Large Errors Research Articles

Application of CF and DMAS Technology to Improve the Quality of Reflector Images Reconstructed from Echoes Measured by an Antenna Array

Reliability and sensitivity of ultrasonic control is determined by the noise level of the reflector image and its resolution. Application of CF- or DMAS-technology in various combinations is promising, as these technologies are simple enough, practically do not require additional computational resources, are applied to echo signals measured by conventional flaw detectors working with antenna arrays. In numerical and model experiments it is demonstrated that the application of these methods allows to increase the resolution of reflector images more than twice and to reduce the noise level by more than 20 dB. In the numerical experiment it is shown that phase distortions due to complex refractive and reflection coefficients lead to the fact that even with precisely known parameters of the experiment when working on a direct beam on a transverse wave the indication of the crack tip can shift from its true position by about a wavelength. For the solution of defectometry tasks this is a very large error. But, if phase correction is performed when reconstructing the reflector image, the crack tip indication coincides with its real position. CF- and DMAS-technologies have shown their workability also when working with noisy echoes.

Denoising proton reference dosimetry spectrum using a large area ionization chamber - physical basis and type A uncertainty.


Reference dosimetry measurement in a pencil beam scanning system can exhibit dose fluctuation due to intra-spill spot positional drift. This results in a noisy reference dosimetry measurement against energy which could introduce errors in monitor unit calibration. The aim of this study is to investigate the impact of smoothing the reference dosimetry measurements on the type A uncertainty.
Methods:
The reference dosimetry measurement (D_w/MU) with a PTW 34045 advanced Markus chamber placed at 2 cm depth and a 10 x 10 cm^2 scanned field are performed for 98 energy layers on five non-consecutive days using a water tank. The PTW 34089 large area ionization chamber (LAIC) is placed at the same depth and the charges are measured with a single spot irradiation (M_spot^LAIC). (D_w/MU) and M_spot^LAIC are fitted with a linear and quadratic function to obtain a smooth plot of (D_w/MU) against the proton energy (reference dosimetry curve). Type A uncertainty of the measured reference dosimetry curve is compared against the de-noised fitted curve. 
Results:
The repeatability of reference dosimetry measurement shows relative difference of up to 2.3% across the five days. The linear and quadratic fits between LAIC charges and the (D_w/MU) from PTW 34045 show a high R^2 values of more than 0.95. The maximum type A uncertainty of the de-noised reference dosimetry curve is lower (0.69% at 70.2 MeV) compared to the measured one (0.88% at 77.5 MeV). However, the average type A uncertainty of the denoised curve across all energies is higher compared to the measurements (0.50% versus 0.43%).
Conclusion:
We have presented the physical basis and procedure for fitting the charges measured with a LAIC to the reference dosimetry curve. The fitted reference dosimetry curve avoids large error in any energy layer but increases the average type A uncertainty across energies and should be used with caution. 

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Investigation on the effect of probe stiffness on surface roughness in single crystal copper nanomachining process

Surface roughness is one of the most important features in nanoscale machining. In this paper, molecular dynamics simulations have been performed to investigate the combination effect of the tool stiffness, machining parameters and the tool tip geometry on the surface roughness of the single-crystal copper workpiece in the nano machining process by diamond tool. To consider the effects of probe beam dynamics in calculating the workpiece surface roughness, the probe cantilever is modeled as an equivalent spring-mass system that the tip mass is added to its end. The differential equation of the probe motion is solved simultaneously with the molecular dynamics equations for the tool tip and the workpiece and the effect of probe stiffness on the surface roughness is obtained. The result shows that a higher cutting speed and depth of cut and lower tool radius lead to a larger effect of probe stiffness on the surface roughness of the workpiece. Also, for the negative rake angles, considering the tool as rigid creates a large error in determining the surface roughness.

Contribution of radio occultation data to atmospheric river landfall forecasts in the eastern North Pacific

AbstractThis study assesses the impact of Global Navigation Satellite System radio occultation (RO) data assimilation (DA) on low‐predictability atmospheric river (AR) landfall forecasts. The period of study is October–March 2020–2022, focusing on AR events over the eastern North Pacific. Eighteen AR landfall events with large forecast errors in the Global Forecast System of the National Centers for Environmental Prediction are selected. Two numerical experiments are conducted for each AR event. One assimilates RO data using a non‐local excess phase operator to improve its initial condition (labeled as EPH forecast), while the other does not (reference run; labeled as REF forecast). Relative to REF, the root‐mean‐squared errors of integrated water vapor transport (IVT), moisture, and wind are reduced by EPH in the main AR activity areas throughout the whole forecast period, except for zonal winds. EPH significantly reduces the underestimation of strong IVT occurrence rate by 2.6%–4.8% and the underestimation of IVT intensity by 15.7–35.0 kg·m−1·s−1, compared to REF. EPH also substantially improves the precipitable water distribution and AR‐related precipitation over the ocean. Despite these basin‐wide improvements, AR coverage, location, and precipitation over land are not significantly affected by RO DA. Specifically, the main contribution of RO DA to the root‐mean‐squared error reduction of AR forecast is the wind improvement prior to AR landfall and the mid‐level moisture improvement after AR landfall. In summary, this study highlights the potential positive contributions of RO DA to AR forecasts, and explores the mechanisms and spatial and temporal characteristics of the improvements.

Machine Learning-Based Spectral Reconstruction for Equivalence Ratio Measurement in Premixed Air-Methane Flames Using RGB Imaging

ABSTRACT This paper introduced a machine learning-based method for reconstruction spectral information from RGB images for measurements of the equivalence ratio (Φ) of premixed air-methane flames. Digital color cameras capture color and spatial information of hydrocarbon premixed flames in the visible band. The color of the flame is the result of spectral integration of chemiluminescence in the visible wavelength band, and direct use for flame equivalence ratio measurements would result in large errors due to low spectral resolution. The mapping function was used to reconstruct the spectral characteristics of the premixed methane flame for the RGB image, which were built by three types of machine learning methods: support vector regression (SVR), random forest (RF) regression, and Levenberg-Marquardt backpropagation neural network (LM-BPNN), respectively. Finally, LM-BPNN-based reconstructed spectral features are used for the development of Φ measurement model, which measurement error below 0.01, accurately measures the equivalence ratio of premixed methane flames within the range of 0.73 to 1.47. Further analyze the two-dimensional spatial distribution of flame equivalence ratio.

On Large Amplitude Vibrations of the Softening Duffing Oscillator at Low Excitation Frequencies—Some Fundamental Considerations

The Duffing equation containing a cubic nonlinearity is probably the most popular example of a nonlinear oscillator. For its harmonically excited, slightly damped, and softening version, stationary large amplitude solutions at subcritical excitation frequencies are obtained when standard semi-analytical methods like Harmonic Balance or Perturbation Analysis are applied. These solutions have the shape of a nose in the amplitude-frequency diagram. In prior work, it has been observed that these solutions may contain large errors and that high ansatz orders may be necessary when applying the Harmonic Balance or other semi-analytical methods to make them converge. Some of these solutions are observed to be asymptotically stable, while in most cases, they are unstable. The current paper aims to give a descriptive explanation for this behavior of the nose solutions, which is mainly related to the exact solution of the free undamped vibrations. Based on this, approximations of the nose solutions are calculated with a procedure combining properties of Perturbation Analysis and Harmonic Balance. Therein, the exact solution of the free undamped vibrations is taken as the zeroth approximation, while higher-order solution parts are calculated by balancing the harmonics, and the phase shift of the zeroth approximation is calculated by a residuum minimization. This method just requires the solution of a system of linear algebraic equations, while systems of nonlinear algebraic equations have to be solved in the case of directly applying Harmonic Balance.

Subaperture moving strategy and related systematic errors in stitching interferometry of X-ray mirrors

For high-precision metrology of X-ray mirrors, the subaperture moving strategy significantly impacts the accuracy of stitching interferometry. In this work, we investigated the systematic errors associated with various subaperture moving strategies of rotational stitching (RS), rotational displacement stitching (RDS), and displacement stitching (DS) in measuring X-ray mirrors. The effects of stitching strategies on a flat mirror and a spherical mirror with a radius of curvature (RoC) of 180 m and 60 m were compared. For the mirror with RoC of 60 m, the DS strategy showed the smallest height error of 3.3 nm (RMS) between algorithm-based stitching and the Nanometer Optical component Measuring machine (NOM), while RS showed a large error of 37.4 nm (RMS). The different regions of the interferometer aperture have different amplitudes of retrace errors, defocusing errors, and lateral distortion, which can lead to accumulations of systematic errors using the RS strategy. By reducing the pixel size from 0.268 mm/pixel to 0.089 mm/pixel, the measurement accuracy using the same RDS strategy improved from 7.4 nm (RMS) to 4.4 nm (RMS). Based on the optimal stitching strategy, the measurement accuracy of residual figure error of the X-ray mirror with a radius of curvature of 30 m reaches 0.2 nm (RMS).

Potential of light-cone sum rules without semiglobal quark-hadron duality

This work addresses the calculation of local form factors involved in the theoretical predictions of semileptonic B-meson decays at low q2. We present a new approach to the method of QCD light-cone sum rule with B-meson light-cone distribution amplitudes. In our strategy, we bypass the semiglobal quark-hadron duality (QHD) approximation which usually contributes an unknown and potentially large systematic error to the prediction of form factors. We trade this improvement for an increased reliance on higher-order contributions in perturbation theory. Unlike the systematic error from semiglobal QHD, truncation errors are assessable and systematically improvable, hence allowing robust predictions of form factors. For the transitions B→π,ρ,K(*), our predictions agree with the literature for all form factors at q2=0. Published by the American Physical Society 2024

Static Map Construction Based on Dense Constraints and Graph Optimization

The construction of scene point-cloud maps is an important prerequisite for the registration-based localization of autonomous vehicles. In order to address the issues of the large cumulative error and low utilization efficiency of sensing information in existing SLAM methods, this paper proposes an offline static point-cloud map construction method. The key frames are described in the form of local maps, and after removing dynamic objects from the local map, it is used for inter-frame registration in a parallelized manner. The poses generated through registration are then used to construct dense constraints for global graph optimization, ultimately resulting in a global point-cloud map. The proposed method is evaluated in both simulated and real-world environments, demonstrating its feasibility.

DebSDF: Delving Into the Details and Bias of Neural Indoor Scene Reconstruction.

In recent years, the neural implicit surface has emerged as a powerful representation for multi-view surface reconstruction due to its simplicity and State-of-the-Art performance. However, reconstructing smooth and detailed surfaces in indoor scenes from multi-view images presents unique challenges. Indoor scenes typically contain large texture-less regions, making the photometric loss unreliable for optimizing the implicit surface. Previous work utilizes monocular geometry priors to improve the reconstruction in indoor scenes. However, monocular priors often contain substantial errors in thin structure regions due to domain gaps and the inherent inconsistencies when derived independently from different views. This paper presents DebSDF to address these challenges, focusing on the utilization of uncertainty in monocular priors and the bias in SDF-based volume rendering. We propose an uncertainty modeling technique that associates larger uncertainties with larger errors in the monocular priors. High-uncertainty priors are then excluded from optimization to prevent bias. This uncertainty measure also informs an importance-guided ray sampling and adaptive smoothness regularization, enhancing the learning of fine structures. We further introduce a bias-aware signed distance function to density transformation that takes into account the curvature and the angle between the view direction and the SDF normals to reconstruct fine details better. Our approach has been validated through extensive experiments on several challenging datasets, demonstrating improved qualitative and quantitative results in reconstructing thin structures in indoor scenes, thereby outperforming previous work.

A novel property to modify weighted [formula omitted] minimization for improved compressed sensing

Weighted l1 minimization schemes are common methods to achieve compressed sensing (CS). However, they fail in the presence of inaccurate prior knowledge or improper scaling of weights due to inappropriately assigned large weights causing large and destructive errors in signal recovery. This paper proposes a theory-based algorithm to identify and correct such destructive weights for each signal entry. The enhancement is achieved through a novel sparsity-inducing property (SIP) which establishes a necessary condition for successful signal recovery. SIP outperforms existing properties such as coherence, restricted isometry property, and nullspace property by indicating which signal entries fail to be recovered. This unique advantage enables us to correct destructive weights that do not satisfy the SIP condition, making signal recovery successful where it previously failed. Results from many numerical experiments demonstrate that our proposed method can improve the signal recovery capability, robustness, and stability of the weighted l1 minimization for a wide range of applications, including sparse and compressive signal recovery, noise-aware recovery, sparse error correction, fast image acquisition, and sub-Nyquist sampling.

Enhanced Accuracy in State-of-Charge Estimation for Lithium-Ion Batteries in Electric Vehicles Using Augmented Adaptive Extended Kalman Filter

Accurate assessment of state of charge (SoC) is crucial for the safety and efficiency of lithium-ion batteries used in electric vehicles (EVs). For precise SoC estimation in EV BMSs, an Augmented Adaptive Extended Kalman Filter (AAEKF) has been proposed in this research. To improve the precision of SoC estimation, AAEKF approach combines Kalman filtering methods with adaptive robust control concepts. The effectiveness of the AAEKF algorithm is shown by simulation results and confirmed by comparison with data from LiFePO4 battery research. The proposed AAEKF SoC estimation matches measurement results with errors of less than 3%. Adaptive Extended Kalman Filter (AEKF) is quite sensitive to the accuracy of the battery model, as errors in this region can lead to large estimate errors. Faster convergence to actual SoC, improved real-time responsiveness, accuracy, and resilience to disturbances and parameter fluctuations are all results of AAEKF's efficient handling of nonlinearities in battery systems, adaptation to system changes, errors (Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), and overall performance. The maximum estimate error stays under 5% at various external temperatures.

Multiple scale method integrated physics-informed neural networks for reconstructing transient natural convection

This study employs physics-informed neural networks (PINNs) to reconstruct multiple flow fields in a transient natural convection system solely based on instantaneous temperature data at an arbitrary moment. Transient convection problems present reconstruction challenges due to the temporal variability of fields across different flow phases. In general, large reconstruction errors are observed during the incipient phase, while the quasi-steady phase exhibits relatively smaller errors, reduced by a factor of 2–4. We hypothesize that reconstruction errors vary across different flow phases due to the changing solution space of a PINN, inferred from the temporal gradients of the fields. Furthermore, we find that reconstruction errors tend to accumulate in regions where the spatial gradients are smaller than the order of 10−6, likely due to the vanishing gradient phenomenon. In convection phenomena, field variations often manifest across multiple scales in space. However, PINN-based reconstruction tends to preserve larger-scale variations, while smaller-scale variations become less pronounced due to the vanishing gradient problem. To mitigate the errors associated with vanishing gradients, we introduce a multi-scale approach that determines scaling constants for the PINN inputs and reformulates inputs across multiple scales. This approach improves the maximum and mean errors by 72.2% and 6.4%, respectively. Our research provides insight into the behavior of PINNs when applied to transient convection problems with large solution space and field variations across multiple scales.

Unreported large errors in a common method for sound source localization of marine mammals.

Confidence intervals of location of calling marine mammals, derived from time differences of arrival (TDOA) between receivers, depend on errors of TDOAs, receiver location, clocks, sound speeds, and location method. Simulations demonstrate Ishmael, a TDOA locator based on uncorrected least squares minimization (ULSM), yields errors with mean, standard deviation, and maximum of 0.1, 0.2, and 0.9 km, respectively, due to sensitivity to inputs and numerical implementation when applied to scenarios with minuscule errors; e.g., five clock-synchronized receivers residing on the vertices of a square with one in its center. This sensitivity can mask other causes of location error due to small uncertainties in receiver location and sound speed. Realistic uncertainties of sound speed up to ±7.5 m/s lead to errors up to 4 km. With unsynchronized clocks and common practice of correcting TDOA from synchronization measurements at the start and end of an experiment, errors of location are 10 to 1000 km. These problems occur because ULSM was not designed to account for all errors. ULSM is also available in PAMGuard and other systems and is used to study behavior and abundance of calling marine mammals. ULSM is briefly compared to another method designed to account for errors.

Mathematical model and experimental verification of self-centering energy dissipative brace equipped with disc spring and wedge block

The novel self-centering energy dissipation brace composed of disc spring group and wedge block group (DW-SCB) is introduced. The working mechanism of the DW-SCB and the force mechanism of each component are described. The stress analysis of the disc spring group (DSG) and the wedge block group (WBG) were carried out, and the DW-SCB theoretical prediction model was established. Three specimens with different disc spring preload lengths were fabricated, tested, and discussed. Furthermore, the established theoretical model was verified, and the theoretical model correction method considering the processing and assembly errors of the DSG was proposed. The tested data show that the hysteresis curve of DW-SCB is a typical flag shape, and there is almost no residual displacement after multiple loading. An increase in the preload length of the disc spring increases the yield load and bearing capacity of the DW-SCB but has no change on the stiffness of DW-SCB. All the components of the three specimens were not damaged after multiple loadings, which show that the DW-SCB has stable multiple seismic performance and recoverability. Significant, although the machining error of the disc spring leads to a large error between the experimental and theoretical curve of the DSG, the error correction method of the DSG proposed in this study can improve the prediction accuracy of the theoretical model, making the DW-SCB theoretical prediction model more accurate and reliable. The theoretical model considering the error of DSG can better predict the stiffness and load of DW-SCB, which can provide a reference for engineering design and application.

Beam dynamics studies on a specific radio-frequency quadrupole injector for superconducting linacs

Radio-frequency quadrupole (RFQ) linear accelerators are widely used at the front end of large-scale hadron accelerator facilities owing to their extraordinary performance of progressive bunching, high-efficiency acceleration and low emittance growth. Meanwhile, it is worth noting that RFQ linacs gradually become the only normal conducting accelerators in modern continuous-wave high-power superconducting hadron linacs. As a result, high-quality beams at the exit of RFQ linacs with respect to more strict beam dynamics requirements are demanded, where minimizing the emittance growth is one of the most critical design targets. Not only a small rms emittance is required for an easy rms match to the following superconducting cavities but also a small total emittance is needed to reduce the beam loss in the superconducting cavities. In this paper, the design parameters of some typical RFQ injectors for superconducting linacs worldwide were compared, and the beam dynamics design of a 162.5 MHz, 2.1 MeV, continuous-wave RFQ linac was performed. Guided by the design approach called MEGLET (Minimizing Emittance Growth via Low Emittance Transfer), the beam dynamics design of the example RFQ injector was completed with high transmission efficiency, low emittance growth, and large error tolerance. The design result could be a reference for similar RFQ linacs, and the beam dynamics design approach is useful and applicable for other linacs intended to improve the figures of merit.

Tibial contact points cannot be used to determine internal-external axial rotation of the native tibiofemoral joint

BackgroundIn the study of tibiofemoral kinematics of the native knee, internal-external (IE) axial rotation is a motion of interest. Locations of contact by the femur on the tibia (termed tibial contact points) have been used to determine IE rotations but such rotations might not be useful due to large error. Hence, our objective was to determine whether tibial contact points are useful in quantifying IE rotations of the native knee. MethodFluoroscopic images of the native knee were analyzed from 25 subjects who performed a weight-bearing deep knee bend. For each subject, 3D bone + cartilage models were created. Following 3D model-to-2D image registration, anterior-posterior (AP) positions of the lowest points and the tibial contact points were computed for each femoral condyle at 0°, 30°, 60°, and 90° of flexion. IE rotations were the angles between lines connecting points in the medial and lateral tibial compartments at different flexion angles. ResultsBased on the lowest points, the tibia rotated internally on the femur primarily during the first 30° of flexion. In this range, mean internal tibial rotation based on tibial contact points was negligible but internal tibial rotation was significantly greater based on lowest points (0° vs 7°, p = 0.0002). At 90° of flexion, the difference was maintained (1.8° vs 8.3°, p = 0.0007). ConclusionWhile tibial contact points are useful in the study of wear of tibial inserts in total knee arthroplasty (TKA), tibial contact points considerably underestimate internal tibial rotation during flexion in the native knee and should not be used to quantify tibiofemoral kinematics.

Measurement of surface tension and contact angle of solar salt at high temperature with axisymmetric drop-shape analysis

As interfacial properties of common materials enabled many practical applications, those for molten salts would do the same. While their properties are important for the safety and design of thermal energy storage and modular nuclear reactors, they are hard to obtain because of the measurements done in confined spaces inside a furnace at high temperatures. Moreover, a small amount is preferred due to the high cost of salts. To overcome these limitations, an accurate method to measure surface tension, γ, and contact angles, θ, of molten salts were sought. As a steppingstone toward using more complex salts, solar salt was used because of safety and availability. Because molten salts typically show low θ, they provide large errors by incorrectly predicting volume and/or geometry. Therefore, various solid materials on which solar salts show high θ were sought and boron nitride was identified to provide θ > 60°. Later, solar salt sessile droplets were analyzed with the axisymmetric drop-shape analysis (ADSA) method to measure γ over a temperature range, which showed a good agreement with literature. Thus, this paper sets an experimental protocol that can be possibly used for other complex and hazardous salts that are scarcer and hard to handle. This paper extends the current knowledge by identifying solid surfaces on which the salts show low wettability for accurate measurements. Compared to other accurate methods, such as maximum bubble pressure method, this method used a small amount of salt (< 100 μL) while keeping a high accuracy.

The research explores the predictive capacity of the shear strength of reinforced concrete walls with different cross-sectional shapes using the XGBoost model.

Structurally, the lateral load-bearing capacity mainly depends on reinforced concrete (RC) walls. Determination of flexural strength and shear strength is mandatory when designing reinforced concrete walls. Typically, these strengths are determined through theoretical formulas and verified experimentally. However, theoretical formulas often have large errors and testing is costly and time-consuming. Therefore, this study exploits machine learning techniques, specifically the hybrid XGBoost model combined with optimization algorithms, to predict the shear strength of RC walls based on model training from available experimental results. The study used the largest database of RC walls to date, consisting of 1057 samples with various cross-sectional shapes. Bayesian optimization (BO) algorithms, including BO-Gaussian Process, BO-Random Forest, and Random Search methods, were used to refine the XGBoost model architecture. The results show that Gaussian Process emerged as the most efficient solution compared to other optimization algorithms, providing the lowest Mean Square Error and achieving a prediction R2 of 0.998 for the training set, 0.972 for the validation set and 0.984 for the test set, while BO-Random Forest and Random Search performed as well on the training and test sets as Gaussian Process but significantly worse on the validation set, specifically R2 on the validation set of BO-Random Forest and Random Search were 0.970 and 0.969 respectively over the entire dataset including all cross-sectional shapes of the RC wall. SHAP (Shapley Additive Explanations) technique was used to clarify the predictive ability of the model and the importance of input variables. Furthermore, the performance of the model was validated through comparative analysis with benchmark models and current standards. Notably, the coefficient of variation (COV %) of the XGBoost model is 13.27%, while traditional models often have COV % exceeding 50%.

Invited perspectives: safeguarding the usability and credibility of flood hazard and risk assessments

Abstract. Flood hazard and risk assessments (FHRAs) and their underlying models form the basis of decisions regarding flood mitigation and climate adaptation measures and are thus imperative for safeguarding communities against the devastating consequences of flood events. In this perspective paper, we discuss how FHRAs should be validated to be fit for purpose in order to optimally support decision-making. We argue that current validation approaches focus on technical issues, with insufficient consideration of the context in which decisions are made. To address this issue, we propose a novel validation framework for FHRAs, structured in a three-level hierarchy: process based, outcome based, and impact based. Our framework adds crucial dimensions to current validation approaches, such as the need to understand the possible impacts on society when the assessment has large errors. It further emphasizes the essential role of stakeholder participation, objectivity, and verifiability in assessing flood hazard and risk. Using the example of flood emergency management, we discuss how the proposed framework can be implemented. Although we have developed the framework for flooding, our ideas are also applicable to assessing risk caused by other types of natural hazards.