Reduction In RMS Error Research Articles

Enhanced single-frame interferometry via hybrid conv-transformer architecture for ultra-precise phase retrieval

Precise dynamic single-frame interferometry based on virtual phase shifting technique remains challenging due to the difficulty in satisfying the requirements for the quality and amount of fine-grained fringe’s interferograms. Here we introduce a novel deep learning architecture, the Transformer Encoder-Convolution Decoder Phase Shift Network (TECD-PSNet), that achieves high-fidelity interferogram reconstruction. TECD-PSNet seamlessly integrates the strengths of transformer blocks in capturing global descriptions and convolution blocks in efficient feature extraction. A key process is the incorporation of a residual local negative feedback enhancement mechanism that adaptively amplifies losses in high-error regions to boost fine-grained detail sensitivity. This approach enables accurate phase retrieval for diverse pupil shapes, enhancing adaptability to various optical setups, while significantly reducing the amount of training data required. Experiments demonstrate a 22.9% improvement in PSNR for reconstructed interferograms and a 36.7% reduction in RMS error for retrieved phases compared to state-of-the-art methods.

A Cubature Kalman Filter for parameter identification and output-feedback attitude control of liquid-propellant satellites considering fuel sloshing effects

The problem of parameter identification and output-feedback attitude stabilization is addressed for liquid-propellant satellites in the presence of fuel sloshing. The sloshing is described by a 2-degree-of-freedom pendulum. A torsional spring-damper system is utilized to model the flexibility of the diaphragm passive strategy and the fuel viscosity. By employing the control Lyapunov function methodology, a nonlinear backstepping-type state-feedback control law is constructed to stabilize the satellite and cancel the fuel sloshing. Then, to make the controller more feasible in practice, the required states of the controller, stiffnesses, and dampers are considered to be unavailable. Thus, a Cubature Kalman Filter is developed to estimate the system states and identify the fuel parameters. The state-feedback control law in conjunction with the Cubature Kalman Filter approach provides an output-feedback control law. This work owns the following two major novelties. One is identifying the stiffness and damping coefficients of the fuel and another is developing a Cubature Kalman Filter for estimating the resultant nonlinear system states. Lastly, comparative simulations verify and highlight the main results of the proposed methodology in relation to the existing methods. The findings suggest that the Cubature Kalman Filter offers potential advantages over the methods outlined in previous works, particularly in terms of reducing RMS error and improving state estimation convergence. The average reduction in RMS error for state convergence is 24 %, while the estimation process is found to be 63 % faster when compared to existing methods.

An in-silico NMR laboratory for nuclear magnetic shieldings computed via finite fields: Exploring nucleus-specific renormalizations of MP2 and MP3.

We developed and implemented a method-independent, fully numerical, finite difference approach to calculating nuclear magnetic resonance shieldings, using gauge-including atomic orbitals. The resulting capability can be used to explore non-standard methods, given only the energy as a function of finite-applied magnetic fields and nuclear spins. For example, standard second-order Møller-Plesset theory (MP2) has well-known efficacy for 1H and 13C shieldings and known limitations for other nuclei such as 15N and 17O. It is, therefore, interesting to seek methods that offer good accuracy for 15N and 17O shieldings without greatly increased compute costs, as well as exploring whether such methods can further improve 1H and 13C shieldings. Using a small molecule test set of 28 species, we assessed two alternatives: κ regularized MP2 (κ-MP2), which provides energy-dependent damping of large amplitudes, and MP2.X, which includes a variable fraction, X, of third-order correlation (MP3). The aug-cc-pVTZ basis was used, and coupled cluster with singles and doubles and perturbative triples [CCSD(T)] results were taken as reference values. Our κ-MP2 results reveal significant improvements over MP2 for 13C and 15N, with the optimal κ value being element-specific. κ-MP2 with κ = 2 offers a 30% rms error reduction over MP2. For 15N, κ-MP2 with κ = 1.1 provides a 90% error reduction vs MP2 and a 60% error reduction vs CCSD. On the other hand, MP2.X with a scaling factor of 0.6 outperformed CCSD for all heavy nuclei. These results can be understood as providing renormalization of doubles amplitudes to partially account for neglected triple and higher substitutions and offer promising opportunities for future applications.

Lithium-Ion Battery Parameter Identification for Hybrid and Electric Vehicles Using Drive Cycle Data

This paper proposes an approach for the accurate and efficient parameter identification of lithium-ion battery packs using only drive cycle data obtained from hybrid or electric vehicles. The approach was experimentally validated using data collected from a BMW i8 hybrid vehicle. The dual polarization model was used, and a new open circuit voltage equation was proposed based on a simplification of the combined model, with the aim of reducing the number of parameters to be identified. The parameter identification was performed using NEDC data collected on a rolling road dynamometer; the results showed that the proposed model improved the accuracy of terminal voltage estimation, reducing the peak voltage error from 2.16% using the Nernst model to 1.28%. Furthermore, the robustness of these models in maintaining accuracy when new drive cycles were used was evaluated by comparing WLTC simulations with experimental measurements. The proposed model showed improved robustness, with a reduction in RMS error of more than 50% compared to the Nernst model. These findings are significant because they will improve the accuracy of model-based battery management systems used in electric vehicles, allowing for improved performance prediction without the requirement of recharacterization for different drive cycles or individual cell characterization.

Vision-Guided MPC for Robotic Path Following Using Learned Memory-Augmented Model.

The control of the interaction between the robot and environment, following a predefined geometric surface path with high accuracy, is a fundamental problem for contact-rich tasks such as machining, polishing, or grinding. Flexible path-following control presents numerous applications in emerging industry fields such as disassembly and recycling, where the control system must adapt to a range of dissimilar object classes, where the properties of the environment are uncertain. We present an end-to-end framework for trajectory-independent robotic path following for contact-rich tasks in the presence of parametric uncertainties. We formulate a combination of model predictive control with image-based path planning and real-time visual feedback, based on a learned state-space dynamic model. For modeling the dynamics of the robot-environment system during contact, we introduce the application of the differentiable neural computer, a type of memory augmented neural network (MANN). Although MANNs have been as yet unexplored in a control context, we demonstrate a reduction in RMS error of 21.0% compared with an equivalent Long Short-Term Memory (LSTM) architecture. Our framework was validated in simulation, demonstrating the ability to generalize to materials previously unseen in the training dataset.

The Hearing-Aid Speech Perception Index (HASPI) Version 2

This paper presents a revised version of the Hearing-Aid Speech Perception Index (HASPI). The index is based on a model of the auditory periphery that incorporates changes due to hearing loss and is valid for both normal-hearing and hearing-impaired listeners. It is an intrusive metric that compares the time-frequency envelope and temporal fine structure (TFS) of a degraded signal to an unprocessed reference. The first modification to HASPI is an extension to the range of envelope modulation rates considered in the metric. HASPI applies a lowpass filter to the time-frequency envelope modulation, and in the new version this single filter is replaced by a modulation filterbank. The temporal fine structure (TFS) analysis in the original version of HASPI is replaced by the filterbank outputs at higher modulation rates that represent auditory roughness and periodicity. The second modification is replacing the parametric model combining envelope and TFS measurements used in the original version with an ensemble of neural networks. The improved version of HASPI is compared to the original version for datasets from five experiments that encompass noise and nonlinear distortion, frequency compression, ideal binary mask noise suppression, speech modified using a noise vocoder, and speech in reverberation. The new version of HASPI is shown to have a statistically-significant reduction in RMS error compared to the original version for most of the data considered, and to be significantly more accurate for speech in reverberation.

Method to calibrate a full-Stokes polarimeter based on variable retarders.

We present a calibration method for a full-Stokes polarimeter. The polarimeter uses two liquid-crystal variable retarders (LCVR) and a linear polarizer to measure the four Stokes parameters. The calibration method proposed in this paper calculates the errors in the experimental setup by fitting the experimental intensity measurements for a set of calibration samples to a theoretical polarimeter with errors. The errors calculated in the method include the axes alignment errors and the errors in the retardance values of both LCVRs. The resulting calibration parameters are verified by measuring the polarization state of a light beam passing through a rotating linear polarizer, a half-wave plate, and a quarter-wave plate and comparing with the predictions for an ideal, error-free polarimeter. It is found that an average reduction in rms error of 55.8% can be obtained with the proposed method.

Modeling astronomical adaptive optics performance with temporally filtered Wiener reconstruction of slope data.

We build on a long-standing tradition in astronomical adaptive optics (AO) of specifying performance metrics and error budgets using linear systems modeling in the spatial-frequency domain. Our goal is to provide a comprehensive tool for the calculation of error budgets in terms of residual temporally filtered phase power spectral densities and variances. In addition, the fast simulation of AO-corrected point spread functions (PSFs) provided by this method can be used as inputs for simulations of science observations with next-generation instruments and telescopes, in particular to predict post-coronagraphic contrast improvements for planet finder systems. We extend the previous results presented in Correia and Teixeira [J. Opt. Soc. Am. A31, 2763 (2014)JOAOD60740-323210.1364/JOSAA.31.002763] to the closed-loop case with predictive controllers and generalize the analytical modeling of Rigaut etal. [Proc. SPIE3353, 1038 (1998)PSISDG0277-786X10.1117/12.321649], Flicker [Technical Report (W. M. Keck Observatory, 2007)], and Jolissaint [J. Eur. Opt. Soc.5, 10055 (2010)1990-257310.2971/jeos.2010.10055]. We follow closely the developments of Ellerbroek [J. Opt. Soc. Am. A22, 310 (2005)JOAOD60740-323210.1364/JOSAA.22.000310] and propose the synthesis of a distributed Kalman filter to mitigate both aniso-servo-lag and aliasing errors while minimizing the overall residual variance. We discuss applications to (i)analytic AO-corrected PSF modeling in the spatial-frequency domain, (ii)post-coronagraphic contrast enhancement, (iii)filter optimization for real-time wavefront reconstruction, and (iv)PSF reconstruction from system telemetry. Under perfect knowledge of wind velocities, we show that ∼60 nm rms error reduction can be achieved with the distributed Kalman filter embodying antialiasing reconstructors on 10m class high-order AO systems, leading to contrast improvement factors of up to three orders of magnitude at few λ/D separations (∼1-5λ/D) for a 0 magnitude star and reaching close to one order of magnitude for a 12 magnitude star.

Accounting for Correlated Observation Error in a Dual-Formulation 4D Variational Data Assimilation System

Appropriate specification of the error statistics for both observational data and short-term forecasts is necessary to produce an optimal analysis. Observation error stems from instrument error, forward model error, and error of representation. All sources of observation error, particularly error of representation, can lead to nonzero correlations. While correlated forecast error has been accounted for since the early days of atmospheric data assimilation, observation error has typically been treated as uncorrelated until relatively recently. Thinning, averaging, and/or inflation of the assigned observation error variance have been employed to compensate for unaccounted error correlations, especially for high-resolution satellite data. In this study, the benefits of accounting for nonzero vertical (interchannel) correlation for both the Advanced Technology Microwave Satellite (ATMS) and Infrared Atmospheric Sounding Interferometer (IASI) in the NRL Atmospheric Variational Data Assimilation System-Accelerated Representer (NAVDAS-AR) are assessed. The vertical observation error covariance matrix for the ATMS and IASI instruments was estimated using the Desroziers method. The results suggest lowering the assigned error variance and introducing strong correlations, especially in the moisture-sensitive channels. Strong positive impact on forecast skill (verified against both the ECMWF analyses and high-quality radiosonde data) is shown in both the ATMS and IASI instruments. Additionally, the convergence of the iterative solver in NAVDAS-AR can be improved by small modifications to the observation error covariance matrices, resulting in further reduction in RMS error.

Characterizing PUF disk passive air samplers for alkyl-substituted PAHs: Measured and modelled PUF-AIR partition coefficients with COSMO-RS.

Isomers of alkyl-substituted polycyclic aromatic hydrocarbons (PAHs) and dibenzothiophenes are modelled with COSMO-RS theory to determine the effectiveness and accuracy of this approach for estimation of isomer-specific partition coefficients between air and polyurethane foam (PUF), i.e., KPUF-AIR. Isomer-specific equilibrium partitioning coefficients for a series of 23 unsubstituted and isomeric alkyl-substituted PAHs and dibenzothiophenes were measured at 22 °C. This data was used to determine the accuracy of estimated values using COSMO-RS, which is isomer specific, and the Global Atmospheric Passive Sampling (GAPS) template approach, which treats all alkyl-substitutions as a single species of a given side-chain carbon number. A recently developed oligomer-based model for PUF was employed, which consisted of a 1:1 condensed pair of 2,4-toluene-diisocyanide and glycerol. The COSMO-RS approach resulted in a significant reduction in the RMS error associated with simple PAHs and dibenzothiophene compared with the GAPS template approach. When used with alkylated PAHs and dibenzothiophenes grouped into carbon-number categories, the GAPS template approach gave lower RMS error (0.72) compared to the COSMO-RS result (0.87) when the latter estimates were averaged within the carbon-number-based categories. When the isomer-specific experimental results were used, the COSMO-RS approach resulted in a 21% reduction in RMS error with respect to the GAPS template approach, with a 0.57 RMS error for all alkylated PAHs and dibenzothiophenes studied. The results demonstrate that COSMO-RS theory is effective in generating isomer-specific PUF-air partition coefficients, supporting the application of PUF-based passive samplers for monitoring and research studies of polycyclic aromatic compounds (PACs) in air.

Modeling of an Enhanced Rain Attenuation Prediction for Equatorial Region

Presented in this paper is an empirical model for long-term rain attenuation prediction on slant path. The slant path prediction model is derived using data collected from tropical region and the formula proposed is based on Gaussian distribution. The proposed model shows a significant improvement over the existing prediction models with considerable reduction in RMS error as compared with other testing models.

Estimation of wind forcing and analysis of near-inertial motions generated by a storm in a submarine canyon with an ensemble Kalman filter

The impact of high-frequency winds on the generation and propagation of inertial currents in the Palamós submarine canyon (northwestern Mediterranean) during a severe storm on 9–16 November 2001 is evaluated in an ocean circulation model. Moored current meter time series collected in and around the canyon during the storm are assimilated with an ensemble adjustment Kalman filter (EAKF) to adjust wind forcing through a simultaneous state and parameter estimation approach. Winds are included as time-dependent parameters, which are updated in each assimilation step as part of the model state. A simulation forced by the estimated wind significantly improves simulation with winds from the atmospheric reanalysis (the RMS error reduction is about 50%). This is due to the higher energy for the estimated wind at inertial period in the clockwise rotating component, which enhances generation of near-inertial motions. The surface inertial energy however does not decay as rapidly compared to a simulation with data assimilation. It is suggested that the submesoscales, which are present in the data assimilated simulation, are effective in channeling a spatially heterogeneous vertical propagation of near-inertial motions.

AN ASSESSMENT OF VIDEO-BASED BATHYMETRY ESTIMATION IN A MACRO TIDAL ENVIRONMENT

This paper investigates the utility of video systems for estimating coastal bathymetry in a highly energetic and strongly macro-tidal environment using a linear wave theory inversion approach (cBathy). cBathy has been improved recently (Holman, et al. 2013), introducing a non-linear fit procedure of a phase structure over an area around a point of interest and a Kalman filter. cBathy has been extensively tested in micro/meso tidal environments but never in a macro tidal environment. Now the model is put to the test for the first time in these tidal conditions at Porthtowan, Cornwall in the United Kingdom. The combination of the macro-tidal range and energetic waves means that the accuracy of cBathy is compromised. A novel solution for macro tidal areas is presented in this paper where the pixels lose their fixed position and float around based on the corresponding tidal elevation. cBathy and the modified code are subsequently compared to topographical RTK-GPS measurements covering the inter-tidal area during spring tide. Depending on the tide and waves, the accuracy of the depth estimates varies, but during spring tide (6-7 metre tidal range) the results with the tidal correction are improved significantly. A reduction in RMS-error of 25% for the inter-tidal zone can be found in the most extreme cases.

Attenuated second-order Møller–Plesset perturbation theory: performance within the aug-cc-pVTZ basis

Attenuated second-order Møller-Plesset perturbation theory (MP2) within the finite aug-cc-pVTZ (aTZ) basis set is developed for inter- and intra-molecular non-bonded interactions. A single attenuation parameter is optimized on the S66 database of 66 intermolecular interactions, leading to a very large RMS error reduction by a factor of greater than 5 relative to standard MP2/aTZ. Attenuation introduces an error of opposite sign to basis set superposition error (BSSE) and overestimation of dispersion interactions in finite basis MP2. A variety of tests including the S22 set, conformer energies of peptides, alkanes, sugars, sulfate-water clusters, and the coronene dimer establish the transferability of the MP2(terfc, aTZ) model to other inter and intra-molecular interactions. Direct comparisons against attenuation in the smaller aug-cc-pVDZ basis shows that MP2(terfc, aTZ) often significantly outperforms MP2(terfc, aDZ), although at higher computational cost. MP2(terfc, aDZ) and MP2(terfc, aTZ) often out-perform MP2 at the complete basis set limit. Comparison of the two attenuated MP2 models against each other and against attenuation using non-augmented basis sets gives insight into the error cancellation responsible for their remarkable success.

Risk Score Inference for Bridge Maintenance Project Using Evolutionary Fuzzy Least Squares Support Vector Machine

Bridges are essential infrastructure of the transportation network. Therefore, maintenance tasks are mandatory to prevent these structures from degradation over time. In practice, funding availability for maintenance projects is often confined and this necessitates prioritization of different bridges that are in need of remedial activities. The writers aim to construct an artificial intelligence (AI) approach, evolutionary fuzzy least-squares support-vector machine (LSSVM) inference model (EFLSIM), for prioritizing bridges based on risk scores (RSs). In EFLSIM, fuzzy logic (FL) is utilized to enhance the capability of approximate reasoning and to deal with subjective information, which is obtained from human judgment. The inference model employs LSSVM as a supervised learning technique to infer the fuzzy input-output mapping relationship. Differential evolution (DE) is integrated into the model to optimize its tuning parameters. Experimental results and comparison illustrates that EFLSIM can successfully absorb and simulate human knowledge in the bridge-assessment process. Additionally, the newly built model has outperformed other benchmark approaches in terms of both reliability and accuracy. A 10-fold cross-validation process has demonstrated that the EFLSIM has achieved more than 38% reduction in RMS error compared to other benchmark methods. Thus, the proposed AI approach is a promising tool to support decision-makers in bridge-maintenance planning.

Operational implementation of a hybrid ensemble/4D‐Var global data assimilation system at the Met Office

AbstractWe describe the development and testing of the hybrid ensemble/4D‐Var global data assimilation system that was implemented operationally at the Met Office in July 2011, giving an average reduction of RMS errors of just under 1%. The scheme uses the extended control variable technique to implement a hybrid background error covariance that combines the standard climatological covariance with a covariance derived from the 23‐member operational ensemble MOGREPS‐G. Unique features of the Met Office scheme include application of a horizontal ‘anti‐aliasing’ filter to the ensemble error modes, a vertical localization scheme based uniquely on a modification of the climatological stream function covariance, and inflation of the climatological covariance to maintain the analysis fit to observations. Findings during development include a significantly greater impact of the scheme in 3D‐Var than 4D‐Var, a clear positive impact from the combination of the anti‐aliasing filter and vertical localization, and a relatively small sensitivity to full coupling of the ensemble and 4D‐Var systems. Supplementary experiments suggest that the ability of the ensemble to capture coherent ‘Errors of the Day’ is key to the improvements in forecast skill.A particular problem encountered during development was significantly poorer tropical verification scores when measured against own analyses. In contrast, verification against independent (ECMWF) analyses gave scores that were much more consistent with those against observations.

Role of convection and diffusion on DCE-MRI parameters in low leakiness KHT sarcomas

The solid tumor is an abnormal environment that is resistant to systemically delivered drugs. Increased plasma leakiness and extracellular matrix density along with poor lymphatic function can result in interstitial flow that attenuates the effectiveness of therapeutics. This study expands upon a previously presented magnetic resonance (MR) imaging-based porous media model by investigating low permeability tumors, where interstitial flow may have increased effect on systemically delivered solutes. The solute transport of the porous media model is compared to that of experiment and the two-compartment model. Small non-necrotic tumors (n=3) were MR-imaged, serially, for 90min after a bolus injection of Gd-based contrast agent (CA). These data provided for the calculation of experimental CA concentration over 90min, while only early time points (15min) were used to create vascular permeability, Ktrans, maps for the porous media model. A Ktrans scale factor (range=1.3–2.5) in the porous media model was found to account for the reduction of permeability (measured by two-compartment model) due to interstitial flow. The optimized porous media simulations showed: 1) better dynamic CA behavior agreement with the experimental data than the two-compartment model (>33% reduction of RMS error); 2) similar spatial CA distribution trends across tumor with increased uptake at the tumor boundary.

Feature-specific structured imaging

We present a feature-specific imaging system based on the use of structured light. Feature measurements are obtained by projecting spatially structured illumination onto an object and collecting all the reflected light onto a single photodetector. Principal component features are used to define the illumination patterns. The optimal linear minimum mean-square error (LMMSE) operator is used to generate object estimates from the measured features. We study the optimal allocation of illumination energy into each feature measurement in the presence of additive white Gaussian detector noise and optical blur. We demonstrate that this new imaging approach reduces imager complexity and provides improved image quality in high noise environments. Compared to the optimal LMMSE postprocessing of a conventional image, feature-specific structured imaging provides a 38% rms error reduction and requires 400 times fewer measurements for a noise standard deviation of sigma = 2 x 10(-3). Experimental results validate these theoretical predictions.

Improvement in Determination of Ice Water Content from Two-Dimensional Particle Imagery. Part I: Image-to-Mass Relationships

Abstract Ice water content in natural clouds is an important but difficult quantity to measure. The goal of a number of past studies was to find average relationships between the masses and lengths of ice particles to determine ice water content from in situ data, such as those routinely recorded with two-dimensional imaging probes. The general approach in these past studies was to measure maximum length L and mass M of a dataset of ice crystals collected at a ground site. Linear regression analysis was performed on the logarithms of the data to estimate an average mass-to-length relationship of the form M = αLβ. Relationships were determined for subsets of the dataset based on crystal habit (shape) as well as for the full dataset. In this study, alternative relationships for determining mass using the additional parameters of width W, area A, and perimeter P are explored. A 50% reduction in rms error in the determination of mass relative to using L alone is achieved using a single parameter that is a combination of L, W, A, and P. The new parameter is designed to take into account the shape of the ice particle without the need to classify the crystals first. An interesting result is that, when applied to the test dataset, the same reduction in rms error is also shown to be achievable using A alone. Using A alone facilitates the reanalysis and improvement of the determination of ice water content from large existing datasets of two-dimensional images, because A is simply the number of occulted pixels in the digital images. Possible sources of error in this study are investigated, as is the usefulness of first segregating the particles into crystal habits.

Near‐surface retrieval of air temperature and specific humidity using multisensor microwave satellite observations

A new method for instantaneous retrievals of near‐surface specific humidity (Qa) and air temperature (Ta) over the oceans was developed by combining satellite microwave observations from Advanced Microwave Sounding Unit‐A (AMSU‐A), Special Sensor Microwave Imager (SSM/I), and Special Sensor Microwave Temperature Sounder (SSM/T‐2). Several retrieval methods based on linear regression are presented that include both single‐sensor and multisensor approaches. The most accurate linear regression algorithms combined AMSU‐A and SSM/I observations. The Qa and Ta retrieval methods had RMS differences of 0.83 g/kg and 1.53°C, respectively, relative to direct surface observations. These differences were found to be significantly lower than those computed from previously published algorithms applied to daily values. An independent validation data set showed that the current Qa retrieval method led to a reduction in RMS error of 0.5 g/kg and reduced bias at the lowest and highest Qa values. Likewise, the current Ta retrieval method had a reduction in RMS errors of 1.0°C from previously published methods. Improvements in both retrievals can be attributed to inclusion of the AMSU‐A 52.8 GHz channel, which helps distinguish variations near the surface from those at higher levels of the atmosphere. Selection of a retrieval approach represents a trade‐off between accuracy, coverage, and temporal extent. Global coverage every 6 hours is approximately 80% using two NOAA satellites; however, such coverage would rely on use of a single‐sensor AMSU‐A approach with somewhat degraded accuracy. A multisensor approach using AMSU‐A and SSM/I on the current NOAA and DMSP satellites will at best provide global coverage over 1 day.