Noise In Measured Data Research Articles

Determination of SS321 pitting stage in FeCl3 solution based on electrochemical noise measurement data using artificial neural network

Artificial neural network (ANN) was used successfully to determine the pitting stage of stainless steel 321 (SS321) based on electrochemical noise (ECN) data. The ANN inputs were the parameters derived from the analysis of ECN data. The target values were the pitting stage of SS321 in FeCl3 solution, which were determined using galvanostatic electrochemical impedance spectroscopy (GEIS) tests. The ANN was validated by using a large number of available experimental data. Four approaches have been used for ANN development. The approaches differ in the number and type of inputs. The results showed that the best performance of ANN was achieved when all ECN analysis parameters were used as ANN inputs. These ECN analysis parameters were derived from the time domain, the frequency domain and the time-frequency domain analysis methods. Based on the results of this study, ANN has a very good performance in determining the pitting stage based on ECN analysis results.

The Influence of Traffic and Meteorological Parameters Assessment on Noise Emission

The main purpose of that study was an experimental effort to determine the correlation between various traffic and meteorological parameters and sound pollution created as a result of the that traffic.
 This paper presents theresearch results oftraffic noise level recorded inthestreet which is a part of the road system of Bielsko-Biala city. Measured noise data was compared to the registered traffic flows, share of heavy vehicles in the traffic flow and to the velocity of vehicles in the traffic flow. To determine the vehicle velocity from recorded video a velocity detection algorithm was used. It was created based on the MatLab package and allows to determine the vehicle velocity in the movie frame.
 The studies are at the first step towards obtaining general assumptions related to the work leading to the formulation of a semi-empirical model of acoustic impact of traffic flow.
 The paper shows the strong correlation between traffic flow and sound level. At the same time, it presents a slight impact of the structure and the speed of vehicles on the level of noise. However, weather conditions don’t have any influence on the created sound pollution.

Reactivity and Delayed Neutron Precursors’ Concentration Estimation Based on Recursive Nonlinear Dynamic Data Reconciliation Technique

Reactivity is widely used as the paramount means for defining nuclear reactor status, but there is no physical sensor available to measure this parameter directly. Similarly delayed neutron precursors’ concentrations, the source of the delayed neutrons which play important role in reactor control cannot be measured directly. Hence, these quantities must be calculated or estimated from observed neutron flux transients. Recently, Kalman filter (KF) and its variants have been used for the reactivity estimation. A key limitation of KF and all of its variants is that they cannot account for the constraints on state variables systematically, which may cause the filter’s instability. In this paper, a constrained estimator based on the recursive nonlinear dynamic data reconciliation technique is proposed for the estimation of reactivity and delayed neutron precursors’ concentrations in the nuclear reactor. The effectiveness of the proposed technique has been evaluated through the step test signal in the presence of measurement noise and experimental power variation data sets collected from one of the Indian reactors.

1.5 MW 풍력 터빈 소음 측정 및 저주파 소음 특성 분석

The noise from a 1.5 MW wind turbine was measured and the apparent sound power level, tonal audibility, and spectrum balance were analyzed. The apparent sound power level and tonal audibility were analyzed according to IEC 61400-11: ed3. (2012-11). The measured noise data at the turbine site was mainly in the north-west (NW) and north-north-west (NNW) directions, and approximately 500 and 250 samples of total noise and background noise data were obtained. Three tone components were observed in the low frequency region below 100 Hz. The tones in the low frequency critical band with a center frequency of 78.1 Hz were found to have a higher level than the hearing threshold. The possibility of wind turbine noise annoyance was also analyzed through the spectrum balance analysis.

High-Frequency Noise Characterization and Modeling of SiGe HBTs

For heterojunction bipolar transistors (HBTs), the noise correlation influences the noise parameters and is therefore relevant for transistor modeling, especially at sufficiently high frequencies. In order to predict the noise correlation, it is necessary to determine the noise transit time in the noise transport model. In this paper, the noise correlation and noise transit time are predicted from the high current model without measured noise data. The four noise parameters are calculated based on the predicted noise transit time and the Y-parameter noise calculation methodology. The extracted noise transit time is validated by the measured result over bias and temperature, which is determined by fitting NFmin of the silicon-germanium (SiGe) HBT between Y-parameter noise calculation methodology and tuner-based noise measurement. It was found that the noise transit time is independent of frequency but dependent on bias and temperature. Also, the results indicate that the calculated four noise parameters based on the extracted noise transit time have a good agreement with the tuner-based noise measurement.

Identification of the Noise Model in the Time Series of GNSS Stations Coordinates Using Wavelet Analysis

Analysis of the time series of coordinates is extremely important in geodynamic research. Indeed, the correct interpretation of coordinate changes may facilitate an understanding of the diverse geophysical processes taking place in the earth’s crust. At present, when rigorously processing global navigation satellite system (GNSS) observations, the influence of deformations in the surface of the earth’s crust is not considered. This article presents signal modelling for the influence on the analysis of noise occurring in the time series of GNSS station coordinates. The modelling of coordinate time series was undertaken using the classic least-squares estimation (LSE) method and the inverse continuous wavelet transform (CWT). In order to determine the type of noise character, the coefficient spectral index was used. Analyses have demonstrated that the nature of noise in measurement data does not depend on the signal estimation method. The differences between classic modelling (LSE) of the time series with annual and semiannual oscillation and signal reconstruction are very small ( Δ κ = 0.0 ÷−0.2).

Next Generation of Phonon Tests of Lorentz Invariance Using Quartz BAW Resonators.

We demonstrate technological improvements in phonon sector tests of the Lorentz invariance that implement quartz bulk acoustic wave oscillators. In this experiment, room temperature oscillators with state-of-the-art phase noise are continuously compared on a platform that rotates at a rate of order of a cycle per second. The discussion is focused on improvements in noise measurement techniques, data acquisition, and data processing. Preliminary results of the second generation of such tests are given, and indicate that standard model extension coefficients in the matter sector can be measured at a precision of order 10-16 GeV after taking a year's worth of data. This is equivalent to an improvement of two orders of magnitude over the prior acoustic phonon sector experiment.

Multi-objective Optimization of Departure Procedures at Gimpo International Airport

Most aviation communities have increasing concerns about the environmental impacts, which are directly linked to health issues for local residents near the airport. In this study, the environmental impact of different departure procedures using the Aviation Environmental Design Tool (AEDT) was analyzed. First, actual operational data were compiled at Gimpo International Airport (March 20, 2017) from an open source. Two modifications were made in the AEDT to model the operational circumstances better and the preliminary AEDT simulations were performed according to the acquired operational procedures. Simulated noise results showed good agreements with noise measurement data at specific locations. Second, a multi-objective optimization of departure procedures was performed for the Boeing 737–800. Four design variables were selected and AEDT was linked to a variety of advanced design methods. The results showed that takeoff thrust had the greatest influence and it was found that fuel burn and noise had an inverse relationship. Two points representing each fuel burn and noise optimum on the Pareto front were parsed and run in AEDT to compare with the baseline. The results showed that the noise optimum case reduced Sound Exposure Level 80-dB noise exposure area by approximately 5% while the fuel burn optimum case reduced total fuel burn by 1% relative to the baseline for aircraft-level analysis.

The influence of vegetation and surrounding traffic noise parameters on the sound environment of urban parks

The main aim of this study was to investigate the effects of vegetation and traffic-noise parameters on the sound environment of urban parks. Eight parks of different sizes and varying proximity to the city's ring road were selected in Antwerp, Belgium. The sound environment was evaluated with a dual approach, using primarily simulated traffic data from the surrounding roads and then measurement noise data from mobile devices within the parks. Percentile weighted sound levels were calculated considering various indicators (LA10, LA50, LA90, LAeq.) with special emphasis on background noise (LA90) and peak values (LA10).Results showed that simulated noise levels were slightly overestimated compared to the actual ones. Within the parks very small differences were found no matter whether measurement points were examined individually or aggregated on grids. Overall, background noise (LA90) presented more fluctuations than LA10. At the same time, the average noise levels both for LA90 and LA10 were higher in the surrounding environment of the parks - compared to the inside – most probably because of traffic sound sources and the proximity to main roads. Additional analysis was also performed within the parks for the identification of “hot” and “cold” spots for LA90 using GIS tools. Relationships between noise levels and morphological features of the surrounding environment were also identified. The final step of analysis dealt with the effects of tree or grass areas in noise indices. The effect of additional sources other than traffic is also explained as part of the limitations and the actual findings of this research.

Urban noise mapping with a crowd sensing system

Noise pollution poses a serious threat to people living in cities today. To alleviate the negative impact of noise pollution, an urban noise mapping can be helpful. In this paper, we present the design of NoiseSense, a crowd sensing system for housing a real-time urban noise mapping service. A major challenge in building such a system is caused by the sparsity problem of the limited noise measurement data from smartphones. To tackle this challenge, we propose a hybrid approach including a neighborhood-based noise level estimation method and a semi-supervised tensor completion algorithm for inferring noise levels for locations without measurements by smartphone users. This approach leverages a variety of urban data sources, such as Point of Interests, road networks, and check-in data. We also provide a noise prediction method for forecasting the noise levels in the next few hours. We implemented the system and developed an APP for smartphone users. We conducted experiments and field study. The experimental results show that the proposed approach is superior in inferring noise levels merely with sparse measurements from smartphone users. And the prediction approach also outperforms other baseline methods.

Improved Imaging Performance in Super-Resolution Localization Microscopy by YALL1 Method

In super-resolution localization microscopy, e.g., stochastic optical reconstruction microscopy or photoactivated localization microscopy, a long acquisition time is required because of stochastic imaging nature, which limits its application in dynamic imaging for live cell. To overcome the limitation, one approach based on compressed sensing (CS) has been used in the previous reports. However, the imaging performance obtained by this method may be affected due to the use of interior point method (IPM). To address the problem, in this paper, we introduce an alternative CS reconstruction method and apply the recently developed YALL1 (your algorithm for L1 norm problems) method to super-resolution imaging model. Two types of numerical simulation experiments were performed to evaluate the performance of the proposed method. In case 1, the microscopy data from a single frame was simulated, which was used to evaluate the performance of YALL1 in single-emitter detection. In case 2, the dynamic microscopy data from a series of time points was generated, which was used to evaluate the performance of YALL1 in resolving fine structures. The results show that compared with the previous reported IPM method, the localization accuracy of super-resolution is improved by the proposed YALL1 method, even if there is high emitter density and noise in measurement data. In addition, the imaging time can also be reduced, because fewer imaging cycles are required for reconstructing the final super-resolution image by YALL1 method. Hence, the technique provides the potential in imaging fast cellular processes.

Measurement of friction and noise from piston assembly of a single-cylinder motorbike engine at realistic speeds

This paper presents a thorough experimental study of piston assembly friction and noise in a single-cylinder motorbike engine operating at low speeds. The friction of the piston ring pack is evaluated using a foil strain gauge with minimal cylinder modification on the thrust side. The technique involves transmitting deformations through the cylinder bore and recording reflections from the lubricated interface as the piston assembly passes. Under these conditions, the piston side forces and the thermal deformations on the output side of the strain gauge sensor are critical. Therefore, the proposed methodology is designed under controlled operating conditions. The overall deformation of the piston assembly is analysed to measure the primary reflection due to friction between the piston assembly and the cylinder wall. Simultaneously, the piston assembly noise is recorded on the thrust side of the engine block using a microphone. Taking measured noise data into account, possible piston slap events resulting from varied engine speeds are taken into account using continuous wavelet signal analysis. The calibration procedure for both tests is also illustrated. The measured friction results show that the strain gauge technique is a challenging work in providing realistic results to enhance current technology. For low engine speeds, a higher contribution is noted by boundary friction at the top dead centre reversal, extending to the position of maximum combustion pressure in the power stroke. Furthermore, the main contribution of the piston slap is estimated at the thrust side when the piston assembly passes at the beginning of the combustion stroke. These results can also be attributed as data to validate piston ring models in terms of friction and piston slap.

FRF-based structural parameters estimation using strain data: sensitivity equation, measurement and excitation selection

In this paper, a structural model updating technique is presented using frequency domain representation of incomplete strain data to identify the location and severity of structural damages. The most important challenges of model updating methods such as selection of excitation, measurement locations and interested frequency ranges are addressed. Distribution of strain energy in all structural elements and norms of the derived sensitivity equation are utilized for the selection of measurement and excitation locations. The important effect of different level of noise in measurement data and modelling error on the accuracy and robustness of the proposed method are investigated. A bowstring truss and a two-storey single-bay frame are used to evaluate the numerical ability of proposed method. The results prove the potentials of the proposed method in identifying the location and severity of damage in the presence of different errors.

Analysis of Bursty Impulsive Noise in Low-Voltage Indoor Power Line Communication Channels: Local Scaling Behaviour

Power line communication (PLC) channels are prone to multipath propagation due to impedance mismatch, and impulsive noise whose characteristics are still not well established in the literature. Moreover, measurements show that this impulsive noise appear in bursts, non-Gaussian and cyclostationary and as such cannot be modeled as the convenient additive white Gaussian noise (AWGN). Transceivers optimized for AWGN may not necessarily perform well for the PLC noise. Therefore, investigating the characteristics of PLC noise is very important for accurate modeling of the same. This study presents multifractal analysis of bursty impulsive noise measured from power line networks from three different environments. We employ multifractal detrended fluctuation analysis, which is a well-developed multifractal analysis technique for non-stationary time series data and easy to implement to analyze measured noise data. Results show that power line noise exhibits both long-range dependence (LRD) and multifractal scaling behavior with different strengths depending on the environments where they were captured. The multiscaling behavior is due to long-range correlation inherent in the power line noise. The source of this local multiscaling behavior is determined by analysis a shuffled series of the original data captured from the power network. Multifractal analysis is able to show clearly both the strengths and frequency of occurrence of bursts occurring in PLC noise which can then be applied in accurate modeling of the noise. The significance of these results is that new power line noise models should be developed that captures both LRD and multifractal scaling for more accurate performance evaluation of power line communication systems. The existing noise models though able to replicate temporal dependence of PLC noise, are not able to capture this local scaling behavior which results show is inherent in PLC noise.

A meshfree method for inverse wave propagation using collocation and radial basis functions

A stable and highly accurate meshfree scheme based on strong form collocation associated with radial basis functions and explicit time integration is proposed to solve ill-posed inverse wave propagation problems. Tikhonov regularization technique and the L-curve criterion are employed to deal with the noise measurement data before solving the governing equation to get a stable solution. Appropriate weights are first derived for imposing boundary conditions and known conditions from the measurement data to yield the optimal convergence for inverse problems. Compared to the required iterative procedures in common methods, no iteration is needed in the proposed method which leads to the time-saving computation and avoids the instability resulting from the iterative methods. Dispersion analysis describes that this method has much less dispersion error than the classical finite element method (FEM) and popular weak form meshfree methods, and stability analysis is performed to predict the critical time step for the explicit time integration. Another outstanding advantage of this method is that it is effortless to extend the solution scheme of one-dimensional inverse problem to high-dimensional inverse problems. Furthermore, the influences of the high noise into measurement data are evaluated, which demonstrate that even handling with very high noise in the input data the proposed algorithm can achieve stable and high-accuracy solutions.

Experimental Study on the Effect of Number of Bubble Occurrences on Tip Vortex Cavitation Noise Scaling Law

A novel scaling law for the tip vortex cavitation (TVC) noise was determined, employing the Rankine vortex model, the Rayleigh–Plesset equation, the lifting surface theory, the boundary layer effect, and the number of bubbles generated per unit time (N0). All terms appearing in the final derived scaling law are well known three-dimensional (3D) lifting surface parameters, except for N0. In this study, the dependence of N0 with inflow velocity and hydrofoil dimension is investigated experimentally while trying to retain the same TVC patterns among different experimental conditions. Afterward, the effect of N0 on the TVC noise is analyzed. Optimal TVC observation conditions are determined from consideration of cavitation number and Reynolds number of two comparable conditions. Two geometrically scaled hydrofoils are concurrently placed in a cavitation tunnel for the hydrofoil size variation experiment. Wall effects and flow field interaction are prevented with the aid of computational fluid dynamics. Images taken with a high‐speed camera are used to count N0 by visual inspection. The noise signals at all conditions are measured and an acoustic bubble counting technique, to supplement visual counting, is devised to determine N0 acoustically from the measured noise data. The broad-band noise scaling law incorporating N0 and the International Towing Tank Conference (ITTC) cavitation noise estimation rule for hydrofoil are both applied to estimate the TVC noise level for comparison with the measured noise level. The noise level estimated by the broad-band noise scaling law accounting for the acoustically estimated N0 gives the best agreement with the measured noise level.

Sequential filtering for surface wind speed estimation from ambient noise measurement

Many research results show that ocean ambient noise and wind speed are highly relevant, and the surface wind speed can be effectively inverted using ocean noise data. In most deep-sea cases, the ambient noise of medium frequency is mainly determined by the surface wind, and there is a conventional relationship between them. This paper gives an equation which shows this relationship firstly, and then a surface-wind inversion method is proposed. An efficient particle filter is used to estimate the speed distribution, and the results exhibit more focused close to the actual wind speed. The method is verified by the measured noise data, and analysis results showed that this approach can accurately give the trend of sea surface wind speed.

Variance analysis for model updating with a finite element based subspace fitting approach

Recently, a subspace fitting approach has been proposed for vibration-based finite element model updating. The approach makes use of subspace-based system identification, where the extended observability matrix is estimated from vibration measurements. Finite element model updating is performed by correlating the model-based observability matrix with the estimated one, by using a single set of experimental data. Hence, the updated finite element model only reflects this single test case. However, estimates from vibration measurements are inherently exposed to uncertainty due to unknown excitation, measurement noise and finite data length. In this paper, a covariance estimation procedure for the updated model parameters is proposed, which propagates the data-related covariance to the updated model parameters by considering a first-order sensitivity analysis. In particular, this propagation is performed through each iteration step of the updating minimization problem, by taking into account the covariance between the updated parameters and the data-related quantities. Simulated vibration signals are used to demonstrate the accuracy and practicability of the derived expressions. Furthermore, an application is shown on experimental data of a beam.

A Phase Change Memory Cell With Metal Nitride Liner as a Resistance Stabilizer to Reduce Read Current Noise for MLC Optimization

Relatively large noise of phase change material is one of the obstacles for realization of multilevel cell (MLC) using phase change memory (PCM) technology. We experimentally verify that the noise in PCM can be lowered as much as $\sim 4$ times by a novel PCM cell which utilizes metal nitride liner to provide an alternative conductive path to the amorphous region with large noise. Program-and-verify (PNV) simulation based on noise measurement data confirms that the small noise of a PCM cell with metal nitride liner improves the MLC performance either by reducing number of PNV iterations or bit-error rate.

Synchrophasor measurement‐based correlation approach for dominant mode identification in bulk power systems

This study proposes a novel synchrophasor measurement-based correlation approach to identify the dominant oscillation modes in bulk power systems. In the proposed approach, the reference channel (RC) of cross-correlation (CC) is optimally selected based on residue analysis. With the selected RC, the CC of field-measurement data is formed to extract the free-decay system responses. Then, eigensystem realisation algorithm (ERA) is applied to the extracted responses to estimate the system state-space model. A practical model order selection strategy for ERA is proposed to determine the system model order. Further, cross-coherence spectrum is employed to distinguish the dominant modes from the eigenvalues of the estimated state-space model. The proposed method can achieve accurate and robust solutions in the presence of different levels of errors and noises in measurement data. The effectiveness of the proposed method has been verified in a 16-generator, 68-bus test system as well as the China Southern Power Grid system.