Estimation Of Physical Parameters Research Articles

Dynamic modelling and a dual vector modulated improved model predictive control with auto tuning feature of active front‐end converters for distributed energy resources

AbstractThe operational performance of grid‐connected active front‐end converters (AFEs) faces challenges arising from the intricate interplay among phase‐locked loop (PLL) non‐linearities, grid impedance, and conventional control strategies, resulting in compromised stability. This study introduces a refined approach to dynamic model predictive control (MPC) by integrating recursive least squares (RLS) for the precise estimation of physical model parameters, thereby addressing stability concerns. Unlike conventional methodologies, the proposed enhanced RLS‐based MPC approach, equipped with an auto‐tuning feature, allows for the design of controllers without a prerequisite understanding of exact external dynamics. Notably, this technique exhibits exceptional disturbance rejection capabilities. The evaluation of the cost function at each sampling interval facilitates the determination of optimal switching states based on predicted variables. Gate pulses for the switches of the AFEs are generated accordingly. Employing a simulation platform, the proposed control structure's performance across varied conditions is comprehensively assessed, encompassing alterations in grid impedance and system non‐linearities. The method adeptly integrates inherent non‐linearities within the system, showcasing exceptional robustness in diverse dynamic scenarios. To further substantiate the efficacy of the proposed control system over conventional approaches, simulation results are validated using a laboratory hardware platform equipped with Typhoon HIL and dSPACE real‐time emulators, providing tangible evidence of the proposed control system's effectiveness in real‐world hardware setups. The multifaceted approach, encompassing precise parameter estimation, predictive control, auto‐tuning, disturbance rejection, robust design, and real‐time evaluation, collectively establishes a resilient foundation for enhancing and maintaining the overall stability of the system across diverse operating scenarios.

Accurate reconstruction of 4D spectral–spatial images from sparse-view data in continuous-wave EPRI

In continuous-wave electron paramagnetic resonance imaging (CW EPRI), data are collected generally at densely sampled views sufficient for achieving accurate reconstruction of a four dimensional spectral–spatial (4DSS) image by use of the conventional filtered-backprojection (FBP) algorithm. It is desirable to minimize the scan time by collection of data only at sparsely sampled views, referred to as sparse-view data. Interest thus remains in investigation of algorithms for accurate reconstruction of 4DSS images from sparse-view data collected for potentially enabling fast data acquisition in CW EPRI. In this study, we investigate and demonstrate optimization-based algorithms for accurate reconstruction of 4DSS images from sparse-view data. Numerical studies using simulated and real sparse-view data acquired in CW EPRI are conducted that reveal, in terms of image visualization and physical-parameter estimation, the potential of the algorithms developed for yielding accurate 4DSS images from sparse-view data in CW EPRI. The algorithms developed may be exploited for enabling sparse-view scans with minimized scan time in CW EPRI for yielding 4DSS images of quality comparable to, or better than, that of the FBP reconstruction from data collected at densely sampled views.

How many random observations are needed for good phase coverage of a periodic source?

The specific problem considered is the number of radial velocity measurements required to obtain good estimates of physical parameters of binary star. It is assumed that observations are made at random binary phases. The loss of information due to poor phase coverage is explored, and a suggested limit on the largest acceptable gap introduced. The statistical distribution of maximum gap lengths can then be used to specify the minimum number of velocity measurements to obtain good phase coverage with a specified confidence limit. The effects of non-zero orbital eccentricity are discussed, as are the ramifications of having multiple binary targets. The theory is also applicable to the characterisation of the radial velocity curves induced by exoplanets on their host stars, provided that the periods and eccentricities are known (from e.g. transit observations).

A Hierarchical Method for Locating the Interferometric Fringes of Celestial Sources in the Visibility Data

In source detection in the Tianlai project, locating the interferometric fringe in visibility data accurately will influence downstream tasks drastically, such as physical parameter estimation and weak source exploration. Considering that traditional locating methods are time-consuming and supervised methods require a great quantity of expensive labeled data, in this paper, we first investigate characteristics of interferometric fringes in the simulation and real scenario separately, and integrate an almost parameter-free unsupervised clustering method and seeding filling or eraser algorithm to propose a hierarchical plug and play method to improve location accuracy. Then, we apply our method to locate single and multiple sources’ interferometric fringes in simulation data. Next, we apply our method to real data taken from the Tianlai radio telescope array. Finally, we compare with unsupervised methods that are state of the art. These results show that our method has robustness in different scenarios and can improve location measurement accuracy effectively.

A Novel Image Formation Model for Descattering.

In the field of image descattering, the image formation models employed for restoration approaches are often simplified. In these models, scattering distribution is uniform in homogeneous media when transmission is fixed. Through specifically designed experiments, we discover that scattering exhibits non-uniform characteristics even in homogeneous media. Neglecting non-uniform scattering in these models limits their accuracy in representing scattering distribution, resulting in existing image descattering approaches inadequate. To tackle these issues, this paper proposes a novel image formation model for image descattering, considering more physical parameters, such as zenith angle, azimuth angle, scattering phase function, and camera focal length. Our model describes the light transfer process in scattering media more accurately. For image descattering, we introduce corresponding algorithms for parameter estimation in our model and simultaneous restoration from degraded images. Experimental evaluations demonstrate the effectiveness of our proposed model in various tasks, including physical parameter estimation, pure-scattering removal, image dehazing, and underwater image restoration. In terms of calculating parameters, our results are close to the real values; in terms of underwater image restoration, our work outperforms the state-of-art methods; in terms of image dehazing, our work promotes the performance of existing methods by replacing previous models with our model.

The Advantages of Global Photometric Models in Fitting Transit Variations

Estimation of planetary orbital and physical parameters from light-curve data relies heavily on the accurate interpretation of Transit Timing Variation (TTV) measurements. In this letter, we review the process of TTV measurement and compare two fitting paradigms—one that relies on making transit-by-transit timing estimates and then fitting a TTV model to the observed timings, and one that relies on fitting a global flux model to the entire light-curve data set simultaneously. The latter method is achieved either by solving for the underlying planetary motion (often referred to as “photodynamics”), or by using an approximate or empirical shape of the TTV signal. We show that, across a large range of the transit S/N regime, the probability distribution function of the mid-transit time significantly deviates from a Gaussian, even if the flux errors do distribute normally. Treating the timing uncertainties as if they are distributed normally leads, in such a case, to a wrong interpretation of the TTV measurements. We illustrate these points using numerical experiments and conclude that a fitting process that relies on a global flux fitting, rather than the derived TTVs, should be preferred.

Beagle-agn I: simultaneous constraints on the properties of gas in star-forming and AGN narrow-line regions in galaxies

ABSTRACT We present the addition of nebular emission from the narrow-line regions (NLR) surrounding active galactic nuclei (AGNs) to beagle (BayEsian Analysis of GaLaxy sEds). Using a set of idealized spectra, we fit to a set of observables (emission-line ratios and fluxes) and test the retrieval of different physical parameters. We find that fitting to standard diagnostic-line ratios plus [O ii] λ3726, λ3729/[O iii] λ5007, H β/H α, [O i] λ6300/[O ii] λ3726, λ3729, and H α flux, degeneracies remain between dust-to-metal mass ratio ($\xi _\rm {d}^{\small NLR}$) and ionization parameter ($U_\rm {s}^{\small NLR}$) in the NLR gas, and between slope of the ionizing radiation ($\alpha _{\small PL}$, characterizing the emission from the accretion disc around the central black hole) and total accretion-disc luminosity ($L_\rm {acc}$). Since these degeneracies bias the retrieval of other parameters even at maximal signal-to-noise ratio (S/N), without additional observables, we suggest fixing $\alpha _{\small PL}$ and dust-to-metal mass ratios in both NLR and H ii regions. We explore the S/N in H β required for un-biased estimates of physical parameters, finding that S/N(H β) ∼ 10 is sufficient to identify an NLR contribution, but that higher S/N is required for un-biased parameter retrieval (∼20 for NLR-dominated systems, ∼30 for objects with approximately equal H β contributions from NLR and H ii regions). We also compare the predictions of our models for different line ratios to previously published models and data. By adding [He ii] λ4686-line measurements to a set of published line fluxes for a sample of 463 AGN NLR, we show that our models with $-4\lt \hbox{$\log U_{\small S}^{\small NLR}$}\lt -1.5$ can account for the full range of observed AGN properties in the local Universe.

A deep learning-based stripe self-correction method for stitched microscopic images

Stitched fluorescence microscope images inevitably exist in various types of stripes or artifacts caused by uncertain factors such as optical devices or specimens, which severely affects the image quality and downstream quantitative analysis. Here, we present a deep learning-based Stripe Self-Correction method, so-called SSCOR. Specifically, we propose a proximity sampling scheme and adversarial reciprocal self-training paradigm that enable SSCOR to utilize stripe-free patches sampled from the stitched microscope image itself to correct their adjacent stripe patches. Comparing to off-the-shelf approaches, SSCOR can not only adaptively correct non-uniform, oblique, and grid stripes, but also remove scanning, bubble, and out-of-focus artifacts, achieving the state-of-the-art performance across different imaging conditions and modalities. Moreover, SSCOR does not require any physical parameter estimation, patch-wise manual annotation, or raw stitched information in the correction process. This provides an intelligent prior-free image restoration solution for microscopists or even microscope companies, thus ensuring more precise biomedical applications for researchers.

Using deep learning to accelerate magnetic resonance measurements of molecular exchange.

Real-time monitoring and quantitative measurement of molecular exchange between different microdomains are useful to characterize the local dynamics in porous media and biomedical applications of magnetic resonance. Diffusion exchange spectroscopy (DEXSY) is a noninvasive technique for such measurements. However, its application is largely limited by the involved long acquisition time and complex parameter estimation. In this study, we introduce a physics-guided deep neural network that accelerates DEXSY acquisition in a data-driven manner. The proposed method combines sampling pattern optimization and physical parameter estimation into a unified framework. Comprehensive simulations and experiments based on a two-site exchange system are conducted to demonstrate this new sampling optimization method in terms of accuracy, repeatability, and efficiency. This general framework can be adapted for other molecular exchange magnetic resonance measurements.

An Evaluation of Non-Gaussian Data Assimilation Methods in Moist Convective Regimes

Abstract Obtaining a faithful probabilistic depiction of moist convection is complicated by unknown errors in subgrid-scale physical parameterization schemes, invalid assumptions made by data assimilation (DA) techniques, and high system dimensionality. As an initial step toward untangling sources of uncertainty in convective weather regimes, we evaluate a novel Bayesian data assimilation methodology based on particle filtering within a WRF ensemble analysis and forecasting system. Unlike most geophysical DA methods, the particle filter (PF) represents prior and posterior error distributions nonparametrically rather than assuming a Gaussian distribution and can accept any type of likelihood function. This approach is known to reduce bias introduced by Gaussian approximations in low-dimensional and idealized contexts. The form of PF used in this research adopts a dimension-reduction strategy, making it affordable for typical weather applications. The present study examines posterior ensemble members and forecasts for select severe weather events between 2019 and 2020, comparing results from the PF with those from an ensemble Kalman filter (EnKF). We find that assimilating with a PF produces posterior quantities for microphysical variables that are more consistent with model climatology than comparable quantities from an EnKF, which we attribute to a reduction in DA bias. These differences are significant enough to impact the dynamic evolution of convective systems via cold pool strength and propagation, with impacts to forecast verification scores depending on the particular microphysics scheme. Our findings have broad implications for future approaches to the selection of physical parameterization schemes and parameter estimation within preexisting data assimilation frameworks. Significance Statement The accurate prediction of severe storms using numerical weather models depends on effective parameterization schemes for small-scale processes and the assimilation of incomplete observational data in a manner that faithfully represents the probabilistic state of the atmosphere. Current generation methods for data assimilation typically assume a standard form for the error distributions of relevant quantities, which can introduce bias that not only hinders numerical prediction, but that can also confound the characterization of errors from the model itself. The current study performs data assimilation using a novel method that does not make such assumptions and explores characteristics of resulting model fields and forecasts that might make such a method useful for improving model parameterization schemes.

Differential Flatness-Based Parameter Estimation for Suspended Load Drones

The transportation of goods by drones using cable towing has recently attracted considerable attention. When flying a suspended load drone, any discrepancy between the mathematical model and the actual drone deteriorates control performance. However, because some physical parameters are difficult to measure, creating an accurate mathematical model is extremely difficult. Therefore, we propose a parameter estimation method using differential flatness that can be extended for application to suspended load drones. This method overcomes the problem of dealing with higher-order derivatives of flat outputs and enables the estimation of physical parameters. In this study, we experimentally show that the proposed method improves trajectory tracking performance.

Forecasting of volumetric flow rate of Ergene river using machine learning

Today, the significance of the estimation of physical parameters has considerably increased; for example, the prediction of water flow rate (WFR) is one of the types that will gain a substantial importance among the others. The predictions of WFR of rivers play a prominent role in the plans and constructions of new water dams, or to operate the ones that were formerly constructed. In this study, a variety of machine learning algorithms have been suggested in the estimations of one-ahead instantaneous measurement of river WFR. In this regard, four different prediction algorithms including long short-term memory (LSTM) neural network, adaptive neuro-fuzzy inference system (ANFIS) with fuzzy c-means (FCM), ANFIS with subtractive clustering (SC), and the ANFIS with grid partition (GP) were initially trained and secondly tested. The study region was selected as Inanli measurement station, which is located on the Ergene River. A cumulative of 134 different models were formed using these algorithms. Instantaneous WFR predictions in terms of WFR data have shown that ANFIS-FCM and ANFIS-SC had given the best statistical error outcomes. Accordingly, the values of 0.34 m3/s, 0.72 m3/s, and 0.9728 have been found as a result of machine learning establishment corresponding to statistical accuracy results of MAE, RMSE, and R values, respectively. Namely, it has been concluded that error values of the ANFIS-FCM and ANFIS-SC computations have outcome to be sufficiently good. Also, it was concluded and shown in the current study that both tools of the ANFIS can be two efficacious methods to WFR forecasting.

Seismic pre-stack inversion for physical and anisotropic parameters in fractured shale reservoirs

AbstractThe estimation of physical and anisotropic parameters is of great importance for the characterization of fractured reservoirs. Vertical fractures developing in a laterally isotropic (VTI) setting are equivalent to orthotropic anisotropic (OA) media common in stratified fractured shale reservoirs. To obtain independent anisotropic and physical information, a novel reflection coefficient approximation containing physical and anisotropic parameters is derived to improve the stability of the inversion for orthotropic media. To simplify the equation for the reflection coefficient, an approximate rock physics model is constructed using the approximate theory of rock modulus. The estimated parameters are reduced from nine to six. The accuracy analysis reveals that the new reflection coefficient is appropriate and suitable for inversion. In addition, a stepwise Bayesian amplitude versus angle and azimuth (AVAZ) inversion method with smooth background constraints is developed to estimate the anisotropic and physical parameters from the azimuthal seismic data. The smooth background constraint improves the robustness of the inversion. And the stepwise inversion strategy solves the problem that the contribution of the fracture parameter to the reflection coefficient is smaller than the other parameters. Synthetic cases show that the proposed stepwise Bayesian AVAZ inversion method is feasible in estimating the anisotropic parameters for OA media even when the signal-to-noise ratio is 2. The field cases show that the proposed inversion method has good stability and robustness in predicting shale reservoirs with vertical or near-vertical fractures.

Thermal uncertainty analysis of a single particle model for a Lithium-Ion cell

In recent years, the sustained increase in the worldwide demand for lithium batteries goes side by side with the need for reliable methods to assess battery performance. Of particular importance is assessing Lithium-Ion cell’s thermal behavior given its role on hazard and aging of batteries. An essential task towards developing battery management systems is the estimation of physical parameters and their uncertainties in terms of both models and observations. Consequently, this paper analyzes the uncertainty in a thermal single particle model for a lithium-ion cell. In the first part of the manuscript, we explore the model adequacy by analyzing the forward and backward uncertainty propagation in the model in terms of diffusion coefficients, reaction rate constants, and observations of cell voltage. In the second part of the manuscript, we infer the cell’s reversible heat given the energy balance equation and the cell’s temperature observations. We argue that the methods proposed here may be extended to analyze other more general lithium-ion models.

Estimation of Depth, Physical Parameters, and Location Using Nonlinear Inverse Modeling of Magnetic Data by Bhattacharya Method in a Field of NW Iran

Estimation of Depth, Physical Parameters, and Location Using Nonlinear Inverse Modeling of Magnetic Data by Bhattacharya Method in a Field of NW Iran

DiveNet: Dive Action Localization and Physical Pose Parameter Extraction for High Performance Training

The tremendous progress of deep convolution neural networks has shown promising results on the classification of various sports activities. However, the accurate localization of a particular sports event or activity in a continuous video stream is still a challenging problem. The accurate detection of sports actions enables the comparison of different performances, objectively. In this work, we propose the DiveNet action localization module to detect the springboard diving sports action in an unconstrained environment. We used Temporal Convolution Network (TCN) over a backbone feature extractor to localize diving actions, with low latency. We estimate the divers center of mass (COM) trajectory and the peak dive height using the temporal demarcations provided by the action localization step via the projectile motion formula. In addition, we train a DiveNet pose regression network, which extends the Unipose architecture with direct physical parameter estimation, i.e COM and 2D joint keypoints. We propose a new homography computation method between the diving motion plane and the image-view for each dive. This enables the representation of physical parameters in metric scale, without any calibration. We release the first publicly available diving sports video dataset, recorded at 60 Hz with a static camera setup for different springboard heights. DiveNet action localization achieves an accuracy of 95% with a single frame latency (< 25 ms). The DiveNet pose regression model shows competitive results around 70% PCK on different diving pose datasets. We achieve COM accuracy of 6 pixels, dive peak height sensitivity of 20 cm and mean joint angle errors around 10 degrees.

Full-waveform inversion based on generalized Rényi entropy using patched Green's function techniques.

The estimation of physical parameters from data analyses is a crucial process for the description and modeling of many complex systems. Based on Rényi α-Gaussian distribution and patched Green's function (PGF) techniques, we propose a robust framework for data inversion using a wave-equation based methodology named full-waveform inversion (FWI). From the assumption that the residual seismic data (the difference between the modeled and observed data) obeys the Rényi α-Gaussian probability distribution, we introduce an outlier-resistant criterion to deal with erratic measures in the FWI context, in which the classical FWI based on l2-norm is a particular case. The new misfit function arises from the probabilistic maximum-likelihood method associated with the α-Gaussian distribution. The PGF technique works on the forward modeling process by dividing the computational domain into outside target area and target area, where the wave equation is solved only once on the outside target (before FWI). During the FWI processing, Green's functions related only to the target area are computed instead of the entire computational domain, saving computational efforts. We show the effectiveness of our proposed approach by considering two distinct realistic P-wave velocity models, in which the first one is inspired in the Kwanza Basin in Angola and the second in a region of great economic interest in the Brazilian pre-salt field. We call our proposal by the abbreviation α-PGF-FWI. The results reveal that the α-PGF-FWI is robust against additive Gaussian noise and non-Gaussian noise with outliers in the limit α → 2/3, being α the Rényi entropic index.

Quadrotor physical parameters online estimation

Online estimation of unmanned aerial vehicles' physical parameters is an essential tool for aerodynamic and control design. This paper numerically evaluates two methods for physical parameter estimation for a quadrotor. The estimation methods are based on recently introduced techniques that relax the persistency of excitation constraint in the least-squares estimation methods.

Input-state-parameter-noise identification and virtual sensing in dynamical systems: A Bayesian expectation-maximization (BEM) perspective

Structural identification and damage detection can be generalized as the simultaneous estimation of input forces, physical parameters, and dynamical states. Although Kalman-type filters are efficient tools to address this problem, the calibration of noise covariance matrices is cumbersome. For instance, calibration of input noise covariance matrix in augmented or dual Kalman filters is a critical task since a slight variation in its value can adversely affect estimations. The present study develops a Bayesian Expectation-Maximization (BEM) methodology for the uncertainty quantification and propagation in coupled input-state-parameter-noise identification problems. It also proposes the incorporation of input dummy observations for stabilizing low-frequency components of the latent states and mitigating potential drifts. In this respect, the covariance matrix of the dummy observations is also calibrated based on the measured data. Additionally, an explicit formulation is provided to study the theoretical observability of the Bayesian estimators, which helps characterize the minimum sensor requirements. Ultimately, the BEM is tested and verified through numerical and experimental examples, wherein sensor configurations, multiple input forces, and abrupt stiffness changes are investigated. It is confirmed that the BEM provides accurate estimations of states, input, and parameters while characterizing the degree of belief in these estimations based on the posterior uncertainties driven by applying a Bayesian perspective.

Surface catalytic mechanism-theoretical study under conditions of differential square-wave voltammetry

Differential square-wave voltammetry (DSWV) is the most recent modification of square-wave voltammetry (SWV) developed for advancing the performances of the technique for both analytical and kinetics applications. The differential current-measuring protocol in DSWV leads to improved voltammetric features of the forward and backward current components, in particular when slow i.e., quasireversible or irreversible electrode reactions are studied. In the present theoretical work catalytic electrode mechanism of surface bounded redox species (surface EC’ mechanism) is studied under conditions of the new technique, where E denotes the electrode reaction and C’ refers to the irreversible follow-up regenerative chemical reaction. Presented theoretical data provides a general overview of the EC’ reaction scheme, implying some specific voltammetric features which can be exploited for estimation of relevant physical parameters of the electrode reaction E and the regenerative chemical reaction C’.