Extrapolation Technique Research Articles

Intelligent RCS Extrapolation Technology of Target Inspired by Physical Mechanism Based on Scattering Center Model

In this paper, a technology named SCM−ANN combining physical scattering mechanisms and artificial intelligence is proposed to realize radar cross-section (RCS) extrapolation of non-cooperative conductor targets with higher efficiency. Firstly, an adaptive scattering center (SC) extraction algorithm is used to construct the scattering center model (SCM) for non-cooperative targets from radar echoes in the low-frequency band (LFB). Secondly, an artificial neural network (ANN) is constructed to capture the nonlinear relationship between the real LFB echoes and those reconstructed from the SCM. Finally, the SCM is used to reconstruct echoes in the high-frequency band (HFB), and these reconstructions, together with the trained ANN, optimize the extrapolated HFB RCS. For the SCM−ANN technology, physical mechanistic modes are used for trend prediction, and artificial intelligence is used for regression optimization based on trend prediction. Simulation results show that the proposed method can achieve a 50% frequency extrapolation range, with an average prediction error reduction of up to 40% compared with the traditional scheme. By incorporating physical mechanisms, this proposed approach offers improved accuracy and an extended extrapolation range compared with the RCS extrapolation techniques relying solely on numerical prediction.

Decision support system (DSS) for facilities rehabilitation and management (part 2): development of optimized intervention strategy

PurposeThis paper (Part 2 of 2) aims to expand the previously developed performance assessment model (PAM) into a decision support system (DSS) to produce optimized decisions on rehabilitation of facilities.Design/methodology/approachThe research includes a comprehensive PAM that integrates analytical hierarchy process-multi attribute utility theory methods for the physical, operational, and environmental aspects of the mosque, as described in Part 1 of the study. An optimized intervention tool is added to the PAM to complete the DSS development. The tool determines the optimum maintenance decisions that are in line with the budget and condition criticality. The deterioration measures are created using a deterministic straight-line extrapolation technique, and the intervention actions used in the optimization are specified through literature review and in-depth interviews with 11 experts. The optimization model was simplified into a user-friendly visual basic application and applied to an existing facility case study depicting its useful functionality.FindingsThe optimization model recommendation of interventions recovered the overall condition rating index (CRI) of the facility while maintaining budget constraints. The CRI in the first approach (minimum budget to achieve acceptable overall performance) was improved from 6.5 to 7.76, costing 275,850 SAR. While the second approach (Maximum recovery with a selected budget constraint, 300,000 SAR in this case) CRI was improved from 6.5 to 8.3, costing 299,850 SAR.Originality/valueThe presented DSS in this paper offers a tool that enables the rehabilitation and overall management of any facility by its managers. The DSS can also be further customized to fit the facility manager’s specific strategies and achieve desired objectives.

A Laboratory Study of the Effects of Size, Density, and Shape on the Wave‐Induced Transport of Floating Marine Litter

AbstractFloating marine litter is transported by several mechanisms, including surface waves. In studies of marine litter transport, the wave‐induced drift is set to be equal to the Stokes drift, corresponding to the Lagrangian‐mean wave‐induced drift of an infinitesimally small tracer. Large‐scale experiments are used to show how the wave‐induced drift of objects of finite size depends on their size, density, and shape. We observe increases in drift of 95% compared to Stokes drift for discs with diameters of 13% of the wavelength, up to 23% for spheres with diameters of 3% of the wavelength, whereas drift is reduced for objects that become submerged such as nets. We investigate what these findings may imply for the transport of plastic pollution in realistic wave conditions and we predict an increase in wave‐induced drift for (very) large plastic pollution objects. The different extrapolation techniques we explore to make this prediction exhibit a large range of uncertainty.

Panamax cargo-vessel excessive-roll dynamics based on novel deconvolution method

This study presents a state-of-the-art extreme-value-prediction methodology based on deconvolution that can be utilized in marine, offshore, and naval-engineering applications. First, a measured gust-windspeed dataset is utilized to illustrate the accuracy of the deconvolution method. Second, a real-time roll dynamics raw dataset measured onboard an operating loaded TEU2800 container vessel is analyzed, and the vessel motion data are measured during numerous trans-Atlantic crossings. The risk of container loss owing to excessive rolling motion is a key issue in cargo vessel transportation. The complex nonlinear and nonstationary characteristics of incoming waves and the associated cargo vessel movements render it challenging to accurately forecast excessive vessel roll angles. When a loaded cargo vessel sails through a harsh stormy environment, higher-order dynamic motion effects become evident and the effect of nonlinearities may increase significantly. Meanwhile, laboratory testing are affected by the wave parameters and similarity ratios used. Consequently, raw/unfiltered motion data obtained from cargo vessels traversing in adverse weather conditions provide valuable insights into cargo vessel reliability. Parametric extrapolations based on certain functional classes are typically employed to extrapolate and fit probability distributions estimated from the underlying dataset. This investigation aims to present an alternative nonparametric extrapolation methodology based on the intrinsic properties of the raw underlying dataset without introducing any assumptions regarding the extrapolation functional class.This novel extrapolation deconvolution method is suitable for contemporary marine-engineering and design applications, as well as serves as an alternative to existing reliability methods. The prediction accuracy of the deconvolution methodology is demonstrated by comparing it with a modified four-parameter Weibull-type extrapolation technique. Compared with its counterpart sub-asymptotic statistical methods, such as the modified Weibull-type fit, peaks over the threshold, and generalized Pareto, the advocated deconvolution method is superior in term of its extrapolation numerical stability.

Overcoming Data Challenges in Population Forecasting at the Subnational Level: A Review

Population forecasts are a vital part of preparing development plans at the subnational level. Appropriate forecasting methods will determine the direction of regional development policy, but limited data regarding demographic issues, geographic issues, time period issues, or a combination of these three issues is a challenge. This research aims to systematically review previous research on population forecasting at the subnational level based on the methods used, challenges faced, and solutions implemented in order to provide recommendations for further research on how to solve the data problems faced. To answer this question, a "systematic literature review" was used to filter the literature using four criteria so that 27 papers were found. As a result, it was found that 71% of papers on the Asian continent experienced challenges with limited data and demographic issues. In comparison, 57% of papers from the Australian continent experienced challenges with a combination of geographic issues and time periods. The solutions used to overcome demographic issues are divided into two, each with 43% of papers completing data using data extrapolation techniques or determining appropriate methods according to data availability. Meanwhile, to overcome the combination of geographic issues with time periods, 43% of papers use a solution to compare methods to determine the appropriate method.

An efficient numerical approach for singularly perturbed time delayed parabolic problems with two-parameters

ObjectivesThe main objective of this work is to design an efficient numerical scheme is proposed for solving singularly perturbed time delayed parabolic problems with two parameters.ResultsThe scheme is constructed via the implicit Euler and non-standard finite difference method to approximate the time and space derivatives, respectively. Besides, to enhance the accuracy and order of convergence of the method Richardson extrapolation technique is employed. Intensive numerical experimentation has been done on some model examples. Further, the layer behavior of the solutions is presented using graphs and observed to agree with the existing theories. Finally, error analysis of the scheme is done and observed that the proposed method is parameter uniform convergent with the order of convergence (Δt)2+h2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\left( (\\Delta t )^2+h^2\\right) $$\\end{document}

Investigation of a modified IEA 15MW wind turbine including a winglet with a new mid-fidelity acoustic toolchain

This paper presents the applicability of a new hybrid tool chain for wind turbine rotor turbulent boundary-layer trailing-edge noise (TBL-TEN) to complex rotor geometries. The research introduces a rapid, high-fidelity 2D CFD/CAA method, which enables the assessment of innovative low-noise profile designs concerning TBL-TEN, coupled with a mid-fidelity extrapolation technique for the 3D analysis, aiming to minimize computational time essential for design processes. This procedure is applied here to compare the changes in performance, load distribution and noise reduction if a winglet is added to a large-scale wind turbine, using the example of the IEA Wind TCP Task 47 15-MW-reference turbine [12]. In the first step, a 3D CFD calculation of the entire turbine is performed in order to derive the local flow parameters and thus the input variables for the 2D CAA. These results for the 2D TEN are then extrapolated for the entire turbine using the fast summation and extrapolation method TAP (Turbine acoustic prediction) [1]. As only data for the TEN is included here, only this noise source for the winglet can be analyzed. For a more complete comparison, a 3D scale-resolving simulation was set up for a static wind tunnel situation in order to be able to qualitatively classify the tip/vortex interaction noise that was not recorded by the procedure mentioned above.

Short-Term and Imminent Rainfall Prediction Model Based on ConvLSTM and SmaAT-UNet.

Short-term precipitation forecasting methods are mainly divided into statistical forecasting, numerical model-based forecasting, and radar image extrapolation techniques. The two methods based on statistical prediction and numerical model have the disadvantages of being unstable and generating large errors. Therefore, this study proposes the use of deep learning for radar image extrapolation for precipitation forecasting, in particular by developing algorithms for ConvLSTM and SmaAT-UNet. The ConvLSTM model is a fusion of a CNN (Convolutional Neural Network) and LSTM (Long Short-Term Memory network), which solves the challenge of processing spatial sequence data, which is a task that traditional LSTM models cannot accomplish. At the same time, SmaAT-UNet enhances the traditional UNet structure by incorporating the CBAM (Convolutional Block Attention Module) attention mechanism and replacing the standard convolutional layer with depthwise separable convolution. This innovative approach aims to improve the efficiency and accuracy of short-term precipitation forecasting by improving feature extraction and data processing techniques. Evaluation and analysis of experimental data show that both models exhibit good predictive ability, with the SmaAT-UNet model outperforming ConvLSTM in terms of accuracy. The results show that the performance indicators of precipitation prediction, especially detection probability (POD) and the Critical Success index (CSI), show a downward trend with the extension of the prediction time. This trend highlights the inherent challenges of maintaining predictive accuracy over longer periods of time and highlights the superior performance and resilience of the SmaAT-UNet model under these conditions. Compared with the statistical forecasting method and numerical model forecasting method, its accuracy in short-term rainfall forecasting is improved.

R-linear and strong convergence of Tseng methods for solving certain split and non-split problems

In the recent past, authors have fondly assumed the co-coercive condition for solving certain class of split inverse problems. Recently, some attempts have been made to relax this very strict condition, however, only few results exists in literature in such case. In this paper, we present some new Mann-type Tseng methods for solving split monotone variational inclusion and common fixed point problems for a finite collection of quasi-pseudocontractive operators. We prove strong convergence and R − linear convergence rate results of our methods, while the co-coerciveness property is dispensed with. Our methods incorporate the relaxation, inertial extrapolation and self-adaptive step size techniques to achieve better performance and faster convergence. With the aid of relevant numerical experiments, we showcase the performance profile of our methods in comparison with recent methods in literature.

A New Cyclic Cohesive Zone Model for Fatigue Damage Analysis of Welded Vessel

A new cyclic cohesive zone fatigue damage model is proposed to address the fatigue problem spanning high and low cycle stages. The new damage model is integrated with the damage extrapolation technique to improve calculation efficiency. The model's effectiveness in regulating the low-cycle fatigue evolution rate, overall fatigue damage evolution rate, and stress level at the fatigue turning point is assessed through the comparison of the S-N curves. The fatigue damage model's high precision is proved based on the minor deviation of stress at the turning point of the S-N curve from the actual scenario. Finally, the fatigue damage evolution is simulated considering the effects of pre-load pressure and welding residual stress. It is observed that laser welding induces a significant residual tensile stress, accelerating fatigue damage evolution, while compressive loading impedes fatigue damage progression.

Uridine 5'-Diphospho-glucuronosyltransferase 1A3 (UGT1A3) Prediction of Hepatic Clearance of Organic Anion Transporting Polypeptide 1B3 (OATP1B3) Substrate Telmisartan by Glucuronidation Using In Vitro-In Vivo Extrapolation (IVIVE).

The prediction of pharmacokinetic parameters for drugs metabolised by cytochrome P450 enzymes has been the subject of active research for many years, while the application of in vitro-in vivo extrapolation (IVIVE) techniques for non-cytochrome P450 enzymes has not been thoroughly evaluated. There is still no established quantitative method for predicting hepatic clearance of drugs metabolised by uridine 5'-diphospho-glucuronosyltransferases (UGTs), not to mention those which undergo hepatic uptake. The objective of the study was to predict the human hepatic clearance for telmisartan based on in vitro metabolic stability and hepatic uptake results. Telmisartan was examined in liver systems, allowing to estimate intrinsic clearance (CLint, in vitro) based on the substrate disappearance rate with the use of liquid chromatography tandem mass spectrometry (LC-MS/MS) technique. Obtained CLint, in vitro values were corrected for corresponding unbound fractions. Prediction of human hepatic clearance was made from scaled unbound CLint, in vitro data with the use of the well-stirred model, and finally referenced to the literature value of observed clearance in humans, allowing determination of the essential scaling factors. The in vitro scaled CLint, in vitro by UGT1A3 was assessed using three systems, human hepatocytes, liver microsomes, and recombinant enzymes. Obtained values were scaled and hepatic metabolism clearance was predicted, resulting in significant clearance underprediction. Utilization of the extended clearance concept (ECC) and hepatic uptake improved prediction of hepatic metabolism clearance. The scaling factors for hepatocytes, assessing the in vitro-in vivo difference, changed from sixfold difference to only twofold difference with the application of the ECC. The study showed that taking into consideration hepatic uptake of a drug allows us to obtain satisfactory scaling factors, hence enabling the prediction of in vivo hepatic glucuronidation from in vitro data.

TISE-LSTM: A LSTM model for precipitation nowcasting with temporal interactions and spatial extract blocks

Precipitation nowcasting has a profound impact on humanity and society, especially in areas with heavy rainfall, playing a central role in alerting against rainstorm disasters. At present, numerous deep learning-based methods have been proposed and proven superior to traditional radar echo extrapolation techniques like the Recurrent Neural Networks (RNNs). Our study introduces a novel precipitation forecasting model named TISE-LSTM, which can use the real image of the past half hour to predict the radar echo image of the next hour. By integrating the TIB and SEB into TISL-LSTM, we can alleviate the inherent issue observed in existing models, which is the decrease in forecast accuracy for high radar echo regions with extended extrapolation time. On both the CIKM23017 and AHEM real radar echo datasets, the performance of TISL-LSTM (including POD, FAR, CSI and HSS) is improved compared to the second-ranked model by 7.53%, 2.32%, 8.93%, 2.6%, and 14.84%, 6.18%, 8.92%, 18.12%, respectively, when the precipitation threshold is set to 40. Moreover, our model obtained an optimal MAE, MSE, SSIM score. Predicted images and the graphs of each metric over extrapolation time both demonstrate that our model accurately forecasts regions with high radar echoes even with a one-hour extrapolation.

A polynomial-correction Navier-Stokes characteristic boundary condition

In an effort to assess the efficacy of different non-reflecting boundary conditions (NRBCs) in high-order solvers, several NRBCs have been implemented in the context of the discontinuous Galerkin (DG) discretization. The methods considered are based on buffer zone techniques and boundary-imposed conditions. The former mainly utilizes the sponge layer (SL), the perfectly matched layer (PML) and the supersonic layer (SSL), while the latter uses a Riemann extrapolation (RE) technique and a novel polynomial-correction Navier-Stokes characteristic boundary condition (PC-NSCBC). The developed PC-NSCBC method reconstructs an explicit boundary state, instead of modifying the equations on the boundary or specifying ad hoc compatibility conditions on corners and edges. This property pairs well with a Riemann solver, as is typical in a DG method. Moreover, the boundary state can be assembled either from a one-dimensional perspective (NSCBC1D) or using partial knowledge of multi-dimensional effects (NSCBC3D).Four different benchmark problems are considered: a 1D linear acoustic pulse in a quiescent flow, a 2D non-linear pressure pulse in a quiescent flow, an isentropic vortex and a shear-flow vortex shedding instability. Results imply that mainly the PML performs better than most other approaches. This is especially true for acoustically-dominated flows. Alternatively, for flow conditions predominantly less acoustic in nature and strongly non-linear, instabilities in the PML may arise. The SL and SSL both suggest that a larger layer is required to deliver comparable results to the PML. The proposed (PC-)NSCBC methods demonstrate reasonable success with no instabilities reported, which is impressive given the comparatively less computational effort required. Partial inclusion of the transverse terms in the NSCBC3D noticeably enhances the output of the NSCBC1D, albeit further investigation is required to determine an optimal, universal expression for its transverse tuning coefficient.

Value of information from fatigue monitoring of the monopile foundation of an offshore wind turbine

Value of information (VoI) allows quantifying benefits of adopting structural health monitoring (SHM) even before implementation by altering the prior risk of structural failure using the probabilities of damage detection from SHM data. The VoI is the reduction in the expected failure cost when SHM data becomes available. In this study, an optimized time of SHM deployment for fatigue crack growth in an offshore wind turbine (OWT) monopile foundation is achieved by maximising VoI. The prior probabilities of failure are obtained from stochastic analysis of dynamic responses to random wind and waves characteristic of North Sea environment. Short-term stress time histories are statistically processed to determine the probability distribution functions of their extrema and ranges and statistical extrapolation techniques are then implemented to analyse the long-term probability distributions of axial stresses. The long-term stress ranges and numbers of cycles are determined by the rainflow count and used in fatigue reliability analysis. The probability of detecting a fatigue crack by SHM is modelled and used for calculating the OTW failure risk after repair is conducted based on SHM outcome. The optimal deployment time of the fatigue crack monitoring system for the analysed 5-MW OWT is determined as 21 years past erection.

Accelerated numerical solutions for discretized Black–Scholes equations

Abstract Accepted by: Aris Syntetos This study thoroughly investigates the efficiency of advanced numerical extrapolation methods aimed at enhancing the convergence of vector sequences in the realm of mathematical finance. Our focus lies in the application of polynomial extrapolation techniques to calculate finite difference solutions for the Black–Scholes (BS) equation—an indispensable model in options pricing. The performance of our algorithms undergoes rigorous evaluation through a comprehensive analysis involving both simulated and real-world data. Notably, our experiments uncover that a stochastic scheme, incorporating two extrapolation strategies and a random relaxation parameter, outperforms other proposed methods, excelling in both convergence and stability metrics. Our findings underscore the potential of this numerical extrapolation method to enhance the efficiency of financial calculations, particularly in the realm of option pricing. This innovation holds promise for refining financial models and addressing specific challenges within the field of mathematical programming, providing effective solutions to the primary computational bottlenecks commonly encountered in financial decision-making.

Transport coefficients of warm dense matter from Kohn-Sham density functional theory

We present a comprehensive study of transport coefficients including DC electrical conductivity and related optical properties, electrical contribution to the thermal conductivity, and the shear viscosity via ab initio molecular dynamics and density functional theory calculations on the “priority 1” cases from the “Second Charged-Particle Transport Coefficient Workshop” [Stanek et al., Phys. Plasmas (to be published 2024)]. The purpose of this work is to carefully document the entire workflow used to generate our reported transport coefficients, up to and including our definitions of finite size and statistical convergence, extrapolation techniques, and choice of thermodynamic ensembles. In pursuit of accurate optical properties, we also present a novel, simple, and highly accurate algorithm for evaluating the Kramers–Kronig relations. These heuristics are often not discussed in the literature, and it is hoped that this work will facilitate the reproducibility of our data.

Simultaneous variable selection and parameters estimation for longitudinal data subject to missingness and covariates measurement error

Longitudinal studies are indispensable to study the change over time in a response variable. The main challenge of such studies is the presence of missing values. Another challenge in these studies is that covariates may be subject to measurement error. In such studies, variable selection, especially if the data are subject to measurement error and missingness, is crucial. Variable selection may lead to biased results in case of ignoring the missing values. Also, measurement error in covariates can negatively affect the accuracy of the estimates if not treated properly. Variable selection for longitudinal data that suffers from missing values and measurement error in covariates is not well explored in literature. In this article, we propose and develop a simultaneous variable selection and parameter estimation method for longitudinal data that suffers from intermittent missing values and covariates measurement error. The penalized weighted generalized estimating equations is used to account for the missingness in the longitudinal response, and simulation selection extrapolation techniques is used to account for the covariate measurement error. A simulation study is conducted to assess the performance of the proposed method. Also, the applicability of the proposed method is demonstrated using the Longitudinal Internet Studies for Social sciences data.

Low cycle fatigue behavior and life prediction of a directionally solidified alloy

Low cycle fatigue behavior and life prediction of a directionally solidified alloy

Implementation of the Projective Quantum Eigensolver on a Quantum Computer.

We study the performance of our previously proposed projective quantum eigensolver (PQE) on IBM's quantum hardware in conjunction with error mitigation techniques. For a single qubit model of H2, we find that we are able to obtain energies within 4 millihartree (2.5 kcal/mol) of the exact energy along the entire potential energy curve, with the accuracy limited by both the stochastic error and the inconsistent performance of the IBM devices. We find that an optimization algorithm using direct inversion of the iterative subspace can converge swiftly, even to excited states, but stochastic noise can prompt large parameter updates. For the 4-site transverse-field Ising model at its critical point, PQE with an appropriate application of qubit tapering can recover 99% of the correlation energy, even after discarding several parameters. The large number of CNOT gates needed for the additional parameters introduces a concomitant error that, on the IBM devices, results in a loss of accuracy despite the increased expressivity of the trial state. Error extrapolation techniques and tapering or postselection are recommended to mitigate errors in PQE hardware experiments.

Improved Accuracy for Trotter Simulations Using Chebyshev Interpolation

Quantum metrology allows for measuring properties of a quantum system at the optimal Heisenberg limit. However, when the relevant quantum states are prepared using digital Hamiltonian simulation, the accrued algorithmic errors will cause deviations from this fundamental limit. In this work, we show how algorithmic errors due to Trotterized time evolution can be mitigated through the use of standard polynomial interpolation techniques. Our approach is to extrapolate to zero Trotter step size, akin to zero-noise extrapolation techniques for mitigating hardware errors. We perform a rigorous error analysis of the interpolation approach for estimating eigenvalues and time-evolved expectation values, and show that the Heisenberg limit is achieved up to polylogarithmic factors in the error. Our work suggests that accuracies approaching those of state-of-the-art simulation algorithms may be achieved using Trotter and classical resources alone for a number of relevant algorithmic tasks.