Influence Of Measurement Errors Research Articles

THE INFLUENCE OF OBSERVATIONAL ERRORS IN GAIA DR3 ON THE RECONSTRUCTION OF GLOBULAR CLUSTER ORBITS ON A COSMOLOGICAL TIMESCALE

In recent years, the emerging field of astronomy focused on the history of galaxy formation, known as Galactic Archaeology, has been gaining popularity. Globular clusters have been involved in many key processes occurring in the Milky Way, making their study, particularly the reconstruction of their orbits, significantly important. The Gaia DR3 catalog provides parameters for 165 globular clusters, such as proper motions, radial velocity, and heliocentric distance, with certain accuracy. Therefore, it is important to examine the influence of measurement errors in these parameters on the initial data when converting to the Galactocentric coordinate system and, consequently, on the shape of the orbits. We integrated the orbits of globular clusters 10 billion years lookback. For physical justification during the integration, we used the external dynamic potential with the individual number 411321 from the cosmological simulation database IllustrisTNG-100, which best reproduces the potential of the Milky Way. The integration was performed using the parallel N-body code φ-GPU, based on a fourth-order Hermite scheme with hierarchical individual block timesteps. A total of 1,000 randomizations of the initial data were created considering a normal distribution of errors, and the influence of errors on the scatter of initial velocities and on the shape of the orbits was examined. The parameters with the largest relative errors are proper motions and radial velocity, while the smallest errors are in heliocentric distance. It was found that 85% of the globular clusters have relative errors in all parameters of no more than 10%, and 5.4% have errors of no more than 1%. Investigating the influence of measurement errors for clusters with different magnitudes of relative errors, we concluded that for most globular clusters, the influence of measurement errors on the shape of the orbits is not significant. Consequently, it is possible to reconstruct the orbits with high accuracy for these clusters. Since the reconstruction of globular cluster orbits involves cosmological timescales, accounting for measurement errors is an important aspect of the preparatory procedure before the main integration.

Functional partially linear single index models with measurement error in both functional and real covariates

In this paper, we investigate the functional partially linear single index models with contaminated functional and real covariates when the functional sample grid is dense. To eliminate the influence of measurement error on the estimations of the proposed models, a two-stage estimation procedure is introduced. In the first stage, we utilize a kernel presmoothing regression to denoise the corrupted functional curves. In the second stage, the fitted functional curves are applied to replace the original ones to obtain the predictor of unknown parameter in parametric component with the attenuation correction method and then we derive the predictor of unknown link function in nonparametric part with kernel method. Meanwhile, in order to improve computational efficiency, functional slice inverse regression method is employed to estimate the functional single index parameter. The asymptotic properties are also stated in this process at the situation of independent and identical distribution sample under some mild conditions. A simulation study and real data analysis are further explored to illustrate the good performance of our methodology.

ECD-CDGI: An efficient energy-constrained diffusion model for cancer driver gene identification.

The identification of cancer driver genes (CDGs) poses challenges due to the intricate interdependencies among genes and the influence of measurement errors and noise. We propose a novel energy-constrained diffusion (ECD)-based model for identifying CDGs, termed ECD-CDGI. This model is the first to design an ECD-Attention encoder by combining the ECD technique with an attention mechanism. ECD-Attention encoder excels at generating robust gene representations that reveal the complex interdependencies among genes while reducing the impact of data noise. We concatenate topological embedding extracted from gene-gene networks through graph transformers to these gene representations. We conduct extensive experiments across three testing scenarios. Extensive experiments show that the ECD-CDGI model possesses the ability to not only be proficient in identifying known CDGs but also efficiently uncover unknown potential CDGs. Furthermore, compared to the GNN-based approach, the ECD-CDGI model exhibits fewer constraints by existing gene-gene networks, thereby enhancing its capability to identify CDGs. Additionally, ECD-CDGI is open-source and freely available. We have also launched the model as a complimentary online tool specifically crafted to expedite research efforts focused on CDGs identification.

Convex model-based regularization method for force reconstruction

In the process of reconstructing structural forces, the influence of measurement errors and inherent model inaccuracies cannot be ignored. These errors exhibit a degree of correlation, and the presence of such correlation inevitably affects the quantification of uncertainties in force reconstruction. Objectively, the inherent ill-posed nature of structural inverse problems makes it difficult to obtain the forces to be identified, subject to uncertainties and highly susceptible to perturbations. Consequently, this paper introduces a force reconstruction regularization approach that explicitly considers the correlation of uncertainty parameters based on the truncated singular value regularization method. The primary objective is to refine the influence of uncertainties on the reconstructed force bounds with greater precision. When determining robust regularization parameters, the generalized cross-validation method and convex modelling approach are introduced to consider the uncertainty and its correlation in solving inverse problems. The proposed approach is rigorously validated through a comprehensive numerical case study. Error indexes and dispersion indices are employed to analyze the impact of different levels of noise and correlation on force reconstruction results. The force bounds obtained using the proposed method are compared with Monte Carlo simulation results. Finally, the validity of the proposed method is verified by an experiment with a four-story shear frame.

Robust compressed ghost imaging against environmental influence factors.

Ghost imaging based on sparse sampling is sensitive to the environmental influence factors frequently encountered in practice, such as instrumental drift and ambient light change, which could cause degradation of image quality. In this manuscript, we report a robust compressed sensing technique which could effectively reduce the influence of measurement errors on image quality. For demonstration purposes, we implement the proposed technique to ghost imaging, namely differential compressed sensing ghost imaging (DCSGI). By applying differential measurements n times, the first n Taylor expansion polynomials of the error could be eliminated in n-order DCSGI. It has been verified theoretically and experimentally that DCSGI works well with typical errors which exists in the realities of ghost imaging applications, while the conventional approach can hardly. In addition, the proposed technique may also replace conventional compressed sensing in other applications for anti-interference high-quality reconstruction.

Non-destructive Thermal Monitoring of Temperature and Flow Rate of the Heat Carrier in a Heating Device

Conducting various calculations for the heating system of a building under operational conditions is impossible without knowledge of the temperature and flow rate of the heat carrier at specific points. Contact measurement means are not always applicable, especially for small-diameter pipelines. Measurement techniques based on the theory of non-destructive thermal inspection allow for remote and timely acquisition of all necessary information. The object of the study is the floor node of the building's heating system. The subject of the study is the temperature and flow rate of the heat carrier passing through the heating device. The research aim is to determine the temperature and flow rate of the heat carrier passing through the heating device under operational conditions. The research method involves the theory of non-destructive thermal inspection, as well as heat balance and heat transfer equations for a vertical flat wall. Research findings reveal that during thermovisual inspection, the temperature of the heat carrier in the supply and return pipelines of the floor node respectively amounted to 51.6 ℃ and 49.6 ℃. The flow rate of the heat carrier through the heating device model MS-140M2-500, with a heat transfer coefficient of 8.5 W/(m²⋅K), was determined to be 195.0 kg/h. Recommendations include utilizing a thermal imager with an absolute measurement error not exceeding ±0.15 ℃ for determining the flow rate of the heat carrier through the heating device. The influence of measurement error of indoor air temperature on the accuracy of determining the heat carrier flow rate is insignificant (not exceeding ±2%). The relative error in determining the heat transfer coefficient of the heating device does not exceed ±1%.

Accelerated Degradation Reliability Modeling Based on Multiple Uncertainty Wiener Processes

Numerous scholars' studies usually adopt the Wiener process to establish industrial robot performance degradation models and randomize the drift coefficients to characterize the differences in individual degradation rates, but they do not take into account the influence of measurement errors on the accuracy of the models. Aiming at the above problems, combining the Wiener process and the accelerated degradation model, the accelerated degradation reliability model of industrial robots based on the multi-uncertainty Wiener process is established, and by adding the factor of measurement error, the model is able to more accurately characterize the influence of external factors on the degradation process of industrial robots. Bayesian theory, Markov chain Monte Carlo method and Bootstrap method are used to estimate the unknown parameters in the model. Finally, the accuracy of the proposed parameter estimation method and model is verified by simulating the industrial robot performance degradation data with Monte Carlo method.

INSTITUTIONAL EXPERIENCES OF RACIAL DISCRIMINATION AND COGNITIVE FUNCTION: INSTRUMENTAL VARIABLE ANALYSIS

Abstract In addition to enduring the ongoing and persistent legacy of structural racism in the United States, Black adults often encounter sources of social stress, like racial discrimination, that have documented associations with health outcomes. As inequities in cognitive aging persist for Black adults, research has worked to examine modifiable factors, like racial discrimination, that may serve as the basis for intervention development. The dysregulation of inflammatory biomarkers has been proposed as one pathway through which racial discrimination becomes embodied to contribute to racial inequities in cognitive function. We assessed how racial discrimination occurring in institutional settings affects cognitive function among a sample of Black and white adults from the Coronary Artery Risk Development in Young Adults (CARDIA) study. To mitigate the influences of measurement error and unmeasured or residual confounding on our findings, we used an instrumental variable analysis to estimate the unbiased, causal association between racial discrimination in institutional settings and a battery of standardized cognitive function measures. We found that increased experiences of racial discrimination had adverse impacts on cognitive function. Understanding how racial discrimination in these settings influences cognitive outcomes supplies insight into points of potential intervention at institutional and policy levels.

On the Selection of Item Scores or Composite Scores for Clinical Prediction

Recent shifts to prioritize prediction, rather than explanation, in psychological science have increased applications of predictive modeling methods. However, composite predictors, such as sum scores, are still commonly used in practice. The motivations behind composite test scores are largely intertwined with reducing the influence of measurement error in answering explanatory questions. But this may be detrimental for predictive aims. The present paper examines the impact of utilizing composite or item-level predictors in linear regression. A mathematical examination of the bias-variance decomposition of prediction error in the presence of measurement error is provided. It is shown that prediction bias, which may be exacerbated by composite scoring, drives prediction error for linear regression. This may be particularly salient when composite scores are comprised of heterogeneous items such as in clinical scales where items correspond to symptoms. With sufficiently large training samples, the increased prediction variance associated with item scores becomes negligible even when composite scores are sufficient. Practical implications of predictor scoring are examined in an empirical example predicting suicidal ideation from various depression scales. Results show that item scores can markedly improve prediction particularly for symptom-based scales. Cross-validation methods can be used to empirically justify predictor scoring decisions.

Quantifying the Impact of Measurement Errors in Consistent Linear Partial Least Squares Structural Equation Modelling: A Monte Carlo Investigation

Measurement errors wield the potential to introduce uncertainties and inaccuracies, casting shadows on data quality and jeopardizing the integrity of structural relationships. Notably robust against measurement errors, Partial Least Squares Structural Equation Modelling (PLS-SEM) has historically maintained a reputation for resilience. However, recent insights have unveiled its susceptibility to these errors, instigating a revaluation of its standing in the Structural Equation Modelling landscape. Overlooking measurement errors in PLS-SEM carry consequential repercussions, notably tainting the accuracy of structural relationships and introducing bias. This effect becomes particularly pronounced when dealing with an insufficient understanding of the intricate structural dynamics. Unfortunately, PLS-SEM currently lacks an all-encompassing remedy to address this concern. Consequently, the quantification of measurement errors impact in PLS-SEM gains paramount importance, fostering a demand for innovative strategies to propel its effectiveness forward. Notably, contemporary investigations have unmasked PLS-SEM's vulnerability to non-orthogonal errors. This revelation challenges the notion of its imperviousness to the detrimental influence of measurement errors, necessitating a comprehensive evaluation of its performance under such conditions. This study leveraged simulated data to extract empirical findings and employed parameters biasedness analysis. This analysis led to the determination that the stability of the PLS-SEM algorithm is compromised when exposed to diverse measurement error scenarios. Consequently, the outcomes generated exhibit both instability and bias. This bias becomes increasingly conspicuous as the magnitude of measurement errors intensifies. Thus, the study proposes avenues for elevating the robustness of PLS-SEM.

Rapid identification of erosion thinning and scaling thickening of inner wall of circular tube based on inverse heat conduction problem method

Due to the erosion of fluid and the deposition of fouling in pipes, the internal defects of pipelines are widespread and seriously threaten the safety of industrial production. In this paper, an indirect inversion model based on modified one-dimensional correction method (MODCM) is proposed to identify the erosion thinning and fouling deposition of the inner wall of a two-dimensional circular tube. By constructing the relationship between the equivalent thermal conductivity and the geometric parameters of defect, the inversion of defects is transformed into the inversion of the equivalent thermal conductivity, which overcomes the difficulty of re-gridding caused by the change of the geometric domain during the iterative process, and effectively improves the calculation efficiency. In order to explore the validity and timeliness of the constructed models, the numerical experiments are carried out. The influence of measurement errors, thermal conductivity of pipe and thermal conductivity of fouling on the identification results are systematically analyzed. The research results show that the thermal conductivities of pipes and fouling have little effect on the inversion results, and the inversion accuracy decreases with the increase of measurement errors. When the measurement error is σ = 0.5 ℃, the identification error remains about 0.2 %, which shows that the method has good robustness. In addition, compared with the typical direct identification models of Levenberg-Marquardt method (LMM) and conjugate gradient method (CGM), the proposed indirect inversion model is superior in computational efficiency and tracking ability of discontinuous boundaries.

Modeling and Optimization of Connected and Automated Vehicle Platooning Cooperative Control with Measurement Errors.

This paper presents a cooperative control method for connected and automated vehicle (CAV) platooning, thus specifically addressing the challenge of sensor measurement errors that can disrupt the stability of the CAV platoon. Initially, the state-space equation of the CAV platooning system was formulated, thereby taking into account the measurement error of onboard sensors. The superposition effect of the sensor measurement errors was statistically analyzed, thereby elucidating its impact on cooperative control in CAV platooning. Subsequently, the application of a Kalman filter was proposed as a means to mitigate the adverse effects of measurement errors. Additionally, the CAV formation control problem was transformed into an optimal control decision problem by introducing an optimal control decision strategy that does not impose pure state variable inequality constraints. The proposed method was evaluated through simulation experiments utilizing real vehicle trajectory data from the Next Generation Simulation (NGSIM). The results demonstrate that the method presented in this study effectively mitigates the influence of measurement errors, thereby enabling coordinated vehicle-following behavior, achieving smooth acceleration and deceleration throughout the platoon, and eliminating traffic oscillations. Overall, the proposed method ensures the stability and comfort of the CAV platooning formation.

Robust Exploration Based Finite Control Set Predictive Current Control for SPMSM Drives Under Influence of Measurement Error

To improve the performance and the robustness of finite control set predictive current control (PCC) under the influence of current measurement error in the actual system, robust exploration based finite control set predictive current control (RE-PCC) is proposed in this paper. Firstly, to analyze directly transient and steady state of system operation, a voltage cost function without weighting factor is designed based on predicted voltage error vector, reference voltage vector and evaluated voltage vector. Secondly, the robust exploration is designed to overcome the influence of measurement error by combing the amplitude and phase attribute between the predicted voltage error vector and the ideal reference voltage vector. Thirdly, based on the robust exploration, the desired sector and the prior vector set is derived for cost function evaluation. It is great benefit to computational burden and switching losses by reducing the seven vectors to three. Finally, the RE-PCC method is experimentally compared with an improved PCC method in terms of robustness, current quality, torque ripple, dynamic characteristics and efficiency. The experimental results verify the high robustness and superior control performance of RE-PCC method in surface-mounted permanent magnet synchronous motor (SPMSM) drives.

Aneurysmal growth in type-B aortic dissection: assessing the impact of patient-specific inlet conditions on key haemodynamic indices.

Type-B aortic dissection is a cardiovascular disease in which a tear develops in the intimal layer of the descending aorta, allowing pressurized blood to delaminate the layers of the vessel wall. In medically managed patients, long-term aneurysmal dilatation of the false lumen (FL) is considered virtually inevitable and is associated with poorer disease outcomes. While the pathophysiological mechanisms driving FL dilatation are not yet understood, haemodynamic factors are believed to play a key role. Computational fluid dynamics (CFD) and 4D-flow MRI (4DMR) analyses have revealed correlations between flow helicity, oscillatory wall shear stress and aneurysmal dilatation of the FL. In this study, we compare CFD simulations using a patient-specific, three-dimensional, three-component inlet velocity profile (4D IVP) extracted from 4DMR data against simulations with flow rate-matched uniform and axial velocity profiles that remain widely used in the absence of 4DMR. We also evaluate the influence of measurement errors in 4DMR data by scaling the 4D IVP to the degree of imaging error detected in prior studies. We observe that oscillatory shear and helicity are highly sensitive to inlet velocity distribution and flow volume throughout the FL and conclude that the choice of IVP may greatly affect the future clinical value of simulations.

Evaluation and improvement of residence time distribution analysis methods

Although residence time distributions (RTDs) are frequently used in wetland studies, there is no consensus about the analysis method. Generally, two types of methods can be used to analyze RTD data: the method of moments and the RTD modeling method. RTD curves are obtained by tracer tests. The real RTD curves are usually imperfect due to the presence of random measurement errors, the unreasonable sampling intervals, and the premature termination of the tracer test. This study aims to find a better RTD analysis method providing robust results under these unfavorable factors, which includes: (i) evaluating the existing RTD analysis methods; (ii) improving the existing RTD analysis method using the Rational model. The influence of measurement error, sampling interval, and ending time in tracer tests on RTD analysis methods was first simulated. The results showed that effective volume ratio was a preferable indicator compared with the variance of RTD. The stability of the moment method decreased with the increase of the measurement error and sampling interval, and the accuracy of the moment method decreased with the decrease of the test time. Although the RTD modeling method showed better stability and accuracy, the implicit premise is that the RTD model adequately describes the observed data. Further, 36 observed RTD data sets were used to test the Tanks in Series (TIS) model and the empirical Rational model. The results showed that the widely used TIS model was only effective in partial cases, while the newly proposed Rational model showed broader applicability. Both the moment method and the TIS modeling method were proven to be inadequate. Finally, the moment method was improved by using Rational models to pre-process RTD data. Although the improved method is still affected by random measurement errors, it can significantly increase the accuracy of the moment method when sampling points are small or the test ends prematurely.

Chlorinated solvents source identification by nonlinear optimization method.

In this work, chloride ions were used as conservative tracers and supplemented with conservative amounts of chloroethenes (PCE, TCE, Cis-DCE, 1,1-DCE), chloroethanes (1,1,1-TCA, 1,1-DCA), and the carbon isotope ratios of certain compounds, the most representative on the sites studied, which is a novelty compared to the optimization methods developed in the scientific literature so far. A location of the potential missing sources is then proposed in view of the balances of the calculated mixing fractions. A test of the influence of measurement errors on the results shows that the uncertainties in the calculation of the mixture fractions are less than 11%, indicating that the source identification method developed is a robust tool for identifying sources of chlorinated solvents in groundwater.

Development and Traceability Method of a 100 kA Reference Impulse Current Measuring System

In order to improve the measurement accuracy of high impulse current, this paper presents the system composition, performance improvement and, traceability method of a 100 kA impulse current measuring system. Since the scale factor of the measuring system can be determined by the product of the scale factor of the components such as the shunt and the digital recorder, a method of component quantity traceability is proposed. Based on a shunt with rated current of 100 kA, a stable measuring system is developed. In addition, a reference impulse current calibrator and a step current generator for traceability are developed, so that the impulse scale factor of the shunt considering skin effect can be accurately calibrated. A single influence quantity independent quantification method was proposed. The influence of measurement error of 100 kA impulse current measuring system is analyzed quantitatively. Based on the high current test results and theoretical analysis, the measurement uncertainty components of peak current and time parameters are evaluated. The expanded uncertainty of the scale factor and time parameters were 0.4% ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> = 2) and 0.8% ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> = 2) respectively. Evaluation results demonstrate that the developed measuring system can accurately reproduce and measure the high exponential lightning impulse current.

A decoupled algorithm for cable parameter identification by frequency‐ratio‐offset

AbstractThis paper proposes a frequency‐ratio‐offset (FRO)‐based parameter identification algorithm for real bridge cables. By introducing specific time and space scaling factors, the dynamic equation is fully nondimensionalized and reduced to a most concise form, independent of all distributed cable parameters. As a result, the frequency equation has only two variables, which can be easily solved to obtain the universal dimensionless frequency–length curve under specified boundary conditions. Based on these curves, a decoupled identification algorithm is constructed, using the newly defined FRO as a bridge to directly connect the dimensionless beam and its real‐world counterpart. The algorithm enables a simultaneous determination of any two of the three cable parameters using a pair of measured frequencies. Examples involving both laboratory tests and real‐life applications demonstrate the effectiveness and accuracy of the algorithm. Based on a statistical analysis of the fitness of dozens of predicted and measured frequencies, a guideline for frequency selection is suggested, and the influence of measurement error on the identification results is discussed. The proposed algorithm could become a convenient tool for engineers to efficiently identify multiple parameters (e.g., tension and bending stiffness) for a broad class of bridge cables.

3次元レーザースキャナによる地震被害建物の変形量評価

In order to obtain knowledge regarding damage mechanisms of building located above surface of the 2016 Kumamoto earthquake fault, the residual deformations of the building were investigated with 3D point cloud data obtained by a laser scanner. The 3D point cloud data contain noise due to measurement error and diffuse reflection. Residual deformations were grasped accurately with method reducing the influence of measurement error by using RANSAC algorism which is one of robust estimations. The validity of the obtained results is also confirmed by comparing other research and evaluating the error.

Data fusion method for wireless sensor network based on machine learning

In order to improve the energy consumption balance between wireless sensor nodes and reduce the energy consumption of nodes in the process of data fusion, a machine learning based data fusion method for wireless sensor networks is proposed. Through the establishment and training of wireless sensor network model, the compressed sensing method is used to collect wireless sensor network data, and the multi-dimensional de aggregation class analysis algorithm is used to de duplicate the collected data. Using the spatial correlation between the data collected by multiple sensor nodes, the DCS method is used to process the abnormal data of WSN network. In order to eliminate the influence of measurement error on the fusion accuracy, the WSN network data is preliminarily fused by combining the adaptive theory with the batch estimation fusion algorithm. Based on the preliminary fusion results of WSN network data, the Bayesian inference method in machine learning algorithm is used to further fuse WSN network data. The experimental results show that the number of surviving nodes is large and the energy consumption is low when using this method for data fusion. The energy consumption between wireless sensor nodes has a certain balance, which proves that this method has a good data fusion effect.