Significant Noise Levels Research Articles

Noise impact evaluations of an outdoor air filter tower in urban blocks

Although air filter towers are commonly used to improve air quality in urban areas, their operation often produces significant noise levels, and their noise impact is unknown. In this research paper, a case study on the noise impacts of an outdoor air filter tower in city open areas and on building façades is conducted by noise mapping. Noise levels around the filter tower were first measured in situ, and a prediction model for noise mapping was built and well-calibrated by comparing the predicted and measured results. Noise mapping was then carried out to investigate noise exposure in city open areas and on façades in four typical urban blocks with different morphology. Simulated results highlight noise level improvement with the increase of façade height at some high-rise buildings whose heights are higher than the neighboring ones. In addition, urban morphology, including building height and density, as well as the distance between the source and the building, has an impact on noise exposure. The utilization of sound-absorbing building envelopes has been identified as an effective means of reducing noise on building façades, with a maximum attenuation of approximately 4 dB(A) observed, but the effectiveness of this method is limited at certain positions. This study provides valuable insights into the impact of filter towers on noise levels and offers suggestions for urban sound planning.

An autonomous and heuristic approach for extracting bridge frequencies from passing vehicles

The Federal Highway Administration (FHWA) recognizes a need for cost-effective and practical structural health monitoring (SHM) techniques to manage the bridge network. System identification (SI) techniques are the first step in developing SHM frameworks to extract bridge dynamic properties (natural frequencies, damping ratios, and mode shapes). One promising indirect SI technique uses accelerometer-instrumented vehicles to measure the dynamic properties of bridges, such that a few instrumented vehicles could economically monitor a large network of bridges. This study proposes an autonomous framework called autonomous peak picking variational mode decomposition (APPVMD), which leverages an ensemble of signal processing techniques and heuristic models to autonomously extract bridge frequencies from instrumented vehicles. The proposed framework does not require prior information or model-informed training. The framework is tested on four vehicles and 39 unique bridge classes using finite element (FE) models to assess the feasibility of deploying it into practice. The results of this study indicate that the algorithm can make successful bridge frequency extractions in more than 69.2% of the bridge cases using any vehicle and a maximum value of 97.4% using an SUV, while considering the effects of road surface roughness. The influence of bridge stiffness, mass, and span length on the algorithm’s success rate is discussed. It is concluded that flexural rigidity and span length play a significant role in the accuracy of APPVMD. The combined effects of bridge inertia and damping cause heavier bridges to have a higher amplitude signal for a longer duration, increasing the algorithm’s successful detection rate of bridge frequency. Next, the authors examine the effect of reducing the tire stiffness in the truck model on the success of the APPVMD algorithm. The customized truck outperformed the original truck and the SUV by successfully extracting the bridge frequencies of 97.4% of the bridge cases and by having more bridge frequencies predicted in a higher confidence region than the SUV. In addition, the customized truck model is used to verify the APPVMD’s robustness to large levels of sensor noise. Even at significant levels of white noise (1% SNR), as the success rate for bridge frequency extraction using the customized truck only drops to 84.6%. Finally, the influence of vehicle speed on the APPVMD algorithm’s effectiveness is studied. Generally, slower speeds resulted in more successful predictions of bridge frequency, but minor improvements in the accuracy of the frequency predictions were observed at slightly increased velocities, 13.4 m/s (30 mph) and 17.9 m/s (40 mph), but a reduction in accuracy was observed at higher velocities, 22.3 m/s (50 mph).

Force amplification at the wheel hub due to the split in the air-cavity mode for a rolling tire

Reduction of tire/road noise has become an important issue for EV's since powertrain noise has largely been eliminated. A tire's air-cavity mode is known to be a significant contributor to increased forces at the wheel hub, which can result in significant interior noise levels near 200 Hz. Moreover, the single natural frequency of a static, undeformed tire can separate into two neighboring frequencies for a rolling, deformed tire due to the Doppler effect. In this study, the evolution of the frequency split at different speeds was observed in Tire Pavement Test Apparatus (TPTA) tests by measuring the dynamic force at the hub under rotation and load. Similar results were obtained using FE simulations to generate Campbell diagrams. Through the FE simulation, it has been shown that there can be force amplification at the hub when the split frequencies couple with adjacent treadband structural modes near 200 Hz. In addition, both material and geometrical modifications were applied to the base model to find values that would reduce force levels at the split frequencies. Finally, it is suggested that a target speed needs to be determined when evaluating a tire since the two natural frequencies are strongly influenced by rotation speed.

Characteristics of Background Noise in the Oldest-1 Array Deployed on the Oldest Part of the Pacific Plate

ABSTRACT We characterize the sources of background noise recorded by an ocean-bottom seismometer array deployed on the oldest oceanic lithosphere of the western Pacific plate. This igneous oceanic crust is blanketed up to 1000 m cover of sediments, making it a unique place to investigate correlations between sediment thickness and background noise levels in the marine environment. Although the data are limited, we found that the thicker sediments attenuated short-period noise levels and amplified long-period noise levels, in agreement with observations made for seismic arrivals in onshore sedimentary basins. Comparison of the noise levels from our Oldest-1 deployment with those (1) in the western Pacific basin, (2) near the Mariana trench, and (3) offshore New Zealand indicates that the Oldest-1 has the highest noise levels in the double-frequency (secondary) microseism band by up to 7 dB. This result was unexpected given the greater water depths (averaging 5800 m) for the Oldest-1 array. However, even at these great depths, we find a strong correlation between significant wave heights and background noise levels in the short-period microseism band (2–5 s). In addition, the Oldest-1 array was subjected to a great number of intense close typhoons than the other arrays and was proximal to a great number of taller seamounts than the other arrays. Finally, tracking noise source azimuths using station pairs led to the identification of a possible new source for the single-frequency microseism band in the coastal areas of Papua New Guinea and Australia.

Exploring the optimality of approximate state preparation quantum circuits with a genetic algorithm

We study the approximate state preparation problem on noisy intermediate-scale quantum (NISQ) computers by applying a genetic algorithm to generate quantum circuits for state preparation. The algorithm can account for the specific characteristics of the physical machine in the evaluation of circuits, such as the native gate set and qubit connectivity. We use our genetic algorithm to optimize the circuits provided by the low-rank state preparation algorithm introduced by Araujo et al., and find substantial improvements to the fidelity in preparing Haar random states with a limited number of CNOT gates. Moreover, we observe that already for a 5-qubit quantum processor with limited qubit connectivity and significant noise levels (IBM Falcon 5T), the maximal fidelity for Haar random states is achieved by a short approximate state preparation circuit instead of the exact preparation circuit. We also present a theoretical analysis of approximate state preparation circuit complexity to motivate our findings. Our genetic algorithm for quantum circuit discovery is freely available at https://github.com/beratyenilen/qc-ga.

Title: Hip and lower limbs 3D motion tracking using a double-stage data fusion algorithm for IMU/MARG-based wearables sensors

This paper presents an algorithm to estimate the relative orientation of body segments during 3D motion tracking, either as multiple individual body segments or as an articulated body chain. For this purpose, a double-stage data fusion algorithm (algorithm-based system) was implemented for inertial measurement units (IMU)/Magnetic Angular Rate and Gravity (MARG)-based wearable sensors during gait. The methodology considers two stages of complementary filters for the data fusion of the IMU/MARG sensors and a Proportional-Integral controller (PI) to incorporate the processed data. Quaternions were employed for the orientation estimation, a Direction Cosine Matrix quaternion-based (DCM) to estimate the relative orientation between the body mobile coordinate system and the inertial reference coordinate system, and Euler angles for the orientation representation. A customized Ambulatory Sensing Motion System (ASMS), consisting of seven monitoring devices was used for the evaluation. The double-stage algorithm-based system was compared with a digital motion processor (DMP) performance. Experimental results of the double-stage algorithm-based system indicated a 0.7° RMSE (M1) and 1° RMSE (M1-M7) with mean values lower than 1° concerning the DMP outcomes. Based on the Bland-Altman analysis, the level of agreement indicated that the double-stage algorithm-based system is neither underestimated nor overestimated and is suitable for the DMP outcome. No singularity conditions nor significant levels of noise or drift were observed in the conducted experiments. The results demonstrate the feasibility of using the proposed method for 3D human motion tracking of multiple body segments, individually or as an articulated body chain. Besides, it suits different IMU/MARG sensor-based wearable sensors.

Real-Time Instance Segmentation Models for Identification of Vehicle Parts

Automated assessment of car damage is a major challenge in the auto repair and damage assessment industries. The domain has several application areas, ranging from car assessment companies, such as car rentals and body shops, to accidental damage assessment for car insurance companies. In vehicle assessment, the damage can take many forms, from scratches, minor dents, and major dents to missing parts. Often, the assessment area has a significant level of noise, such as dirt, grease, oil, or rush, which makes accurate identification challenging. Moreover, in the repair industry, identifying a particular part is the first step in obtaining an accurate labor and part assessment, where the presence of different car models, shapes, and sizes makes the task even more challenging for a machine-learning model to perform well. To address these challenges, this study explores and applies various instance segmentation methodologies to determine the best-performing models. This study focuses on two genres of real-time instance segmentation models, namely, SipMask and YOLACT, owing to their industrial significance. These methodologies were evaluated against a previously reported car parts dataset (DSMLR) as well as an internally curated dataset extracted from local car repair workshops. The YOLACT-based part localization and segmentation method outperformed other real-time instance mechanisms with an mAP of 66.5. For the workshop repair dataset, SipMask++ reported better accuracy for object detection with a mAP of 57.0, with outcomes for A P I o U = . 50 and A P I o U = . 75 reporting 72.0 and 67.0, respectively, whereas YOLACT was observed to be a better performer for A P s with 44.0 and 2.6 for object detection and segmentation categories, respectively.

Entropy-Based Anomaly Detection for Gaussian Mixture Modeling

Gaussian mixture modeling is a generative probabilistic model that assumes that the observed data are generated from a mixture of multiple Gaussian distributions. This mixture model provides a flexible approach to model complex distributions that may not be easily represented by a single Gaussian distribution. The Gaussian mixture model with a noise component refers to a finite mixture that includes an additional noise component to model the background noise or outliers in the data. This additional noise component helps to take into account the presence of anomalies or outliers in the data. This latter aspect is crucial for anomaly detection in situations where a clear, early warning of an abnormal condition is required. This paper proposes a novel entropy-based procedure for initializing the noise component in Gaussian mixture models. Our approach is shown to be easy to implement and effective for anomaly detection. We successfully identify anomalies in both simulated and real-world datasets, even in the presence of significant levels of noise and outliers. We provide a step-by-step description of the proposed data analysis process, along with the corresponding R code, which is publicly available in a GitHub repository.

Effects of Urban Form on Ambient Air Filter Noise Exposure in Open Areas

Although ambient air filters are commonly used to improve air quality in urban areas, their operation often produces significant noise levels. In this study, we investigated and addressed the issue of ambient air filter noise exposure in open areas of Chinese cities, with a focus on various typical urban forms. Firstly, fifteen common urban blocks with different forms and of 250 × 250 m each were chosen as sample sites, for which urban form indices and noise distribution indices were defined. Then noise mapping was conducted to investigate air filter noise exposure in open areas and the effects of urban form indices on noise distribution indices. Results show that urban form has considerable effects on filter noise exposure in open areas. Among the six urban form indices examined, the distance between the first-row building and the air filter was found to be the most critical factor affecting noise levels with the highest correlation coefficient (R = 0.754). The orientation of the first-row building shows the significant resisting effect on both average and background noise levels. Furthermore, the resisting effect of first-row buildings can create a maximum reduction of 12.0 dB (A) for peak noise. These indices could be profiled and used as an “a priori” tool for urban sound environment planning.

Acoustic contamination assessment during the transition between the COVID-19 restrictions and reactivation: A exploratory analysis in Guayaquil

Noise pollution is becoming a critical health risk for city life. In 2020, the COVID-19 pandemic forced many cities to implement several mobility restrictions. These restrictions changed human activity patterns and decreased the noise levels and noise pollution that often affect urban settings. As the number of infections decreased, so did the outdoor activities, influencing the population’s perception of noise. This paper aims to evaluate the changes in noise levels associated with mobility restrictions between 2020 and 2021 in Guayaquil, Ecuador. This study used crowdsourcing with the help of smartphones and mobile applications to collect geo-referenced environmental noise data. The data was used to generate noise maps in different time frames. Finally, noise level maps were created using GIS-based tools to identify the urban areas that experienced the highest noise level variation during the study period. The results show that the most significant noise increase occurred at night. Furthermore, when analyzing noise level changes in different urban areas, the western area of Guayaquil was the one that experienced the most significant noise level variation. Findings inform the perception of noise pollution and could potentially serve as a reference for decision-makers during the proposal of public policies that ensure a better quality of life for its citizens.

Quadratic Multilinear Discriminant Analysis for Tensorial Data Classification

Over the past decades, there has been an increase of attention to adapting machine learning methods to fully exploit the higher order structure of tensorial data. One problem of great interest is tensor classification, and in particular the extension of linear discriminant analysis to the multilinear setting. We propose a novel method for multilinear discriminant analysis that is radically different from the ones considered so far, and it is the first extension to tensors of quadratic discriminant analysis. Our proposed approach uses invariant theory to extend the nearest Mahalanobis distance classifier to the higher-order setting, and to formulate a well-behaved optimization problem. We extensively test our method on a variety of synthetic data, outperforming previously proposed MDA techniques. We also show how to leverage multi-lead ECG data by constructing tensors via taut string, and use our method to classify healthy signals versus unhealthy ones; our method outperforms state-of-the-art MDA methods, especially after adding significant levels of noise to the signals. Our approach reached an AUC of 0.95(0.03) on clean signals—where the second best method reached 0.91(0.03)—and an AUC of 0.89(0.03) after adding noise to the signals (with a signal-to-noise-ratio of −30)—where the second best method reached 0.85(0.05). Our approach is fundamentally different than previous work in this direction, and proves to be faster, more stable, and more accurate on the tests we performed.

Active Vibration Control of a Rotating Mechanical System with a Rigid Coupling using Active Disturbance Rejection Control

The work aims to develop an active control method for mitigating torsional vibration of a rotating mechanical system with a rigid coupling, driven by an electric motor, particularly for suppressing vibration associated with its mechanical resonances. It is commonly known that torsional oscillations in a rotating system, which is normally used for various power transmission systems, can generate significant levels of vibration and noise. In this work, therefore, an active control system that was based on the Active Disturbance Rejection Control approach was utilized to develop an active control system that was robust against uncertainties associated with the internal dynamics and external disturbances affecting the system. The dynamic interactions between the inertias of the electric motor and the load, which were connected through elastic shafts and a rigid coupling, were investigated. The Extended State Observer was used to estimate the generalized disturbance consisting of not only the external disturbances but also the disturbances associated with internal dynamics of the rotating system. The effectiveness of the developed controller was demonstrated, which showed that in the case of the varying load inertia, the control system could still perform well in mitigating the torsional vibration.

Theoretical studies of vibrations and noise of grinding belt machines

The operation of belt grinding machines is usually accompanied by a significant level of noise and vibration, which adversely affect the health of operators and the accuracy of processing the final product. It is known that in the woodworking industry there is an increased risk of myocardial infarction due to significant noise in the workplace. The purpose of the article is a theoretical analysis of the occurrence of noise and vibrations of belt grinding machines and the construction of appropriate mathematical models. The influence of various noise sources in the machines under consideration was analyzed: electric motors, belt drives, grinding belts, axes of cutting units, and workpieces being ground. The solution of the vibration equations of the tape and the workpiece made it possible to obtain an analytical solution for the vibroacoustic characteristics. The obtained solutions are in satisfactory agreement with the experimental data and allow the design to develop engineering solutions that reduce the sound pressure level.

ENTROPY APPROACH FOR FILTERING ACOUSTIC EMISSION SIGNALS

The acoustic emission (AE) method has been successfully used in recent years to monitor the condition of industrial and civil infrastructure structures. In AE, time-of-arrival (ToA) estimation is considered a key parameter for the accurate location of a growing defect. This paper describes an entropy-based filtering approach to improve the ToA estimation of a signal in a noisy environment compared to the commonly used Akaike Information Criterion (AIC). The proposed method consists of coarsening the input data using the Crutchfield-Packard algorithm and calculating the local (instantaneous) entropy. In the present study, it is demonstrated that the local entropy of the noise component of the signal the local entropy of the useful (informative) part can be distinguished. As a result, the approach allows filtering out the noise component by selecting appropriate filtering parameters. The proposed approach has been tested on experimental data obtained on an aluminium plate used to pinpoint an AE source. The entropic approach demonstrates higher accuracy in final target location of AE sources at significant noise levels compared to classical AIC.

Learning physics-consistent particle interactions.

Interacting particle systems play a key role in science and engineering. Access to the governing particle interaction law is fundamental for a complete understanding of such systems. However, the inherent system complexity keeps the particle interaction hidden in many cases. Machine learning methods have the potential to learn the behavior of interacting particle systems by combining experiments with data analysis methods. However, most existing algorithms focus on learning the kinetics at the particle level. Learning pairwise interaction, e.g., pairwise force or pairwise potential energy, remains an open challenge. Here, we propose an algorithm that adapts the Graph Networks framework, which contains an edge part to learn the pairwise interaction and a node part to model the dynamics at particle level. Different from existing approaches that use neural networks in both parts, we design a deterministic operator in the node part that allows to precisely infer the pairwise interactions that are consistent with underlying physical laws by only being trained to predict the particle acceleration. We test the proposed methodology on multiple datasets and demonstrate that it achieves superior performance in inferring correctly the pairwise interactions while also being consistent with the underlying physics on all the datasets. While the previously proposed approaches are able to be applied as simulators, they fail to infer physically consistent particle interactions that satisfy Newton's laws. Moreover, the proposed physics-induced graph network for particle interaction also outperforms the other baseline models in terms of generalization ability to larger systems and robustness to significant levels of noise. The developed methodology can support a better understanding and discovery of the underlying particle interaction laws, and hence, guide the design of materials with targeted properties.

Tolerance For the Pixelation Effect in Shear Measurement

Images taken by space telescopes typically have a superb spatial resolution, but a relatively poor sampling rate due to the finite CCD pixel size. Beyond the Nyquist limit, it becomes uncertain how much the pixelation effect may affect the accuracy of galaxy shape measurement. It is timely to study this issue given that a number of space-based large-scale weak lensing surveys are planned. Using the Fourier_Quad method, we quantify the shear recovery error as a function of the sampling factor Q, i.e., the ratio between the FWHM of the point-spread function (PSF) and the pixel size of the CCD, for different PSFs and galaxies of different sizes and noise levels. We show that sub-percent-level accuracy in shear recovery is achievable with single-exposure images for Q ≲ 2. The conclusion holds for galaxies much smaller than the PSF, and those with a significant level of noise.

Assessing Occupational Noise Exposure and Blood Pressure of Cabin Personnel of an Indonesian Diesel Train

Noise may cause serious health problems, including physiological responses, specifically in the cardiovascular system. This study aims to assess occupational noise exposure and analyze the correlation between occupational noise and blood pressure in cabin and station personnel. The participants were 30 cabin personnel (train drivers) and 30 station personnel. The cabin had a Leq of 90.3 dBA while the noise level at Poncol Station was still below the threshold limit value (TLV), i.e., 75.8 dBA. The noise exposure assessment also included noise exposure profiling. The Chi-square test showed that noise influenced systolic and diastolic blood pressure changes. Furthermore, for train drivers, the t-test showed a difference in systolic and diastolic between before and after the activity. As for station personnel, there was a difference in systolic, but contrarily it was not the case in diastolic. According to the noise profile, the train whistle had the most significant noise level at 120 dBA. Moreover, the use of a train whistle regularly may raise the noise level exposure to cabin staff and even station workers. This study contributes to scientific evidence that occupational noise might affect blood pressure

Detecting limit cycles in stochastic time series

The emergence of oscillatory behaviour represents fundamental information about the interactions of the underlying system. In biological systems, oscillations have been observed in experimental data, but due to the significant level of noise, it is difficult to characterize whether observed dynamics based on time series, are truly limit cycles. Here, we present a simple three step method to identify the presence of limit cycles in stochastic systems. Considering input from one-dimensional time series, as are typically obtained in experiments, we propose statistical measures to detect the existence of limit cycles. This is tested on models from chemical networks, and we investigate how the underlying dynamics can be separated depending on the noise level and length of the series.

Bipolar fuzzy based least squares twin bounded support vector machine

Data classification is a key domain of research in real-world applications. One of the big challenges of real-world data classification is to tackle the presence of noise and outliers. In this paper, we handle the computational cost of Bipolar evaluation pairs score value based support vector machine model by formulating two efficient variants, referred to as bipolar fuzzy least squares support vector machines and bipolar fuzzy least squares twin bounded support vector machines, in which the score value is obtained as a bipolar evaluation pair with membership and non-membership functions. The solution of primal problems is attained by solving a system of linear equations that leads to less training time complexity unlike other fuzzy based twin models obtain the solution by handling two quadratic programming problems. Furthermore, our proposed models minimize the impact of noise in the data and facilitate the separation of support vectors from noise. The proposed works have been computationally analyzed on many publicly available real-world benchmark datasets as well as simulated artificial datasets in the non-linear case for different significant levels of noise, namely 0% (noise-free) and 5% (noise-corrupted). In comparison to other similar models, our suggested model is showing phenomenal generalization performance by controlling the negative impact of noise and needs substantially less training time. The results of the proposed models are also validated using quality metrics including AUC, F1-score, G-mean, and Precision Predictive Value.

Incorporating injection stage into DFIT analysis for permeability estimation, and its significance

Diagnostic fracture injection tests (DFIT) have been broadly used in unconventional reservoirs to derive properties such as initial formation pressure, formation permeability, and closure pressure. DFIT involve pumping a small volume of untreated fluid into the formation to form a small crack. Formation permeability is usually obtained by modeling fluid leakoff during well shut-in. Prevailing studies assume constant fluid pressure boundary conditions on the fracture walls, constant leakoff coefficient, or both. However, the results deduced from these assumptions can introduce tremendous errors because the fluid pressure inside the fracture dissipates fast as fluid leaks off. Therefore, this study proposes a material balance approach to analyze DFIT data and obtain formation permeability. The proposed analysis incorporates fluid leakoff during both injection and well shut-in periods. According to the material balance relationship, volume of water injected during hydraulic fracturing should be equal to the summation of leakoff volume during hydraulic fracturing, leakoff volume during shut-in period and residual fracture volume. Thus, we propose leakoff models for both fracture propagation and shut-in periods. These models are then validated through a few synthetic cases. The model accounting for leakoff during fracture propagation is modified to more accurately calculate the leakoff volume. The model for the shut-in period is solved using the Fourier transform and the eigenfunction expansion method. The results suggest that the proposed DFIT analysis approach can provide a good estimation of the formation permeability. The proposed solution can be used in the field even when pressure data contain significant levels of noise. We showed that more accuracy can be achieved using this method compared to available solutions in the literature.