Error Model Research Articles

Analyzing method comparison data with skew-normal measurement error models: incorporating generalized scale mixtures and varying degrees of freedom

Comparing two measurement methods is vital in various fields, such as medical research, epidemiology, economics, and environmental studies, to determine whether a new measurement method can be used interchangeably with an existing one. Measurement error models (MEMs) are commonly used for this purpose, where the methods have different measuring scales. However, these models often assume normality, which can be problematic when dealing with skewed and heavy-tailed data. To address this issue, we propose the replicated measurement error model (RMEM) under scale mixtures of skew-normal (SMSN) distributions with different levels of skewness and heavy tails of the true covariate and error distributions. Our primary aim is to assess the extent of similarity and agreement between two measurement methods using this model. The expectation conditional maximization (ECM) approach is applied to fit the model. A simulation study is conducted to evaluate the effectiveness and robustness of the proposed methodology and is illustrated by analyzing systolic blood pressure data. The probability of agreement is used further to assess the agreement between the two measurement methods. The findings indicate that the proposed model effectively analyses replicated method comparison data with measurement errors, even when there are outliers, skewness, and heavy-tailedness.

The value of Sigma-metrics in laboratory medicine.

While Six Sigma is used in different disciplines to improve quality, Tony Badric and Elvar Theodorsson in a recent paper in CCLM have questioned Six Sigma application in medical laboratory concluding Six Sigma has provided no value to medical laboratory. In addition, the authors have expanded their criticism to Total Analytical Error (TAE) model and statistical quality control. To address their arguments, we have explained the basics of TAE model and Six Sigma and have shown the value of Six Sigma to medical laboratory.

Deep neural network-based prediction of tsunami wave attenuation by mangrove forests

The goal of this research is to develop a model employing deep neural networks (DNNs) to predict the effectiveness of mangrove forests in attenuating the impact of tsunami waves. The dataset for the DNN model is obtained by simulating tsunami wave attenuation using the Boussinesq model with a staggered grid approximation. The Boussinesq model for wave attenuation is validated using laboratory experiments exhibiting a mean absolute error (MAE) ranging from 0.003 to 0.01. We employ over 40,000 data points generated from the Boussinesq numerical simulations to train the DNN. Efforts are made to optimize hyperparameters and determine the neural network architecture to attain optimal performance during the training process. The prediction results of the DNN model exhibit a coefficient of determination (R2) of 0.99560, an MAE of 0.00118, a root mean squared error (RMSE) of 0.00151, and a mean absolute percentage error (MAPE) of 3%. When comparing the DNN model with three alternative machine learning models— support vector regression (SVR), multiple linear regression (MLR), and extreme gradient boosting (XGBoost)— the performance of DNN is superior to that of SVR and MLR, but it is similar to XGBoost.•High-accuracy DNN models require hyperparameter optimization and neural network architecture selection.•The error of DNN models in predicting the attenuation of tsunami waves by mangrove forests is less than 3%.•DNN can serve as an alternate predictive model to empirical formulas or classical numerical models.

Comparison of non-linear time series models (Beta-t-EGARCH and NARMAX models) with Radial Basis Function Neural Network using Real Data

This paper presents a comparison of three different non-linear time series modelling approaches: NARMAX (Non-linear Autoregressive Moving Average with Exogenous Inputs), Beta-t-EGARCH (Beta t Exponential Generalized Autoregressive Conditional Heteroscedasticity), and Radial Basis Function Neural Networks (RBFNN) applied to weekly stock market index data. We will explain three types of models and compare their compositions and structures. Then, we will show which model gives better predictions. To study series data, the comparison involved analysing the structure of the model and its errors in various time series models and summarising their findings. We divide the data into two parts: training data to structure the time series and testing. The training data tests the model's predictions. Then, we can analyse the model with the errors and the best deterrence predictions. After selecting the NARMAX and Beta-t-EGARCH models, we test them with specific criteria. The best choice is finding the model with the lowest average errors. For this study, we analysed the weekly average closing of the Aramco 2222 index from 15 December 2019 to 16 July 2023 and made 187 observations.

Comparison of non-linear time series models (Beta-t-EGARCH and NARMAX models) with Radial Basis Function Neural Network using Real Data

This paper presents a comparison of three different non-linear time series modelling approaches: NARMAX (Non-linear Autoregressive Moving Average with Exogenous Inputs), Beta-t-EGARCH (Beta t Exponential Generalized Autoregressive Conditional Heteroscedasticity), and Radial Basis Function Neural Networks (RBFNN) applied to weekly stock market index data. We will explain three types of models and compare their compositions and structures. Then, we will show which model gives better predictions. To study series data, the comparison involved analysing the structure of the model and its errors in various time series models and summarising their findings. We divide the data into two parts: training data to structure the time series and testing. The training data tests the model's predictions. Then, we can analyse the model with the errors and the best deterrence predictions. After selecting the NARMAX and Beta-t-EGARCH models, we test them with specific criteria. The best choice is finding the model with the lowest average errors. For this study, we analysed the weekly average closing of the Aramco 2222 index from 15 December 2019 to 16 July 2023 and made 187 observations.

Group-theoretic error mitigation enabled by classical shadows and symmetries

Estimating expectation values is a key subroutine in quantum algorithms. Near-term implementations face two major challenges: a limited number of samples required to learn a large collection of observables, and the accumulation of errors in devices without quantum error correction. To address these challenges simultaneously, we develop a quantum error-mitigation strategy called symmetry-adjusted classical shadows, by adjusting classical-shadow tomography according to how symmetries are corrupted by device errors. As a concrete example, we highlight global U(1) symmetry, which manifests in fermions as particle number and in spins as total magnetization, and illustrate their group-theoretic unification with respective classical-shadow protocols. We establish rigorous sampling bounds under readout errors obeying minimal assumptions, and perform numerical experiments with a more comprehensive model of gate-level errors derived from existing quantum processors. Our results reveal symmetry-adjusted classical shadows as a low-cost strategy to mitigate errors from noisy quantum experiments in the ubiquitous presence of symmetry.

Experimental and simulation study on the performance of a solar assisted multi-source heat pump drying system in Zhengzhou area

Zhengzhou of Henan province is an important agricultural production base. To improve the performance of the combined drying system and promote the development of agricultural products in Zhengzhou area, a solar assisted multi-source heat pump (SMSHP) drying system was proposed based on a traditional solar assisted air source heat pump (SASHP) drying system. The simulation models were developed for the air source heat pump (ASHP), SASHP, and SMSHP drying system respectively. The coefficient of performance, energy consumption and specific moisture extraction rate of the drying systems under different seasons were analyzed. Moreover, to validate the simulation models, an experimental platform of the combined drying system was designed and set up, and the experimental tests were carried out on the ASHP, SASHP, and SMSHP drying system. The simulation results revealed that the SMSHP drying system had lower energy consumption, higher coefficient of performance and specific moisture extraction rate than that of the SASHP drying system. The most significant performance enhancement was observed in spring, where the SMSHP system demonstrated a 22.69 % increase in coefficient of performance, a 19.2 % increase in specific moisture extraction rate, and a 16.11 % decrease in energy consumption compared to the SASHP drying system. Comparing with the experimental data, the simulation model errors of the energy consumption, coefficient of performance, and specific moisture extraction rate of the SMSHP drying system were 3.19 %, 0.28 %, and 2.27 %, respectively. Therefore, the accuracy of the simulation results was confirmed. To better calculate the heat provided by the solar collection system, the concept of the tank heating guarantee rate was first introduced, and it was found to be more scientific than the solar energy guarantee rate. At last, The economic and environmental benefit of the drying systems were analyzed during the life cycle, and the SMSHP drying system exhibited better comprehensive performance than the SASHP drying system. This paper provides valuable insights into the comprehensive utilization of solar energy and the control logic of solar assisted air source heat pump combined drying system under different seasons.

Nonlinear compartmental modeling to monitor ovarian follicle population dynamics on the whole lifespan.

In this work, we introduce a compartmental model of ovarian follicle development all along lifespan, based on ordinary differential equations. The model predicts the changes in the follicle numbers in different maturation stages with aging. Ovarian follicles may either move forward to the next compartment (unidirectional migration) or degenerate and disappear (death). The migration from the first follicle compartment corresponds to the activation of quiescent follicles, which is responsible for the progressive exhaustion of the follicle reserve (ovarian aging) until cessation of reproductive activity. The model consists of a data-driven layer embedded into a more comprehensive, knowledge-driven layer encompassing the earliest events in follicle development. The data-driven layer is designed according to the most densely sampled experimental dataset available on follicle numbers in the mouse. Its salient feature is the nonlinear formulation of the activation rate, whose formulation includes a feedback term from growing follicles. The knowledge-based, coating layer accounts for cutting-edge studies on the initiation of follicle development around birth. Its salient feature is the co-existence of two follicle subpopulations of different embryonic origins. We then setup a complete estimation strategy, including the study of structural identifiability, the elaboration of a relevant optimization criterion combining different sources of data (the initial dataset on follicle numbers, together with data in conditions of perturbed activation, and data discriminating the subpopulations) with appropriate error models, and a model selection step. We finally illustrate the model potential for experimental design (suggestion of targeted new data acquisition) and in silico experiments.

Предиктивное моделирование эксплуатационных свойств пеностекла с использованием моделей машинного обучения на основе линейной регрессии

Building construction requires the use of efficient thermal insulation materials such as foam glass in view of energy conservation. The paper considers predictive modelling of the performance properties of foam glass using machine learning models. The paper presents a mathematical description of the additives impact in the charge on the properties of foam glass. Nine charge compositions for foam glass synthesis were developed and their main microstructure parameters were determined. The authors tested the regression models using the JupyterNotebook software environment and the SciKit-Learn library in the Python programming language. The paper analyses the regression equation coefficients and estimates the modelling error. The obtained results confirm the effectiveness of predictive modelling of foam glass performance properties on the basis of linear regression.

The spatial spillover effect and its attenuation boundary of urban economy on port efficiency.

Cities are commonly recognized as the immediate hinterland of ports and play a crucial role in fostering the sustainable development of ports. Therefore, it is imperative to investigate the influence of cities on ports. By employing panel data from 2001 to 2021 for both ports and cities in the Bohai Rim region, this study examines the spatial spillover effect of urban economy on port efficiency using the spatial error model (SEM). The findings show that urban economies have a significant spatial spillover effect on port efficiency, but this effect diminishes across different spatial matrices. In particular, the geographical matrix demonstrates a stronger spatial spillover effect of the urban economy on port efficiency. These research findings help to establish a collaborative mechanism for port-city development and provide useful insights for government management decision-making.

A Novel Approach to Surface Roughness Virtual Sample Generation to Address the Small Sample Size Problem in Ultra-Precision Machining.

Surface roughness is one of the main bases for measuring the surface quality of machined parts. A large amount of training data can effectively improve model prediction accuracy. However, obtaining a large and complete surface roughness sample dataset during the ultra-precision machining process is a challenging task. In this article, a novel virtual sample generation scheme (PSOVSGBLS) for surface roughness is designed to address the small sample problem in ultra-precision machining, which utilizes a particle swarm optimization algorithm combined with a broad learning system to generate virtual samples, enriching the diversity of samples by filling the information gaps between the original small samples. Finally, a set of ultra-precision micro-groove cutting experiments was carried out to verify the feasibility of the proposed virtual sample generation scheme, and the results show that the prediction error of the surface roughness prediction model was significantly reduced after adding virtual samples.

A New Grid Zenith Tropospheric Delay Model Considering Time-Varying Vertical Adjustment and Diurnal Variation over China

Improving the accuracy of zenith tropospheric delay (ZTD) models is an important task. However, the existing ZTD models still have limitations, such as a lack of appropriate vertical adjustment function and being unsuitable for China, which has a complex climate and great undulating terrain. A new approach that considers the time-varying vertical adjustment and delicate diurnal variations of ZTD was introduced to develop a new grid ZTD model (NGZTD). The NGZTD model employed the Gaussian function and considered the seasonal variations of Gaussian coefficients to express the vertical variations of ZTD. The effectiveness of vertical interpolation for the vertical adjustment model (NGZTD-H) was validated. The root mean squared errors (RMSE) of the NGZTD-H model improved by 58% and 22% compared to the global pressure and temperature 3 (GPT3) model using ERA5 and radiosonde data, respectively. The NGZTD model’s effectiveness for directly estimating the ZTD was validated. The NGZTD model improved by 22% and 31% compared to the GPT3 model using GNSS-derived ZTD and layered ZTD at radiosonde stations, respectively. Seasonal variations in Gaussian coefficients need to be considered. Using constant Gaussian coefficients will generate large errors. The NGZTD model exhibited outstanding advantages in capturing diurnal variations and adapting to undulating terrain. We analyzed and discussed the main error sources of the NGZTD model using validation of spatial interpolation accuracy. This new ZTD model has potential applications in enhancing the reliability of navigation, positioning, and interferometric synthetic aperture radar (InSAR) measurements and is recommended to promote the development of space geodesy techniques.

Modeling and parameter identification of rate-dependent hysteresis behavior based on modified-generalized Prandtl–Ishlinskii model

The intrinsic characteristic of piezoelectric actuators (PEA), known as hysteresis, has been demonstrated to diminish the capability and stability of the system significantly. This paper proposes a modified-generalized Prandtl–Ishlinskii (MGPI) model to describe the rate-dependent hysteresis in piezoelectric actuators. The developed model incorporates a voltage change rate function to replace the first part of the generalized Prandtl–Ishlinskii (GPI) model. Additionally, the model integrates the cubic polynomial into the envelope function, along with the dynamic thresholds and weights. When describing the hysteresis of the piezoelectric actuator (PEA), the model parameters are identified using the Improved Grey Wolf Optimizer (IGWO) algorithm. To prevent the algorithm from getting trapped in local optima, the cubic chaotic mapping is utilized for population initialization, as well as a nonlinear convergence factor, and the Levy flight strategy factor is introduced to update the Wolf pack’s position. The rate-dependent hysteresis behavior of a PEA under excitation in the 1–200 Hz frequency range was experimentally measured. The measured data were used to demonstrate the validity of the proposed MGPI model. The relative root-mean-square error and the relative maximum error of the MGPI model are 1.41% and 6.00%, respectively, which are lower than those of the GPI model, which are 3.15% and 10.58%. Under the composite frequency driving, the outputs of the GPI model and MGPI model were compared with the measured data of the PEA, the results suggest that the MGPI model and the IGWO algorithm can more accurately describe the rate-dependent hysteresis of the piezoelectric actuators.

Beyond thinking fast and slow: Implications of a transtheoretical model of clinical reasoning and error on teaching, assessment, and research

From dual process to a family of theories known collectively as situativity, both micro and macro theories of cognition inform our current understanding of clinical reasoning (CR) and error. CR is a complex process that occurs in a complex environment, and a nuanced, expansive, integrated model of these theories is necessary to fully understand how CR is performed in the present day and in the future. In this perspective, we present these individual theories along with figures and descriptive cases for purposes of comparison before exploring the implications of a transtheoretical model of these theories for teaching, assessment, and research in CR and error.

Measurement error in longitudinal earnings data: evidence from Germany

We present evidence on the extent of measurement error in German longitudinal earnings data. Qualitatively, we confirm the main result of the international literature: longitudinal earnings data are relatively reliable in a cross section but much less so in first differences. Quantitatively, in the cross section our findings are very similar to those of Bound and Krueger (J Labor Econ 9:1–24, 1991) and Pischke (J Bus Econ Stat 13:305–314, 1995) for the United States while we find even stronger evidence that first-differencing exacerbates measurement error problems. We also show that measurement error in our survey data is not “classical” as it is negatively correlated with administrative earnings and positively autocorrelated over an extended period of time. Additionally, we estimate a model of measurement error stemming from underreporting of transitory earnings shocks in combination with a white-noise component and make a number of methodological contributions. Our results are robust to the use of two different linked survey-administrative data sets and various other sensitivity checks.

INCREASING THE ACCURACY OF THE POWER LINES MODELING BASED ON PMU

The article discusses the modeling of power lines based on PMU data to assess the state of overhead lines. Current approaches to solving the problem are analyzed. The advantages and disadvantages of these approaches are presented. It has been established that when modeling power lines are traditionally represented in the form of a - circuits scheme, which leads to methodological modeling errors. To reduce the methodological error of modeling, overhead lines (OHLs) can be presented in the form of equations with distributed parameters, which allows for maximum accuracy. However, distributed line equations contain hyperbolic functions and reduce performance in real-time simulations. Based on this, it is noted that the selection of an adequate mathematical model of overhead lines with the appropriate accuracy of synchronized vector measurements in real time when assessing the state of power lines is an important stage of modeling. The article discusses overhead line models using equations with distributed parameters, a -circuits, an equivalent - circuits and chain -circuits depending on the number of links. The results of calculating the mode based on the modeling accuracy using the example of a 500 kV overhead line are presented, as well as the results of comparing various equivalent circuits. It is shown that when modeling overhead line modes based on the results of PMU measurements, representing power lines as chain circuits with links 50 km or less in length is appropriate, since it makes it possible to obtain a model accuracy that is adequate to the PMU measurements. Methodological errors in modeling ultra-high voltage transmission lines using the equations of long lines have been studied. The obtained results can be used for monitoring, analysis and operational management of the electric power system

Numerical investigate on the heat transfer performance and mechanical analysis of a water sublimator

Water sublimators play a critical role in efficiently managing the high thermal load of spacecraft in a vacuum environment, ensuring their proper functioning. This article focuses on a novel cold plate water sublimator driven by a porous sublimation tube structure, which is still in the theoretical research stage. Numerical methods are employed to study the evaporation, freezing, and sublimation processes, with validation against experimental data. The average absolute error of the four operating conditions is 1.33 K, and the ice layer thickness error is 8.33 %. Additionally, a method to calculate the effective thermal conductivity of porous aluminum foam is proposed, with a model error of 9 %. The sublimator's heat transfer characteristics are significantly influenced by the porosity of the aluminum foam and sublimation tubes. The impacts on temperature behavior, ice thickness, feed water pressure, and stress are carefully examined. Results reveal aluminum foam porosity of 0.7 and sublimation tube porosity of 0.3. Moreover, the study explores how different heat loads affect the sublimator's operation mode. Heat loads above 2 W/cm2 cause evaporation and dryness within the sublimation tube, while excessively small heat loads lead to ice formation and increased stress on the aluminum foam structure. These findings provide valuable insights for water sublimator design.

Conglomerate rock interface debonding characteristics and damage evolution based on cohesive phase-field method

Rock failure often occurs due to uneven mechanical strength and discontinuous strain at the interface. However, due to the complex interaction mechanism between different phases of conglomerate, it is necessary to improve the crack growth model at the conglomerates' interface. In view of the physical structure and mechanical properties of conglomerate materials, this paper innovatively adopts the cohesive phase field model to study the material deformation and crack growth of conglomerate materials. Different from the traditional phase field model, this model characterises the real structure of the interface by adding an interface phase field, which is used to simulate the debonding of the interface during crack propagation and damage evolution. The regularization of the phase field is carried out to ensure the smoothness of the interface. The transition state of the interface region is determined by the element and the additional phase field. The energy equation is coupled with the crack propagation process using the crack surface density function. The evolution of the damaged phase field and displacement field is driven by the principle of energy minimization. The innovative assumption is that there is a virtual crack at the crack tip, and the interface related elements show a certain cohesive softening form during the damage evolution, which is the main way to realize the particularity of interface mechanics. By controlling the crack dispersion state and stiffness degradation of the material, several ideal softening models are innovatively assumed according to the cohesive force change law to satisfy the mechanical properties and stress function constraints. When cutting blocks n=2.5×104, the errors of the linear model, exponential model, and compound inverse model are 1.76%, 7.48%, and 5.19%, respectively. The results of the exponential model agree well with the stress strength (the error is 2%) and interface crack kinking angle (68.89°). The simulation results indicate that the deformation process of the conglomerate can be divided into three stages: elastic deformation stage, crack propagation stage, and stable load stage. The tensile strength of conglomerate is positively correlated with interface's fracture toughness and proportion of gravel area, respectively.

Switching diffusions for multiscale uncertainty quantification

High-fidelity simulations are increasingly called-upon to resolve the nucleation of instabilities such as crack initiation and propagation and fluid mixing. While multiscale modeling capabilities have progressed rapidly over recent decades, associated simulations still present a computational burden, thus often preempting a systematic statistical analysis of related problems. Given the paucity of data that relate micro-structure to macroscale behavior, and the unavoidable modeling errors at both scales, the lack of a probabilistic characterization significantly limits the value of highly-resolved numerical simulators. The goal of the present research is to develop a probabilistic model that encodes pathwise relationships between micro and macro scales. Specifically, we develop a switching diffusion model to relate damage evolution, characterized at the microscale, to system performance identified with a macroscale property. Microscale behavior is modeled as a Markov switching process with a finite state space while the macroscale counterpart is modeled as a continuous-state diffusion process. The interaction between macro and micro scales is captured by coupling these two processes. Calibrated by data, the switching diffusion model can generate, with minimal computational effort, sample paths with prescribed statistics. The proposed model contributes new capabilities and perspectives at the interface of multiscale simulation, uncertainty quantification, and stochastic modeling.

Active suspension hierarchical control with parameter uncertainty and external disturbance of electro-hydraulic actuators

In order to improve the ride comfort and handling stability of the electro-hydraulic active suspension system, a hierarchical control strategy is proposed. For the active suspension system with body mass uncertainty and safety constraints, an enhanced constraint adaptive backstepping controller is designed to generate the target force of body vertical motion. When dealing with constraints, nonlinear filters are introduced, backstepping technology and quadratic Lyapunov function are used to combine the control target and domain constraints, and the vertical displacement of the body and the suspension deflection control index are synthesized into a single controlled variable, so that the control variable converging to zero meets the suspension dynamic displacement limit. The ride comfort and safety of the active suspension system are improved. Secondly, stability analysis is conducted on the zero dynamic error system to ensure that all safety performance indicators are bounded. The effects of external disturbances, parameter uncertainties and modelling errors on the force tracking accuracy of asymmetric cylinder electrohydraulic systems are addressed. A backstepping dynamic surface control method based on a nonlinear perturbation observer is proposed, which estimates the composite perturbation terms such as modelling error and external perturbation, and uses the estimated value of the nonlinear observer to design a feedforward compensating control term to offset the effect of the composite perturbation on the system performance. In addition, the dynamic surface control method is used to calculate the derivative of the target force input, which avoids the derivative explosion phenomenon and reduces the computational complexity of the system. Simulation test results show that this method reduces the computational complexity of the system and improves the control performance. And under random and bumpy road conditions, the root-mean-square value of sprung mass acceleration performance index is reduced by 48.354 % and 72.677 % compared with passive suspension, which improves the ride comfort of the vehicle.