this study was to establish the noise generation actual condition of large machine in plant. most of the plant's large-scale machines are designed and manufactured overseas, so it is difficult to access basic information. The regulatory standards, management plans and measurement methods for these machines are insufficient. It is necessary to obtain information about the noise storage devices installed for warranty and the noise reduction device when designing the equipment. Investigation of the occurrence of large-scale machine noise at domestic plant sites was conducted. The sound insulation design of the industrial safety and health care law benchmark and the large-scale machine compound noise design of the equipment are carried out.

Anisotropic source modelling for turbulent jet noise prediction.

An anisotropic component of the jet noise source model for the Reynolds-averaged Navier–Stokes equation-based jet noise prediction method is proposed. The modelling is based on Goldstein's generalized acoustic analogy, and both the fine-scale and large-scale turbulent noise sources are considered. To model the anisotropic characteristics of jet noise source, the Reynolds stress tensor is used in place of the turbulent kinetic energy. The Launder–Reece–Rodi model (LRR), combined with Menter's ω-equation for the length scale, with modified coefficients developed by the present authors, is used to calculate the mean flow velocities and Reynolds stresses accurately. Comparison between predicted results and acoustic data has been carried out to verify the accuracy of the new anisotropic source model.This article is part of the theme issue ‘Frontiers of aeroacoustics research: theory, computation and experiment’.

Study on the application of point cloud denoising in Gobi desert surface

Affected by the noise of desert environment and UAV mechanical system, the surface point cloud data will be mixed with noise points when we extracted the data from photos of UAV with overlapping regions. These noise points will seriously reduce the precision of DEM and DLG. In this paper, we use a denoising combination of two filter methods, Topscan filter is used to remove large-scale noise points and bilateral filter is used to deal with small noise during the surface point cloud. Experiments shows, the denoising combination can remove the noise points and improve the quality of aerophotogrammetry products.

Observation of Large-Scale Shot Noise in a High-Current Electron Beam

The character of the noise component of an electron beam formed by an explosive-emission cathode has been experimentally investigated. It is shown that its source is a large-scale shot noise related to the manifestation of ectonic structures in the electron beam. The measured quantitative characteristics of ectons are in good agreement with previously obtained data and estimates.

An Exploratory Survey of Sound Levels in New York City Restaurants and Bars

For several decades, there has been a significant need to better educate the public about noise pollution. A small number of small-scale studies have focused on the sound levels of restaurants and their impact on health and hearing. There have also been an increasing number of media articles stating that eating and drinking venues are getting increasingly loud making it more difficult for people to connect with others in conversation. This study reports on an exploratory large-scale noise survey of sound levels of 2376 restaurants and bars in New York City using a novel smart-phone application and categorized them based on how quiet or loud they were. The results suggest that: 1) A significant number of venues have high sound levels that are not conducive to conversation and may be endangering the health of patrons and employees, 2) that the reported sound levels by the venue managers on their online public business pages generally underestimated actual sound levels, and 3) the average sound levels in restaurants and bars are correlated by neighborhood and type of cuisine.

Public cloud computing for seismological research: Calculating large-scale noise cross-correlations using ALIYUN

Public cloud computing for seismological research: Calculating large-scale noise cross-correlations using ALIYUN

Validation of large scale noise exposure modelling by long-term measurements

Abstract Large scale noise exposure modelling is used in epidemiological research projects as well as for noise mapping and strategic action planning. Such calculations should always be accompanied by an assessment of uncertainty, on the one hand to check for systematic deviations and on the other hand to investigate the sources of uncertainty to address them in future studies. Within the SiRENE (Short and Long Term Effects of Transportation Noise Exposure) project, a large scale nationwide assessment of Switzerland’s road, railway, and aircraft noise exposure was conducted for the year 2011. In the present follow-up study, we equipped 180 sleeping and/or living room windows with sound level meters for one week. The resulting dataset was used to validate noise exposure modelling within SiRENE. For the noise metric LDEN the comparison revealed a difference of 1.6 ± 5 dB(A) when taking all measurements into account. After removing measurement sites with noise mitigation measures not considered in the modelling, the difference to the calculation was reduced to 0.5 ± 4 dB(A). As major sources of uncertainty, the position accuracy and topicality of infrastructure and building geometries, the traffic modelling as well as the acoustic source and propagation models were identified.

Dynamics of the stochastic chemostat with Monod-Haldane response function

The stochastic chemostat model with Monod-Haldane response function is perturbed by environmental white noise. This model has a global positive solution. We demonstrate that there is a stationary distribution of the stochastic model and the system is ergodic under appropriate conditions, on the basis of Khasminskii’s theory on ergodicity. Sufficient criteria for extinction of the microbial population in the stochastic system are established. These conditions depend strongly on the Brownian motion. We find that even small scale white noise can promote the survival of microorganism populations, while large scale noise can lead to extinction. Numerical simulations are carried out to illustrate our theoretical results.

Adaptive Communications for Stochastic Networks

Wireless networks have crucial needs for increasing efficiency in the spectrum usage. Adaptive communication is a key enabler for enhancing the spectral efficiency (SE) of wireless networks. This paper develops a framework for design and analysis of stochastic wireless networks, in which nodes employ diversity techniques and are randomly distributed in space. The signal-to-interference-plus-noise ratio (SINR) distribution is determined for finite and infinite stochastic networks at the output of the optimum combiner (OC). The statistics of the SINR at the OC output are used to design adaptive communication systems with diversity in the presence of small- and large-scale fading, interference, and noise. Slow adaptive modulation for any diversity order is analyzed in the presence of an interference field modeled as a Poisson point process. The results are applied to evaluate the benefits of adaptive modulation techniques with diversity for maximizing the SE in a stochastic network.

An efficient 3D point cloud data denoising algorithm for ship block visual measurement

For the 3D point cloud data aquired by laser scanning measurement of ship block, an efficient denoising algorithm based on image and normal vector threshold judgement is proposed. Firstly, large scale noise points are eliminated using global threshold judgement based image, then Kuwahara filter algorithm is used for data smoothing and a denoising algorithm based on normal vector threshold judgement is proposed to eliminate noises point excluding ship manufacture sections. The experiment result demonstrates that not only the proposed denoising algorithm keeps key data points but also avoids bluring point cloud boundary and eliminates noise points effectively.

Evaluating underwater ambient noise levels across a changing Arctic environment

As a result of climate change, the Arctic underwater acoustic environment is undergoing an unprecedented transformation of its ambient noise sources, including an expanded wind fetch over open water and an intensification of maritime transportation lanes across Northern routes. This comparative study synthesizes multi-annual passive acoustic monitoring (PAM) data collected at various Alaskan Arctic environments (Bering Strait, Chukchi Plateau, Chukchi, and Beaufort Seas) in order to evaluate the statistics of ambient noise across the changing region. Mean levels at significant frequency ranges are presented, as well as percentile PSD levels in 1-Hz frequency bins. Remote sensing sea ice data is utilized to separate each annual dataset into 3 different regimes: open water, covered, and transition (when ice is forming or breaking up). During each, the dominant large-scale noise sources are characterized and the relationship between noise and local wind speed is evaluated. Through manual inspection, ship transits and seismic surveys have been extracted, allowing ambient level associations on the scale of hours or days. Consistently across all years, the Chukchi Plateau site, which is the most distant from shore, presents the lowest overall ambient noise levels (20-40 dB lower than other locations), potentially indicating a new Arctic ambient noise baseline.

Rapid updating if 3D road traffic noise maps in large cities

A rapid updating system of three-dimensional road traffic noise maps is presented. The system described in this paper primarily consists of two parts. The first part is an optimized noise update algorithm, which can significantly improve the update speed for large scale noise maps by reducing the amount of calculation work. To make full use of scattered computing resources, the second part is a distributed computation system with high simplicity and extensibility and the ability of self-balance. For traffic flow data gathered in large cities, a model that uses a speed-density relationship is developed to estimate the traffic volume from the Global Positioning System (GPS) data collected by floating cars. Finally, an experiment was performed to analyze the efficiency of both the distributed computation system and the optimized algorithm. The outcomes show that the distributed computation system has a near-linear speedup and the optimized algorithm can update a noise map of a 5.25 km2 area with 446,367 noise receiver points using about 96 s. Thus, for a large city with an area of approximately 500 km2, such as the central area of Guangzhou, its noise map can be updated hourly using only 3 personal computers.

Improved sparse representation with low-rank representation for robust face recognition

In this paper, an approach to learn a robust sparse representation dictionary for face recognition is proposed. As well known, sparse representation algorithm can effectively tackle slight occlusion problems for face recognition. However, if images are corrupted by heavy noise, performance will be not guaranteed. In this paper, to enhance the robustness of sparse representation to serious noise in face images, we integrate low rank representation into dictionary learning to alleviate the influence of unfavorable factors such as large scale noise and occlusion. Among which we extract eigenfaces by singular value decomposition (SVD) from the low rank pictures to reduce dictionary atoms and, thereby, optimize the efficiency of improved algorithm. Otherwise, we characterize each image using the histogram of orientated gradient (HOG) feature which has been proven to be an effective descriptor for face recognition in particular. The performance of the proposed Low-rank and HOG feature based ESRC (LH_ESRC) algorithm on several popular face databases such as the Extended Yale B database and CMU_PIE face database shows the effectiveness of our method. In addition, we evaluate the robustness of our method by adding different proportions of randomly noise and block occlusion and real disgusts. Experimental results illustrate the benefits of our approach.

Mesh Denoising using Extended ROF Model withL1Fidelity

This paper presents a variational algorithm for feature-preserved mesh denoising. At the heart of the algorithm is a novel variational model composed of three components: fidelity, regularization and fairness, which are specifically designed to have their intuitive roles. In particular, the fidelity is formulated as an L1 data term, which makes the regularization process be less dependent on the exact value of outliers and noise. The regularization is formulated as the total absolute edge-lengthed supplementary angle of the dihedral angle, making the model capable of reconstructing meshes with sharp features. In addition, an augmented Lagrange method is provided to efficiently solve the proposed variational model. Compared to the prior art, the new algorithm has crucial advantages in handling large scale noise, noise along random directions, and different kinds of noise, including random impulsive noise, even in the presence of sharp features. Both visual and quantitative evaluation demonstrates the superiority of the new algorithm.

High-frame-rate Video Denoising for Ultra-low Illumination

In this study, we present a denoising algorithm for high-frame-rate videos in an ultra-low illumination environment on the basis of Kalman filtering model and a new motion segmentation scheme. The Kalman filter removes temporal noise from signals by propagating error covariance statistics. Regarded as the process noise for imaging, motion is important in Kalman filtering. We propose a new motion estimation scheme that is suitable for serious noise. This scheme employs the small motion vector characteristic of high-frame-rate videos. Small changing patches are intentionally neglected because distinguishing details from large-scale noise is difficult and unimportant. Finally, a spatial bilateral filter is used to improve denoising capability in the motion area. Experiments are performed on videos with both synthetic and real noises. Results show that the proposed algorithm outperforms other state-of-the-art methods in both peak signal-to-noise ratio objective evaluation and visual quality.