Noise Reduction Effect Research Articles

Optimal Design and Experimental Verification of Low Radiation Noise of Gearbox

Reducing the radiated noise of a gearbox is a difficult problem in aviation, navigation, machinery, and other fields. Structural improvement is the main means of noise reduction for a gearbox, and it is realized primarily through contribution analysis and structure optimization. However, these approaches have certain limitations. In this study, a low-noise design method for a gearbox that combines the two approaches is proposed, and experimental verification is performed. First, a finite element/boundary element model is established using a single-stage herringbone gearbox. Considering the vibration excitation of the gear system, the radiation noise of a single-stage gearbox is predicted based on the modal acoustic transfer vector (MATV) method. Subsequently, the maximum field point of the radiated noise is determined, and the acoustic transfer vector (ATV) analysis and modal acoustic contribution (MAC) analysis are conducted to determine the region that contributes significantly to the radiated noise of the field point. The optimization region is selected through the panel acoustic contribution (PAC) analysis. Next, to reduce the normal speed in the optimization region, topology optimization is performed. According to the topology optimization results, four different noise reduction structures are added to the gearbox, and the low-noise optimization models are established respectively. Finally, by measuring the radiated noise of the gearbox before and after optimization under a given working condition, the validity of the radiated noise prediction method and the low-noise optimization design method are verified by comparing the simulation and experimental data. A comparison of the four optimization models proves that the noise reduction effect can be achieved only by adding a noise reduction structure to the center of the density nephogram.

A Noise Reduction Method for Four-Mass Vibration MEMS Gyroscope Based on ILMD and PTTFPF.

In this paper, the structure and working principle of four-mass vibration MEMS gyroscope (FMVMG) are introduced, and the working modes of FMVMG are simulated and analyzed. On the basis of this, an improved noise reduction method based on interval local mean decomposition (ILMD) and parabolic tracking time-frequency peak filtering (PTTFPF) is proposed. PTTFPF can resample the signal along a parabolic path and select the optimal filtering trajectory, but there is still a contradiction, choosing a short window length may lead to good signal amplitude retention, but the random noise reduction effect is not good, while choosing a long window length may lead to serious amplitude attenuation, but the random noise reduction effect is better. In order to achieve a better balance between effective signal amplitude preservation and random noise reduction, the ILMD method was used to improve PTTFPF. First, the original signal was decomposed into product functions (PFs) by local mean decomposition (LMD) method, and the sample entropy (SE) of each PF was calculated. The PFs are divided into three different components. Then, short window PTTFPF is used for useful PF and long window PTTFPF is used for mixed PF, noise PF is directly removed. Then the final signal is reconstructed. Finally, the denoised useful PF and mixed PF are reconstructed to obtain the final signal. The proposed ILMD-PTTFPF algorithm was verified by temperature experiments. The results show that the denoising performance of the ILMD-PTTFPF algorithm is better than that of traditional wavelet threshold denoising and Kalman filtering.

Study on the Extraction of CT Images with Non-Uniform Illumination for the Microstructure of Asphalt Mixture.

An adaptive image-processing method for CT images of asphalt mixture is proposed in this paper. Different methods are compared according to the error analysis calculated between the real gradation and 3D reconstruction gradation. As revealed by the test results, the adaptive image-processing method was effective in carrying out different brightness homogenization processes for each image. The Wiener filter with 7 × 7 size filter was able to produce a better noise reduction effect without compromising image sharpness. Among the three methods, the adaptive image-processing method performed best in the accuracy of coarse aggregate recognition, followed by the ring division method and the global threshold segmentation method. The error of the gradation extracted by the adaptive image-processing method was found to be lowest compared with the real gradation. For a variety of engineering applications, the developed method helps to improve the analysis of CT images of asphalt mixtures.

A Novel Lidar Signal-Denoising Algorithm Based on Sparrow Search Algorithm for Optimal Variational Modal Decomposition

Atmospheric lidar is susceptible to the influence of light attenuation, sky background light, and detector dark currents during the detection process. This results in a large amount of noise in the lidar return signal. To reduce noise and extract a useful signal, a novel denoising method combined with variational modal decomposition (VMD), the sparrow search algorithm (SSA) and singular value decomposition (SVD) is proposed. The SSA is used to optimize the number of decomposition layers K and the quadratic penalty factor α values of the VMD algorithm. Some intrinsic mode function (IMF) components obtained from the VMD-SSA decomposition are grouped and reconstructed according to the interrelationship number selection criterion. Then, the reconstructed signal is further denoised by combining the strong noise-reduction ability of SVD to obtain a clean lidar return signal. To verify the effectiveness of the VMD-SSA-SVD method, the method is compared and analysed with wavelet packet decomposition, empirical modal decomposition (EMD), ensemble empirical modal decomposition (EEMD), and adaptive noise-complete ensemble empirical modal decomposition (CEEMD), and its noise-reduction effect is considerably improved over that of the other four methods. The method can eliminate the complex noise in the lidar return signal while retaining all the details of the signal. The signal is not distorted, the waveform is smoother, and far-field noise interference can be suppressed. The denoised signal is closer to the real signal with higher accuracy, which shows the feasibility and the practicality of the proposed method.

Sound absorption and insulation performance of a finite cylindrical micro-perforated panel absorber.

This paper describes the theoretical prediction, simulation research, and experimental verification conducted to understand the potential of finite cylindrical micro-perforated panel absorbers (FC-MPPAs) in noise control. The simplified Rayleigh integral method is extended to predict the dissipation of sound energy in cylindrical micro-perforations with sub-millimeter aperture. The sound absorption and insulation performance of a FC-MPPA and the influence of its structural parameters on its acoustic performance were studied by model simulation and experiment, and the directional distribution of the sound field was revealed. The simulation and experimental results show that the FC-MPPA has unique acoustic characteristics different from the panel-type absorber, and its structural parameters are closely related to the acoustic-energy dissipation efficiency. Moreover, the absorption coefficient of FC-MPPA is a function of the incidence angle and frequency of the sound wave, and its acoustic properties are controlled by the coupling between the micro-perforated panel and the acoustic modes in the annular cavity domain. These studies can provide help in the design and application of FC-MPPA to achieve better noise reduction effect.

Numerical modeling and field test of sonic crystal acoustic barriers.

The rapid development of highway traffic has gradually deteriorated the acoustic environment along the line. Sonic crystal theory provides new ideas for traffic acoustic barrier. However, the lack of practical numerical models and field test verifications has restricted the promotion and application of sonic crystal acoustic barriers (SCABs). In this study, a field test was conducted to study the noise reduction performance of SCAB. The SCAB exhibits excellent wave attenuation in the band gap, when compared with concrete acoustic barriers (CABs) along highways, the noise reduction performance in the band gap is improved by 0.5-2.1dB(A), especially at the local peak in the highway noise spectrum. However, from the perspective of total insertion loss, CAB performs better than SCAB in all distances in the protected area. Next, the 3D FEM model is established based on the highway site and validated by the measured results. Compared with the commonly used 2D model, the 3D FEM model is more practical for considering the top diffraction and ground reflection, which influence the noise reduction performance a lot and need to be considered. To improve the noise reduction performance of SCAB, three types of optimization measures are explored. The gradient combination of scatterers can effectively improve the noise reduction effect in the low-frequency band gap, especially the high- to low-gradient layout. Besides, not only the porous sound-absorbing material but also the microperforated plates can improve the noise reduction effect, especially outside the band gap. The larger perforation rates and smaller apertures of microperforated plate are preferred in SCAB. This work provides field test support and promotes the application of SCABs in traffic noise control.

Research on Start-stop standby energy storage element participating in wind power filtering under the influence of power quality disturbance

In order to realize effective noise reduction of wind power under the influence of complex power quality disturbance(PQD), an application fusion method of improved smoothing and noise reduction strategy and start-stop energy storage element is proposed in this paper. Firstly, the complex power quality disturbance is filtered and de-noised based on Wiener filtering(WF) algorithm to obtain stable disturbance output. Then, a filtering self-built library(FSBL) is established to perform parallel operations of multiple filtering algorithms and obtain the optimal output power according to the constraints of time scale. After that, a hybrid energy storage system(HESS) with start-stop standby energy storage element is proposed. According to the maximum entropy difference drop point and permutation entropy test, the start-stop instructions of standby energy storage elements can be achieved. Finally, the state of charge(SOC) is optimized according to fuzzy control constraints. In order to verify the effectiveness of the proposed method, this paper constructs the wind power output based on the hardware experimental platform. The power quality disturbance database is further constructed to verify and analyze power quality disturbance types from simple to complex. The results show that the proposed method is reliable. And the hybrid energy storage system with start-stop standby energy storage elements can be widely used in many fields and scenarios.

Optimal design of lightweight acoustic metamaterials for low-frequency noise and vibration control of high-speed train composite floor

Low-frequency noise and vibration are new challenges faced in high-speed trains. Conventional sound insulation and absorption materials or structures have insufficient efficiency for low-frequencies. This paper studies how to design lightweight acoustic metamaterials and apply them to high-speed train composite floors, for low-frequency noise and vibration control. First, through in-situ experiments, the interior noise characteristics and sound source distributions of a high-speed train were measured and analysed, including the sound transmission characteristics of the composite floor. Second, based on the finite element method, a vibration analysis model of the composite floor was established and validated. The target frequency of noise and vibration control was determined. Third, based on the multi-parametric optimisation method, a lightweight and low-frequency acoustic metamaterial (beam-like resonator) for the low-frequency noise and vibration control was designed, and the band gap properties of the beam-like resonator were obtained. Finally, the noise reduction effects of the beam-like resonator installed on different positions of the high-speed train composite floor were analysed, including vibration transfer ratio, vibration response and radiated sound power. The results demonstrate that the weight of the optimal beam-like resonator could be reduced by 60.7 %, compared to the initial solution prior to optimisation design. When the beam-like resonators are installed on the wooden floor, the vibration transfer ratio of the target frequency is reduced drastically, the overall acceleration level is reduced by 2.9 dB, and the overall radiated sound power level is reduced by 3.9 dB. The lightweight acoustic metamaterial designed in this paper has an obvious effect on the low-frequency noise and vibration control of composite structures.

Design of Noise-Reducing Two-Stage Cage Control Valve and Its Fluid Characteristics and Cavitation Study

For all kinds of media noise, noise reduction valve cage cannot meet the equal percentage flow characteristics, and for manufacturing and processing and other technical problems, research and design of new noise reduction two-stage cage control valve are necessary. The valve adopts noise reduction valve cage and curve valve cage with noise reduction, forming a two-stage noise reduction, with uniform flow, diffusion, and noise reduction effect, to meet the regulating valve equal percentage flow characteristics. The spool is evenly stressed and not easy to loosen and fall off, and the small opening is not easy to vibrate and shake. The flow characteristics of the noise-reducing two-stage cage control valve are studied, and the fluid flow coefficients, flow characteristic curves, the relationship of flow coefficients with nominal diameter at the same stroke, and the relationship of flow coefficients with different nominal diameters at different strokes are analyzed. The relationship between nominal diameter constant opening and cavitation coefficient with inlet velocity, the relationship between inlet velocity and cavitation coefficient with nominal diameter at half opening, and the relationship between inlet velocity and cavitation coefficient with nominal diameter at full opening were studied, and the cavitation generation and its effect on noise were analyzed. The noise and cavitation test showed that noise is very small and cavitation is difficult to produce, all meeting the noise reduction requirements.

Noise Reduction Method of Nanopore Based on Wavelet and Kalman Filter

Nanopore detection technology has now developed into an indispensable tool for single molecule experiments, in which useful information on molecular properties can be obtained from the ion current flow induced by individual molecules, but the relatively high background noise affects the data analysis. Therefore, a nanopore signal noise reduction method based on wavelet transform and Kalman filter is proposed, which can achieve multi-scale decomposition and optimal estimation of the nanopore signal. The real measurement shows that the Kalman filter based on the wavelet mode maxima method reduces the root mean square (RMS) of the background noise by 17.8%, and the noise reduction effect is better than the traditional Kalman filter method.

Porous asphalt mixture use asphalt rubber binders: Preparation and noise reduction evaluation

To prepare low-noise asphalt pavement with excellent noise reduction and road performance, a large amount of crumb rubber was added to asphalt binder to prepare high viscosity modified asphalt rubber, with Sasobit and waste cooking oil (WCO) selected as viscosity-reducing additives to reduce the viscosity. Moreover, the study took the prepared porous asphalt rubber mixture (PARM) as the research object and formed a systematic evaluation method for the noise reduction performance of the asphalt mixture. The temperature sweep test based on DSR, the dynamic creep test based on UTM, and the tire drop test were used to explore the damping performance of the asphalt mixture. Due to the softening effect of WCO and the high asphalt-aggregate ratio, the damping capacity of PARM with WCO is stronger. It is worth mentioning that the addition of crumb rubber can indirectly improve the damping performance of the asphalt mixture by increasing the asphalt-aggregate ratio. The impedance tube test results show that the air voids is the critical factor affecting the sound absorption performance of the asphalt mixture. The sound absorption coefficient of porous asphalt mixture (PAM) with the largest air voids is higher than PARM, and both are significantly higher than AC-13. In addition, the tire drop and pendulum instruments were designed to simulate and test tire pavement's vibration and friction noise. It can be found that the PARM mixed with WCO showed the best noise reduction effect.

Hybrid Model Based on an SD Selection, CEEMDAN, and Deep Learning for Short-Term Load Forecasting of an Electric Vehicle Fleet

Forecasting the aggregate charging load of a fleet of electric vehicles (EVs) plays an important role in the energy management of the future power system. Therefore, accurate charging load forecasting is necessary for reliable and efficient power system operation. A hybrid method that is a combination of the similar day (SD) selection, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and deep neural networks is proposed and explored in this paper. For the SD selection, an extreme gradient boosting (XGB)-based weighted k-means method is chosen and applied to evaluate the similarity between the prediction and historical days. The CEEMDAN algorithm, which is an advanced method of empirical mode decomposition (EMD), is used to decompose original data, to acquire intrinsic mode functions (IMFs) and residuals, and to improve the noise reduction effect. Three popular deep neural networks that have been utilized for load predictions are gated recurrent units (GRUs), long short-term memory (LSTM), and bidirectional long short-term memory (BiLSTM). The developed models were assessed on a real-life charging load dataset that was collected from 1000 EVs in nine provinces in Canada from 2017 to 2019. The obtained numerical results of six predictive combination models show that the proposed hybrid SD-CEEMDAN-BiLSTM model outperformed the single and other hybrid models with the smallest forecasting mean absolute percentage error (MAPE) of 2.63% Canada-wide.

Nonharmonic Control of the Blade-Vortex Interaction Noise of Electrically Controlled Rotor

Electrically controlled rotor, also known as swashplateless rotor, represents an active rotor system that, due to the use of a trailing edge flap system instead of a swashplate, not only enables primary control but also conveniently reduces the blade-vortex interaction (BVI) noise of the rotors through active control. The effect of nonharmonic inputs on the control of the BVI noise of electrically controlled rotors based on their unique trailing edge flap systems has been investigated in this paper. To this end, an analytical model for the vortex interaction-induced load and noise of electrically controlled rotor is first established based on the viscous vortex particle method, the Weissinger-L blade model, and the Ffowcs Williams-Hawkings (FW-H) equation. On this basis, a simulation study of flap nonharmonic control for BVI noise reduction in electrically controlled rotors is carried out. According to the mechanism of the BVI in electrically controlled rotors, the second quadrant flap nonharmonic control is used to reduce the advancing side BVI noise, and the effects of different control waveforms and amplitudes on the peak value and directivity of the BVI noise of the sample electrically controlled rotors are analyzed to reveal the noise reduction mechanism of flap nonharmonic control. Subsequently, the effect of the third quadrant flap nonharmonic control on BVI noise on the retreating side of the sample electrically controlled rotors is investigated. The results show that flap nonharmonic control has little effect on miss distance and that it controls BVI noise mainly by reducing the wake vortex strength on the advancing and retreating sides, which may lead to an increase in rotor noise in other regions; the noise reduction effect of flap nonharmonic control for different blade preindex angles indicates that suitable preindex angles coupled with flap nonharmonic control help optimally reduce noise.

Temporal-Spatial Analysis of the Essentiality of Hub Proteins in Protein-Protein Interaction Networks

Hubs are generally defined as nodes with a high degree centrality, and they are important for maintaining the stability of complex networks. Previous studies have shown that hub proteins tend to be essential in protein-protein interaction (PPI) networks, providing us with a new way to analyze the essentiality of proteins. Unfortunately, most of the existing studies leverage static PPI networks that are both incomplete and noisy and ignore the temporal and spatial characteristics of PPI networks. Benefiting from the development of high-throughput technologies, abundant multi-biological datasets have been accumulated and can be used for network analysis. To reexamine the relationship between the network centrality and protein essentiality in PPI networks, in this study, we integrated PPI networks with gene expression data and subcellular localization information to construct temporal-spatial dynamic PPI networks. Based on the constructed temporal-spatial dynamic PPI networks, we introduced the maximum degree centrality (MDC) method to evaluate the essentiality of hub proteins. Our results illustrate that the integration of gene expression data or subcellular localization information can significantly reduce noise effects and improve the identification accuracy of essential proteins through the temporal-spatial analysis with disparate sources of PPI networks. Moreover, we redefined hubs and classified them into two types: temporospatial hubs and static hubs. The results show that temporospatial hub proteins are more likely to be essential.

A real-time data processing method based on twice weighted improved Kalman filter algorithm

The data obtained by sensor is strong randomness and deviates from Gaussian distribution. The Extended Kalman Filter (EKF) is cost-effective and easy to cause large errors while processing data. At the same time, the accuracy of the data prediction is not high enough. Therefore, an improved filter based on the original EKF is proposed to improve the prediction accuracy and noise reduction effect in this paper, which combines the predicted value and the measured value of the EKF twice to find the root mean square to obtain a more accurate predicted value. The noise reduction effect of EKF and TWEKF is compared by using MATLAB. And the root means square error (RMSE), signal-to-noise ratio (SNR), smoothness (S) and other quantitative judgment indexes prove that TWEKF has better noise reduction effect than EKF.

Application of the complementary ensemble empirical mode decomposition for the identification of simulation model parameters and groundwater contaminant sources

Identification of groundwater contaminant sources (GCSs) relies on actual observed data, and the observed data directly affects the accuracy of the identification results. However, the observed data inevitably contains noise due to accidental and systematic errors, and the identification results of GCSs based on the observed data containing noise are usually not reliable. This seriously restricts the rational design of contamination remediation plans and contamination risk assessment. To solve this problem, complementary ensemble empirical mode decomposition (CEEMD) was applied to the joint identification of simulation model parameters and GCSs information in this study for the first time. The wavelet analysis and CEEMD methods were used to reduce the noise in observed concentrations respectively, and the noise reduction effect of the two methods was compared and analyzed. Then the simulated concentrations, observed concentrations and noise-reduced observed concentrations with the best noise reduction effect were then applied to identify simulation model parameters and GCSs information, respectively, after which the corresponding identification results were compared and analyzed. The results showed that CEEMD could reduce the noise contained in the observed concentrations more effectively and make it closer to the actual values when compared with the wavelet analysis method. The accuracy of identification results based on the noise-reduced observed concentrations by was improved. As the noise contained in the observed concentrations increased, the noise reduction effect of the CEEMD decreased. Nevertheless, the accuracy of the identification results based on the noise-reduced observed concentrations was still higher than that of those based on the observed concentrations with the same intensity noise without denoising.

Deconstruction and Realization of Sports Entity Simulation Based on Fish Swarm Algorithm.

Due to the limitation of sports movement, the current simulation technology of sports entities is prone to deficiencies in capturing dynamic motion figures and is prone to lack of accuracy. It is also affected by external noise and brightness. To solve these problems, this paper proposes a sports entity simulation based on the fish swarm algorithm and compares the figure effectiveness, figure segmentation, core point, and noise reduction effect of the two in the shooting figure. Through the comparison, it is found that the figure is more appropriate to the real moving figure, the motion capture is more accurate, and the number of core points is related to the accuracy of motion capture. The more core points, the more accurate the motion capture, and the noise reduction effect is also increased by 20.3%, which reduces the impact of brightness on the motion simulation. The difference in the effect of the traditional simulation technology (particle swarm algorithm) and the entity simulation based on the fish swarm algorithm was also compared. The combination with the artificial fish swarm algorithm is to simulate the moving entity and learn from some reference data. By comparing the data between the two after the experiment, it is concluded that the fish swarm algorithm is more effective in the simulation of sports entities.

Noise reduction in the neonatal intensive care unit: An exploratory study

PurposeTo assess nurses' knowledge and perspectives on the impact of excessive noise exposure on infants’ neurodevelopment, and on the clinical utility of a noise meter. MethodsAn online and written survey was administered to 66 nurses in a level II-III NICU pertaining to nurses’ demographic, perception of noise, and efficacy of a noise meter. Quantitative and qualitative responses were descriptively and thematically analyzed, respectively. ResultsA total of 32 (49%) nurses responded, of these 24 (75%) were aware of the risks associated with exposure to excessive noise on infants' neurodevelopmental outcomes, and almost half believed that the noise meter was not an effective noise reduction intervention. Conclusion: There is an increased need to further educate NICU staff regarding the negative effects of excessive noise on infants’ neurodevelopment, and to provide ongoing feedback on sound levels in the NICU to empower staff to enact sustainable noise reduction interventions.

10dB Quantum-Enhanced Michelson Interferometer with Balanced Homodyne Detection.

Future generations of gravitational-wave detectors (GWD) are targeting an effective quantum noise reduction of 10dB via the application of squeezed states of light. In the last joint observation run O3, the advanced large-scale GWDs LIGO and Virgo already used the squeezing technology, albeit with a moderate efficiency. Here, we report on the first successful 10dB sensitivity enhancement of a shot-noise limited tabletop Michelson interferometer via squeezed light in the fundamental Gaussian laser mode, where we also implement the balanced homodyne detection scheme that is planned for the third GWD generation. In addition, we achieved a similarly strong quantum noise reduction when the interferometer was operated in higher-order Hermite-Gaussian modes, which are discussed for the GWD thermal noise mitigation. Our results are an important step toward the targeted quantum noise level in future GWDs and, moreover, represent significant progress in the application of nonclassical states in higher-order modes for interferometry, increased spatial resolution, and multichannel sensing.

Methods of Assessing the Effectiveness of Filter Elements in Power Electronics

Newly constructed devices must meet a number of requirements in terms of the level of generated disturbances. To achieve an acceptable level in such devices, filters are installed—one of the cheapest ways to reduce interference in devices. One of the key elements of the filter that is responsible for the effectiveness of noise reduction are ferrites. Unfortunately, for various devices, an individual approach should be used in the selection of filters and, accordingly, ferrites. Due to the fact that ferrites from different manufacturers do not have standardized characteristics and information on their effectiveness, the selection of the right ferrite is a very time-consuming process. Therefore, this article will present the possibilities of quickly determining selected ferrite parameters in order to ensure the necessary level of noise reduction. For this purpose, assumptions from the CISPR 17 standard will be used. For selected types of ferrites, a large number of measurements were carried out in order to determine the optimal computational algorithm for adjusting ferrite characteristics to the designed conditions. The performance of these tests will be the basis for conducting tests on a larger number of ferrites, as well as for the development of possible standardization procedures.