Search

Since November 2021, the Tianwen-1 mission has activated its scientific instrument, the Mars Ion and Neutral Particle Analyzer (MINPA), to detect particles within Martian space. To evaluate the reliability of the plasma parameters from the MINPA measurements, in this study, we analyze and reduce the background signal (or noise) appearing in the MINPA data and then calculate the plasma moments based on the noise-reduced data. Remarkably, our findings reveal a strong correlation between the velocity measurements from MINPA and those from the Solar Wind Ion Analyzer (SWIA) onboard the MAVEN spacecraft, underscoring MINPA's accuracy. Similarly, temperature measurements correlate with SWIA data, albeit with a tendency towards underestimation and greater variability. A significant limitation, however, is MINPA's 2 π field of view (FOV), which restricts its ability to observe ions omnidirectionally, leading to a substantial underestimation of number density and thermal pressure compared to SWIA measurements. Addressing this challenge necessitates a sophisticated approach that fully accommodates the FOV constraints to derive accurate values for these parameters. Moreover, our comprehensive investigation into the noise origins traced it back to electronic noise within MINPA's circuitry. This study confirms MINPA's operational efficacy and potential to yield dependable plasma parameters with further procedures and contributes valuable insights for the design of future scientific instruments. [ABSTRACT FROM AUTHOR]

The resolution and noise levels of input images directly affect the three-dimensional (3D) structure-from-motion (SfM) reconstruction performance. Conventional super-resolution (SR) methods focus too little on denoising, and latent image noise becomes worse when resolution is improved. This study proposes two SR denoising training algorithms to simultaneously improve resolution and noise: add-noise-before-downsampling and downsample-before-adding-noise. These portable methods preprocess low-resolution training images using real-world noise samples instead of altering the basic neural network. Hence, they concurrently improve resolution while reducing noise for an overall cleaner SfM performance. We applied these methods to the existing SR network: super-resolution convolutional neural network, enhanced deep residual super-resolution, residual channel attention network, and efficient super-resolution transformer, comparing their performances with those of conventional methods. Impressive peak signal-to-noise and structural similarity improvements of 0.12 dB and 0.56 were achieved on the noisy images of Smartphone Image Denoising Dataset, respectively, without altering the network structure. The proposed methods caused a very small loss (<0.01 dB) on clean images. Moreover, using the proposed SR algorithm makes the 3D SfM reconstruction more complete. Upon applying the methods to non-preprocessed and conventionally preprocessed models, the mean projection error was reduced by a maximum of 27% and 4%, respectively, and the number of 3D densified points was improved by 310% and 7%, respectively. [ABSTRACT FROM AUTHOR]

Aiming at the difficulty of feature extraction of rotor vibration signals under strong noise, a fault diagnosis method based on mesh search variational mode decomposition (VMD) parameters combined with fine composite multiscale spread entropy (RCMDE-Relieve-F) is proposed. Based on time-domain energy entropy, kurtosis and Pearson correlation coefficient, a new index grid was formed to search VMD to decompose the optimal K of the original signal, a value was reconstructed, RCMDE in the reconstructed signal was extracted as the characteristic value, and input was filtered into the generalized regression neural network (GRNN) for training and fault pattern recognition using Relief-F dimensionality reduction. The effectiveness of VMD noise reduction parameter selection was verified by numerical simulation and comparison with other decomposition methods. Moreover, the fault simulation experiment results showed that compared with other algorithm models, the mesh search-optimized VMD combined with the RCMDE-Relief-F-GRNN method could effectively filter noise with accuracy of 96 %. [ABSTRACT FROM AUTHOR]

Background/Objectives: To assess the impact of a vendor-agnostic deep learning model (DLM) on image quality parameters and noise reduction in dental cone-beam computed tomography (CBCT) reconstructions. Methods: This retrospective study was conducted on CBCT scans of 93 patients (41 males and 52 females, mean age 41.2 years, SD 15.8 years) from a single center using the inclusion criteria of standard radiation dose protocol images. Objective and subjective image quality was assessed in three predefined landmarks through contrast-to-noise ratio (CNR) measurements and visual assessment using a 5-point scale by three experienced readers. The inter-reader reliability and repeatability were calculated. Results: Eighty patients (30 males and 50 females; mean age 41.5 years, SD 15.94 years) were included in this study. The CNR in DLM reconstructions was significantly greater than in native reconstructions, and the mean CNR in regions of interest 1-3 (ROI1-3) in DLM images was 11.12 ± 9.29, while in the case of native reconstructions, it was 7.64 ± 4.33 (p < 0.001). The noise level in native reconstructions was significantly higher than in the DLM reconstructions, and the mean noise level in ROI1-3 in native images was 45.83 ± 25.89, while in the case of DLM reconstructions, it was 35.61 ± 24.28 (p < 0.05). Subjective image quality assessment revealed no statistically significant differences between native and DLM reconstructions. Conclusions: The use of deep learning-based image reconstruction algorithms for CBCT imaging of the oral cavity can improve image quality by enhancing the CNR and lowering the noise. [ABSTRACT FROM AUTHOR]

ABSTRACT Objective Methods Results Conclusion Noise reduction during surgical procedures leads to improved surgical performance and results. The caesarean birth (CB) is an exceptional operation and a life changing experience. Through the introduction of staff education and implementation of audiovisual feedback, we intended to reduce noise, and subsequently reduce surgical complications and increase the well‐being of patients and staff.During Phase I, blinded baseline measurements of noise were conducted. Phase II started after staff education and structured questionnaires on subjective noise and stress were added, and in Phase III audiovisual feedback was introduced. Mean and peak noise levels over the time of the procedure were obtained in A‐weighted decibels (dB(A)). Kruskal–Wallis H tests were performed to evaluate the impact of interventions on noise levels. Questionnaires were evaluated using descriptive statistics; stress‐scores were compared using independent sample t‐tests.Ninety planned CBs were included. Median noise levels were 62.85 dB(A) at baseline. They decreased significantly to 60.60 dB(A) (Phase II) and 59.25 dB(A) (Phase III), respectively. This reduction of 3.6 dB(A) leads to a subjective noise reduction of around 20%. Significant differences for A‐weighted and peak noise levels during actual surgery were found after combining staff education with audiovisual feedback. In Phase III, staff reported less stressful noise. Stress also decreased significantly in the patient group. Beeping machines and telephones were identified as the most stressful sources of noise.We show that noise reduction during CB is both necessary and possible. Diminished subjective perception of noise and stress are positive impacts of this intervention. Staff education and audiovisual feedback can help to provide a calm and lower stress environment for patients and staff during caesarean births. [ABSTRACT FROM AUTHOR]

Purpose: An adult version of an app giving users the control over the level of the volume, microphone directionality and noise reduction was adapted for children. The main purpose of this study was to evaluate the effect of changes made to microphone directionality and noise reduction in the myPhonak Junior (the app) on Speech intelligibility in challenging listening environments in children and teens. Methods: The randomized, non-blinded interventional study with a single group of subjects involved two study visits with a home trial in-between. In the final study session speech assessment in noise was conducted in three different, randomly assigned conditions: default mode (Autosense Sky OS), preffered (self-adjusted) and the extreme condition. Questionnaire based assessment was conducted to assess the subjective benefit of using the app in different daily situations. Results: The best scores (speech results in noise) were achieved with the preferred setting and the default Autosense Sky OS setting was significantly better than the extreme setting. The self-reported benefit through the questionnaire indicates significantly better result when adjusting the hearing aids through the app. Conclusion: The app is an easy-to-use way of controlling the level of noise reduction and the beam forming for children 11 years and older. It has the potential to help customizing the hearing aids beyond the default setting and helping to improve speech understanding in noise. [ABSTRACT FROM AUTHOR]

Magnetic Resonance Imaging (MRI) is often constrained by long acquisition times. Accelerated acquisition techniques can reduce scan time but may introduce artifacts and decrease image resolution. Additionally, MRI data invariably contains noise (often Gaussian or Rician), further impacting image quality and diagnostic value. Conventional approaches often address noise and undersampling artifacts separately, leading to suboptimal image reconstruction. This work proposes a novel ensemble of CycleGAN models integrated within the Joint Sensitivity Encoding (JSENSE) framework to address these challenges in a comprehensive manner. Each CycleGAN within the ensemble is specifically trained to target distinct aspects of image degradation. This approach leverages the complementary strengths of the models to achieve a superior image reconstruction. Experiments demonstrate the effectiveness of the proposed ensemble model, outperforming conventional methods and our prior work in terms of visual quality and quantitative metrics. Significant gains were observed at higher acceleration factors and greater noise levels. The integration of CycleGANs with JSENSE yielded superior structural similarity and image clarity compared to traditional parallel MRI (pMRI) and single-model methods. [ABSTRACT FROM AUTHOR]

As a technological advantage of ultrasonic non-destructive testing, fully focused imaging can accurately feedback the defective characteristics of the inspected object, greatly improving the detection efficiency. This article aims to address the challenges of outdated and low detection rates in the detection technology of branch pipe fillet welds. The full matrix acquisition (FMC) and total focus method (TFM) ultrasonic detection technology are used for detection and defect image feature analysis. Firstly, a multi-mode, fully focused real-time imaging software system was developed to address the specificity of the detection object; secondly, a phased array detection system based on 64 elements was constructed; finally, a region wavelet denoising method based on TFM images was proposed to solve the problem of artifacts caused by poor coupling; and based on the feature extraction method for a minimum rectangle, we analyzed the size, position, angle, and other information regarding defects. Through experiments, it has been found that this technology can effectively improve the detection efficiency of branch pipe weld defects, with a detection rate of 100%. Based on the partition fusion denoising method, the defect imaging quality can be further improved; at the same time, based on the feature extraction method, the error is 0.1 mm, the length range of various defects is 2.3 mm–6.3 mm, the width range is 0.6 mm–0.8 mm, and the angle range is 52°–75°, which can provide an application basis for the localization, classification, and risk assessment of corner weld defects in branch pipes. [ABSTRACT FROM AUTHOR]

Natural gas pipelines are an essential part of the economy. Natural gas pipelines may leak after aging, strong vibration signals may be generated in the pipeline when leakage occurs, and vibration signals may be noisy. Traditional variational mode decomposition (VMD) noise reduction methods need to set parameters in advance, and so may not achieve the best decomposition effect. To solve this problem, this paper proposes a method for pipeline leakage location based on the sparrow search algorithm (SSA) optimization of VMD combined with generalized quadratic cross-correlation. The method first calculates the original signal-to-noise ratio (SNR), and if the SNR is low, wavelet threshold denoising is used to process the signal. Then, SSA optimization is used to refine the two key parameters of VMD (penalty parameter α and mode decomposition number K) based on sample entropy. Subsequently, the signal undergoes decomposition into K intrinsic mode function (IMF) components through VMD according to the obtained analysis parameter combination. Then, the IMF components are screened to obtain the reconstructed signal. Finally, the noise reduction signal is obtained. The signal delay after noise reduction is obtained through a generalized quadratic cross-correlation and the accurate leakage position is obtained using the delay. Experiments showed that the minimum relative error of this method could reach 0.6%, which was more accurate than the traditional VMD method, and effectively improved the accuracy of noisy signals in pipeline leakage locations. [ABSTRACT FROM AUTHOR]

AbstractIn this paper, a novel approach was introduced for cross flow fan design by incorporating bionic microstructures on the blade surface. Two types of bionic microstructures, riblet and convex hull, were constructed and studied to achieve high aerodynamic efficiency and low noise design. Numerical simulations were conducted to understand the influence of bionic microstructures on the fan’s aerodynamic and acoustic performance, and the impact of design parameters on these performances was investigated. After printing the bionic microstructure fan samples, the experimental tests were carried out. The flow field and sound field data of the fans with bionic microstructures and the original fan were compared. The results demonstrated that both riblet and convex hull microstructures effectively improved the fan’s aerodynamic performance by reducing turbulent kinetic energy on the blade surface. Additionally, the microstructures inhibited boundary layer separation on the suction surface of adjacent blades, which contributed to reducing vortex noise. However, the pressure gradient formed near the convex hull and the edge of the small-diameter riblet resulted in deterioration in the noise of the corresponding fan. By optimizing the diameter and spacing of the riblet microstructure, the best noise reduction performance was achieved for the riblet fan. The optimized fan showed a 5.3% increase in maximum air volume flow rate and a 2.4 dB(A) reduction in noise. Overall, the cross flow fan design method based on bionic microstructures has valuable application potential in improving the aerodynamic and acoustic performance of various rotating machinery. [ABSTRACT FROM AUTHOR]

The mining face under the close coal seam group is affected by the superposition of the concentrated stress of the overlying residual diagonally intersecting coal pillar and the mining stress, which can easily cause the instability and damage of the section coal pillars during the process of mining back to the downward face. Additionally, the traditional methods of monitoring such as numerical simulation, drilling peeping, and acoustic emission fail to realize the real-time and accurate deformation monitoring of the internal deformation of the section coal pillars. The introduction of the drill-hole-implanted fiber-optic grating monitoring method can realize real-time deformation monitoring for the whole area inside the coal pillar, which solves the short board problem of coal pillar deformation monitoring. However, fiber-optic monitoring is easily disturbed by the external environment, which is especially sensitive to the background noise of the complex underground mining environment. Therefore, taking the live chicken and rabbit well of Shaanxi Daliuta Coal Mine as the engineering background, the ensemble empirical modal decomposition (EEMD) is introduced for primary noise reduction and signal reconstruction by the threshold determination (DE) algorithm, and then the singular matrix decomposition (SVD) is introduced for secondary noise reduction. Finally, a machine learning algorithm is combined with the noise reduction algorithm for the prediction of the fiber grating strain signals of coal pillar in a zone, and DBO-LSTM-BP is constructed as the prediction model. The experimental results demonstrate that compared with the other two noise reduction prediction models, the SNR of the EEMD-DE-SVD-DBO-LSTM-BP model is improved by 0.8–2.3 dB on average, and the prediction accuracy is in the range of 88–99%, which realizes the over-advanced prediction of the deformation state of the coal column in the section. [ABSTRACT FROM AUTHOR]

In order to solve the problem that the sound signal of belt conveyor is seriously disturbed by reverberation and background noise in the acoustic diagnosis, the reasons of reverberation and the composition of sound signal were analyzed, an acoustic signal enhancement method based on super directional beamforming (SBF) and improved singular value decomposition ( ISVD) was proposed. Firstly, the optimal frequency band was selected based on the maximum energy variation to determine the frequency band with more fault information. Then, SBF was used to remove the interference of reverberation on the sound signal, ISVD method was used to reduce the noise of the signal after reverberation, and the envelope spectrum of the signal was analyzed. The fault characteristic frequency measured in practice was compared with the theoretical fault characteristic frequency, and the fault characteristics of the belt conveyor were extracted. Finally, the test was designed, the data were collected, and the data collected from the coal mine site were analyzed and verified by using this method. The method was compared with the weighted prediction error algorithm ( WPE), the linear constrained minimum variance ( LCMV) and the recursive least square method (RLS). The research results show that: comparing with the original signal, the amplitude at the inner circle fault characteristic frequency of 153. 1 Hz and the frequency doubling of 312. 5 Hz in the envelope spectrum of test data processed by SBFISVD method is significantly improved, and the signal-to-noise ratio is significantly increased from - 31. 39 dB to - 25. 4 dB. The sound signal enhancement method based on SBF-ISVD has remarkable effect on removing reverberation and reducing noise, and the bearing fault feature extraction effect is good, which can realize the sound signal enhancement of belt conveyor under complex ambient noise. [ABSTRACT FROM AUTHOR]

Low-voltage AC distribution system in numerous loads generates a large amount of noise, which can weaken the arc fault characteristics to lead much more difficult to detect series arc faults, seriously threatening the safety of electricity consumption. Therefore, to solve the problem of insufficient arc fault detection capability in noise background, the paper proposes a series arc fault detection method based on IDOA-SR-VMD and ensemble learning. By comparing the high-order harmonic characteristics of resistive, inductive, and capacitive load arc faults before and after occurrence, the fault frequency distribution range is determined. Subsequently, adaptive SR and VMD methods are employed for noise reduction and feature enhancement, constructing a multi-layered signal processing model and outputting the reconstructed signal. KPCA algorithm is utilized for signal dimensionality reduction, generating a feature matrix used as input for the Stacking ensemble learning model to achieve accurate diagnosis and load classification of arc faults in noisy background. The method achieved significant improvements in diagnostic accuracy and load classification accuracy, reaching 99.5% and 98.25%, respectively. Comparative analysis with other methods validated the effectiveness and superiority of the proposed approach. In summary, the method provides a reliable solution for arc fault detection in noisy backgrounds, with broad prospects for practical applications. [ABSTRACT FROM AUTHOR]

Aiming at the low-frequency line spectrum noise characteristics of power equipment noise, based on the principle of energy degradation, this paper combines the energy degradation sound insulation structure with the dynamic vibration absorption technology for the first time and applies it to the research field of noise control of power equipment in substations. Dynamic vibration absorption technology is used to effectively control low-frequency vibration and noise. Considering that there is an upper limit to the capacity of DVA, the sound-vibration energy degradation design of the transformer is completed by setting a sound insulation structure on the outside of the original transformer housing. It is analyzed that the vibration energy of the sound insulation structure in the specific frequency band is significantly reduced compared to the transformer housing, realizing efficient degradation of the vibration energy of the transformer housing and effective isolation of sound radiation. Through the optimized design of dynamic vibration absorption for the sound insulation structure, the structural sound isolation ability at the target frequency is further strengthened, and the system noise radiation level is greatly reduced under the action of multiple mechanisms at the target frequency, verifying the feasibility and high efficiency of the optimal DVAs energy degradation design of the transformer. [ABSTRACT FROM AUTHOR]

The acoustic and aerodynamic fields of blunt porous plates are examined experimentally in an effort to mitigate trailing-edge bluntness noise. The plates are characterized by a single dimensionless porosity parameter identified in previous works that controls the influence of porosity on the sound field. Hot-wire anemometry interrogates the velocity field to connect turbulence details of specific regions to flow noise directivity and beamforming source maps. Porous plates are demonstrated to reduce the bluntness-induced noise by up to 17 dB and progressively suppress broadband low-frequency noise as the value of the porosity parameter increases. However, an increase in this parameter also increases the high-frequency noise created by the pores themselves. The same highly perforated plate characterized by a large value of the porosity parameter reduces the bluntness-induced vortex shedding that is present in the wake of the impermeable plate. Lastly, pore shape and positional alignment are shown to have a complex effect on the acoustic field. Among the porosity designs considered, plates with circular pores are most effective for low-frequency noise reductions but generate high-frequency noise. No meaningful difference is found between the acoustic spectra from plates of the same open-area fraction with pores aligned along or staggered about the flow direction. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Featured Application: The Flexible Soundproofing Metapanel (FSM) developed in this study has potential applications where lightweight, flexible, and effective noise control solutions are crucial. This study addresses the critical challenge of developing lightweight, flexible soundproofing materials for contemporary applications by introducing an innovative Flexible Soundproofing Metapanel (FSM). The FSM represents a significant advancement in acoustic metamaterial design, engineered to attenuate noise within the 2000–5000 Hz range—a frequency band associated with significant human auditory discomfort. The FSM's novel structure, comprising a box-shaped frame and vibrating membrane, was optimized through rigorous finite element analysis and subsequently validated via comprehensive open field tests for enclosure-type soundproofing. Our results demonstrate that the FSM, featuring an optimized configuration of urethane rubber (Young's modulus 6.5 MPa) and precisely tuned unit cell dimensions, significantly outperforms conventional mass-law-based materials in sound insulation efficacy across target frequencies. The FSM exhibited superior soundproofing performance across a broad spectrum of frequency bands, with particularly remarkable results in the crucial 2000–5000 Hz range. Its inherent flexibility enables applications to diverse surface geometries, substantially enhancing its practical utility. This research contributes substantially to the rapidly evolving field of acoustic metamaterials, offering a promising solution for noise control in applications where weight and spatial constraints are critical factors. [ABSTRACT FROM AUTHOR]

Noise pollution negatively impacts people's mental and physiological health. Unfortunately, not only is noise present in hospital environments, but its level frequently exceeds recommended thresholds. The efficacy of passive acoustic absorbers in reducing indoor noise in these scenarios has been well-documented. Conversely, given their inorganic composition and their origin in the petrochemical industry, most of these materials present a risk to human health. Over the last few years, there has been a notable increase in research on eco-friendly, low-toxicity, and biocompatible materials. This work outlines a methodology for fabricating recycled acoustic panels from plastic bottles and PET felt composites. This study encompasses three key objectives: (i) a comprehensive biocompatibility assessment of the panels, (ii) an evaluation of their thermal and acoustic properties, and (iii) their applicability in several case studies to evaluate potential acoustic enhancements. Specifically, antifungal resistance tests, Volatile Organic Compound (VOC) emission assessment, and cell viability experiments were conducted successfully. Additionally, experimental procedures were performed to determine the thermal conductivity and thermal resistance of the proposed material, along with its sound absorption coefficients in diffuse field conditions. Finally, the potential benefits of using this biomaterial in healthcare environments to reduce noise and improve acoustic comfort were demonstrated. [ABSTRACT FROM AUTHOR]

The effect of urban green spaces on traffic noise reduction has been extensively studied at the level of single vegetation, hedges, etc., but there is a lack of corresponding studies at the scale of spatial patterns of urban green spaces. Therefore, this study aims to analyze the relationship between the spatial pattern of urban green space and the change in green space's noise reduction capacity. Through the morphology spatial pattern analysis method, this analysis divides the urban green space in the Fuzhou high-tech zone into seven types of elements with different ecological definitions and simulates the noise condition of the urban environment with the presence of green space as well as without the presence of green space by computer simulation, calculates the distribution map of the noise reduction produced by the urban green space, and analyzes the correlation between the seven types of green space elements and the noise reduction with the geographically weighted regression modeling analysis. The study finds that (1) Urban green space patterns can significantly affect the net noise reduction of green space. Areas with high green coverage can produce a stronger green space noise reduction effect. (2) More complex green space shapes and more fragmented urban green space can produce higher noise reduction. (3) The green space close to the source of noise can exert a stronger noise reduction effect. Therefore, in the process of planning and design, from the perspective of improving the urban acoustic environment, the configuration of high-quality green spaces in areas with higher levels of noise pollution should be given priority, which may have better noise reduction effects. [ABSTRACT FROM AUTHOR]

Copyright of GeSec: Revista de Gestao e Secretariado is the property of Sindicato das Secretarias e Secretarios do Estado de Sao Paulo (SINSESP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A novel neural network adaptive filter algorithm is proposed to address the challenge of weak spectral signals and low accuracy in micro-spectrometer detection. This algorithm bases on error backpropagation (BP) and least mean square (LMS), introduces an innovative BP neural network model incorporating instantaneous error function and error factor to optimize the learning process. It establishes a network relationship through the input signal, output signal, error and step factor of the adaptive filter, and defines a training optimization learning method for this relationship. To validate the effectiveness of the algorithm, experiments were conducted on simulated noisy signals and actual spectral signals. Results show that the algorithm effectively denoises signals, reduces noise interference, and enhances signal quality, the SNR of the proposed algorithm is 3-4 dB higher than that of the traditional algorithm. The experimental spectral results showed that the proposed neural network adaptive filter algorithm combined with partial least squares regression is suitable for simultaneous detection of copper and cobalt based on ultravioletvisible spectroscopy, and has broad application prospects. [ABSTRACT FROM AUTHOR]

This study investigates the influence of ferrite shape modifications on the performance and noise characteristics of an induction cooktop. The goal is to optimize the air gap dimensions between ferrites and cookware, enhancing efficiency while managing noise levels. Using finite element method (FEM) simulations, we analyze the spatial distribution of magnetic forces and their harmonics. Eight ferrite shape models were examined, focusing on both outer and inner air gaps. Model #8 (reduced outer air gap) and Model #9 (reduced inner air gap) were experimentally validated. Noise measurements indicated that Model #8 reduced 120 Hz harmonic noise components, while Model #9 increased them due to enhanced excitation forces. Current measurements confirmed that Model #9 achieved higher efficiency, with RMS current reduced to 94.54% of the base model. The study reveals a trade-off between performance and noise: inner air gap reduction significantly boosts efficiency but raises noise levels, whereas outer air gap reduction offers balanced improvements. These findings provide insights for optimizing induction cooktop designs, aiming for quieter operation without compromising efficiency. [ABSTRACT FROM AUTHOR]

Noise pollution in hospitals has increased over the last few years to a level that can threaten the health and productivity of staff and patient safety. There are many control measures to reduce hospital noise. However, there is still no consensus on the best measures. This study aims to prioritize the control measures for reducing hospital noise. The work is divided into three phases. The first phase identifies and categorizes noise sources in hospitals through a review of the state-of-the art literature using Scopus®, ProQuest, PubMed, Google Scholar, Embase,™ and Web of Science™. The second phase identifies possible strategies for reduction of hospital noise and the best criteria for their adoption using findings from the literature review and interviews with corresponding experts. The third phase uses Fuzzy Analytic Hierarchy Process (FAHP) method and the Technique for Order of Preference by Similarity to Ideal Solution (fuzzy TOPSIS) method to weigh the criteria and to prioritize the control measures. Based on the results, hospital noise sources were classified into four groups: outdoor noise sources (29.7%), noise produced by domestic facilities (20.8%), indoor noise from human activities (27.5%), and noise produced by diagnostic and treatment equipment (22%). The study further arrives at a set of 9 criteria and 22 alternatives ranked using FAHP and fuzzy TOPSIS. The criteria's weights were determined using the FAHP method, with feasibility (0.175), effectiveness (0.143), and interference with staff activities (0.140) being the most important criteria. It was found that engineering controls such as substitution of noisy equipment (rank = 1), using acoustic enclosures (rank = 2), using double-glazed windows (rank = 2), and soundproofing walls, doors, and windows (rank = 3) have priority for reducing hospital noise. [ABSTRACT FROM AUTHOR]

Background: Distantly supervised relation extraction (DSRE) aims to identify semantic relations in large-scale texts automatically labeled via knowledge base alignment. It has garnered significant attention due to its high efficiency, but existing methods are plagued by noise at both the word and sentence level and fail to address these issues adequately. The former level of noise arises from the large proportion of irrelevant words within sentences, while noise at the latter level is caused by inaccurate relation labels for various sentences. Method: We propose a novel multi-level noise reduction neural network (MLNRNN) to tackle both issues by mitigating the impact of multi-level noise. We first build an iterative keyword semantic aggregator (IKSA) to remove noisy words, and capture distinctive features of sentences by aggregating the information of keywords. Next, we implement multi-objective multi-instance learning (MOMIL) to reduce the impact of incorrect labels in sentences by identifying the cluster of correctly labeled instances. Meanwhile, we leverage mislabeled sentences with cross-level contrastive learning (CCL) to further enhance the classification capability of the extractor. Results: Comprehensive experimental results on two DSRE benchmark datasets demonstrated that the MLNRNN outperformed state-of-the-art methods for distantly supervised relation extraction in almost all cases. Conclusions: The proposed MLNRNN effectively addresses both word- and sentence-level noise, providing a significant improvement in relation extraction performance under distant supervision. [ABSTRACT FROM AUTHOR]

Conventional asphalt roads are noisy. Currently, there are two main types of mainstream noise-reducing pavements: pore acoustic absorption and damping noise reduction. However, a single noise reduction method has limited noise reduction capability, and porous noise-reducing pavements have a shorter service life. Therefore, this paper aimed to improve the noise-damping performance of porous asphalt mixture by adding rubber granules and extending its service life using electromagnetic induction heating self-healing technology. Porosity and permeability coefficient test, Cantabro test, immersion Marshall stability test, freeze–thaw splitting test, a low-temperature three-point bending experiment, and Hamburg wheel-tracking test were conducted to investigate the pavement performance and water permeability coefficients of the mixtures. A tire drop test and the standing-wave tube method were conducted to explore their noise reduction performance. Induction heating installation was carried out to study the heating rate and healing performance. The results indicated that the road performance of the porous asphalt mixture tends to reduce with an increasing dosage of rubber granules. The road performance is not up to the required standard when the dosage of rubber granules reaches 3%. The mixture's performance of damping and noise tends to increase with the increase of rubber granule dosage. Asphalt mixtures with different rubber granule dosages have different noise absorption properties, and the mixture with 2% rubber granules has the best overall performance (a vibration attenuation coefficient of 7.752 and an average absorption factor of 0.457). The optimum healing temperature of the porous asphalt mixture containing rubber granules and steel wool fibers is 120 °C and the healing rate is 74.8% at a 2% rubber granule dosage. This paper provides valuable insights for improving the noise reduction performance and service life of porous asphalt pavements while meeting road performance standards. [ABSTRACT FROM AUTHOR]

• A novel variable stiffness valve based on PEEK and stainless steel was proposed. • Stress-strain behavior and operational characteristics of novel valve were studied. • Impacts of novel valve on compressor performance and noise experimentally. • Compressor performance improved by 2.6 % and noise reduced by 3–5 dB. Improving valve design is crucial for enhancing the performance of rolling piston type rotary compressor. A novel variable stiffness valve based on PEEK and stainless steel was proposed. The stress-strain behavior of the novel valve was studied, and its operational characteristics, including opening and closing angles and over-compression losses, were analyzed in a compressor. It is shown that opening and closing angle of the new-designed valve were advanced compared to that of the original cylinder by about 2∼3°, leading to a general reduction in over-compression losses, with a maximum decrease of 4.15 W. The impacts of new-designed valve on compressor performance were experimentally investigated. The cooling capacity and coefficient of performance of compressors equipped with new-designed compared to original valve were improved by 1.1–1.7 % and 1.1–2.6 %, respectively. In addition, the input electrical power required for the new valve was observed to be 0.9–1.3 % lower. The incorporation of high-damping material in the new valve design significantly reduced compressor noise by 3–5 dB at the critical frequency bands of 630–1250 Hz. These findings offer valuable insights for enhancing the energy efficiency and noise reduction in rolling piston type rotary compressors. [ABSTRACT FROM AUTHOR]

In the context of controlling axial fan aerodynamic noise, one effective approach involves the redesign of the blade. In the present study, a novel blade design was formulated for a specific model of axial flow fan, targeting the mitigation of the tip leakage vortex (TLV) and subsequent reduction of noise levels. The assessed noise-reduction blades have perforations drilled from the leading edge (LE) to the pressure surface (PS) of the blades. By impeding the tip leakage flow (TLF), these perforations effectively weaken the TLV. The three-dimensional non-constant flow field within the channel is computed employing the shear stress transport (SST) turbulence model. The aerodynamic noise prediction, which is based on the Lighthill's acoustic analogy theory, shows that the designed blades can effectively reduce the aerodynamic noise of the axial fan. Specifically, enhancing the ratio of inlet area to outlet area of the ventilation holes leads to an improved noise reduction effect. Notably, for ratios of 0.44 and 1.06, the designed blades exhibit noise reductions of up to 3.3 dB(A) and 3.9 dB(A), respectively. Meanwhile, the static efficiency of these two fans is reduced by 0.50% and 0.26%, respectively. The study thus provides good theoretical support for the design of axial flow fans in the context of noise reduction. [ABSTRACT FROM AUTHOR]

Acoustic absorption consists basically of the transformation of mechanical energy into thermal energy, carried out through the interaction of the fibers with the sound wave through the breaking of such waves and their meeting with the surface of the fibers. Among the various materials that may be employed to this end, the use of non-woven fabrics has stood out. This study aimed to evaluate the influence of the number of carded non-woven web layers on the mechanical and sound absorption capacity. For this purpose, three groups of non-woven fabrics were produced with different numbers of web layers (24, 40, and 58 layers) of equal thickness and density, composed of a mixture of polyester fibers. Standard test procedures were used to measure the physical properties of the non-woven fabrics, the linear density of the fibers used as raw material, the tensile strength, and air permeability and to assess the sound absorption capacity. In addition, to identify a balance point between sound absorption and equipment productivity, the relationship between the acoustic performance of the non-woven fabrics produced and their production speed was evaluated. The greater the number of web layers is, the higher the absorption coefficient of the non-woven. However, the variation was not significant when the various sound absorption frequencies were observed. In this regard, developing a non-woven fabric with fewer layers was more productive and cost-effective without losing technical performance. [ABSTRACT FROM AUTHOR]

Acoustic barriers are used to reduce the noise of power equipment. To increase their efficiency, an cylindrical top edge is installed, which is an add-on on the top edge of the barrier. To study the acoustic properties of the cylindrical top edge, a mathematical model of a 3-m high barrier was built in the COMSOL Multiphysics program. The mathematical model of the barrier without an cylindrical top edge was verified using the Kurze calculation method. The acoustic characteristics of a superstructure in the form of an cylindrical top edge have been studied. It has been determined that the acoustic efficiency of the cylindrical top edge depends both on the position relative to the upper edge of the barrier and on the distance from the noise source to the barrier. The calculation results show that the greatest changes in sound pressure levels when installing an cylindrical top edge are observed at high frequencies, and the minimum at low frequencies. The acoustic efficiency of the cylindrical top edge at geometric mean frequencies corresponding to low frequencies is approximately 1–2 dB and it can reach up to 25 dB at geometric mean frequencies corresponding to high frequencies. The acoustic characteristics of an cylindrical top edge with different installation angles have been studied. It has been shown that the cylindrical top edge with an installation angle of 0° has the highest acoustic efficiency (8–10 dBA) at a distance from the noise source to the barrier of up to 2 m. At distances from 2 to 5 m, the highest acoustic efficiency (4–8 dBA) is observed when using an antidiffraction device with an installation angle of 90°. Using an cylindrical top edge with an installation angle 180° is advisable when the barrier is located next to the design point at a distance from the barrier to it of less than 5 m. When installing an antidiffraction device, a significantly greater acoustic effect is achieved than when increasing the height of the barrier. The results obtained during the research are recommended to be taken into account when implementing noise reduction measures when choosing the location of an acoustic barrier with an cylindrical top edge relative to the noise source and the design point. [ABSTRACT FROM AUTHOR]

Tensor decomposition methods are inefficient when dealing with low-rank approximation of large-scale data. Randomized tensor decomposition has emerged to meet this need, but most existing methods exhibit high computational costs in handling large-scale tensors and poor approximation accuracy in noisy data scenarios. In this work, a Tucker decomposition method based on randomized block Krylov iteration (rBKI-TK) is proposed to reduce computational complexity and guarantee approximation accuracy by employing cumulative sketches rather than randomized initialization to construct a better projection space with fewer iterations. In addition, a hierarchical tensor ring decomposition based on rBKI-TK is proposed to enhance the scalability of the rBKI-TK method. Numerical results demonstrate the efficiency and accuracy of the proposed methods in large-scale and noisy data processing. [ABSTRACT FROM AUTHOR]

This article presents a comprehensive overview of the various types of mufflers that researchers have studied based on their acoustic performance. Mufflers are a crucial component in noise control to minimize the noise generated by various machines such as internal combustion engines, fans, and other sources that include gas flow. They leverage a combination of resonance, absorption, reflection, and expansion principles to mitigate sound waves. The effectiveness of mufflers in reducing noise pollution is evaluated based on certain parameters, notably transmission loss (TL), insertion loss (IL), and noise reduction (NR). TL serves as an essential parameter to evaluate the acoustic efficiency of mufflers and is widely used by scholars. It is calculated as the difference in sound power at both ends of the muffler in the absence of energy reflecting back to the pipe. Although this measure is easy to compute, it is difficult to obtain accurate measurements. Generally speaking, mufflers play a pivotal role in lowering noise emanating from a vehicle's exhaust system, and they conform to the prevailing noise pollution standards. Consequently, muffler designers aim not only to minimize noise emissions but also to satisfy customer demands. This article examines the design criteria used for mufflers so that researchers can utilize this information to achieve the optimal muffler design. This study reviews various muffler types, including expansion mufflers with circular, elliptical, and polygonal/rectangular cross-sections, mufflers with inlet/outlet pipes with non- perforated extensions, mufflers with inlet/outlet pipes with perforated extensions, multi-chambered mufflers, mufflers with three-pass perforated pipes, mufflers with microperforated plates, plug mufflers, and mufflers with U-shaped pipes (corrugated). [ABSTRACT FROM AUTHOR]

The heating, ventilation, and air conditioning (HVAC) units in vehicles can produce significant noise, lowering the sound quality inside the vehicle and reducing passenger comfort. While noise control methods are available, they can be expensive and harmful to the environment. To address these issues, this study aims to investigate using low-cost and biodegradable natural materials, specifically coconut fibers, for vehicle HVAC noise control. The study utilized coconut fibers, which have sound absorption properties reaching 42 dBA, to treat an actual Perodua HVAC unit. The treatment targeted the HVAC noise spectrum at low, medium, and high blower speeds, resulting in reduced Sound Pressure Levels (SPL) at the passenger's ear position. The composite was applied to the air inlet head and inlet channel of the HVAC system using a specific combination of coconut fiber content. The research identified the sources of noise in the highest contributions that occurred at the blower fan unit and treated the required areas. In terms of numerical data, the results showed that the treatment significantly reduced the noise level by 11 dBA. Additionally, the experiment found that the 8% fiber ratio at low speed decreased by 14.28% following the treatment. Similarly, the fiber ratio at medium and high speeds saw reductions of 15.47% and 17.56%, respectively. This study presents a promising solution for reducing noise in vehicle HVAC units using cost-effective and eco-friendly materials. Future research should focus on optimizing coconut fiber ratios, evaluating long-term durability and biodegradability, validating real-world applicability, and establishing standardized testing protocols to improve and confirm the effectiveness of coconut fiber-based noise control in automotive HVAC systems. [ABSTRACT FROM AUTHOR]

The mechanism of contra-rotating propeller's aerodynamic noise is very complicated，and propeller-driven aircraft is not equipped passive noise reduction components such as nacelle liner to absorb noise during sound transmission，the noise generated by propeller will straightforward radiated to the fuselage and surrounding. Therefore，reducing the intensity of propeller noise is the key to the development of low-noise propeller aircraft. In this paper，a "soft blade" module is formed by placing a "small hole and through channel" structure on the suction front edge of spiral blade tip to balance the peak pressure at the leading edge of blade and reduce the load noise. According to the optimal parameters，the conventional propeller and the soft-blade propeller are manufactured，and the radiation noise measurement is carried out on the basis of the counter-rotating propeller aerodynamic noise test system. The results show that the counter-rotating propeller with perforated structure can effectively reduce the noise as well as ensuring the aerodynamic performance. At the 90° pointing angle，which has the most significant effect on the aircraft cabin，the noise reduction at the second-order passing frequency reaches 5 dB.

Aviation fuel pump has the problem of flow-induced noise in engineering application，which can affect the unit operation stability and staff's working security. To solve these problems，the noise reduction technique for fuel pump is introduced. Firstly，the noise data of fuel pump acquired during the on-land system test of air plane is tested，and the noise amplitude and frequency are analyzed. Then，numerical simulation is conducted to understand the flow regime in the fuel pump. Finally，the optimization design is conducted for impeller and guide vane of fuel pump. The results show that the main reason of flow-induced noise is the pressure fluctuation caused by rotor-stator interaction between impeller and guide vane. The pressure fluctuation can be reduced by increasing the impeller and vane blade number and applying the alternate loading technique. After optimization，the noise of fuel pump is decreased by 6.5 dB.

Abstract This study investigates noise reduction using a periodic arrangement of dual Helmholtz resonators and explores the introduction of defects within this periodic structure. The transfer matrix method was employed to carry out theoretical research. The computations of the interface response function approach results are verified, and consistent outcomes are demonstrated. The simulation results highlight the distinctive dual resonance frequencies of dual Helmholtz resonators. By differentiating dual Helmholtz resonators from traditional Helmholtz resonators, prospective applications for low-frequency noise reduction are envisioned. In this contribution, introducing defects in the middle of perfect dual Helmholtz resonators adds more value to the acoustic filter. In particular, the first neck and cavity of the defective dual Helmholtz resonator. This study shows that introducing a 2D-defect into identical dual Helmholtz resonators can improve the transmission of defect modes by taking advantage of the advantageous interaction of the resonant modes. In such arrangements, the entire structure functioned as a potent selective filter.

Noise reduction in low-dose positron emission tomography (PET) is a well-researched topic aimed at reducing patient radiation doses and improving diagnosis. Software-based noise reduction mainly improves the contrast between regions by reducing the variation of the acquired image. However, it should be performed under appropriate parameters to reduce discrimination. We propose a method that derives optimal noise-reduction parameters using the multi-scale structural similarity index measure and visual information fidelity, which are metrics for image quality assessment. Simulation and experimental studies demonstrated the viability of the proposed algorithm. The contrast-to-noise ratio value of the denoised reconstruction slice, which was used as the optimal parameter, increased approximately three times compared to that of the low-dose slice while preserving the resolution. The results indicate that the proposed method successfully predicted the parameters according to the noise-reduction algorithm and PET system conditions in the sinogram domain. The proposed algorithm should help prevent misdiagnosis and provide standardized medical images for clinical application by performing appropriate noise reduction.

Background: Distantly supervised relation extraction (DSRE) aims to identify semantic relations in large-scale texts automatically labeled via knowledge base alignment. It has garnered significant attention due to its high efficiency, but existing methods are plagued by noise at both the word and sentence level and fail to address these issues adequately. The former level of noise arises from the large proportion of irrelevant words within sentences, while noise at the latter level is caused by inaccurate relation labels for various sentences. Method: We propose a novel multi-level noise reduction neural network (MLNRNN) to tackle both issues by mitigating the impact of multi-level noise. We first build an iterative keyword semantic aggregator (IKSA) to remove noisy words, and capture distinctive features of sentences by aggregating the information of keywords. Next, we implement multi-objective multi-instance learning (MOMIL) to reduce the impact of incorrect labels in sentences by identifying the cluster of correctly labeled instances. Meanwhile, we leverage mislabeled sentences with cross-level contrastive learning (CCL) to further enhance the classification capability of the extractor. Results: Comprehensive experimental results on two DSRE benchmark datasets demonstrated that the MLNRNN outperformed state-of-the-art methods for distantly supervised relation extraction in almost all cases. Conclusions: The proposed MLNRNN effectively addresses both word- and sentence-level noise, providing a significant improvement in relation extraction performance under distant supervision.

The heating, ventilation, and air conditioning (HVAC) units in vehicles can produce significant noise, lowering the sound quality inside the vehicle and reducing passenger comfort. While noise control methods are available, they can be expensive and harmful to the environment. To address these issues, this study aims to investigate using low-cost and biodegradable natural materials, specifically coconut fibers, for vehicle HVAC noise control. The study utilized coconut fibers, which have sound absorption properties reaching 42 dBA, to treat an actual Perodua HVAC unit. The treatment targeted the HVAC noise spectrum at low, medium, and high blower speeds, resulting in reduced Sound Pressure Levels (SPL) at the passenger's ear position. The composite was applied to the air inlet head and inlet channel of the HVAC system using a specific combination of coconut fiber content. The research identified the sources of noise in the highest contributions that occurred at the blower fan unit and treated the required areas. In terms of numerical data, the results showed that the treatment significantly reduced the noise level by 11 dBA. Additionally, the experiment found that the 8% fiber ratio at low speed decreased by 14.28% following the treatment. Similarly, the fiber ratio at medium and high speeds saw reductions of 15.47% and 17.56%, respectively. This study presents a promising solution for reducing noise in vehicle HVAC units using cost-effective and eco-friendly materials. Future research should focus on optimizing coconut fiber ratios, evaluating long-term durability and biodegradability, validating real-world applicability, and establishing standardized testing protocols to improve and confirm the effectiveness of coconut fiber-based noise control in automotive HVAC systems.

As a subcategory of acoustic metamaterials, membrane acoustic metamaterials (MAMs) are lighter and more adjustable than other types of acoustic metamaterials when it comes to low-frequency sound absorption. Currently, their primary application is in air mediums. However, there is a small body of research exploring MAMs' underwater potential, as they are expected to offer a solution for controlling low-frequency sound in underwater environments. This review provides an overview of the basic and derived structures of MAMs, and analyses how to adjust the eigenfrequency of both passive and active MAMs to support researchers who are the field of MAMs. [ABSTRACT FROM AUTHOR]

Approximate policy iteration (API) is a class of reinforcement learning (RL) algorithms that seek to solve the long-run discounted reward Markov decision process (MDP), via the policy iteration paradigm, without learning the transition model in the underlying Bellman equation. Unfortunately, these algorithms suffer from a defect known as chattering in which the solution (policy) delivered in each iteration of the algorithm oscillates between improved and worsened policies, leading to sub-optimal behavior. Two causes for this that have been traced to the crucial policy improvement step are: (i) the inaccuracies in the policy improvement function and (ii) the exploration/exploitation tradeoff integral to this step, which generates variability in performance. Both of these defects are amplified by simulation noise. Deep RL belongs to a newer class of algorithms in which the resolution of the learning process is refined via mechanisms such as experience replay and/or deep neural networks for improved performance. In this paper, a new deep learning approach is developed for API which employs a more accurate policy improvement function, via an enhanced resolution Bellman equation, thereby reducing chattering and eliminating the need for exploration in the policy improvement step. Versions of the new algorithm for both the long-run discounted MDP and semi-MDP are presented. Convergence properties of the new algorithm are studied mathematically, and a post-earthquake disaster response case study is employed to demonstrate numerically the algorithm's efficacy. [ABSTRACT FROM AUTHOR]

When faced with noisy time series data, significant challenges are encountered by clustering algorithms, including noise interference, temporal distortions, and irregular data patterns. In order to cope with the challenge of noisy time series data and to improve the performance of clustering algorithms, a Move-Split-Merge based Fuzzy C-Means algorithm (MSMFCM) is proposed. Firstly, dynamic wavelet basis functions as well as Median Absolute Deviation (MAD) are used to optimize the wavelets to reduce noise and highlight the actual data patterns in the original data. Secondly, a similarity matrix, constructed using the Move-Split-Merge (MSM) edit distance metric, quantitatively assesses the similarity between each pair of time series data points. Thirdly, to improve clustering efficiency, K-means + + is used to optimize the initial centers of the Fuzzy C-Means algorithm. Among twenty datasets, the performance of MSMFCM is compared with that of K-means, K-medoids, Fuzzy C-Means, K-shape, and algorithms incorporating Dynamic Time Warping and the Longest Common Subsequence. Simulation results show that MSMFCM significantly outperforms its closest competitors in the Adjusted Rand Index (ARI) and Normalized Mutual Information (NMI) evaluation indicators, with an average improvement of 26.09% for ARI and 18.86% for NMI. It means that MSMFCM has better clustering performance for noisy time series data, which will provide the application of clustering on a wider range of data. [ABSTRACT FROM AUTHOR]

Noise reduction (NR) algorithms are employed in nearly all commercially available hearing aids to attenuate background noise. However, NR processing also involves undesirable speech distortions, leading to variability in hearing outcomes among individuals with different noise tolerance. Leveraging 30 participants with normal hearing engaged in speech-in-noise tasks, the present study examined whether the cortical measure of neural signal-to-noise ratio (SNR)—the amplitude ratio of auditory evoked responses to target speech onset and noise onset—could predict individual variability in NR outcomes with varying strength, thus serving as a reliable indicator of individual noise tolerance. In addition, we also measured subjective ratings of noise tolerance to see if these measures could capture different perspectives on individual noise tolerance. Results indicated a significant correlation between neural SNR and NR outcomes that intensified with increasing strength of NR processing. While subjective ratings of noise tolerance were not correlated with the neural SNR, noise-tolerance ratings could predict outcomes with stronger NR processing and account for additional variance in the regression model, although the effect was limited. Our findings underscore the importance of accurately assessing an individual's noise tolerance characteristics in predicting perceptual benefits from various NR processing methods and suggest the advantage of incorporating both cortical and subjective measures in the relevant methodologies. [ABSTRACT FROM AUTHOR]

Introduction: Noise reduction (NR) algorithms have been integrated into modern digital hearing aids to reduce noise annoyance and enhance speech intelligibility. This study aimed to evaluate the influences of a novel hearing aid NR algorithm on individuals with severe-to-profound hearing loss. Methods: Twenty-five participants with severe-to-profound bilateral sensorineural hearing loss underwent three tests (speech intelligibility, listening effort, and subjective sound quality in noise) to investigate the influences of NR. All three tests were performed under three NR strength levels (Off, Moderate, and Strong) for both speech in noise program (SpiN) and speech in loud noise program (SpiLN), comprising six different hearing aid conditions. Results: NR activation significantly reduced listening effort. Subjective sound quality assessments also exhibited benefits of activated NR in terms of noise suppression, listening comfort, satisfaction, and speech clarity. Discussion: Individuals with severe-to-profound hearing loss still experienced advantages from NR technology in both listening effort measure and subjective sound quality assessments. Importantly, these benefits did not adversely affect speech intelligibility. [ABSTRACT FROM AUTHOR]

Generated rotor (gerotor) pumps have commercial applications in oil supply pumps and compressors. Unfortunately, they produce an excessive amount of noise while in operation. The present study focuses on reducing the noise, which is caused due to pressure oscillations in the fluid, and vibration levels when a gerotor pump is in operation. Three-dimensional numerical simulations were performed on the gerotor pump using the dynamic mesh method. The flow field was simulated using the commercial software Ansys Fluent. The mesh motion was provided in the form of a user-defined function. Computational fluid dynamics analysis was performed for different combinations of delivery pipe length and diameter, and their effects on delivery pressure were analyzed. It is observed from numerical results that the pressure oscillations are reduced around 20 % by increasing volume of port or pipe region, which in turn reduces the noise levels. It is concluded that the pressure oscillations during the fluid flow are highly dependent on the volume of the delivery pipe. [ABSTRACT FROM AUTHOR]

The Normalized Difference Vegetation Index (NDVI) is a crucial remote-sensing metric for assessing land surface vegetation greenness, essential for various studies encompassing phenology, ecology, hydrology, etc. However, effective applications of NDVI data are hindered by data noise due to factors such as cloud contamination, posing challenges for accurate observation. In this study, we proposed a novel approach for employing a Temporal-Difference Graph (TDG) method to reconstruct low-quality pixels in NDVI data. Regarding spatio-temporal NDVI data as a time-varying graph signal, the developed method utilized an optimization algorithm to maximize the spatial smoothness of temporal differences while preserving the spatial NDVI pattern. This approach was further evaluated by reconstructing MODIS/Terra Vegetation Indices 16-Day L3 Global 250 m Grid (MOD13Q1) products over Northwest China. Through quantitative comparison with a previous state-of-the-art method, the Savitzky–Golay (SG) filter method, the obtained results demonstrated the superior performance of the TDG method, and highly accurate results were achieved in both the temporal and spatial domains irrespective of noise types (positively-biased, negatively-biased, or linearly-interpolated noise). In addition, the TDG-based optimization approach shows great robustness to noise intensity within spatio-temporal NDVI data, suggesting promising prospects for its application to similar datasets. [ABSTRACT FROM AUTHOR]

The quality of images captured digitally or transmitted over networks is distorted by noise during the process. The current methods of image restoration can be ineffective in dealing with intricate noise patterns or may be slow or imprecise. This paper fills this gap by presenting a new second-order continuous-time dynamical system for denoising of images in image restoration. The approach used in this work poses the problem as a convex quadratic program that can, thus, be solved for optimality. The existence and uniqueness of a global solution are theoretically demonstrated, and the condition for the global strong convergence of the system's trajectory is provided. The method presented in this paper is shown to be useful in a number of experiments on image restoration. As for the performance, it is higher than that of other known algorithms, with an average SNR equal to 34.78 and a Structural Similarity Index Measure (SSIM) of 0.959 for the reconstructed images. Such improvements demonstrate the effectiveness of the second-order dynamical system approach in actual image restoration applications. [ABSTRACT FROM AUTHOR]

The diagnosis of faults in wind turbine gearboxes based on signal processing represents a significant area of research within the field of wind power generation. This paper presents an intelligent fault diagnosis method based on ensemble-refined composite multiscale fluctuation-based reverse dispersion entropy (ERCMFRDE) for a wind turbine gearbox vibration signal that is nonstationary and nonlinear and for noise problems. Firstly, improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and stationary wavelet transform (SWT) are adopted for signal decomposition, noise reduction, and restructuring of gearbox signals. Secondly, we extend the single coarse-graining processing method of refined composite multiscale fluctuation-based reverse dispersion entropy (RCMFRDE) to the multiorder moment coarse-grained processing method, extracting mixed fault feature sets for denoised signals. Finally, the diagnostic results are obtained based on the least squares support vector machine (LSSVM). The dataset collected during the gearbox fault simulation on the experimental platform is employed as the research object, and the experiments are conducted using the method proposed in this paper. The experimental results demonstrate that the proposed method is an effective and reliable approach for accurately diagnosing gearbox faults, exhibiting high diagnostic accuracy and a robust performance. [ABSTRACT FROM AUTHOR]