Error Model Research Articles

Population pharmacokinetic modeling of paired plasma–breast milk lamivudine data for estimation of infant exposure in breastfeeding mother–infant pairs

AbstractAround 1.2 million women living with HIV give birth annually, majority of whom will breastfeed their infants while receiving antiretroviral therapy (ART). Lamivudine, a component of first‐line ART regimens crosses from maternal plasma to breast milk, with measurable concentrations in some breastfed infants. Wide variability in plasma‐to‐breast milk transfer has been reported within‐ or across studies, probably due to differences in sampling framework. This work sought to characterize the milk‐to‐plasma transfer of lamivudine, quantify inter‐patient variability and associated factors, and predict exposure of a breastfed infant. We explored data from an observational pharmacokinetic study that included 35 Ugandan mothers and their infants. Mothers received lamivudine doses of 150 mg twice daily or 300 mg once daily as part of their antiretroviral regimen. Pharmacokinetic sampling was undertaken across two visits approximately 8 weeks apart, providing 248 maternal plasma, 256 breast milk‐, and 151 infant blood concentrations, measured across a 24‐h sampling interval. A one‐compartmental model best described the plasma disposition of lamivudine, with first‐order absorption, interindividual variability on clearance and volume of distribution, and a proportional residual error model. A lag in time of plasma‐to‐breast milk drug accumulation was described using an effect compartment model with a milk‐to‐plasma ratio of 1.77. An estimated daily infant dose of 179.3 μg/kg (range: 125.8, 282.3) closely predicted the observed infant steady‐state concentrations and translated into 3.34% (2.13, 7.20) and 3.35% (1.10, 7.15) of the standard daily maternal dose in visits 1 and 2, respectively. The established modeling framework can be extended to other drugs.

Anti-disturbance Adaptive Cruise Control Method Based on H∞ Robust Controller with Optimized Safe Velocity and Following Time Headway

Abstract To alleviate driver workload, many vehicles are now equipped with adaptive cruise control (ACC) systems. However, ACC performance can significantly deteriorate in real-world conditions due to disturbances such as sensor measurement errors, system uncertainties, and modeling errors. Therefore, an innovative anti-disturbance ACC method is proposed based on H∞ robust controller with optimized safe velocity and following time headway to overcome the above problems. Initially, we analyze the longitudinal following characteristics that are undergoing disturbances and establish the ACC system model. Then, the optimization models for the safe velocity and the following time headway during ACC are designed. Subsequently, based on H∞ theory, the design criterion of the H∞ robust controller for ACC is established by utilizing the linear matrix inequality technique. Finally, two typical experiment scenarios are given for hardware-in-the-loop simulation to demonstrate the effectiveness of the proposed method.

Research on Pose Error Modeling and Compensation of Posture Adjustment Mechanism Based on WOA-RBF Neural Network

This paper is aimed to address the issue of decreased accuracy in the ship block docking caused by the structural errors of posture adjustment mechanism. First, inverse kinematic analysis is performed to investigate the sources of static errors in the mechanism. Subsequently, based on the closed-loop vector method, a pose error model for the moving platform is established, which includes eight categories of error terms. The impact of various structural errors on the pose accuracy of the moving platform is then compared and analyzed under both single-limb and multi-limb configurations. Therefore, a compensation method based on the whale optimization algorithm optimized radial basis function neural network is proposed. By transforming pose errors into actuator length errors, it establishes a predictive model between the theoretical pose of the dynamic platform and actuator length errors. After optimizing the network parameters, it yields the actuator length compensation to correct the actual pose of the dynamic platform. Simulation and experimental results validate the effectiveness of this method in enhancing the motion accuracy of the parallel mechanism. The mean pose accuracy of the moving platform is improved by 85.07%, demonstrating a significant compensation effect.

Attitude Control System for Quadrotor Using Robust Monte Carlo Model Predictive Control

Monte Carlo Model Predictive Control (MCMPC) is a kind of non-linear Model Predictive Control (MPC) that determines control inputs using the Monte Carlo method. The Monte Carlo method used in MCMPC can handle discontinuous phenomena, and there have been reports of its application in control systems for quadrotors, where the objective is to maintain the stability of the attitude during collisions. However, as with conventional MPC, concerns remain about control performance degradation due to modeling errors and external disturbances such as unexpected wind gusts. In this study, we propose a novel Robust Monte Carlo Model Predictive Control (RMCMPC), which improves the robustness of MCMPC by considering the hypersurface known from the Sliding Mode Control (SMC) theory. The proposed RMCMPC is applied to a quadrotor attitude control system, and its effectiveness is validated through numerical simulations.

Multi‐timescale optimal scheduling of microgrids for generating new energy output scenarios based on correction error sampling intervals

AbstractMicrogrid can realize energy saving and emission reduction and multi‐energy complementation, but the fluctuation of renewable energy output and the error of day‐ahead scheduling will threaten the stability of microgrid operation. For this reason, this article proposes a microgrid multi‐timescale optimal scheduling method based on new energy output scenario generation. First, the microgrid framework of this article is introduced, and an energy cycle emission reduction model taking into account electricity‐to‐gas conversion is designed; second, a new energy prediction error model is established based on the prediction box and Gaussian hybrid model, and the scenario of wind and photovoltaic power generation is generated by correcting the sampling intervals for error sampling; and then, based on the day‐ahead scheduling plan, an intraday cooling, heating and electricity two‐layer rolling optimization model is established to correct the day‐ahead scenario. Finally, the example analysis shows that the cyclic emission reduction model can realize the recycling of resources, reduce the fuel cost and carbon emission, and the generation of scenarios for wind power can smooth out the fluctuation of new energy power, which, together with the intra‐day two‐layer rolling optimization, can reduce the day‐ahead scheduling error and improve the stability of microgrid operation.

Two-tier stochastic frontier analysis: heterogeneous error distributions and model selection

Two-tier stochastic frontier analysis: heterogeneous error distributions and model selection

High frequency radar error classification and prediction based on K-means methods

This study aims to characterize the high frequency radar and numerically simulated low-frequency filtered currents in the south-eastern Bay of Biscay (study area) using a K-means classification algorithm based on an improved Euclidean Distance calculation method that does not take missing values. The errors between observations and simulations was estimated and predicted based on this classification method. Results indicate that predominantly eastward (northward) currents over the Spanish (French) continental shelf/slope in winter and more variable currents in the west and south-west in summer. The model classification results for circulation characteristics are in relatively good agreement with HF radar results, especially for currents on the Spanish (French) shelf/slope. In addition, the probabilistic relationship between observed and modeled currents was explored, obtaining the probability of occurrence of modeled current groups when each group of observed currents occurs. Finally, predictions of model and observed current errors were made based on the classification results, and it was found that the predictions based on the classification of all data had the smallest errors, with a 17% improvement over the unclassified control experiment. This study provides a foundation for subsequent model error testing, forecast product improvement and data assimilation.

Study on the analytical verification method of geometric error models

Abstract Geometric error compensation is a widely used and effective approach for enhancing the accuracy of Coordinate Measuring Machines (CMMs) and Computer Numerical Control (CNC) machines. The research in this field primarily focuses on the challenging task of developing geometric error modelling techniques. Currently, there are several well-established methods for modelling geometric errors. However, there is scarce availability of methods that can validate the established geometric error models. Therefore, this article proposes an analytical validation method for geometric error models based on the error transformation equation. The principle of the method is elaborately explained, and its correctness is verified through specific model verification example. The method proposed in this article enables rapid and accurate model correction and iteration, effectively reducing the economic and time costs of model validation in the current geometric error compensation process. Therefore, the method provides guidance for standardising the modelling process and could be widely applied to various structural forms of measurement and machining systems.

Enhanced Forecasting of Global Ionospheric Vertical Total Electron Content Maps Using Deep Learning Methods

The ionospheric state holds significant implications for satellite navigation, radio communication, and space weather; however, precise forecasting of the ionosphere remains a formidable challenge. To improve the accuracy of traditional forecasting models, we developed an enhancement model based on the CODE and IRI forecasting methods, termed the Global Ionospheric Maps Forecast Enhancement Model (GIMs-FEM). The results indicated that by extracting the GIM features from existing forecasts and incorporating additional proxies for geomagnetic and solar activity, the GIMs-FEM provided stable and reliable forecasting outcomes. Compared to the original forecasting models, the overall model error was reduced by approximately 15–17% on the test dataset. Furthermore, we analyzed the model’s performance under different solar activity conditions and seasons. Additionally, the RMSE for the C1pg model ranged from 0.98 TECu in the solar minimum year (2019) to 6.91 TECu in the solar maximum year (2014), while the enhanced GIMs (C1pg) model ranged from 0.91 to 5.75 TECu, respectively. Under varying solar activity conditions, the RMSE of GIMs-FEM for C1pg (C2pg) ranged from 0.98 to 6.91 TECu (0.96 to 7.26 TECu). Seasonally, the GIMs-FEM model performed best in the summer, with the lowest RMSE of 1.9 TECu, and showed the highest error in the autumn, with an RMSE of 2.52 TECu.

Error Sensitivity Analysis of a 1T2R Kinematically Redundant Parallel Mechanism With Closed-Loop Chain

Abstract In this paper, a generalized method for error modeling of the spatial 1T2R three degrees-of-freedom kinematically redundant parallel mechanism with a closed-loop chain is proposed, which is based on the matrix differential method. First, the detailed process of generalized error modeling and error analysis are described. Based on the proposed method, the error model of the spatial 3PRR(RR)S-P (P—prismatic joint, R—revolute joint, S—spherical joint, and the underline indicates that the joint is the actuator) kinematically redundant parallel mechanism is established as an example, and the correctness of the error model is verified by combining forward with inverse kinematics. Then, the patterns affecting the output error of the moving platform are discussed for the case where the mechanism contains only static error or dynamic error, respectively. In addition, the error sensitivity indices are defined to evaluate the error sensitivity of the moving platform to different redundant parameters L4 under a certain pose. Finally, in order to identify the key error terms, the sensitivity of the output error of the mechanism to a single error term is analyzed. The results show that the error sensitivity of the spatial kinematically redundant parallel mechanism can be effectively reduced by adjusting the kinematically redundant parameters, so that the mechanism can maintain a low error sensitivity in a certain pose.

Deep evidential learning for radiotherapy dose prediction

Background:As we navigate towards integrating deep learning methods in the real clinic, a safety concern lies in whether and how the model can express its own uncertainty when making predictions. In this work, we present a novel application of an uncertainty-quantification framework called Deep Evidential Learning in the domain of radiotherapy dose prediction. Method:Using medical images of the Open Knowledge-Based Planning Challenge dataset, we found that this model can be effectively harnessed to yield uncertainty estimates that inherited correlations with prediction errors upon completion of network training. This was achieved only after reformulating the original loss function for a stable implementation. Results:We found that (i)epistemic uncertainty was highly correlated with prediction errors, with various association indices comparable or stronger than those for Monte-Carlo Dropout and Deep Ensemble methods, (ii)the median error varied with uncertainty threshold much more linearly for epistemic uncertainty in Deep Evidential Learning relative to these other two conventional frameworks, indicative of a more uniformly calibrated sensitivity to model errors, (iii)relative to epistemic uncertainty, aleatoric uncertainty demonstrated a more significant shift in its distribution in response to Gaussian noise added to CT intensity, compatible with its interpretation as reflecting data noise. Conclusion:Collectively, our results suggest that Deep Evidential Learning is a promising approach that can endow deep-learning models in radiotherapy dose prediction with statistical robustness. We have also demonstrated how this framework leads to uncertainty heatmaps that correlate strongly with model errors, and how it can be used to equip the predicted Dose-Volume-Histograms with confidence intervals.

Robust Driving Control Design for Precise Positional Motions of Permanent Magnet Synchronous Motor Driven Rotary Machines with Position-Dependent Periodic Disturbances

Position-dependent periodic disturbances often limit the accuracy and smoothness of the positional motion of permanent magnet synchronous motor (PMSM)-driven rotary machines. Because the period of these disturbances varies with the motion velocity of the rotary machine, spatial domain control methods such as spatial iterative learning control (SILC) and spatial repetitive control (SRC) have been proposed and applied to improve rotary machine motion control designs. However, problems with learning period convergence and rotary machine dynamics significantly affect transient motion, further constraining the overall motion performance. To address these challenges, this study developed a robust driving control (RDC) that integrates a robust control design with position-dependent periodic disturbance feedforward compensation, rotary machine dynamics compensation, and proportional–proportional integral feedback control. A position-dependent periodic disturbance model was developed using multiple position–sinusoidal signals and identified using a spatial fast Fourier transform. RDC compensates for disturbances and dynamics and considers the effects of model parameter uncertainty and modeling error on the stability of the control system. Several motion control experiments were conducted on a PMSM test bench to compare the RDC, SILC, and SRC. The experimental results demonstrated that although both SILC and SRC can effectively suppress position-dependent periodic disturbances, SILC provides slower position error convergence owing to the learning process, and SILC and SRC result in significant position errors because of the influence of the PMSM-driven rotary machine dynamics. RDC not only suppresses position-dependent periodic disturbances, but also significantly reduces position errors with a reduction rate of 90%. Therefore, the RDC developed in this study effectively suppressed position-dependent periodic disturbances and significantly improved both the transient-state and steady-state position-tracking performances of the PMSM-driven rotary machine.

Identification of heart rate dynamics during treadmill and cycle ergometer exercise: the role of model zeros and dead time

Background The response of heart rate to changes in exercise intensity is comprised of several dynamic modes with differing magnitudes and temporal characteristics. Investigations of empirical identification of dynamic models of heart rate showed that second-order models gave substantially and significantly better model fidelity compared to the first order case. In the present work, we aimed to reanalyse data from previous studies to more closely consider the effect of including a zero and a pure delay in the model. Methods This is a retrospective analysis of 22 treadmill (TM) and 54 cycle ergometer (CE) data sets from a total of 38 healthy participants. A linear, time-invariant plant model structure with up to two poles, a zero and a dead time is considered. Empirical estimation of the free parameters was performed using least-squares optimisation. The primary outcome measure is model fit, which is a normalised root-mean-square model error. Results A model comprising parallel connection of two first-order transfer functions, one with a dead time and one without, was found to give the highest fit (56.7 % for TM, 54.3 % for CE), whereby the non-delayed component appeared to merely capture initial transients in the data and the part with dead time likely represented the true dynamic response of heart rate to the excitation. In comparison, a simple first-order model without dead time gave substantially lower fit than the parallel model (50.2 % for TM, 47.9 % for CE). Conclusions This preliminary analysis points to a linear first-order system with dead time as being an appropriate model for heart rate response to exercise using treadmill and cycle ergometer modalities. In order to avoid biased estimates, it is vitally important that, prior to parameter estimation and validation, careful attention is paid to data preprocessing in order to eliminate transients and trends.

Prediction of building cooling load: an innovative design for GA-SVM and IG-SVM system for the estimation of hourly precision and computational fluctuation range

ABSTRACT Anticipating the loads that buildings will bear is a crucial strategy in the pursuit of energy efficiency and the reduction of emissions. In this paper, we introduce an interactive and comprehensive joint prediction model, specifically crafted to elevate the precision of forecasts related to building loads. The resultant Genetic Algorithm-Support Vector Machine (GA-SVM) prediction model is put into action to provide hourly predictions of cooling loads for buildings. In scenarios where the prediction of building cooling load (BCL) fluctuations becomes imperative, especially in the face of extreme weather conditions, the information granulation (IG) method is employed. The findings of this validation process unveil significant improvements. The coefficient of variation of root mean square error (CV-RMSE) and mean absolute percentage error (MAPE) for the GA-SVM model are reduced by 58.85% and 68.04%, respectively, when compared to the conventional SVM model. Furthermore, in a comparative analysis against three widely utilized prediction models, the SVM model demonstrates a reduction in CV-RMSE and MAPE ranging from 2.04% to 68.04%. The application of the joint prediction model showcases impressive results, with R 2 values ranging from 97.27% to 99.68%, MAPE ranging from 2.59% to 2.84%, and CV-RMSE ranging from only 0.0249 to 0.0319.

Load-bearing characteristics of surface conductor in deepwater gas hydrate formations

The surface conductor is the first structural casing in deepwater natural gas hydrate (NGH) development, bearing the top load while suspending the casings of various layers. NGH decomposition leads to formation settlement, changing the mechanical properties of the formation and reducing the bearing capacity of the surface conductor, threatening the safety and stability of the wellhead. Understanding the bearing characteristics of the surface conductor in the hydrate formation can guide the safe drilling operation in the field. By introducing the negative skin friction theory of pile foundations and based on conventional bearing capacity models, a method for calculating the bearing capacity of surface conductors in NGH formations was developed. Using an NGH drilling simulation apparatus, the accuracy of the bearing capacity theoretical model was verified, empirical coefficients under different conditions were obtained, and the influence of soil parameters, hydrate saturation, and decomposition temperature on the bearing capacity of surface conductors was quantified. The results indicated that compared to clay, sandy soils have higher porosity and significantly weakened strength after the decomposition of NGH; when the hydrate saturation in the formation is 20%, the reduction in bearing capacity of the surface conductor in sand exceeds 30%, and in clay soils, it decreases by 25% after complete decomposition of NGH; as the hydrate saturation increases, the reduction in the bearing capacity of surface conductors after decomposition becomes more significant. Verified through Experimentation, the error of the hydrate-bearing strata-bearing capacity model is around 10%. For short-term test production operations of NGH in water, the design depth for surface conductors is around 100 meters. These research results can provide a scientific theoretical basis for the design of conductor depth below the mud， and reduce operational risks.

Statistical Response of ENSO Complexity to Initial Condition and Model Parameter Perturbations

Abstract Studying the response of a climate system to perturbations has practical significance. Standard methods in computing the trajectory-wise deviation caused by perturbations may suffer from the chaotic nature that makes the model error dominate the true response after a short lead time. Statistical response, which computes the return described by the statistics, provides a systematic way of reaching robust outcomes with an appropriate quantification of the uncertainty and extreme events. In this paper, information theory is applied to compute the statistical response and find the most sensitive perturbation direction of different El Niño–Southern Oscillation (ENSO) events to initial value and model parameter perturbations. Depending on the initial phase and the time horizon, different state variables contribute to the most sensitive perturbation direction. While initial perturbations in sea surface temperature (SST) and thermocline depth usually lead to the most significant response of SST at short and long ranges, respectively, initial adjustment of the zonal advection can be crucial to trigger strong statistical responses at medium range around 5–7 months, especially at the transient phases between El Niño and La Niña. It is also shown that the response in the variance triggered by external random forcing perturbations, such as the wind bursts, often dominates the mean response, making the resulting most sensitive direction very different from the trajectory-wise methods. Finally, despite the strong non-Gaussian climatology distributions, using Gaussian approximations in the information theory is efficient and accurate for computing the statistical response, allowing the method to be applied to sophisticated operational systems. Significance Statement The purpose of this work is to better understand how El Niño–Southern Oscillation (ENSO) responds to changes in its initial state and internal dynamics or external forcings. A statistical quantification of this response allows for the comprehension of the triggering conditions and the effect of climate change on the occurrence frequency and strength of each type of ENSO event. Such a study also allows to detect the most sensitive perturbation directions, which has practical significance in guiding anthropogenic activities. The approach used to study the response in this work is through the framework of information theory, which allows for an unbiased and robust assessment of the statistical response that is not affected by the turbulent dynamics of the system.

Modelling motion energy in psychotherapy: A dynamical systems approach.

In this study we introduce an innovative mathematical and statistical framework for the analysis of motion energy dynamics in psychotherapy sessions. Our method combines motion energy dynamics with coupled linear ordinary differential equations and a measurement error model, contributing new clinical parameters to enhance psychotherapy research. Our approach transforms raw motion energy data into an interpretable account of therapist-patient interactions, providing novel insights into the dynamics of these interactions. A key aspect of our framework is the development of a new measure of synchrony between the motion energies of therapists and patients, which holds significant clinical and theoretical value in psychotherapy. The practical applicability and effectiveness of our modelling and estimation framework are demonstrated through the analysis of real session data. This work advances the quantitative analysis of motion dynamics in psychotherapy, offering important implications for future research and therapeutic practice.

Triple junction benchmark for multiphase-field models combining capillary and bulk driving forces

Abstract A benchmark problem is formulated which is well suited for the validation of mesoscopic phase-field models for grain-boundary migration in polycrystals. First, an analytical steady-state solution of the sharp moving boundary problem is derived for a symmetric lamellar structure, which is valid for arbitrary bulk driving forces and triple junction angles. Characteristic quantities are identified to reduce the parameter space which in turn allows a systematic comparison of simulations and analytical results. Various multiphase-field (MPF) formulations are compared which approximate the sharp interface problem in terms of a diffuse regularization. An interfacial thickness convergence study reveals that the model error is largely dependent on the ratio of bulk to interfacial stabilizing force as well as the underlying model formulation. An additional grid convergence study highlights the efficiency of a more advanced discretization scheme. The results can be used to guide the selection of appropriate models and to estimate the interface thickness and spatial resolution required to achieve a given accuracy target. The post-processing framework consists of a fully automated determination of well-defined metrics from the phase field simulation data, eliminating human bias and facilitating reproducibility. The corresponding code is made openly available to assist the materials science and engineering community in validating MPF, multi-order parameter and similar model developments. We believe that this work provides a reliable benchmark procedure to better understand the potentials and limitations of current multiphase-field models as well as alternative approaches.

Assessing the potential accuracy of a small-sized goniometer with extended dynamic range based on nuclear magnetic resonance

This paper evaluates potential accuracy characteristics of a small-sized goniometer based on nuclear magnetic resonance with an extended dynamic range. This required constructing a model of goniometer errors, estimating its accuracy based on this model, and formulating practical recommendations for the design of such a device based on the accuracy assessment. To evaluate the accuracy of a nuclear goniometer, a theoretical model was built that makes it possible to determine the optimal operating parameters of the cell gas mixture, the ranges of their permissible changes, the sensitivity of the goniometer, and the dependence of its characteristics on external and internal factors. In particular, the dependence of output signal of the device on the parameters of gas mixture and optical pumping has been determined. For a goniometer with a cell volume of 8 cm3, the optimum temperature is 130 °C, and the optimum intensity of the pumping radiation is 5 mW. The dependence of output signal on the measured angle of rotation was also established, as well as the noise and error dependence of the device on the permissible values of its parameters. Based on the model built, parameters of a goniometer with a cell volume of 8 cm3 were determined; the maximum angular sensitivity of such a goniometer with complete suppression of technical noise is δφsen=1.0 arcsec. The greatest contribution to the angle measurement error is from the instability of pumping power Ip (ΔIp/Ip=0.05) – 85 %, magnetic field B0 (ΔB0/B0=10-8) – 13 %, temperature T (ΔT/T=0,1) – 2 %. The goniometer under consideration corresponds to the medium accuracy class, δφtot≥10 arcsec. It could be used in optical manufacturing for operational control, calibration, and certification of optical products. To improve the angular accuracy of the goniometer, it is necessary to increase the stability of the laser pumping intensity

Data Quality and Data Governance: Investigating the Impact on Data Science Outcomes

Abstract: The purpose of this study is to evaluate the effects that high data quality and effective data governance have on the outcomes of data science, with a particular emphasis on the vital roles that these factors play in improving decision-making within businesses. The rising use of data science projects by enterprises for the purpose of digital transformation presents major hurdles, particularly with regard to the trustworthiness of data and compliance with appropriate legal frameworks. A high data quality improves model accuracy, decision-making efficiency, and error reduction, while good governance boosts compliance with rules, data security, and accessibility, according to the findings of the study, which were derived from an analysis of data collected from professionals working in data science and related sectors. These findings demonstrate the synergy that exists between data quality and governance, underscoring the fact that businesses may obtain more dependable and trustworthy outcomes by prioritizing these components, which in turn supports digital transformation programs that are effective and responsible.