Noise Modeling Method Research Articles

Global prediction and analysis for helicopter noise footprint based on acoustic modes.

Aiming to address the significant external noise produced by helicopter rotors, an integrated process based on acoustic modes has been developed for predicting the global noise footprint. The proposed method has the advantages of global prediction, analysis, and low computational costs. The key steps involve constructing an acoustic modal database and efficient prediction of noise footprint. First, the global noise model is utilized to map the time-domain sound pressure to amplitudes and coefficients in the acoustic modal domain. Acoustic mode coefficients representing various flight states are systematically cataloged in the database. Subsequently, the coefficients are extracted based on the characteristic parameters of trajectory elements, allowing for the evaluation of noise radiation in the acoustic modal domain. To investigate the impact of descent angle and advance ratio on the noise footprint, a simulation study is conducted in the forward and approach flight with an AS350 helicopter. Results indicated that, for a region with 1800 observers, the computational time of the developed approach requires only one-fifth of the time compared to the traditional Rotorcraft Noise Model method. This remarkable reduction in computation time, along with the global predictive capability, supports the practicality of efficient noise footprint assessment in both military and civilian contexts.

Learning real-world heterogeneous noise models with a benchmark dataset

Noise modeling is an important research field in computer vision; the design of an accurate model for imaging sensor noise depends on not only a comprehensive benchmark dataset of the real world, but also a precise design of the noise modeling algorithm. However, due to the inaccurate estimation method of noise-free images and limited shooting scenes, the current realistic datasets could not describe the diverse noise properties sufficiently. Moreover, popular parametric noise models are not sophisticated enough to characterize the real-world noise exactly. In this work, we first construct a more comprehensive dataset of the real world by capturing more indoor and outdoor scenes under different lighting conditions using diverse smartphones, then we propose a non-parametric noise estimation method capable of modeling the spatial heterogeneity of real-world noise patterns. Specifically, in order to characterize the spatial heterogeneity of real-world noise, we assume a non-i.i.d Gaussian distribution and propose a deep convolutional neural network (DCNN)-based approach for learning pixel-wise noise variance maps. To learn the pixel-wise variance map, we have constructed a variance estimation network mapping from the conditional signals (clean image, ISO, and camera model) to surrogate labels obtained from the nonlocal search of similar patches from the clean-noisy image pair. Finally, we conducted denoising and classification experiments using different kinds of simulated noisy images, compared to the Poisson-Gaussian and Noise Flow noise models, the proposed method achieves denoising performance improvements (PSNR) of 1.13 dB and 2.51 dB respectively on the proposed real-world test dataset, denoising and classification results on the real noisy data captured by mobile phones have verified that our approach is more accurate than current noise modeling methods.

Vibration shock disturbance modeling in the rotating machinery fault diagnosis: A generalized mixture Gaussian model

In real-world industrial environments, complex background noises are composed of various components, deviating from a simple Gaussian distribution like shock noise. In this work, a robust noise modeling method based on the mixture of exponential power (MoEP) distributions is developed to address this issue. To proficiently extract the fault characteristics, the signal’s 2-D representation is attained via Fast-SC, both of the fault features’ low-rankness and the complex noise are combined in a signal model. Then, a penalized function of the noise model is combined to further improve the performance of the method. The model is designated as the PMoEP enhanced low-rank model (PMoEP-LR). The Generalized Expectation–Maximization (GEM) algorithm is utilized to estimate the low-rank spectral correlation matrix and deduce all parameters of the PMoEP-LR model. Finally, the enhanced envelope spectrum (EES) is used to detect the defect characteristic. The efficacy of the proposed method is showcased by analyzing both simulated and real data.

Beyond supervision: An unsupervised spatio-temporal point cloud noise modeling for event vision sensor

Noise modeling is a fundamental unexplored problem in many event camera applications. The noise modeling methods of conventional cameras are inadequate for capturing the intricate noise patterns unique to event cameras. To address this gap, we propose a novel event camera-driven noise modeling approach specifically designed to account for the unique characteristics of event camera data. Given the challenges in generating ground truth data, the proposed method is designed using unsupervised statistical techniques. The proposed noise model is camera-aware, i.e. it allows adaptability to the unique noise patterns of each sensor. It also facilitates asynchronous event processing as applied to downstream computer vision tasks. To validate the proposed noise modeling approach, we conducted extensive experiments in the context of denoising tasks. The results indicate that our noise model accurately represents the noise, leading to state-of-the-art performance across various datasets.

Comparison of data-based and modeled-based analysis of aircraft departure noise using noise monitor network recordings

A comparison of data-based regression analysis and modeled-based analysis of aircraft departure noise variations using airport noise monitor network recordings is presented. Accurate modeling of aircraft noise is useful for the assessment of departure procedures and provides insight into the noise reduction potential of noise abatement flight procedures. In addition, many airports employ monitor networks that continuously record noise data produced by aircraft on departure, which can be used for assessing potential factors leading to variation in departure noise and for comparison to results from modeling methods. In this paper, departure noise variations of Boeing 737-800 aircraft are correlated to departure climb procedure factors using both data-based approaches incorporating recorded noise collected at the Seattle International Airport monitor network, and a method to model departure noise from on altitude, ground track, and ground speed from recorded operational surveillance data. Examples using both approaches indicate altitude and velocity result in the highest correlating factors for noise at the three noise monitor locations studied. Limitations and areas of needed improvement for the different noise modeling methods are highlighted.

Providing acoustical advice for the purpose of strategic land-use planning

Strategic land-use planning is the process where stakeholders work together to design future land-use to achieve more desirable economic, social or environmental outcomes. There is often uncertainty around the type of acoustical advice required for the purpose of informing strategic land-use planning decisions. There are also limitations to the level of technical detail that can be made available when little is known about the nature or locations of potential future development. EMM Consulting Pty Limited’s experience in recent years has provided opportunities to work on various strategic land-use planning projects. This experience has enabled an understanding of what is required by relevant stakeholders to appropriately inform decisions as well as what is possible from a technical perspective. The importance of establishing clear expectations around technical outcomes and uncertainties is highlighted, as well as the importance of collaboration with all relevant stakeholders. A strong understanding of the planning framework the project will be approved and operate within is critical. The importance of having an appropriate authority and/or framework to facilitate the implementation of required noise measures and a plan to transition existing land use is discussed. The benefits and limitations of a Noise Management Precinct, as described in the Noise Policy for Industry (NSW EPA, 2017), are identified. Noise modeling methods and technical assumptions that could be utilized to predict and manage likely impacts are described.

Unsupervised Noise Reductions for Gravitational Reference Sensors or Accelerometers Based on the Noise2Noise Method.

Onboard electrostatic suspension inertial sensors are important applications for gravity satellites and space gravitational-wave detection missions, and it is important to suppress noise in the measurement signal. Due to the complex coupling between the working space environment and the satellite platform, the process of noise generation is extremely complex, and traditional noise modeling and subtraction methods have certain limitations. With the development of deep learning, applying it to high-precision inertial sensors to improve the signal-to-noise ratio is a practically meaningful task. Since there is a single noise sample and unknown true value in the measured data in orbit, odd-even sub-samplers and periodic sub-samplers are designed to process general signals and periodic signals, and adds reconstruction layers consisting of fully connected layers to the model. Experimental analysis and comparison are conducted based on simulation data, GRACE-FO acceleration data, and Taiji-1 acceleration data. The results show that the deep learning method is superior to traditional data smoothing processing solutions.

An Improved Noise Modeling Method Using a Quasi-Physical Zone Division Model for AlGaN/GaN HEMTs

Accurate characterization of transistor noise performance is significant for low-noise amplifier (LNA) design. The conventional empirical noise model contains too many fitting parameters and thus relies on a large number of on-wafer noise measurements, especially the ambient temperature effects modeling. In this article, an improved noise modeling method using the quasi-physical zone division (QPZD) theory is proposed. The boundary potential calculation in the traditional modeling method is improved by considering the effects of the charge depletion region when the transistor is in the ON-state, which can result in at least two orders of magnitude improvement in the accuracy of the noise power spectral density (PSD). With this improvement, the noise performance can be accurately predicted without adding any fitting parameters in the noise PSD models. Furthermore, ambient temperature modeling of the noise performance is realized by considering the ambient temperature effects of drain–source current <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${I}_{\text {ds}}$ </tex-math></inline-formula> with a modified <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${V}_{\text {off}}$ </tex-math></inline-formula> model and some other significant intrinsic parameters. Verification shows that the proposed QPZD noise model with the improved boundary potential calculation method can well predict the noise performance and scattering parameters at different ambient temperatures. The method of this study can be applied to physical-based noise modeling of GaN high-electron-mobility transistors (HEMTs) and LNA design.

Semi-Blindly Enhancing Extremely Noisy Videos With Recurrent Spatio-Temporal Large-Span Network.

Capturing videos under the extremely dark environment is quite challenging for the extremely large and complex noise. To accurately represent the complex noise distribution, the physics-based noise modeling and learning-based blind noise modeling methods are proposed. However, these methods suffer from either the requirement of complex calibration procedure or performance degradation in practice. In this paper, we propose a semi-blind noise modeling and enhancing method, which incorporates the physics-based noise model with a learning-based Noise Analysis Module (NAM). With NAM, self-calibration of model parameters can be realized, which enables the denoising process to be adaptive to various noise distributions of either different cameras or camera settings. Besides, we develop a recurrent Spatio-Temporal Large-span Network (STLNet), constructed with a Slow-Fast Dual-branch (SFDB) architecture and an Interframe Non-local Correlation Guidance (INCG) mechanism, to fully investigate the spatio-temporal correlation in a large span. The effectiveness and superiority of the proposed method are demonstrated with extensive experiments, both qualitatively and quantitatively.

Astronomically forced changes in groundwater reservoirs and sea level during the greenhouse world

<p indent="0mm">Sea level rise caused by global warming is one of the biggest challenges facing humankind. Deepening the understanding of the mechanism of global sea level change during the greenhouse period is the key to meeting this challenge. Large-scale, rapid sea-level changes frequently occurred during the greenhouse period lacking ice sheets. However, conventional sea level change models cannot explain this phenomenon, exposing their shortcomings. Estimation of global pore space in sediments and big data studies have indicated that global sea level changes may also be related to climate-related continental groundwater volume changes. We review the mechanism of sea level change caused by astronomically driven continental groundwater activities and propose a new Sponge Continent hypothesis: astronomical forcing drives climate change, causing filling and discharge of continental aquifers (by analogy, a sponge), which may in turn impact large-scale changes in global sea levels and inland lake level during the greenhouse period. We summarize recent technological breakthroughs related to the reconstruction of deep time sea- and lake-level changes. First, lake level change has been proposed as an indicator for the fluctuation of continental groundwater volume. Second, the sedimentary noise model and SediRate-Fischer method were proposed for the reconstruction of sea- and lake-level changes from sedimentary successions. Among these, the sedimentary noise model is developed based on the principle of the Milankovitch theory of climate change and time series analysis techniques. The noise model contains two complementary methods: dynamic noise after orbital tuning (DYNOT) and lag-1 autocorrelation coefficient (<italic>ρ</italic>₁). The DYNOT method measures the noise intensity in paleoclimate time series, i.e., the ratio of non-Milankovitch variation to total variation. By calculating the lag-1 autocorrelation coefficient of the target paleoclimate time series, the <italic>ρ</italic>₁ model can independently test the noise changes related to sea level variations. The <italic>ρ</italic>₁ value decreases with the increase of noise intensity, and vice versa. The relative intensity of the sedimentary noise is anti-phased with the sea level change. When the sea level increases, the sedimentary noise decreases, and vice versa. This model can also be applied to the studies of lake level changes. Third, cyclo-magnetostratigraphy can provide a global synchronous stratigraphic correlation framework, which is vital for the evaluation of the aquifer eustasy and Sponge Continent hypothesis. The hypothesis of aquifer eustasy and Sponge Continent can be supported by geological evidence from the greenhouse period. This paper reviews recent progress related to astronomically forced sea level changes and/or lake level changes in chronological order. Some of them support the predicted antiphase relationship between the sea level curve and the contemptuous lake level curve as predicted by the Sponge Continent hypothesis. However, many unsolved issues about the nature and mechanisms of sea level change are to be explored. First, estimates of groundwater volume remain in dispute. Second, there is a lack of quantitative indicators for deep time groundwater variations. Third, the timing and mechanism of groundwater changes in continental aquifers are still unconstrained. Potential research directions are also discussed, including groundwater storage estimate, research methods, storage-drainage mechanism of groundwater reservoirs, etc.

The Charge-Based Flicker Noise Model for HEMTs

The flicker noise modeling is of great significance in many applications of high electron mobility transistors (HEMTs) in low noise circuits. In this letter, a novel charge-based flicker noise model for HEMTs is proposed, which introduces the trap-charge fluctuation-based flicker noise modeling method to the charge-based MIT virtual source (MVS) HEMT model. This model is highly compatible with the MVS HEMT model. It can predict the flicker noise performance in a physically rigorous and accurate way. This model is verified by the excellent agreement between simulations and measurement results.

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

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.

Research and Modeling of Photovoltaic Array Channel Noise Characteristics

The photovoltaic array can be used as a medium for carrier communication to realize monitoring of photovoltaic components. Photovoltaic array channel noise, especially the pulse-type noise therein, seriously interferes carrier communication, so it is necessary to grasp the characteristics of the photovoltaic array channel noise. Photovoltaic array channel noise modeling is a key process when conducting anti-noise immunity tests of monitoring equipment. Based on the time-domain waveform of photovoltaic series channel noise which is measured in a photovoltaic power station, this paper proposes a photovoltaic array noise modeling method of Wavelet Peak-Type Markov chain, and studies the influence on modeling accuracy when different mother wavelets are adopted for modeling. From the simulation results, root mean square errors of the predicted output for Haar, Biorthogonal and Daubechies wavelet-based function modeling case are 0.9614 V, 1.4915 V and 0.7928 V, respectively, validating that Daubechies wavelet-based function is the best wavelet-based function of modeling. In the case that the peak of original noise reaches 20 V, the predicted mean absolute error of this model is only 0.4926 V, which not only verifies the applicability of the Wavelet Peak-Type Markov chain model to the photovoltaic array channel noise, but also verifies the applicability to the pulse-type noise.

Unsupervised Deep Noise Modeling for Hyperspectral Image Change Detection

Hyperspectral image (HSI) change detection plays an important role in remote sensing applications, and considerable research has been done focused on improving change detection performance. However, the high dimension of hyperspectral data makes it hard to extract discriminative features for hyperspectral processing tasks. Though deep convolutional neural networks (CNN) have superior capability in high-level semantic feature learning, it is difficult to employ CNN for change detection tasks. As a ground truth map is usually used for the evaluation of change detection algorithms, it cannot be directly used for supervised learning. In order to better extract discriminative CNN features, a novel noise modeling-based unsupervised fully convolutional network (FCN) framework is presented for HSI change detection in this paper. Specifically, the proposed method utilizes the change detection maps of existing unsupervised change detection methods to train the deep CNN, and then removes the noise during the end-to-end training process. The main contributions of this paper are threefold: (1) A new end-to-end FCN-based deep network architecture for HSI change detection is presented with powerful learning features; (2) An unsupervised noise modeling method is introduced for the robust training of the proposed deep network; (3) Experimental results on three datasets confirm the effectiveness of the proposed method.

Prospect and Theory of GNSS Coordinate Time Series Analysis

The long-term accumulated coordinate time series of Global Navigation Satellite System (GNSS) reference stations provide basic data for geodesy and geodynamic studies. Through improving precise GNSS data processing models and strategies, together with investigating physical mechanism and properly modeling of the non-linear variations, the precise position and velocity of GNSS stations can be estimated. This will not only help interpret the plate tectonics reasonably, establish and maintain the terrestrial reference frame, but also contribute to investigating the geodynamic processes, such as post-glacial rebound, sea level change, as well as snow and ice redistribution inversion, et al. In this paper, the basic theory and methodology of GNSS coordinate time series analysis is reviewed by introducing the model estimation of station coordinates time series and the signal analysis. Then, noise modeling technique for GNSS coordinate time series is outlined. Particularly, a rigorous 3-D noise modeling method is proposed to best describe the stochastic process. After that, the advances in investigating physical mechanism of the non-linear variations in GNSS coordinate time series are summarized. Finally, we outline the scientific and engineering applications of GNSS coordinate time series, and discuss the prospect of future research.

Computing intrinsic noise of the genetic regulation modeled by Hill functions

Intrinsic noise embedded in stochastic gene regulation due to low copy number of species has been studied using the approach of theoretical modeling and computational simulation, including the standard methods of stochastic simulation algorithm (SSA) and linear noise approximation (LNA). At average cell level, Hill functions are widely used as a compact format to represent gene regulation involving multi-transcription-factor binding and cooperativity. Heuristic SSA and LNA methods (hSSA and hLNA) have been applied to study stochastic models employing Hill functions. It is however unclear which modeling and simulation method is suitable to characterize intrinsic noise of Hill-type gene regulation with sufficient accuracy and computational efficiency. In this work, we perform noise analysis of two gene regulatory models represented by second-order activating and inhibitory Hill functions, seeking to evaluate the performance of five existing noise modeling methods. Specifically, SSA and LNA are applied to the full models that are expanded from the Hill functions containing only elementary reactions, while hSSA and hLNA are applied to reduced models where the Hill function is heuristically used. In addition, we characterize intrinsic noise using the slow-scale LNA (ssLNA) method that is recently proposed to deal with models with both fast and slow time scales. Using SSA as ground truth, we find that hSSA and hLNA underestimate the level of intrinsic noise in the Hill-type models, despite of high computational efficiency. The ssLNA approach calculates noise with comparable accuracy as SSA and LNA, while requesting much less computational resources. In addition, the chemical Langevin equation (CLE) under the same slow-scale framework simulates single-cell stochastic trajectories as accurately as SSA yet with significantly lower computational demands. This study shows that ssLNA complemented by slow-scale CLE offers a computational platform that out-performs the other four methods in characterizing intrinsic stochasticity of the Hill-type genetic models.

Novel Time Series Modeling Methods for Gyro Random Noise Used in Internet of Things

In the micro-strapdown inertial navigation systems of Internet of Things, modeling and filtering of gyro random noise are a useful approach to reducing sensor error and enhancing navigation accuracy. Time series is a popular choice for the gyro random noise modeling process. This paper contains two parts to research the modeling methods of gyro random noise. In the first part, for the problem that conventional autoregressive (AR) modeling methods for fiber optic gyro random noise require mass data due to the slow convergence speed of parameter estimation, furthermore, these methods do not perform well if there are too few samples (e.g., less than 3000), and a novel AR modeling method based on the adaptive Kalman filter is proposed. The proposed method can work with smaller sample sizes due to its rapid parameter estimation convergence speed; its algorithm also yields very precise results. These advantages make it well-suited to applications that require a fast AR modeling method for gyro random noise. In the second part, an experiment reveals that the random noise characteristic of some micro-electromechanical systems gyros is adapted to the moving average (MA) model. However, from the available literature, research on a gyro random noise modeling method based on an MA model is very rare. Therefore, we discuss and analyze two MA modeling methods for gyro random noise and give the comparisons between their modeling results.

Large signal and noise properties of heterojunction AlxGa1−xAs/GaAs DDR IMPATTs

Simulation studies are carried out on the large signal and noise properties of heterojunction (HT) AlxGa1−xAs/GaAs double drift region (DDR) IMPATT devices at V-band (60 GHz). The dependence of Al mole fraction on the aforementioned properties of the device has been investigated. A full simulation software package has been indigenously developed for this purpose. The large signal simulation is based on a non-sinusoidal voltage excitation model. Three mole fractions of Al and two complementary HT DDR structures for each mole fraction i.e., six DDR structures are considered in this study. The purpose is to discover the most suitable structure and corresponding mole fraction at which high power, high efficiency and low noise are obtained from the device. The noise spectral density and noise measure of all six HT DDR structures are obtained from a noise model and simulation method. Similar studies are carried out on homojunction (HM) DDR GaAs IMPATTs at 60 GHz to compare their RF properties with those of HT DDR devices. The results show that the HT DDR device based on N-AlxGa1−x As/p-GaAs with 30% mole fraction of Al is the best one so far as large signal power output, DC to RF conversion efficiency and noise level are concerned.

Recursive least squares identification of hybrid Box–Jenkins model structure in open-loop and closed-loop

Inspired by the fact that, in order to obtain a global optimal solution, a continuous plant should be identified simultaneously with the noise model, a simple but effective identification method is firstly proposed for hybrid Box–Jenkins structure in open-loop and close-loop. Two recursive generalized extended least squares algorithms are developed for different plant models. In recursive computations, the idea of auxiliary model has been applied to make the global recursive identification possible, and the idea of delay compensation has been introduced to handle the identification of SOPDT plant model effectively. Meanwhile, the online implementation issues of recursive algorithms are discussed. The two proposed algorithms can be further extended to closed-loop systems by an appropriate closed-loop setup. The simulation examples demonstrate the accuracy and effectiveness of the proposed method in open-loop and closed-loop.

Random Response Analysis of a Large-Size Cooling Tower Subjected to the Stationary Earthquake Motion

The response of a large-size cooling tower with 250m high subjected to the seismic action are investigated by both random vibration theory and response spectrum method. Shell element is taken to model the tower body, and beam element is used for the circular foundation and supporting columns. The earthquake motion input is a colored filtered white noise model and mode superposition method is adopted to analyze the random response of the large-size cooling tower. The paper presents the power spectrum density functions (PDF) and standard deviation of the displacement of the top and characteristic node, and the analysis results indicate that the results of the stationary random vibration theory and the response spectrum method are the same order of magnitude. The power spectrum density function of the bottom node stress is obviously bigger than the one at the top and the throat, and the random response of meridonal stress is dominated at the top. In addition, the peak frequency position of the power spectrum density function is different from the corresponding stress.