High Frequency Noise Research Articles

A Dynamic High-Frequency Noise Suppression Method of Gyroscope for Inertial Stabilized Platforms Based on an Adaptive Compensatory Disturbance Observer

A Dynamic High-Frequency Noise Suppression Method of Gyroscope for Inertial Stabilized Platforms Based on an Adaptive Compensatory Disturbance Observer

Study on the Sound Absorption Characteristics and Noise Reduction Mechanism of Coconut-Shell-Activated Carbon Particles and Coconut Fiber Composite Biomass Sound-Absorption Materials

Abstract Sound absorbing materials are widely used in the field of automotive industry. Biomass materials are abundant in nature, and some biomass has natural sound absorption and noise reduction properties. Biomass sound absorption material is green and pollution-free, and has obvious noise reduction effect on middle and high frequency noise, large specific surface area, light weight, and strong sound absorption effect. In order to analyze the sound absorption and physical properties of biomass sound absorbing materials, the noise reduction performance of the different structure of biomass sound absorbing materials were analyzed. In this paper, the biomass sound absorbing materials coconut fiber and coconut shell activated carbon particles were used to make samples. Coconut shell activated carbon sound absorption material (CSAC) was made. The cylindrical holes was made and filled with coconut fiber materials to form composite sound absorbing materials (CSAC-F). The acoustic performance of impedance tube was tested based on the acoustic absorption coefficient, and the physical performance was studied by means of Scanning Electron Microscope (SEM), Brunauer, Emmett and Teller (BET), X-ray diffraction (XRD), Fourier Transform Infrared Spectrometer (FTIR), Thermogravimetric (TGA) and other detection methods. In contrast, the sound absorption effect of CSAC-F was better in the middle and low frequency range. The microstructure of the material was analyzed and the mechanism of noise reduction was studied. This research will provide a new way for the research and development of sound absorbing materials in the automotive industry, and biomass sound absorbing materials have potential applications in the noise absorption and vibration control of automotive interior.

Synthesis of Highly Porous Graphene Oxide-PEI Foams for Enhanced Sound Absorption in High-Frequency Regime.

High-frequency noise exceeding 1 kHz has emerged as a pressing public health issue in industrial and occupational settings. In response to this challenge, the present study explores the development of a graphene oxide-polyethyleneimine (GO-PEI) foam (GPF) featuring a hierarchically porous structure. The synthesis and optimization of GPF were carried out using a range of analytical techniques, including Raman spectroscopy, scanning electron microscopy (SEM), Braunauer-Emmett-Teller (BET) analysis, X-ray diffraction (XRD), and Fourier-transform infrared spectroscopy (FT-IR). To evaluate its acoustic properties, GPF was subjected to sound absorption tests over the 1000-6400 Hz frequency range, where it was benchmarked against conventional melamine foam. The findings demonstrated that GPF with a GO-to-PEI composition ratio of 1:3 exhibited enhanced sound absorption performance, with improvements ranging from 15.0% to 118%, and achieved a peak absorption coefficient of 0.97. Additionally, we applied the Johnson-Champoux-Allard (JCA) model to further characterize the foam's acoustic behavior, capturing key parameters such as porosity, flow resistivity, and viscous/thermal losses. The JCA model exhibited a superior fit to the experimental data compared to traditional models, providing a more accurate prediction of the foam's complex microstructure and sound absorption properties. These findings underscore GPF's promise as an efficient solution for mitigating high-frequency noise in industrial and environmental applications.

Impact of T-Shape Drain Doping Engineering on the Analog/RF and High-Frequency Noise Parameters of Junctionless Si/ Si0.7Ge0.3 FET: A Numerical Simulation Study

Impact of T-Shape Drain Doping Engineering on the Analog/RF and High-Frequency Noise Parameters of Junctionless Si/ Si0.7Ge0.3 FET: A Numerical Simulation Study

Target setting and practical Improvement for sound quality of electric vehicles

For electric vehicles, because there is no masking effect of combustion engine noise, the middle frequency and high frequency noise of electric components become distinct and annoying. Electric driven unit, electric compressor, electric pump, and motors of seat adjusting device are always major noise sources for electric vehicles. Some of those noise signals are steady, some are transient and some are time-variable. So only SPL is hard to reflect human's true perception. Psychoacoustic metrics, such as loudness, roughness, sharpness, and tonality, is hard to evaluate all complicated NVH phenomenon of electric vehicles using one metrics. In this paper, the author tested different operation noises of above-mentioned electric components of several popular EVs, and finalized proper psychoacoustic metrics for different operations based on loudness, modulation, tonality and objective evaluation. Furthermore, the author studied target setting for dedicated metrics, and effective improving methods for sound quality of EVs from the point of source and path. For example, effective designing for acoustic encapsulation of EDU and electric compressor, and good insulation performance designing for dash inner and trunk floor trim. Keywords: Sound quality, Psychoacoustic metrics, electric vehicle, objective evaluation, target setting

Expanding road noise cancellation to higher frequencies

Active vehicle Road Noise Cancellation (RNC) systems effectively reduce persistent vehicle rumble and booming noise which is very difficult to control with traditional NVH measures, particularly at low frequencies. Therefore, more and more vehicles are adapting RNC systems in their production applications. Most current RNC systems cancel low frequency structure-born noise, which is typically below 400 Hz. Although it provides appreciable noise attenuation, remaining high frequency noise contents still requires mitigation for a premium cabin experience. This study targets a next generation RNC system development which extends noise cancellation frequency range up to 1 kHz. High frequency RNC system (HF-RNC) development begins with analyzing limiting factors of the current RNC systems. Theoretical and practical limitations of the current system are discussed. Then, new technology and enablers to overcome those limitations are introduced. New enablers investigated herein in includes feedforward microphones, low latency system, headrest speakers and microphones, and head tracking system. HF-RNC system architecture is defined to comprise extended features and enablers. Low latency software is ported into a development platform which is based on a commercial DSP. The new HF-RNC system demonstrates noise cancellation up to 1 kHz when applied to an electric vehicle.

Porous acoustic metamaterial for simultaneous control of high and low frequency machinery noise: Case study of a water pump.

An acoustic metamaterial (AMM) consisting of a porous material (melamine foam) layer above a symmetrical labyrinthine metamaterial, incorporating a micro-hole and micro-slit cover plate, is proposed to simultaneously mitigate low and high frequency noise from industrial machineries. Theoretical model of sound absorption by this AMM is developed and validated numerically and experimentally. Sensitivity analysis indicates that increasing the length of the labyrinthine pathway and cover plate thickness and decreasing the slit width, slit length, and hole diameter shifts the peak sound absorption to lower frequencies. This material is successfully applied as a sound absorptive enclosure of a 0.5 hp water pump to reduce its sound pressure levels across widely separated frequencies of 1414-2245 Hz (high frequency) and 176-222 Hz (low frequency). This study offers guidelines to noise control engineers for controlling low and high frequency noise in industrial machineries.

Effect of impact kinematic filters on brain strain responses in contact sports.

Impact kinematics are widely employed to investigate mechanisms of traumatic brain injury (TBI). However, they are susceptible to noise and artefacts; thus, require data filtering. Few studies have focused on how data filtering affects brain strain most relevant to TBI. Here, we report that impact-induced brain strains are much less sensitive to data filtering than kinematics based on three filtering methods: CFC180, lowpass 200Hz, and a new method called Head Exposure to Acceleration Database in Sport (HEADSport). Using mouthguard-measured head impacts in elite rugby (N=5694), average Euclidean distances between the three filtered angular velocity profiles and their unfiltered counterparts are used to identify three groups of impacts with large variations: 90-95th, 95-99th, and >99th percentile. From each group, 20 impacts are randomly selected for simulation using the anisotropic Worcester Head Injury Model (WHIM) V1.0. HEADSport and CFC180 are the most and least effective, respectively, in suppressing "unphysical artefacts" shown as sharp spikes with a rather short impulse duration (e.g., <3 ms) in angular velocity. However, maximum principal strain (MPS), especially that in the corpus callosum, is much less sensitive to data filtering compared to kinematic peaks (e.g., reduction of 3% vs. 47% and 90% for peak angular velocity and acceleration with HEADSport for impacts of >99th percentile). These findings confirm that the brain acts as a low-pass filter, itself, to suppress high frequency noise in impact kinematics. Therefore, brain strain can serve as a common metric for TBI biomechanical studies to maximize relevance to the injury, as it is not sensitive to kinematic filters.

Prediction of wind power ramp events via a self-attention based deep learning approach

With the increasing popularity of wind power generation, the importance of accurate wind power ramp events (WPRE) prediction has been further emphasized. At present, WPRE prediction model based on deep learning shows superior performance, which does not need to fully grasp the operation of wind power generation, but ignores the interdependence mechanism of prediction error along the input characteristics of the neural network. This paper develops a self-attention (SAM) and wavelet transform (WT) based hybrid 1DCNN (one-dimensional convolutional neural network) and LSTM (long short term memory networks) for WPRE forecasting. In the proposed model, WT effectively separates the middle and high frequency noise signals in wind power and SAM can redistribute the neural weights in 1DCNN-LSTM, then 1DCNN-LSTM further extracts the space-time information of effective wind power. Finally, the performance of the forecast model proposed in this paper is tested by using the wind power data from the Belgian ELIA website. The experimental results show that, compared with the other six competition models, the proposed model effectively suppresses the influence of randomness, volatility and uncertainty of wind power on the recognition of ramp events. Furthermore, through quantitative analysis, this paper explains the reason of SAM is superior to traditional AM in terms of their application object and weight control range in the proposed model.

Stability of ECoG high gamma signals during speech and implications for a speech BCI system in an individual with ALS: a year-long longitudinal study

Objective. Speech brain–computer interfaces (BCIs) have the potential to augment communication in individuals with impaired speech due to muscle weakness, for example in amyotrophic lateral sclerosis (ALS) and other neurological disorders. However, to achieve long-term, reliable use of a speech BCI, it is essential for speech-related neural signal changes to be stable over long periods of time. Here we study, for the first time, the stability of speech-related electrocorticographic (ECoG) signals recorded from a chronically implanted ECoG BCI over a 12 month period. Approach. ECoG signals were recorded by an ECoG array implanted over the ventral sensorimotor cortex in a clinical trial participant with ALS. Because ECoG-based speech decoding has most often relied on broadband high gamma (HG) signal changes relative to baseline (non-speech) conditions, we studied longitudinal changes of HG band power at baseline and during speech, and we compared these with residual high frequency noise levels at baseline. Stability was further assessed by longitudinal measurements of signal-to-noise ratio, activation ratio, and peak speech-related HG response magnitude (HG response peaks). Lastly, we analyzed the stability of the event-related HG power changes (HG responses) for individual syllables at each electrode. Main Results. We found that speech-related ECoG signal responses were stable over a range of syllables activating different articulators for the first year after implantation. Significance. Together, our results indicate that ECoG can be a stable recording modality for long-term speech BCI systems for those living with severe paralysis. Clinical Trial Information. ClinicalTrials.gov, registration number NCT03567213.

Fracture surface topography measurements analysis of low-alloyed corrosion resistant steel after bending-torsion fatigue tests

In this paper, an assessment of a topography measurement method for fracture surfaces of 10HNAP steel after bending-torsion fatigue tests was performed. Surface roughness was measured by using a non-contact Focus Variation Microscopy (FVM) technique in which the non-measured points (NMPs) and outliers (spikes) were removed by the application of general methods. The results revealed, that the optical measurement method introduced variations in the high-frequency errors, considered as noise within the selected bandwidth. Therefore, the minimization of the high-frequency noise (HFN) was proposed based on an extensive examination of ISO 25178 roughness parameters. Additionally, a general S-filter was applied, as recommended by international standards and commercial software. It was used to identify and remove noise from the measured data after pre-processing. Consequently, levelling and eliminating of NMPs and spikes was successfully performed. Subsequently, the results obtained by using various filters were compared to further assess the impact of different filtration bandwidths. Finally, the proposed procedure was validated by implementing different general functions, such as autocorrelation (ACF), power spectral densities (PSD), and texture direction (TD). It was concluded, that coupled characteristics, including profile and areal measurements, should be studied simultaneously since they are necessary to analyze the fracture surfaces comprehensively.

DESIGN AND EVALUATION OF PIEZOELECTRIC-BASED PASSIVE DAMPING FOR HIGH FREQUENCY NOISE SUPPRESSION

This study focuses on developing a piezoelectric-based passive damper to mitigate high- frequency brake noise. The research investigates different designs of piezoelectric transducer elements to convert mechanical energy into electrical energy using a mass sliding belt test system that simulates real disc brake noise mechanisms. Integration of these electrical elements into the existing mass-sliding belt system is investigated. Five distinct piezoelectric transducer designs (Design-1, Design-2, Design-3, Design-4 and Design-5), each with unique technical specifications, boundary conditions, and scaling, are developed to suppress high frequency noise from friction-induced vibrations. To facilitate effective selection, a scoring method is employed, identifying Design-5 with a cylindrical model as the most efficient transducer design. Evaluation criteria include energy generation, compressive strength, operating temperature, weight, ergonomic usability, cost, and safety. Analyses are performed to assess potential stress-induced damage to the piezoelectric material resulting from the selected design. This study aims to contribute a novel perspective to noise reduction techniques and successfully demonstrates the integration of a piezoelectric transducer and an RLC circuit into a functional system.

Noise analysis of NC-GAAFET cylindrical nanowire with non-uniform interface trap charge

In this article, low to high frequency noise behavior analysis of negative capacitance gate-all-around field effect transistor (NC-GAAFET) MFIS structure Silicon Nanowire (SiNW) device, using Sentauras TCAD simulations, is investigated. The NC-GAAFET SiNW yields on current (ION) 5.31 times larger and off current (IOFF) is significantly reduced by ∼ 105 orders compared to baseline SiNW. The device exhibits an excellent switching ratio of 5.2 × 1014. Average subthreshold swing for the NC-GAAFET is 33 mV/dec compared to 64 mV/dec of baseline nanowire. The Negative-DIBL for the device is −20 mV/V which outshines earlier findings. Furthermore, the drain current noise power spectral density (PSD) Sid, and input referred gate voltage noise PSD (Svg) are comprehensively analyzed in the presence of Gaussian trap (non-uniform) distribution. The analysis indicates that, flicker or (1/f) noise dominates in low frequency regime, generation-recombination (G-R) noise is more influential in mid frequency regime whereas in very high frequency regime diffusion noise is leading. The device exhibits least Sid(NET) and Svg(NET) at tfe = 6 nm compared to baseline, tfe = 5 nm, and 7 nm.

The recital assessment of image fusion based on multi-resolution transforms using smoothness approach proach

Combining the information from the registered source images is the process involved in the image-fusion. In this present manuscript, two fusion rules are explored. The first one is based on the weightage-based rule. The second one is the Smoothness and weightage-based algorithm. Smoothness is used to reduce the noise from the source images. These two methods are independent of the selection of the transform. In this work, Discrete Wavelet Transform is considered to perform the experimentation. The recital comparison was made between multiresolution transforms using maximum selection method, weightage method, and smoothness along with weightage. The source images are generally multi-focused, satellite, panchromatic, and clinical medical images. The experimental results show that more smoothened (in addition with weightage) images (including edges and curves) provide high visual information. The advantage of this approach is proven using the performance metrics such as PSNR, NCC, MI, ESOP, and FSIM. The blocking artifacts are reduced by decomposing the transforms and the high frequency noise in the source image is smoothened by proposed approaches.

Experimental Investigation on Transmission of Sound in To Metallic Enclosures During Static Firing Tests Of Solid Rocket Motor

Satellite launch vehicle during lift-off generates intense dynamic environment mainly the acoustic noise, which in turn acts on the nearby launch pad systems. Reliable operation of these launch pad ground systems needs to be ensured by safeguarding against these dynamic loads with adequate protection measures. For future launch vehicle programs where the launch turnaround time is a crucial aspect, sensitive systems like ground check-out electronic packages are planned to be placed adjacent to the launch vehicle mounting pedestal on the launch pad to ensure minimal time for preparation. The enclosures housing these electronic packages needs to be configured to minimize the transmission of acoustic levels being imposed from the lift-off induced noise. In the present work, an experimental investigation is carried out on acoustic transmission characteristics of metallic enclosures of different material and thickness during the static firing tests of solid rocket motors. Acoustics generated from the supersonic jet noise has a wide frequency band from 100 to 10,000 Hz with sound power peaking near 1,000 Hz. The electronic components and packages, which are generally vulnerable to the high frequency noise, need to be protected from this lift-off induced acoustics. The enclosures are placed in the far field of this jet noise and the transmission loss measurements are obtained. Variation of transmission loss with material and thickness of the enclosures are studied with specific attention to the internal acoustic modes and the coincidence phenomena. Subsequently, the enclosures treated with acoustic blankets of different thickness are investigated to verify its influence on internal acoustic modes and also the additional reduction in transmission of sound in the dominant frequency band of supersonic jet noise.

Research on predicting heat loads based on extracting temporal and spatial features of multiple buildings using data-driven methods

Accurate heat load prediction is the key to ensure the stable operation of thermal system and effective planning of thermal resources. However, current heat load forecasting methods tend to treat individual buildings as isolated entities, ignoring the temporal and spatial correlation between buildings. In this study, we propose a data-driven model based on spatiotemporal coupling to predict short-term heat load. Firstly, the spatial and temporal correlation and the correlation among features of various buildings are analyzed by using the autocorrelation function and Spearman correlation coefficient. Secondly, synchronous wavelet transform is used to eliminate high frequency noise of heat load. Secondly, the spatial and temporal characteristics of building heat load are extracted by using the improved Informer model which is concerned with multi-scale graphs. The experimental results show that the proposed model has better predictive performance than the baseline model. It provides a reference for the accurate regulation of thermal system.

A Multi-Scale Fusion Strategy for Side Scan Sonar Image Correction to Improve Low Contrast and Noise Interference

Side scan sonar images have great application prospects in underwater surveys, target detection, and engineering activities. However, the acquired sonar images exhibit low illumination, scattered noise, distorted outlines, and unclear edge textures due to the complicated undersea environment and intrinsic device flaws. Hence, this paper proposes a multi-scale fusion strategy for side scan sonar (SSS) image correction to improve the low contrast and noise interference. Initially, an SSS image was decomposed into low and high frequency sub-bands via the non-subsampled shearlet transform (NSST). Then, modified multi-scale retinex (MMSR) was employed to enhance the contrast of the low frequency sub-band. Next, sparse dictionary learning (SDL) was utilized to eliminate high frequency noise. Finally, the process of NSST reconstruction was completed by fusing the emerging low and high frequency sub-band images to generate a new sonar image. The experimental results demonstrate that the target features, underwater terrain, and edge contours could be clearly displayed in the image corrected by the multi-scale fusion strategy when compared to eight correction techniques: BPDHE, MSRCR, NPE, ALTM, LIME, FE, WT, and TVRLRA. Effective control was achieved over the speckle noise of the sonar image. Furthermore, the AG, STD, and E values illustrated the delicacy and contrast of the corrected images processed by the proposed strategy. The PSNR value revealed that the proposed strategy outperformed the advanced TVRLRA technology in terms of filtering performance by at least 8.8%. It can provide sonar imagery that is appropriate for various circumstances.

Jet noise of a low bypass turbofan with internal mixing and external plug

Jet noise generated by turbofan engines which are designed for supersonic aircraft operation is expected to remain a challenge for the aerospace industry. For civil supersonic transport, low bypass ratio engines with internal mixing and external plugs are currently seen as promising exhaust architecture to meet aircraft mission and certification requirements. Measurements of the noise from a representative low-bypass ratio long duct mixed flow with external plug exhaust configuration were made, along with corresponding steady-RANS and LES analyses. The measured data showed excess high frequency noise across a wide range of operating conditions as compared to an equivalent single stream jet, or an equivalent uniform jet with internal plug. The absolute levels of the excess noise were relatively insensitive to forward flight, and the excess noise was most dominant when the primary and secondary streams had large velocity and temperature mismatch just upstream of the internal mixer. Preliminary indications from the LES analysis also indicated that significant pressure fluctuations, at the same frequencies which are insensitive to forward flight, develop inside the nozzle as the internal shear layer from the mixer grows and gets accelerated by the external plug. Parametric study test measurements on the fan and core operating conditions were performed, providing support for the hypothesis that the excess noise is generated as the internal shear layer, starting inside the nozzle, gets accelerated by the plug and passes through the weak shock at the nozzle exit where the flow accelerates more and turns over the external plug.

The Parameter Identification of Structure with TMD considering Seismic Soil-Structure Interaction

Parameter identification is of great significance for the postearthquake performance evaluation of structure equipped with tuned mass damper (TMD). However, the soil-structure interaction (SSI) effects have not been considered in the parameter identification of structure with TMD yet, which influence the dynamic characteristics and seismic responses of structures. This paper aims at proposing a framework for identifying the physical parameters of soil-structure-TMD system. Firstly, the accelerated particle swarm optimization (APSO) algorithm is combined with the search space reduction (SSR) method. Then, the frequency response function and transmissibility function are adopted for output-input and input-only cases, respectively, and a simplified mechanical model for soil-structure-TMD system is employed. Next, the measured responses are used to identify the physical parameters of structure with TMD considering SSI effects. Finally, the effectiveness of the proposed identification method is investigated, and the influences of frequency band and noise pollution on the identification performance are discussed. The results show that the proposed strategy can identify the system physical parameters accurately and quickly. It is worth noting that high frequency bands and noise pollution may lead to estimation error, especially for output-only case.

A Feedback Active Control Approach to Road Noise Based on a Single Microphone Sensor to Improve Automotive Cabin Sound Comfort.

Tire-road noise deteriorates the sound quality of a vehicle's interior and affects the driving safety and comfort. Obtaining low interior noise is a challenge for passenger car manufacturers. Traditional passive noise control (PNC) is efficient for canceling high frequency noise but not useful for low frequency noise, while active noise control (ANC), according to the residual error signal, can generate an anti-noise signal to reduce the original noise. Most research has focused on improving the control effect for a feedforward ANC system. However, this paper emphasizes a feedback ANC system based on a signal microphone sensor. There are two main contributions in this study to improve automotive cabin sound comfort. One is that the algorithm of the feedback ANC system using a single microphone sensor without a reference noise signal is proposed based on the Filtered-x Least Mean Square method. The other is that the algorithm applies additive random noise online to estimate the secondary path model. A simulation was implemented based on measured real road noise data, and the simulation results indicate that the proposed feedback ANC system with the single microphone sensor can effectively attenuate road noise. This study shows the feasibility of applying a feedback ANC system in automobiles to increase the cabin sound quality.