Advanced procedure noise model validation using Seattle International Airport noise monitor networks

2021 ◽  
Vol 263 (2) ◽  
pp. 4787-4798
Author(s):  
Ara Mahseredjian ◽  
Jacqueline Thomas ◽  
R. John Hansman
Keyword(s):  
Noise Model ◽  
Noise Sources ◽  
Modeling Methods ◽  
International Airport ◽  
Atmospheric Propagation ◽  
Aircraft Performance ◽  
Ground Noise ◽  
Noise Models ◽  
Monitor Data ◽  
Noise Impacts

Advanced operational flight procedures that utilize modifications to thrust, airspeed, altitude, and configuration can be implemented to mitigate noise impacts for communities surrounding airports. Evaluating and designing such procedures requires accurate modeling of the aircraft performance, source noise, and atmospheric propagation of the source noise to the ground. Modeling frameworks to assess advanced procedures have been developed but must be validated to ensure their results are reasonable. This paper presents validation of such noise models using a network of ground noise monitoring data at Seattle-Tacoma International airport and ADS-B operational radar flight profiles from the OpenSky database. Modeled noise from operational flights of several aircraft types are shown to be consistent with noise monitor data when reasonable flap settings and atmospheric corrections for the actual weather at the time of flight are used. Discrepancies that exist between the modeled and measured noise results are identified to determine where current noise modeling methods must be improved to accurately represent all relevant noise sources.

Effect of Ge-Nanoislands on the Low-Frequency Noise in Si/SiOx/Ge Structures

2013 ◽  
Vol 854 ◽  
pp. 21-27 ◽  
Author(s):  
N.P. Garbar ◽  
Valeriya N. Kudina ◽  
V.S. Lysenko ◽  
S.V. Kondratenko ◽  
Yu.N. Kozyrev
Keyword(s):  
High Surface ◽  
Low Frequency ◽  
Noise Model ◽  
Frequency Noise ◽  
Low Frequency Noise ◽  
Physical Processes ◽  
Noise Sources ◽  
Noise Component ◽  
First Time ◽  
Noise Models

Low-frequency noise of the structures with Ge-nanoclusters of rather high surface density grown on the oxidized silicon surface is investigated for the first time. It was revealed that the 1/f γ noise, where γ is close to unity, is the typical noise component. Nevertheless, the 1/f γ noise sources were found to be distributed nonuniformly upon the oxidized silicon structure with Ge-nanoclusters. The noise features revealed were analyzed in the framework of widely used noise models. However, the models used appeared to be unsuitable to explain the noise behavior of the structures studied. The physical processes that should be allowed for to develop the appropriate noise model are discussed.

Modeling Nonlinear White Noise in GaN HEMT Devices

Frequenz ◽  
2014 ◽  
Vol 69 (1-2) ◽  
pp. 47-55
Author(s):  
Matthias Rudolph ◽  
Laurent Escotte ◽  
Ralf Doerner
Keyword(s):  
Good Accuracy ◽  
Drain Current ◽  
Noise Model ◽  
Noise Prediction ◽  
Noise Sources ◽  
Nonlinear Excitation ◽  
New Approach ◽  
Gan Hemt ◽  
Nonlinear Noise ◽  
Noise Models

Abstract This paper compares new bias-dependent descriptions for the Pucel and Pospieszalski noise models for GaN HEMT devices. It is proposed to replace the traditional descriptions of the noise sources by general noise powers linked to the drain current. A well-behaved bias dependence of the new parameters is observed for both models the new approach reduces uncertainty in the nonlinear noise model extraction. Finally, the performance of the bias-dependent noise models under nonlinear excitation is investigated and it can be shown that Pucel and Pospieszalski models yield comparably good accuracy in noise prediction.

scholarly journals Quantum Divide and Compute: Exploring the Effect of Different Noise Sources

2021 ◽  
Vol 2 (3) ◽  
Author(s):  
Thomas Ayral ◽  
François-Marie Le Régent ◽  
Zain Saleem ◽  
Yuri Alexeev ◽  
Martin Suchara
Keyword(s):  
Success Probability ◽  
Quantum Circuit ◽  
Noise Sources ◽  
Output Distribution ◽  
Tensor Network ◽  
Learning Machine ◽  
Society Annual ◽  
The Impact ◽  
Noise Models ◽  
Detailed Derivation

AbstractOur recent work (Ayral et al. in Proceedings of IEEE computer society annual symposium on VLSI, ISVLSI, pp 138–140, 2020. 10.1109/ISVLSI49217.2020.00034) showed the first implementation of the Quantum Divide and Compute (QDC) method, which allows to break quantum circuits into smaller fragments with fewer qubits and shallower depth. This accommodates the limited number of qubits and short coherence times of quantum processors. This article investigates the impact of different noise sources—readout error, gate error and decoherence—on the success probability of the QDC procedure. We perform detailed noise modeling on the Atos Quantum Learning Machine, allowing us to understand tradeoffs and formulate recommendations about which hardware noise sources should be preferentially optimized. We also describe in detail the noise models we used to reproduce experimental runs on IBM’s Johannesburg processor. This article also includes a detailed derivation of the equations used in the QDC procedure to compute the output distribution of the original quantum circuit from the output distribution of its fragments. Finally, we analyze the computational complexity of the QDC method for the circuit under study via tensor-network considerations, and elaborate on the relation the QDC method with tensor-network simulation methods.

scholarly journals Belief Merging Operators as Maximum Likelihood Estimators

2020 ◽  
Author(s):  
Patricia Everaere ◽  
Sebastien Konieczny ◽  
Pierre Marquis
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimators ◽  
Noise Model ◽  
Belief Merging ◽  
The World ◽  
Natural Noise ◽  
True State ◽  
Merging Process ◽  
Rationality Postulates ◽  
Noise Models

We study how belief merging operators can be considered as maximum likelihood estimators, i.e., we assume that there exists a (unknown) true state of the world and that each agent participating in the merging process receives a noisy signal of it, characterized by a noise model. The objective is then to aggregate the agents' belief bases to make the best possible guess about the true state of the world. In this paper, some logical connections between the rationality postulates for belief merging (IC postulates) and simple conditions over the noise model under consideration are exhibited. These results provide a new justification for IC merging postulates. We also provide results for two specific natural noise models: the world swap noise and the atom swap noise, by identifying distance-based merging operators that are maximum likelihood estimators for these two noise models.

Radiation modes of propeller tonal noise

2021 ◽  
pp. 1-25
Author(s):  
Hanbo Jiang ◽  
Siyang Zhong ◽  
Han Wu ◽  
Xin Zhang ◽  
Xun Huang ◽  
...  
Keyword(s):  
Spherical Harmonics ◽  
Sound Field ◽  
Low Noise ◽  
Noise Model ◽  
Sound Waves ◽  
Tonal Noise ◽  
Noise Sources ◽  
Separate Source ◽  
Radial Forces ◽  
Flow Variables

Abstract This paper focuses on the radiation modes and efficiency of propeller tonal noise. The thickness noise and loading noise model of propellers has been formulated in spherical coordinates, thereby simplifying numerical evaluation of the integral noise source. More importantly, the radiation field can be decomposed and projected to spherical harmonics, which can separate source-observer positions and enable an analysis of sound field structures. Thanks to the parity of spherical harmonics, the proposed model can mathematically explain the fact that thrusts only produce antisymmetric sound waves with respect to the rotating plane. In addition, the symmetric components of the noise field can be attributed to the thickness, as well as drags and radial forces acting on the propeller surface. The radiation efficiency of each mode decays rapidly as noise sources approach the rotating centre, suggesting the radial distribution of aerodynamic loadings should be carefully designed for low-noise propellers. The noise prediction model has been successfully applied to a drone propeller and achieved a reliable agreement with experimental measurements. The flow variables employed as an input of the noise computation were obtained with computational fluid dynamics (CFD), and the experimental data were measured in an anechoic chamber.

scholarly journals LSSVR Model of G-L Mixed Noise-Characteristic with Its Applications

Entropy ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. 629 ◽  
Author(s):  
Shiguang Zhang ◽  
Ting Zhou ◽  
Lin Sun ◽  
Wei Wang ◽  
Baofang Chang
Keyword(s):  
Wind Speed ◽  
Regression Models ◽  
Historical Data ◽  
Least Square ◽  
Noise Model ◽  
Noise Distribution ◽  
Short Term ◽  
Mixed Noise ◽  
Noise Models ◽  
Better Than

Due to the complexity of wind speed, it has been reported that mixed-noise models, constituted by multiple noise distributions, perform better than single-noise models. However, most existing regression models suppose that the noise distribution is single. Therefore, we study the Least square S V R of the Gaussian–Laplacian mixed homoscedastic ( G L M − L S S V R ) and heteroscedastic noise ( G L M H − L S S V R ) for complicated or unknown noise distributions. The ALM technique is used to solve model G L M − L S S V R . G L M − L S S V R is used to predict short-term wind speed with historical data. The prediction results indicate that the presented model is superior to the single-noise model, and has fine performance.

scholarly journals Identifying and mitigating noise sources in precision pulsar timing data sets

2020 ◽  
Author(s):  
Boris Goncharov ◽  
D J Reardon ◽  
R M Shannon ◽  
Xing-Jiang Zhu ◽  
Eric Thrane ◽  
...  
Keyword(s):  
Gravitational Waves ◽  
Physical Models ◽  
Dispersion Measure ◽  
Data Sets ◽  
Pulse Profile ◽  
Noise Sources ◽  
Arrival Times ◽  
Pulsar Timing ◽  
Noise Models ◽  
Pulsar Timing Array

Abstract Pulsar timing array projects measure the pulse arrival times of millisecond pulsars for the primary purpose of detecting nanohertz-frequency gravitational waves. The measurements include contributions from a number of astrophysical and instrumental processes, which can either be deterministic or stochastic. It is necessary to develop robust statistical and physical models for these noise processes because incorrect models diminish sensitivity and may cause a spurious gravitational wave detection. Here we characterise noise processes for the 26 pulsars in the second data release of the Parkes Pulsar Timing Array using Bayesian inference. In addition to well-studied noise sources found previously in pulsar timing array data sets such as achromatic timing noise and dispersion measure variations, we identify new noise sources including time-correlated chromatic noise that we attribute to variations in pulse scattering. We also identify “exponential dip” events in four pulsars, which we attribute to magnetospheric effects as evidenced by pulse profile shape changes observed for three of the pulsars. This includes an event in PSR J1713+0747, which had previously been attributed to interstellar propagation. We present noise models to be used in searches for gravitational waves. We outline a robust methodology to evaluate the performance of noise models and identify unknown signals in the data. The detection of variations in pulse profiles highlights the need to develop efficient profile domain timing methods.

Estimate of PRNU Noise Based on Different Noise Models for Source Camera Identification

2010 ◽  
Vol 2 (2) ◽  
pp. 21-33 ◽  
Author(s):  
Irene Amerini ◽  
Roberto Caldelli ◽  
Vito Cappellini ◽  
Francesco Picchioni ◽  
Alessandro Piva
Keyword(s):  
Noise Model ◽  
Digital Content ◽  
Wavelet Domain ◽  
Multimedia Forensics ◽  
Source Camera Identification ◽  
Camera Identification ◽  
Novel Method ◽  
Photo Response ◽  
Noise Models ◽  
Open Issues

Identification of the source that has generated a digital content is considered one of the main open issues in multimedia forensics community. The extraction of photo-response non-uniformity (PRNU) noise has been so far indicated as a mean to identify sensor fingerprint. Such a fingerprint can be estimated from multiple images taken by the same camera by means of a de-noising filtering operation. In this paper, the authors propose a novel method for estimating the PRNU noise in source camera identification. In particular, a MMSE digital filter in the un-decimated wavelet domain, based on a signal-dependent noise model, is introduced and compared with others commonly adopted for this purpose. A theoretical framework and experimental results are provided and discussed.

scholarly journals Validating Aircraft Noise Models

Proceedings ◽  
2020 ◽  
Vol 59 (1) ◽  
pp. 12
Author(s):  
Ran Giladi ◽  
Eliav Menachi
Keyword(s):  
Noise Level ◽  
Health Hazard ◽  
Aircraft Noise ◽  
Noise Model ◽  
Aviation Industry ◽  
Noise Abatement ◽  
Statistical Variation ◽  
Huge Impact ◽  
Aircraft Type ◽  
Noise Models

Aircraft noise, especially at takeoffs and landings, became a major environmental nuisance and a health hazard for the population around metropolitan airports. In the battle for a better quality of life, wellbeing, and health, aircraft noise models are essential for noise abatement, control, enforcement, evaluation, policy-making, and shaping the entire aviation industry. Aircraft noise models calculate noise and exposure levels based on aircraft types, engines and airframes, aircraft flight paths, environment factors, and more. Validating the aircraft noise model is a mandatory step towards the model credibility, especially when these models play such a key role with a huge impact on society, economy, and public health. Yet, no validation procedure was offered, and it turns out to be a challenging task. The actual, measured, aircraft noise level is known to be subject to statistical variation, even for the same aircraft type at the same situation and flight phase, executing the same flight procedure, with similar environmental factors and at the same place. This study tries to validate the FAA’s AEDT aircraft noise model, by trying to correlate the specific flight path of an aircraft with its measured noise level. The results show that the AEDT noise model underestimates the actual noise level, and four validation steps should be performed to correct or tune aircraft noise databases and flight profiles.

scholarly journals Measurement Noise Model for Depth Camera-Based People Tracking

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4488
Author(s):  
Otto Korkalo ◽  
Tapio Takala
Keyword(s):  
Real Life ◽  
Measurement Noise ◽  
Noise Model ◽  
Depth Camera ◽  
Depth Sensor ◽  
People Tracking ◽  
Plan View ◽  
Depth Cameras ◽  
Depth Sensors ◽  
Noise Models

Depth cameras are widely used in people tracking applications. They typically suffer from significant range measurement noise, which causes uncertainty in the detections made of the people. The data fusion, state estimation and data association tasks require that the measurement uncertainty is modelled, especially in multi-sensor systems. Measurement noise models for different kinds of depth sensors have been proposed, however, the existing approaches require manual calibration procedures which can be impractical to conduct in real-life scenarios. In this paper, we present a new measurement noise model for depth camera-based people tracking. In our tracking solution, we utilise the so-called plan-view approach, where the 3D measurements are transformed to the floor plane, and the tracking problem is solved in 2D. We directly model the measurement noise in the plan-view domain, and the errors that originate from the imaging process and the geometric transformations of the 3D data are combined. We also present a method for directly defining the noise models from the observations. Together with our depth sensor network self-calibration routine, the approach allows fast and practical deployment of depth-based people tracking systems.

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