Measured Noise Levels Research Articles

Self-elimination of phase noise in coherence scanning interferometry for nano-scale measurement

Coherence scanning interferometry (CSI) has been a powerful non-contact technique. In this work, a novel surface recovery algorithm based on phase noise self-correction is proposed for nano-scale measurements. Through spectrum analysis, the surface height is coarsely retrieved from the linear relationship between phase and angular wavenumber. Afterwards, the phase noise is modeled, and a correction array is designed to compensate the noises in surface height. A coefficient is defined to assess the accuracy of correction array, then the surface height is automatically corrected with the coefficient minimized. In the experiments, three step height standards are tested to prove the measurement accuracy, and additional comparisons with a commercial white light profiler and an AFM are provided as well to testify the measurement noise level. At last, a nano-scale grids matrix is measured, where the nano-topography is accurately recovered. All investigations have verified the effectiveness of the proposed method in nano-scale measurements.

A multi-rate sampling PMU-based event classification in active distribution grids with spectral graph neural network

Phasor measurement units (PMUs) are time-synchronized measurement devices that have been proliferated in transmission networks during the last two decades. Recently, there have been efforts to bring this technology to distribution grids for different applications such as three-phase state estimation, fault and event analyses, and phase identification. Streamed time-synchronized voltage and current phasor data can be used for events classification and region identification along distribution feeders to determine the type and location of events, which are important features of any fault and event detection, location, and isolation software. In this paper, the spectral theory-based graph convolution is used for event classification and region identification. The proposed model uses modified graph convolution filters to aggregate the regional multi-rate samples of PMU data, i.e., voltage magnitude and angles from several nodes. Besides these temporal data of the measured nodes, the physical configuration of the network containing edge features are given to the graph convolution network (GCN) to not only classify the event type, but also identify the affected region and location. The proposed graph-based method is tested on a standard test system with capacitor and distributed energy resources-related events, malfunction of voltage regulator, sudden load changes, and different types of faults. The results are compared with baseline methods, Chebyshev graph neural network (GNN), decision tree, logistic regression and K-nearest neighbor using the accuracy, recall, precision and F-1 score metrics. Furthermore, performance sensitivity analysis is carried out with respect to the number of installed PMUs, measurement noise level, size of available historical data, availability of network edge features, and different designs of GNN.

Computationally attractive and statistically efficient estimator for noise resilient TOA localization

Localization technologies have received considerable attentions and been well-studied in past few decades. However, the performance under limited number of sensors and large noise/error environments still needs to be improved for balancing the performance and complexity. Taking these factors into consideration, this paper focuses on the time of arrival (TOA) localization problem where the measurement noise level can be high, the sensor position errors are present and the number of sensors is limited. We propose a location estimator that consists of a coarse solution and a refined estimate to enhance the quality of the coarse solution. The proposed estimator is closed-form and simple to compute. It is asymptotically unbiased, and having the estimation covariance matrix analytically matching the Cramér–Rao lower bound (CRLB). Simulations validate the theoretical results and illustrate the proposed algorithm works well with small number of sensors, has more noise resilient behavior in the large error region than the existing methods, and performs close to the Maximum Likelihood Estimator with ideal initialization.

Environmental Noise Evolution during COVID-19 State of Emergency: Evidence of Peru’s Need for Action Plans

In Peru, as in many countries worldwide, varying degrees of restrictions have been established on the movement of the population after the World Health Organization (WHO) declared the condition of pandemic by COVID-19. In Lima, there have been different degrees of compulsory social immobilization (CSI), and the resumption of activities was planned in three consecutive phases. To analyse and evaluate the influence of such restrictions on the evolution of environmental noise, an investigation was carried out in one of the main avenues in the city of Lima during various successive mobility restriction conditions. The sound pressure level was measured, and the traffic flow was also registered. Considering that in Peru there is no environmental noise monitoring system whatsoever, in situ data are extremely valuable and allow the environmental noise problem to be depicted, even if in a limited area of the big city. The results show that in spite of the strongly restrictive social immobilization conditions, the measured noise levels have remained above the WHO recommendations and often above the Peruvian environmental noise quality standards. The results highlight the need to properly assess the environmental noise and noise sources in the city of Lima as well as the number of people exposed in order to adequately implement effective and cost-efficient noise mitigation action plans.

Evaluation of the Event Detection Level of the Cuban Seismic Network

Abstract The detection level of a seismic network is a measure of its effective ability to record small earthquakes in a given area. It can vary in both space and time and depends on several factors such as meteorological conditions, anthropic noise, local soil conditions—all factors that affect the seismic noise level—as well as the quality and operating condition of the instruments. The ability to estimate the level of detection is of tremendous importance both in the design of a new network and in determining whether a given network can recognize seismicity consistently or needs to be improved in some of its parts. In this article, we determine the detection level of the Cuban seismic network using the empirically estimated seismic noise spectral level at each station site and some theoretical relationships to predict the signal amplitude of a seismic event at individual stations. The minimum local detectable magnitude thus depends on some network parameters such as the signal-to-noise ratio and the number of stations used in the calculation. We also demonstrate the effectiveness of our predictions by comparing the estimated detection level with those empirically determined from one year of data (i.e., the year 2020) of the Cuban seismic catalog. Our analysis shows, on the one hand, in which areas the current Cuban network should be improved, also depending on the regional pattern of faults, and, on the other hand, indicates the magnitude threshold that can be assumed homogeneously for the catalog of Cuban earthquakes in 2020. Because the adopted method can use current measurements of the seismic noise level (e.g., daily), the proposed analysis can also be configured for continuous monitoring of network state quality.

An overview of modern approaches for assessment and mapping of noise pollution

The article substantiates the role of the cartographic research method in the study and control of city noise pollution. The regulatory documentation in scope of studying and mapping the noise load is considered. The requirements for measurement and mapping of noise levels are outlined. Fragments of maps created in Russia and abroad are presented, their strengths and weaknesses are described. The three main approaches to measuring and mapping noise levels, including mobile noise mapping, are characterized. Case-studies on the use of various interpolation methods for calculating the noise load are considered. Proposals are formulated for GIS-based city noise modeling. The main sources of data for direct and indirect city noise modeling are characterized.

Simulation and experiment of ultrasonic phased array for additive manufacturing titanium alloy with columnar crystal structure

The influence of the grain structure of titanium alloys, used for additive manufacturing (AM), on the propagation of ultrasonic beams is of great significance for the non-destructive testing of components manufactured from this material. In order to explore the issues related to ultrasonic testing, such as low signal-to-noise ratio (SNR) and large imaging error of internal defects, an ultrasonic phased array C-scan image was obtained and the measurements of ultrasonic noise level and defect distortion caused by columnar crystals were analysed. Ultrasonic wave theory was combined with the Voronoi algorithm to establish a finite element (FE) model of the AM titanium alloy with a columnar crystal structure. The focusing and scattering of ultrasonic beams in different grain structures was studied and the defect echo signals from the FE model and the experimental results were extracted and compared. Comparisons of the signal-to-noise ratio and the background noise of three typical sections were in good agreement, which shows that the proposed simulation model can be used to analyse the propagation and scattering of beams for the inspection of AM titanium alloys. Finally, suggestions to improve the ultrasonic detection results of AM titanium alloy components and the applicability of the wave simulation model for other polycrystalline materials are discussed.

Applying principles of EIA post-project analysis in the context of suburban infrastructure development

Residential, commercial, and infrastructural construction is a worldwide phenomenon that is monitored and evaluated in various details in different countries in terms of environmental impact. The prediction verification approach in the sense of principles of post-project analysis (PPA) within the EIA process for a residential complex was performed in this study. Re-analyses for selected environmental factors were done, the results of which were compared with reference values measured, modelled, or predicted during the EIA process. Hydro chemical changes and noise levels were selected as the most important factors. The results of the comparison of individual predictions and measurements of noise levels indicate the accuracy of former EIA outcomes. However, it is possible to identify an ever-increasing rate of noise level loading, which is caused by an overall cumulative effect, due to which the predicted values will most probably be exceeded in upcoming years. The results of the comparison of hydro chemical parameters show certain changes after the implementation of the project for some measured factors, such as water saturation by oxygen or pH. In some parts of the monitored water profile, significantly higher values of chlorine ions, sulphates, or surfactants were measured. Results show that the prediction and conclusions regarding selected parameters of EIA evaluation were accurate for the construction phase and the first year’s use of the residential complex; however, they could not precisely identify medium to long-term cumulative effects. Similar studies in the sense of EIA PPA principles focuses on urban development are an opportunity for improving predictions within the EIA process and from the lessons gain more sustainable development could be achieved.

Investigation of Algorithms for Identification of Wave Parameters in Solid-State Wave Gyroscopes without Adjusting Calculations to the Frequency of Signals

To improve the efficiency of production operations of diagnostics, adjustment and control in the manufacture of integrating solid-state wave gyroscopes, algorithms for improving the accuracy of identification of wave parameters from measuring signals without their additional adjustment to the frequency of signals are considered. In the first algorithm for processing measurement results, a virtual transition to the moving axes of standing waves was introduced without taking into account the influence of a quadrature standing wave. It is characterized by the lowest potential accuracy, but it is convenient for the formation of initial approximations in the problems of clarifying optimization of identification functions in other algorithms. The second algorithm is based on direct five-parameter numerical minimization of the error feature by the conjugate gradient method. To avoid local extremes, it is recommended to use it as a clarifier. In this case, the initial search points are calculated by other algorithms and therefore selected near extremum. This algorithm allows identification at the shortest time intervals, which may be required when measuring high angular velocities. In the third algorithm, computational conditionality is improved at short identification intervals for increased level of measurement noise. In the fourth algorithm, the measurement results are processed using numerical procedures of digital demodulation under noise conditions. Comparison of the identification algorithms accuracy was carried out by methods of simulation modeling for theoretically set initial signals. This made possible direct comparison of initial and identified characteristics of wave processes: amplitudes of the main and quadrature standing waves, the angle of the main standing wave and its frequency. For the four algorithms considered, the results of simulation in the absence and presence of noise in the measuring signals are given. In the first case, diagrams of identification errors are analyzed depending on the length of the interval with a fixed and variable proportion of digital sampling frequencies and resonator oscillations. In the second case, there are standard identification errors depending on the noise level in the measuring signals (up to 20 percent of the basic amplitude of oscillations). Comparison of the results obtained showed the advantages and disadvantages of the corresponding algorithms, which is important for their practical adjustment when working with real samples of gyroscopes.

Hybrid data-driven and model-informed online tool wear detection in milling machines

Precision machining tool wear is responsible for low product throughput and quality. Monitoring the tool wear online is vital to prevent degradation in machining quality. However, direct real-time tool wear measurement is not practical. This paper presents residual-based anomaly detection models, combining a hybrid model comprised of a physics-based model and a data-driven model (a decision tree or a neural network) to predict signals of interest (e.g., power or forces) under nominal conditions, followed by Page’s cumulative sum test for detecting tool wear on-line using the computer numerical control machine measurements. The most informative features are ranked using dynamic programming and its approximation variants from real-time measurements and machine settings, such as the width of cut, depth of cut, feed rate and spindle speed, that serve as inputs to the predictive models. The baseline nominal model is incrementally updated with experimental data via a gradient boosted adaptation model to generate the residuals that account for discrepancies between the actual machine data under normal conditions and the baseline nominal model predictions. The hybrid model is validated against 20 Mazak milling machine experimental tests and one Haas run-to-failure experiment. The proposed anomaly detector is applied to synthetic data from simulations of the physics-based model at different operating conditions, measurement noise levels, and tool wear levels, and the methods were able to achieve an overall 92% accuracy in data with 1% noise. The anomaly detection methods based on hybrid model reduced the false alarms of either the data-driven or physical-based models alone, and are found to be capable of good online detection of tool wear.

Application of land use regression to map environmental noise in Shanghai, China.

Urban environment noise has been linked with wide adverse effects on health; however, noise epidemiological researches are hindered by the lack of large-scale population-based exposure assessment. We aimed to measure noise levels over multiple seasons and to establish an LUR model to assess the spatial variability of intra-urban noise and identify its potential sources in Shanghai, China. Forty-minute (LAeq, 40 min) measurements of environmental noise were collected at 144 fixed sites, and each was visited three times (morning, afternoon, and evening) in winter, spring, and summer in 2019. Noise measurements were then integrated with land-use types, road networks, socioeconomic variables, and geographic information systems to construct LUR models. Ten-fold cross-validation was used to test the model performance. A total of 1296 measurements and 29 predicting variables were used to estimate the spatial variation in environmental noise. The annual mean (±standard deviation) of LAeq, 40min, was 62±8dB (A). Significant variations were observed among monitoring sites but not between seasons or time of day. The LUR model explained 79% of the spatial variability of the noise, and the R2 of the ten-fold cross-validation was 0.75. The most contributory predictors of noise level were road-related variables all within the 50-m buffers, followed by urban area within a 50-m buffer, total area of buildings within a 1000-m buffer, and number of restaurant clusters within a 50-m buffer. Farmland area within a 100-m buffer was the only negative variable in the model. A 50-m resolution noise prediction map was produced and suggested high noise level in urban areas and near traffic arteries. LUR can be a robust method for reflecting noise variability in megacities such as Shanghai and may provide an efficient solution for noise exposure assessment in areas where noise maps are not available.

Evaluation of Noise Pollution for Four consecutive years throughout Nowruz festival in Dahok city, Iraq

The study of noise pollution (NSP) is an important subject due to its environmental and health impacts. If the levels of noise prove to be above health criteria, there is a possibility that they may contribute to short- and long-term physiological & psychologically harmful effects. This study aimed to determine the spatial & temporal levels of NSP produced during the Nowruz festival (NZFS) in Duhok city (DKC), Iraq, for 4 consecutive years (2016–2019) and compare the noise levels during these periods. Also, to identify possible sources of noise to assist in the analysis of NSP problematic areas & determine if city noise criteria are exceeded. The noise level measurements were conducted using a sound level meter in 3 different areas of DKC. On Nowruz Eve, 2019, noise reached 102, 92.6, & 70.2 dB in residential, commercial, and silence zones, respectively. The overall study suggests that the city is gradually hurtling towards a high-noise ambience. Most of the results obtained in this work are above the limits allowed both internationally (i.e., the WHO) & locally. This is due to certain erroneous practices in daily activities during NZFS, the spread of electrical generators, as well as heavy traffic in the city. Also, this work could be help to publicize awareness about NSP among the people.

A Bayesian approach for damage assessment in welded structures using Lamb-wave surrogate models and minimal sensing

The increasing mechanical and economical demands in modern systems and structures are forcing an inevitable need for joining dissimilar materials, thus creating the challenge of establishing a process to inspect and monitor dissimilar joints. Condition monitoring is a necessity to ensure that the structures are being safely used and to extend their lifetime. Lamb waves (LWs) are ultrasonic guided waves that are widely used for structural health monitoring of mechanical, aerospace, and civil structures. This paper proposes a novel structural health monitoring approach for damage detection, localization, and assessment using a minimal LW sensor-actuator set-up. More specifically, the proposed framework provides damage detection, localization and assessment within a dissimilar-material joint by Bayesian inference of six parameters of damage extent and location. Finite element simulations are used to simulate the measured LWs and generate a dataset required to train artificial neural networks (ANN), acting as surrogate models for LW simulation with reduced computational cost. The ANN-based LW simulations are further used as forward model within an Approximate Bayesian Computation (ABC) framework to provide probabilistic inference of the damage size and position. The results show that damage of different sizes and locations can be successfully identified with a high level of resolution and with quantified uncertainty. The results also show that data fusion for ABC inference using multiple sensor measurements can be possible with improved inference results. However, a precise and robust damage inference can be achieved using a minimal sensing set-up based on one actuator and two sensing points, with consideration of certain levels of measurement noise. These findings imply a considerable reduction of complexity of LW actuator-sensor networks, and overall, they imply a significant reduction of computational resources and cost for damage detection and assessment in structures, thus providing a step forward towards online/onboard monitoring applications.

An Improved Next Generation Gravity Mission

There is an opportunity to make a major reduction in the acceleration noise level for the first Next Generation Gravity Mission by replacing the accelerometers used on the GRACE Follow-On Mission by a highly simplified version of the Gravitational Reference Sensors flown very successfully on the LISA Pathfinder mission of ESA. The reduced measurement noise level can make possible much-improved measurements of the short-period and short-wavelength variations in the geopotential. This would be particularly from the along-track analysis of the results, which can permit repeat measurements about half a day apart along ground tracks within 200 km of each other over a substantial part of the globe. Such a mission would permit considerably improved testing of geophysical models for the geopotential variations due to changes in the Earth’s mass distribution.

Vehicular traffic noise modelling of urban area-a contouring and artificial neural network based approach.

Road traffic vehicular noise is one of the main sources of environmental pollution in urban areas of India. Also, steadily increasing urbanization, industrialization, infrastructures around city condition causes health risks among the urban populations. In this study, we have explored noise descriptors (L10, L90, Ldn, LNI, TNI, NC), contour plotting and find the suitability of artificial neural networks (ANN) for the prediction of traffic noise all around the Dhanbad township in 15 monitoring stations. In order to develop the prediction model, measuring noise levels of five different hours, speed of vehicles, and traffic volume in every monitoring point have been studied and analyzed. Traffic volume, percent of heavy vehicles, speed, traffic flow, road gradient, pavement, road side carriageway distance factors were taken as input parameter, whereas LAeq as output parameter for formation of neural network architecture. As traffic flow is heterogenous which mainly contains 59%, two wheelers and different vehicle specifications with varying speeds also affect driving and honking behavior which constantly changing noise characteristics. From radial noise diagrams shown that average noise levels of all the stations beyond permissible limit and the highest noise levels were found at the speed of 50-55 km/h in both peak and non-peak hours. Noise descriptors clearly indicate high annoyance level in the study area. Artificial neural network with 7-7-5 formation has been developed and found as optimum due to its sum of square and overall relative error 0.858 and .029 in training and 0.458 and 0.862 in testing phase respectively. Comparative analysis between observed and predicted noise level shows very less deviation up to ± 0.6 dB(A) and the R2 linear values are more than 0.9 in all five noise hours indicating the accuracy of model. Also, it can be concluded that ANN approach is much superior in prediction of traffic noise level to any other statistical method.

Discovering causal structure with reproducing-kernel Hilbert space ε-machines.

We merge computational mechanics' definition of causal states (predictively equivalent histories) with reproducing-kernel Hilbert space (RKHS) representation inference. The result is a widely applicable method that infers causal structure directly from observations of a system's behaviors whether they are over discrete or continuous events or time. A structural representation-a finite- or infinite-state kernel ϵ-machine-is extracted by a reduced-dimension transform that gives an efficient representation of causal states and their topology. In this way, the system dynamics are represented by a stochastic (ordinary or partial) differential equation that acts on causal states. We introduce an algorithm to estimate the associated evolution operator. Paralleling the Fokker-Planck equation, it efficiently evolves causal-state distributions and makes predictions in the original data space via an RKHS functional mapping. We demonstrate these techniques, together with their predictive abilities, on discrete-time, discrete-value infinite Markov-order processes generated by finite-state hidden Markov models with (i) finite or (ii) uncountably infinite causal states and (iii) continuous-time, continuous-value processes generated by thermally driven chaotic flows. The method robustly estimates causal structure in the presence of varying external and measurement noise levels and for very high-dimensional data.

Workers’ Satisfaction vis-à-vis Environmental and Socio-Morphological Aspects for Sustainability and Decent Work

This study examines worker satisfaction vis-à-vis outdoor places in terms of their environmental and socio-morphological aspects. Numerous studies have considered decent work as the eighth goal of sustainable development. However, it is worth investigating outdoor workers’ satisfaction with a view to the practical design of the surrounding context that supports their work in outdoor places. Using bibliometric analysis, this study investigates possible approaches toward providing decent work in a public place in Cairo as a case study, focusing on outdoor workers’ satisfaction. In the bibliometric analysis, this study used query settings in the Scimago database to search for manuscripts published in the previous five years. The result yielded 195 manuscripts that were filtered down to 50 manuscripts and then grouped using VOSviewr Software. Environmental noise and heat assessment analyses were performed using noise level measurements, remote sensing, and the Grasshopper platform. Further, we conducted an ethnographic study employing 77 participant observations. The results show that work hours and time affect worker satisfaction, as do environmental conditions, particularly noise and heat. However, unexpected findings from participant observation in this study do not accord with findings in other scholarly sources, where other observers find workers neither satisfied nor dissatisfied with the spatial morphology in the case study. Per this study, the alignment of worker satisfaction with convenient socio-morphological tangible elements of the workplace and with other environmental aspects should be attained in both specified replicable methods to engender decent work for outdoor workers.

Indoor noise level measurements and subjective comfort: Feasibility of smartphone-based participatory experiments

We designed and performed a participatory sensing initiative to explore the reliability and effectiveness of a distributed network of citizen-operated smartphones in evaluating the impact of environmental noise in residential areas. We asked participants to evaluate the comfort of their home environment in different situations and at different times, to select the most and least comfortable states and to measure noise levels with their smartphones. We then correlated comfort ratings with noise measurements and additional contextual information provided by participants. We discuss how to strengthen methods and procedures, particularly regarding the calibration of the devices, in order to make similar citizen-science efforts effective at monitoring environmental noise and planning long-term solutions to human well-being.

Smart Core and Surface Temperature Estimation Techniques for Health-Conscious Lithium-Ion Battery Management Systems: A Model-to-Model Comparison

Estimation of core temperature is one of the crucial functionalities of the lithium-ion Battery Management System (BMS) towards providing effective thermal management, fault detection and operational safety. It is impractical to measure the core temperature of each cell using physical sensors, while at the same time implementing a complex core temperature estimation strategy in onboard low-cost BMS is also challenging due to high computational cost and the cost of implementation. Typically, a temperature estimation scheme consists of a heat generation model and a heat transfer model. Several researchers have already proposed ranges of thermal models with different levels of accuracy and complexity. Broadly, there are first-order and second-order heat resistor–capacitor-based thermal models of lithium-ion batteries (LIBs) for core and surface temperature estimation. This paper deals with a detailed comparative study between these two models using extensive laboratory test data and simulation study. The aim was to determine whether it is worth investing towards developing a second-order thermal model instead of a first-order model with respect to prediction accuracy considering the modeling complexity and experiments required. Both the thermal models along with the parameter estimation scheme were modeled and simulated in a MATLAB/Simulink environment. Models were validated using laboratory test data of a cylindrical 18,650 LIB cell. Further, a Kalman filter with appropriate process and measurement noise levels was used to estimate the core temperature in terms of measured surface and ambient temperatures. Results from the first-order model and second-order models were analyzed for comparison purposes.

Geo-Spatial Analysis of Noise Levels in a University Community

Noise is identified as a disturbance that could induce different behavioural responses. Its exposure for a long period of time has been an issue of concern. Increasing noise levels is not unusual especially with booming developments and innovations in the University environment. This study investigated noise pollution level (NPL) and its spatial distribution within the Campus of Federal University of Technology, Owerri Nigeria. A digital sound level meter-Extech 407730 was used to measure noise level at the sampling points namely, Student affairs unit, SEET head round-about, School park, Senate round-about and FUTO market designated as SP1,SP2,SP3,SP4 and SP5 respectively. A global positioning system (GPS)-NAVA 300 was used to record the GPS coordinates of the sampling noise hotspots in the university community. The Arc GIS software was used to assess the geospatial mapping and distribution of noise within the study areas. Results revealed that the noise levels in the school campus reached a peak of 75.7dB (A) in FUTO market on day 4 during the hours of 12pm – 1pm. Results of geospatial mapping of the noise level in the study area also revealed that noise level exceeds the maximum permissible limit ranging from 40dB(A) to 50dB(A). Therefore, it was recommended that public enlightenment programs such as workshops and seminars on the health hazards connected with noise pollution within the campus should be held regularly, also, control of noise through the use of sound proof doors, walls, and ceilings that can be installed while erecting buildings within the university community.