Estimation Of Measurement Bias Research Articles

Regularized Iterative Weighted Total Least Squares for Vehicle Mass Estimation

This work addresses the challenge of vehicle mass estimation using a longitudinal vehicle dynamics model, which adopts an errors-in-variables formulation due to the presence of noise-contaminated measurements in both input and output variables. The reduced vehicle dynamics model is ill-conditioned by nature of correlated input variables and a lack of persistent excitation in the measured data. A regularized iterative weighted total least squares (RIWTLS) method is therefore developed and has the advantage of producing parameter uncertainty quantification and measurement bias estimation alongside the estimated system parameters. A complementary adaptive regularization scheme is developed and serves to control the numerical stability of the RIWTLS algorithm based on the conditioning of incoming data. Experimental tests using electric vehicle data and a batch estimation scheme highlight the performance of the proposed RIWTLS algorithm, estimating vehicle mass to within ±1% accuracy.

Kinetic and Static Analysis of Poly-(Adenosine Diphosphate-Ribose) Polymerase-1-Targeted 18F-Fluorthanatrace PET Images of Ovarian Cancer.

The poly-(adenosine diphosphate-ribose) polymerase (PARP) family of proteins participates in numerous functions, most notably the DNA damage response. Cancer vulnerability to DNA damage has led to development of several PARP inhibitors (PARPi). This class of drugs has demonstrated therapeutic efficacy in ovarian, breast, and prostate cancers, but with variable response. Consequently, clinics need to select patients likely to benefit from these targeted therapies. In vivo imaging of 18F-fluorthanatrace uptake has been shown to correspond to PARP-1 expression in tissue. This study characterized the pharmacokinetics of 18F-fluorthanatrace and tested kinetic and static models to guide metric selection in future studies assessing 18F-fluorthanatrace as a biomarker of response to PARPi therapy. Methods: Fourteen prospectively enrolled ovarian cancer patients were injected with 18F-fluorthanatrace and imaged dynamically for 60 min after injection followed by up to 2 whole-body scans, with venous blood activity and metabolite measurements. SUVmax and SUVpeak were extracted from dynamic images and whole-body scans. Kinetic parameter estimates and SUVs were assessed for correlations with tissue PARP-1 immunofluorescence (n = 7). Simulations of population kinetic parameters enabled estimation of measurement bias and precision in parameter estimates. Results:18F-fluorthanatrace blood clearance was variable, but labeled metabolite profiles were similar across patients, supporting use of a population parent fraction curve. The total distribution volume from a reversible 2-tissue-compartment model and Logan reference tissue distribution volume ratio (DVR) from the first hour of PET acquisition correlated with tumor PARP-1 expression by immunofluorescence (r = 0.76 and 0.83, respectively; P < 0.05). DVR bias and precision estimates were 6.4% and 29.1%, respectively. SUVmax and SUVpeak acquired from images with midpoints of 57.5, 110 ± 3, and 199 ± 4 min highly correlated with PARP-1 expression (mean ± SD, r ≥ 0.79; P < 0.05). Conclusion: Tumor SUVmax and SUVpeak at 55-60 min after injection and later and DVR from at least 60 min appear to be robust noninvasive measures of PARP-1 binding. 18F-fluorthanatrace uptake in ovarian cancer was best described by models of reversible binding. However, pharmacokinetic patterns of tracer uptake were somewhat variable, especially at later time points.

Inhibition of Glucose-6-Phosphate Dehydrogenase Activity Attenuates Right Ventricle Pressure and Hypertrophy Elicited by VEGFR Inhibitor + Hypoxia.

Pulmonary hypertension (PH) is a disease of hyperplasia of pulmonary vascular cells. The pentose phosphate pathway (PPP)-a fundamental glucose metabolism pathway-is vital for cell growth. Because treatment of PH is inadequate, our goal was to determine whether inhibition of glucose-6-phosphate dehydrogenase (G6PD), the rate-limiting enzyme of the PPP, prevents maladaptive gene expression that promotes smooth muscle cell (SMC) growth, reduces pulmonary artery remodeling, and normalizes hemodynamics in experimental models of PH. PH was induced in mice by exposure to 10% oxygen (Hx) or weekly injection of vascular endothelial growth factor receptor blocker [Sugen5416 (SU); 20 mg kg-1] during exposure to hypoxia (Hx + SU). A novel G6PD inhibitor (N-[(3β,5α)-17-oxoandrostan-3-yl]sulfamide; 1.5 mg kg-1) was injected daily during exposure to Hx. We measured right ventricle (RV) pressure and left ventricle pressure-volume relationships and gene expression in lungs of normoxic, Hx, and Hx + SU and G6PD inhibitor-treated mice. RV systolic and end-diastolic pressures were higher in Hx and Hx + SU than normoxic control mice. Hx and Hx + SU decreased expression of epigenetic modifiers (writers and erasers), increased hypomethylation of the DNA, and induced aberrant gene expression in lungs. G6PD inhibition decreased maladaptive expression of genes and SMC growth, reduced pulmonary vascular remodeling, and decreased right ventricle pressures compared with untreated PH groups. Pharmacologic inhibition of G6PD activity, by normalizing activity of epigenetic modifiers and DNA methylation, efficaciously reduces RV pressure overload in Hx and Hx + SU mice and preclinical models of PH and appears to be a safe pharmacotherapeutic strategy. SIGNIFICANCE STATEMENT: The results of this study demonstrated that inhibition of a metabolic enzyme efficaciously reduces pulmonary hypertension. For the first time, this study shows that a novel inhibitor of glucose-6-phosphate dehydrogenase, the rate-limiting enzyme in the fundamental pentose phosphate pathway, modulates DNA methylation and alleviates pulmonary artery remodeling and dilates pulmonary artery to reduce pulmonary hypertension.

Classification and estimation of unmeasured process variables in crude oil pre-heat trains subject to fouling deposition

Crude oil refineries use a series of measurement instruments to monitor process units. One of these units is the pre-heat train. Flow rate and temperature measurements are taken from specific locations for monitoring purposes, especially fouling deposition. The availability of these measurements is related to the instrumentation cost, which limits the number of instruments installed on each unit. Dealing with missing measurements requires specific techniques for allowing the estimation of such variables. Data reconciliation and gross error detection can be integrated to improve the accuracy of process measurements. This work presents an integrated approach that considers the estimation of missing measurements, identification and estimation of measurement bias and reconciliation of measured data. The set of reconciled data is used for determining fouling models to predict the thermal performance of a crude oil pre-heat train. The fouling models can also be used for fouling mitigation and optimisation of cleaning schedules.

Extended State based Kalman Filter for Uncertain Systems with Bias

This paper addresses the state estimation for a class of stochastic systems with both uncertain dynamics and measurement bias. By using the idea of uncertainty/disturbance estimation, an extended state based Kalman filter algorithm is developed to estimate the original state, the uncertain dynamics and the measurement bias. Furthermore, a necessary and sufficient condition for the observability of augmented system is presented. Also, the stability of the proposed algorithm is analyzed. It is shown that the proposed filter can achieve unbiased estimation of measurement bias, such that the influence of measurement bias is eliminated. Finally, a simulation study is provided to illustrate the effectiveness of proposed method.

Measurement Bias Estimation in the Problem of Target Tracking

In the problem of target tracking, different types of biases can enter into the measurement collected by sensors due to various reasons. In order to accurately track the target, it is essential to estimate and correct the measurement bias. Considering practical backgrounds, the bias is assumed to be locally stationary Gaussian distributed and an iterative estimation algorithm is proposed. Firstly, a mechanism is established to detect whether the bias switches between different Gaussian distributions. Secondly, the expectation maximization algorithm with the assistance of extended Kalman filtering and smoothing is proposed to iteratively estimate the bias and target state in an offline manner. Simulations show the proposed algorithm can suppress the impact of the measurement bias on target tracking.

Performance analysis of two EM-based measurement bias estimation processes for tracking systems

In target tracking, the measurements collected by sensors can be biased in some real scenarios, e.g., due to systematic error. To accurately estimate the target trajectory, it is essential that the measurement bias be identified in the first place. We investigate the iterative bias estimation process based on the expectation-maximization (EM) algorithm, for cases where sufficiently large numbers of measurements are at hand. With the assistance of extended Kalman filtering and smoothing, we derive two EM estimation processes to estimate the measurement bias which is formulated as a random variable in one state-space model and a constant value in another. More importantly, we theoretically derive the global convergence result of the EM-based measurement bias estimation and reveal the link between the two proposed EM estimation processes in the respective state-space models. It is found that the bias estimate in the second state-space model is more accurate and of less complexity. Furthermore, the EM-based iterative estimation converges faster in the second state-space model than in the first one. As a byproduct, the target trajectory can be simultaneously estimated with the measurement bias, after processing a batch of measurements. These results are confirmed by our simulations.

State‐of‐charge and state‐of‐health estimation with state constraints and current sensor bias correction for electrified powertrain vehicle batteries

Pragmatic approaches are proposed to enhance battery state estimation using Kalman filter (KF) and extended KF. Notable novelties introduced include: the use of state/parameter constraints, asymmetric equivalent circuit model behaviour, inclusion of nominal models, and current sensor measurement bias estimation and compensation. The so-called delta parameters are estimated to handle cell variations, aging, and online deviation of parameters. Strategic simplifications that enable the use of traditional KF algorithm are described. Unique filter structures are presented for state-of-charge and state-of-health estimation, the latter focuses on capacity and impedance estimation. The performance of the proposed approaches is demonstrated on experimental drive-cycle data designed for electric vehicle (EV) and hybrid EV applications.

Instrumented Assessment of Oral Motor Function in Healthy Subjects and People with Systemic Sclerosis.

The aim of the present study was to provide quantitative data of oral function in healthy subjects (HSs), validity of measurements and estimation of measurement bias, as well as quantify oral impairment in persons with scleroderma (SSc). 151 HSs and 12 subjects with SSc were recruited and assessed using instrumented tools, measuring maximal mouth opening; lip strength; and tongue strength, protrusion, retraction, and endurance. Twenty HSs were also retested 3-5weeks later in order to assess the test-retest reliability of the measurements. Intraclass correlation coefficients proved to be satisfactory (>0.8) for both inter-rater and test-retest reliabilities of all measurements except for tongue retraction. In the HS group, maximal mouth opening and tongue and lips strength values were larger (P<0.05) for males than females, while no significant differences were found for other variables. Older subjects had statistically significantly lower tongue retraction values and tongue endurance values than younger subjects. The SSc group showed a statistically significant decrease (P<0.05) in almost all the measurements. Assessment procedures proved to be valid and reliable. Gender and height were predictors of mouth opening, lip and tongue strength, while age correlates with tongue retraction and endurance. Measurements highlighted the strong impact of SSc on oral functions and in particular on tongue protrusion, tongue strength, and endurance.

Linear minimum mean squared estimation of measurement bias driven by structured unknown inputs

In this study, a generalised systematic bias (SB) is presented, which is represented via a dynamic model driven by structured unknown inputs (UI). The online SB estimation is implemented in two steps. In the first step, the state-free SB measurement and the UI-free SB dynamic model are derived in the case that UI-free condition holds. In the second step, the linear minimum mean squared filter is obtained via orthogonal principle, and the sufficient condition of filtering stability is presented. A simulation about target tracking is given to verify the proposed method.

A Comparative Analysis Between Customized and Commercial Systems for Complex Permittivity Measurements on Liquid Samples at Microwave Frequencies

In this paper, different customized systems for microwave permittivity measurements on liquid samples, based on reflectometric measurements, are presented and analyzed. Their performance is compared against the one deriving from the most widely adopted commercial measurement setup. The systems are designed with the aim of providing less expensive solutions without compromising measurement accuracy. The purpose of the first proposed solution is to replace the commercial measurement software exploiting a reformulation of the classical theory. Based on this alternative formulation, a “homemade” probe is built by properly modifying an N-type coaxial connector, thus providing a system requiring a lower quantity of liquid under test. Moreover, a different experimental approach which uses time-domain reflectometry (TDR) instrumentation is presented. Such solution is by far the least expensive, as it allows avoiding the use of costly instrumentation (such as a vector network analyzer). In order to metrologically characterize the proposed solutions, a series of repeated measurements is performed on a set of well-referenced liquids. After extracting the Cole–Cole parameters through each of the considered measurement methods, the resulting type A uncertainty is evaluated. Finally, comparison with literature data allows the estimation of measurement bias. The analysis evidences that custom solutions generally exhibit an accuracy comparable to the one of the commercial solution, with a slight degradation of performance for the TDR-based setup, which, however, compensates for this drawback with its appealing low cost.

Bias detection and estimation in dynamic data reconciliation

Abstract A method for detection and estimation of measurement bias in nonlinear dynamic processes is presented. It employs model-based data reconciliation and requires the examination of the resulting difference between the measured and reconciled values. Since bias is commonly present in process measurements, this technique is an important step toward the ultimate goal of reconciling ‘raw’ process data that may contain bias and gross errors in addition to small random errors. A CSTR example shows that this method does allow for the detection of a single bias in a nonlinear dynamic process whether or not the exact model equations are known.

BIAS Detection and Estimation in Dynamic Data Reconciliation

A method for detection and estimation of measurement bias in nonlinear dynamic processes is presented. It employs model-based data reconciliation and requires the examination of the resulting difference between the measured and reconciled values. Since bias is commonly present in process measurements, this technique is an important step toward the ultimate goal of reconciling “raw” process data that may contain bias and gross errors in addition to small random errors. A CSTR example shows that this method does allow for the detection of a single bias in a nonlinear dynamic process whether or not the exact model equations are known.