Noise reduction mechanism of high-speed railway box-girder bridges installed with MTMDs on top plate

The issue of low-frequency structural noise radiated from high-speed railway(HSR) box-girder bridges(BGBs) is a significant challenge worldwide. Although it is known that vibrations in BGBs caused by moving trains can be reduced by installing multiple tuned mass dampers(MTMDs) on the top plate, there is limited research on the noise reduction achieved by this method. This study aims to investigate the noise reduction mechanism of BGBs installed with MTMDs on the top plate. A sound radiation prediction model for the BGB installed with MTMDs is developed, based on the vehicle–track–bridge coupled dynamics and acoustics boundary element method. After being verified by field tested results, the prediction model is employed to study the reduction of vibration and noise of BGBs caused by the MTMDs. It is found that installing MTMDs on top plate can significantly affect the vibration distribution and sound radiation law of BGBs. However, its impact on the sound radiation caused by vibrations dominated by the global modes of BGBs is minimal. The noise reduction achieved by MTMDs is mainly through changing the acoustic radiation contributions of each plate of the bridge. In the lower frequency range, the noise reduction of BGB caused by MTMDs can be more effective if the installation of MTMDs can modify the vibration frequency and distribution of the BGB to avoid the influence of small vibrations and disperse the sound radiation from each plate.

Analysis of the Influence of Sound Source Position and Electrode Placement on the Stimulation Pattern of the Cochlear Implant under Noise Conditions: Optimization of Auditory Perceptual Effects

Background: Cochlear implants (CI) are widely used to restore hearing in people with severe to profound hearing loss. However, optimizing CI performance, especially in difficult listening environments with background noise, remains a major challenge. Understanding the influence of factors such as sound source position and electrode placement on CI stimulation patterns is critical to improving auditory perception. Methods: In this study, an analysis was conducted to investigate the influence of sound source position and electrode placement on CI stimulation patterns under noisy conditions. For this purpose, a special measurement setup with a CI speech processor-microphone test box was used to simulate realistic listening scenarios and measure CI performance. Results: The results show that the effectiveness of CI noise reduction systems is influenced by factors such as the position of the sound source and electrode placement. In particular, the beamforming ultra zoom mode showed significantly better noise reduction than the omnidirectional mode, especially under real listening conditions. Furthermore, differences in electrode responses indicate individual variability in the CI user experience, highlighting the importance of personalized fitting algorithms. Conclusions: The results demonstrate the importance of considering environmental factors and individual differences when optimizing CI performance. Future research efforts should focus on the development of personalized fitting algorithms and the exploration of innovative strategies, such as the integration of artificial intelligence, to improve CI functionality in different listening environments. This study contributes to our understanding of CI stimulation patterns and lays the foundation for improving auditory perception in CI users.

Noise Reduction of an Axial Piston Pump by Valve Plate Optimization

Current researches mainly focus on the investigations of the valve plate utilizing pressure relief grooves. However,air?release and cavitation can occur near the grooves. The valve plate utilizing damping holes show excellent perfor?mance in avoiding air?release and cavitation. This study aims to reduce the noise emitted from an axial piston pump using a novel valve plate utilizing damping holes. A dynamic pump model is developed,in which the fluid properties are carefully modeled to capture the phenomena of air release and cavitation. The causes of di erent noise sources are investigated using the model. A comprehensive parametric analysis is conducted to enhance the understanding of the e ects of the valve plate parameters on the noise sources. A multi?objective genetic algorithm optimization method is proposed to optimize the parameters of valve plate. The amplitudes of the swash plate moment and flow rates in the inlet and outlet ports are defined as the objective functions. The pressure overshoot and undershoot in the piston chamber are limited by properly constraining the highest and lowest pressure values. A comparison of the various noise sources between the original and optimized designs over a wide range of pressure levels shows that the noise sources are reduced at high pressures. The results of the sound pressure level measurements show that the optimized valve plate reduces the noise level by 1.6 d B(A) at the rated working condition. The proposed method is e ective in reducing the noise of axial piston pumps and contributes to the development of quieter axial piston machines.

A local f-x Cadzow method for noise reduction of seismic data obtained in complex formations

A noise-reduction method with sliding called the local f-x Cadzow noise-reduction method, windows in the frequency-space （f-x） domain, is presented in this paper. This method is based on the assumption that the signal in each window is linearly predictable in the spatial direction while the random noise is not. For each Toeplitz matrix constructed by constant frequency slice, a singular value decomposition （SVD） is applied to separate signal from noise. To avoid edge artifacts caused by zero percent overlap between windows and to remove more noise, an appropriate overlap is adopted. Besides flat and dipping events, this method can enhance curved and conflicting events. However, it is not suitable for seismic data that contains big spikes or null traces. It is also compared with the SVD, f-x deconvolution, and Cadzow method without windows. The comparison results show that the local Cadzow method performs well in removing random noise and preserving signal. In addition, a real data example proves that it is a potential noise-reduction technique for seismic data obtained in areas of complex formations.

A novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise,minimum mean square variance criterion and least mean square adaptive filter

Underwater acoustic signal processing is one of the research hotspots in underwater acoustics.Noise reduction of underwater acoustic signals is the key to underwater acoustic signal processing.Owing to the complexity of marine environment and the particularity of underwater acoustic channel,noise reduction of underwater acoustic signals has always been a difficult challenge in the field of underwater acoustic signal processing.In order to solve the dilemma,we proposed a novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN),minimum mean square variance criterion(MMSVC) and least mean square adaptive filter(LMSAF).This noise reduction technique,named CEEMDAN-MMSVC-LMSAF,has three main advantages:(i) as an improved algorithm of empirical mode decomposition(EMD) and ensemble EMD(EEMD),CEEMDAN can better suppress mode mixing,and can avoid selecting the number of decomposition in variational mode decomposition(VMD);(ii) MMSVC can identify noisy intrinsic mode function(IMF),and can avoid selecting thresholds of different permutation entropies;(iii) for noise reduction of noisy IMFs,LMSAF overcomes the selection of deco mposition number and basis function for wavelet noise reduction.Firstly,CEEMDAN decomposes the original signal into IMFs,which can be divided into noisy IMFs and real IMFs.Then,MMSVC and LMSAF are used to detect identify noisy IMFs and remove noise components from noisy IMFs.Finally,both denoised noisy IMFs and real IMFs are reconstructed and the final denoised signal is obtained.Compared with other noise reduction techniques,the validity of CEEMDAN-MMSVC-LMSAF can be proved by the analysis of simulation signals and real underwater acoustic signals,which has the better noise reduction effect and has practical application value.CEEMDAN-MMSVC-LMSAF also provides a reliable basis for the detection,feature extraction,classification and recognition of underwater acoustic signals.

NOISE REDUCTION SCHEDULING METHOD IN A SHOP FLOOR AND ITS CASE STUDY

Noise reduction in a shop floor is one of the important parts of greenmanufacturing. In a shop floor machine tools are the main noise sources in a shop floor. A newapproach is discovered by investigation that the noise can be obviously reduced in a shop floor byoptimizing the scheduling between work pieces and machine tools. Based on the discovery, a newmethod of noise reduction is proposed. A noise reduction scheduling model in a shop floor isestablished, and the application of the model is also discussed. A case is studied, which shows thatthe method and model are practical.

Use of a plane jet for flow-induced noise reduction of tandem rods

Unsteady wake from upstream components of landing gear impinging on downstream components could be a strong noise source.The use of a plane jet is proposed to reduce this flow-induced noise.Tandem rods with different gap widths were utilized as the test body.Both acoustic and aerodynamic tests were conducted in order to validate this technique.Acoustic test results proved that overall noise emission from tandem rods could be lowered and tonal noise could be removed with use of the plane jet.However,when the plane jet was turned on,in some frequency range it could be the subsequent main contributor instead of tandem rods to total noise emission whilst in some frequency range rods could still be the main contributor.Moreover,aerodynamic tests fundamentally studied explanations for the noise reduction.Specifically,not only impinging speed to rods but speed and turbulence level to the top edge of the rear rod could be diminished by the upstream plane jet.Consequently,the vortex shedding induced by the rear rod was reduced,which was confirmed by the speed,Reynolds stress as well as the velocity fluctuation spectral measured in its wake.This study confirmed the potential use of a plane jet towards landing gear noise reduction.

Noise Reduction of Welding Defect Image Based on NSCT and Anisotropic Diffusion

In order to reduce noise effectively in the welding defect image and preserve the minutiae information, a noise reduction method of welding defect image based on nonsubsampled contourlet transform(NSCT) and anisotropic diffusion is proposed. Firstly, an X-ray welding defect image is decomposed by NSCT. Then total variation(TV) model and Catte_PM model are used for the obtained low-pass component and band-pass components, respectively. Finally, the denoised image is synthesized by inverse NSCT. Experimental results show that, compared with the hybrid method of wavelet threshold shrinkage with TV diffusion, the method combining NSCT with P_Laplace diffusion, and the method combining contourlet with TV model and adaptive contrast diffusion, the proposed method has a great improvement in the aspects of subjective visual effect, peak signal-to-noise ratio(PSNR) and mean-square error(MSE). Noise is suppressed more effectively and the minutiae information is preserved better in the image.

Evaluation of Diesel Engine Noise Reduction Measures Based on Hierarchy Diagnosis

Effect of different noise reduction measures for diesel engines was evaluated based on hierarchy diagnosis. The hierarchy diagnosis chart and hierarchy judgment matrix were given. Through evaluation of noise reduction measures, the main strategies of noise reduction were found. The result shows that the noise reduction level of different frequency belts varies from measure to measure. The reduction capacity of different measures could not add simply, which relates to the problem of parameter matching.

DNN-Based Speech Enhancement Using Soft Audible Noise Masking for Wind Noise Reduction

This paper presents a deep neural network(DNN)-based speech enhancement algorithm based on the soft audible noise masking for the single-channel wind noise reduction. To reduce the low-frequency residual noise, the psychoacoustic model is adopted to calculate the masking threshold from the estimated clean speech spectrum. The gain for noise suppression is obtained based on soft audible noise masking by comparing the estimated wind noise spectrum with the masking threshold. To deal with the abruptly time-varying noisy signals, two separate DNN models are utilized to estimate the spectra of clean speech and wind noise components. Experimental results on the subjective and objective quality tests show that the proposed algorithm achieves the better performance compared with the conventional DNN-based wind noise reduction method.

Joint Noise Reduction and lp-Norm Minimization for Enhancing Time Delay Estimation in Colored Noise

Time delay estimation (TDE) is an important issue in signal processing. Conventional TDE algorithms are usually efficient under white noise environments. In this paper, a joint noise reduction and -norm minimization method is presented to enhance TDE in colored noise. An improved subspace method for colored noise reduction is first performed. Then the time delay is estimated by using an -norm minimization method. Because the clean speech signal form the noisy signal is well extracted by noise reduction and the -norm minimization method is robust, the TDE accuracy can be enhanced. Experiment results confirm that the proposed joint estimation method obtains more accurate TDE than several conventional algorithms in colored noise, especially in the case of low signal-to-noise ratio.

Hybrid device for acoustic noise reduction and energy harvesting based on a silicon micro-perforated panel structure

A kind of hybrid device for acoustic noise reduction and vibration energy harvesting based on the silicon micro- perforated panel （MPP） resonant structure is investigated in the article. The critical parts of the device include MPP and energy harvesting membranes. They are all fabricated by means of silicon micro-electro-mechanical systems （MEMS） tech- nology. The silicon MPP has dense and accurate micro-holes. This noise reduction structure has the advantages of wide band and higher absorption coefficients. The vibration energy harvesting part is formed by square piezoelectric membranes arranged in rows. ZnO material is used as it has a good compatibility with the fabrication process. The MPP, piezo- electric membranes, and metal bracket are assembled into a hybrid device with multifunctions. The device exhibits good performances of acoustic noise absorption and acoustic-electric conversion. Its maximum open circuit voltage achieves 69.41 mV.

Hybrid In-Vehicle Background Noise Reduction for Robust Speech Recognition:The Possibilities of Next Generation 5G Data Networks

This pilot study focuses on employment of hybrid LMS-ICA system for in-vehicle background noise reduction.Modern vehicles are nowadays increasingly supporting voice commands,which are one of the pillars of autonomous and SMART vehicles.Robust speaker recognition for context-aware in-vehicle applications is limited to a certain extent by in-vehicle back-ground noise.This article presents the new concept of a hybrid system which is implemented as a virtual instrument.The highly modular concept of the virtual car used in combination with real recordings of various driving scenarios enables effective testing of the investigated methods of in-vehicle background noise reduction.The study also presents a unique concept of an adaptive system using intelligent clusters of distributed next generation 5G data networks,which allows the exchange of interference information and/or optimal hybrid algorithm settings between individual vehicles.On average,the unfiltered voice commands were successfully recognized in 29.34%of all scenarios,while the LMS reached up to 71.81%,and LMS-ICA hybrid improved the performance further to 73.03%.

RESEARCH ON DYNAMIC BEHAVIOUR AND NOISE REDUCTION OF METAL CUTTING SAW PLATES

Based upon theoretical and experimental methods of acoustics and vibration, the dynamic characteristics of cutting processes have been researched systematically, and a simplified method of quickly estimating the modal parameters of elastic circular plate structures under clamping has also been advanced. Furthermore, we try the best to explore effectively the noise-reducing ways in the sawing process. In fact, satisfactory results have been achieved in practice.

NOISE REDUCTION FOR FAST FADING CHANNEL BY RECURRENT LEAST SQUARES SUPPORT VECTOR MACHINES IN EMBEDDING PHASE SPACES

A new strategy for noise reduction of fast fading channel is presented. Firstly, more information is acquired utilizing the reconstructed embedding phase space. Then, based on the Recurrent Least Squares Sup-port Vector Machines (RLS-SVM), noise reduction of the fast fading channel is realized. This filtering tech-nique does not make use of the spectral contents of the signal. Based on the stability and the fractal of the cha-otic attractor, the RLS-SVM algorithm is a better candidate for the nonlinear time series noise-reduction. The simulation results shows that better noise-reduction performance is acquired when the signal to noise ratio is 12dB.

Fault Diagnosis of Wind Turbine Generator with Stacked Noise Reduction Autoencoder Based on Group Normalization

In order to improve the condition monitoring and fault diagnosis of wind turbines,a stacked noise reduction autoencoding network based on group normalization is proposed in this paper.The network is based on SCADA data of wind turbine operation,firstly,the group normalization(GN)algorithm is added to solve the problems of stack noise reduction autoencoding network training and slow convergence speed,and the RMSProp algorithm is used to update the weight and the bias of the autoenccoder,which further optimizes the problem that the loss function swings too much during the update process.Finally,in the last layer of the network,the softmax activation function is used to classify the results,and the output of the network is transformed into a probability distribution.The selected wind turbine SCADA data was substituted into the pre-improved and improved stacked denoising autoencoding(SDA)networks for comparative training and verification.The results show that the stacked denoising autoencoding network based on group normalization is more accurate and effective for wind turbine condition monitoring and fault diagnosis,and also provides a reference for wind turbine fault identification.

Research and application of inertia-gravity dedusting and noise reduction technology in mine airshaft outlet

Massive dust of exhausted airflow easily diffuses to the industrial square while hoisting shaft is used as return shaft in the retrograde ventilation,and certainly there is a problem of dust scattering and noise exceeding standard. In order to solve the problem,the coupled technology of dedusting and noise reduction in airshaft outlet was investigated and the device with the function of Dedusting and noise Reduction(DR) was originated. The device is named as DR diffuser,which comprehensively utilizes mechanisms of inertia-gravity dedusting and absorption-insulation sound. The DR diffuser has been applied to Xingdong Coal Mine,and the testing results show that:a. the resistance of DR diffuser is lower than 60 Pa;b. the noise level reaches to 49.2 dB(A);c. the dedusting efficiency of above 50 μm is higher than 85 %;d. dynamic pressure recovered from the outlet of main fan is larger than 50 Pa. The above measured data illustrates that comprehensive benefit of the DR diffuser is good,and the device is worth of applying to engineering site.

The Use of Acoustic Quality Assessment in the Management of the Road Noise Reduction Project

The article presents a new approach to the tasks of noise assessment and reduction in the urbanized environment endangered by road noise sources. It was proposed to include the acoustic quality model in the currently applied quantitative noise assessment in the management of urbanized environment. In particular, this model takes into account subjective features of sound quality, i.e.： loudness, sharpness, roughness, and fluctuation strength as well as noise mmoya^ce assessment obtained in laboratory conditions. The proposed way can be used in estimating investment costs of an acoustic barrier at the design stage

Artificial neural network-based determination of denoised optical properties in double integrating spheres measurement

Accurate determination of the optical properties of biological tissues enables quantitative understanding of light propagation in these tissues for optical diagnosis and treatment applications.The absorption(μa)and scattering(μs)coe±cients of biological tissues are inversely analyzed from their diffuse re°ectance(R)and total transmittance(T),which are measured using a double integrating spheres(DIS)system.The inversion algorithms,for example,inverse adding doubling method and inverse Monte Carlo method,are sensitive to noise signals during the DIS measurements,resulting in reduced accuracy during determination.In this study,we propose an arti ficial neural network(ANN)to estimateμa andμs at a target wavelength from the R and T spectra measured via the DIS to reduce noise in the optical properties.Approximate models of the optical properties and Monte Carlo calculations that simulated the DIS measurements were used to generate spectral datasets comprisingμa,μs,R and T.Measurement noise signals were added to R and T,and the ANN model was then trained using the noise-added datasets.Numerical results showed that the trained ANN model reduced the effects of noise inμa andμs estimation.Experimental veri fication indicated noise-reduced estimation from the R and T values measured by the DIS with a small number of scans on average,resulting in measurement time reduction.The results demonstrated the noise robustness of the proposed ANN-based method for optical properties determination and will contribute to shorter DIS measurement times,thus reducing changes in the optical properties due to desiccation of the samples.

The noise spectral characteristics and noise reduction schemes of screw air-source heat pump:A case study

The screw air-source heat pump can cause incessant high noise levels during operation,which might hinder adoption of this energy-efficient heat pump.First,acoustic measurements and comparison testing were performed in this research.The measurements revealed that the compressor is the main noise source of the heat pump,and it shows a multipeak frequency distribution and a wide frequency spectrum under different work conditions,with multiple peaks at 63,250,and 1000 Hz.Then,a compressor sound insulation cover with broadband absorption was proposed,and it was experimentally proven that the insulation cover can reduce the maximum sound pressure level of one unit from 89.8 dBA to 79.1 dBA.Third,we proposed several noise reduction strategies and compared their noise reduction effects using computer simulation.The results showed that the noise problem can be effectively improved through the rational design of the sound barrier and the layout and opening options of heat pump.The distance between the sound barrier and heat pump and the sound attenuation due to diffraction ALa exhibit a U-shaped relation.For buildings of different heights,the optimal heights of noise barrier are proposed.The 5.5-meter is the optimal height of the sound barrier for single-story buildings.The conclusions can be applied to other building projects for heat pump noise reduction.