Sequential Estimation of Thermal Diffusivity for Flash Tests

This brief presents a sequential fusion estimation method for maneuvering target tracking in asynchronous wireless sensor networks. The modeling error caused by asynchronous sampling and communication uncertainties is considered and compensated for by introducing a time-varying fading factor into the unscented Kalman filter (UKF). A square root form of the unscented strong tracking filter (SR-USTF) based on QR decomposition is proposed to improve the stability and performance of the USTF. Moreover, a hybrid sequential fusion estimation method is presented to estimate the state of the target, and the proposed sequential fusion estimation method combines the superiorities of both the SR-USTF and the conventional UKF, and is able to deal with communication uncertainties such as time delay and packet loss in a unified framework. Both simulations and experiments of an E-puck robot tracking example are provided to demonstrate the effectiveness and superiorities of the proposed sequential fusion estimation method.

Abstract. In sequential estimation methods often used in oceanic and general climate calculations of the state and of forecasts, observations act mathematically and statistically as source or sink terms in conservation equations for heat, salt, mass, and momentum. These artificial terms obscure the inference of the system's variability or secular changes. Furthermore, for the purposes of calculating changes in important functions of state variables such as total mass and energy or volumetric current transports, results of both filter and smoother-based estimates are sensitive to misrepresentation of a large variety of parameters, including initial conditions, prior uncertainty covariances, and systematic and random errors in observations. Here, toy models of a coupled mass–spring oscillator system and of a barotropic Rossby wave system are used to demonstrate many of the issues that arise from such misrepresentations. Results from Kalman filter estimates and those from finite interval smoothing are analyzed. In the filter (and prediction) problem, entry of data leads to violation of conservation and other invariant rules. A finite interval smoothing method restores the conservation rules, but uncertainties in all such estimation results remain. Convincing trend and other time-dependent determinations in “reanalysis-like” estimates require a full understanding of models, observations, and underlying error structures. Application of smoother-type methods that are designed for optimal reconstruction purposes alleviate some of the issues.

Sequential fusion estimation for clustered sensor networks

<strong class="journal-contentHeaderColor">Abstract.</strong> In sequential estimation methods often used in oceanic and general climate calculations of the state and of forecasts, observations act mathematically and statistically as forcings. For purposes of calculating changes in important functions of state variables such as total mass and energy, or volumetric current transports, results are sensitive to mis-representation of a large variety of parameters, including initial conditions, prior uncertainty covariances, and systematic and random errors in observations. Here toy models of a mass-spring oscillator and of a barotropic Rossby-wave equation are used to demonstrate many of the issues. Results from Kalman-filter estimates, and those from finite interval smoothing are analyzed. In the filter (and prediction) problem, entry of data leads to violation of conservation and other invariant rules. A finite interval smoothing method restores the conservation rules, but uncertainties in all such estimation results remain. Convincing trend and other time-dependent determinations in "reanalysis" -like estimates require a full understanding of both models and observations.

In sequential estimation methods often used in general climate or oceanic calculations of the state and of forecasts, observations act mathematically and statistically as forcings as is obvious in the innovation form of the equations. For purposes of calculating changes in important functions of state variables such as total mass and energy, or in volumetric current transports, results are sensitive to mis-representation of a large variety of parameters including initial conditions, prior uncertainty covariances, and systematic and random errors in observations. Errors are both stochastic and systematic, with the latter, as usual, being the most intractable. Here some of the consequences of such errors are first analyzed in the context of a simplified mass-spring oscillator system exhibiting many of the issues of far more complicated realistic problems. The same methods are then applied to a more geophysical barotropic Rossby wave plus western boundary current system. The overall message is that convincing trend and other time-dependent determinations in "reanalyis" like estimates requires a full understanding of both models and observations.

The nested case-control design is a relatively new type of observational study whereby a case-control approach is employed within an established cohort. In this design, we observe cases and controls longitudinally by sampling all cases whenever they occur but controls at certain time points. Controls can be obtained at time points randomly scheduled or prefixed for operational convenience. This design with longitudinal observations is efficient in terms of cost and duration, especially when the disease is rare and the assessment of exposure levels is difficult. In our design, we propose sequential sampling methods and study both (group) sequential testing and estimation methods so that the study can be stopped as soon as the stopping rule is satisfied. To make such a longitudinal sampling more efficient in terms of both numbers of subjects and replications, we propose applying sequential sampling methods to subjects and replications, simultaneously, until the information criterion is fulfilled. This simultaneous sequential sampling on subjects and replicates is more flexible for practitioners designing their sampling schemes, and is different from the classical approaches used in longitudinal studies. We newly define the σ-field to accommodate our proposed sampling scheme, which contains mixtures of independent and correlated observations, and prove the asymptotic optimality of sequential estimation based on the martingale theories. We also prove that the independent increment structure is retained so that the group sequential method is applicable. Finally, we present results by employing sequential estimation and group sequential testing on both simulated data and real data on children's diarrhea.

TDRSS user orbit determination is analyzed using a batch least-squares method and a sequential estimation method. It was found that in the batch least-squares method analysis, the orbit determination consistency for Landsat-4, which was heavily tracked by TDRSS during January 1991, was about 4 meters in the rms overlap comparisons and about 6 meters in the maximum position differences in overlap comparisons. The consistency was about 10 to 30 meters in the 3 sigma state error covariance function in the sequential method analysis. As a measure of consistency, the first residual of each pass was within the 3 sigma bound in the residual space.

&lt;sec&gt;&lt;st&gt;Background&lt;/st&gt; Body size is an important patient covariate in scaling drug doses. While total body weight has been the most commonly used size descriptor, fat free mass (FFM) has been advocated as an alternative size descrip-tor to scale drug doses in adults and children. FFM de-scribes the non-fat component of the body thus having a better correlation with the metabolic rate and drug clear-ance (CL). The aim of this work was to evaluate FFM as a covariate in a PK model of unfractionated heparin (UFH) developed in a paediatric population. &lt;/sec&gt; &lt;sec&gt;&lt;st&gt;Methods&lt;/st&gt; Data from 64 infants and children who re-ceived 75–100 IU/kg of UFH during cardiac angiography were analysed. Four plasma samples were collected at baseline and at 15, 30, 45, and 120 min post-dose. UFH concentration (231 measurements) was quantified using a protamine titration assay. UFH effect (164 mea-surements) was quantified using activated partial throm-boplastin time (aPTT). A PKPD model was fitted to the data using the non-linear mixed effects modelling (in NONMEM v7.2). Various patient covariates such as age, weight (Wt), body surface area, and FFM were tested. The final model was evaluated using the likelihood ratio test and visual predictive checks (VPCs). &lt;/sec&gt; &lt;sec&gt;&lt;st&gt;Results&lt;/st&gt; A one-compartment model with linear elimina-tion provided the best fit for the dose-concentration data. Wt and FFM had substantial influence on model fit; FFM was preferred statistically. A linear model provided the best fit for the concentration-effect data using the PPP and D sequential estimation method. Censored PD data (above the upper limit of quantification) were accounted for us-ing likelihood estimation. The PKPD model performed well using visual predictive checks. &lt;/sec&gt; &lt;sec&gt;&lt;st&gt;Conclusion&lt;/st&gt; A PKPD model to describe the time-course of UFH effect was developed in a paediatric population which received a high single bolus dose. FFM was shown to describe drug disposition well and can potentially be used in dose calculation after appropriate evaluation. &lt;/sec&gt;

A modified sequential gradient-based method for the inverse estimation of transient heat transfer coefficients in non-linear one-dimensional heat conduction problems

Sequential multi-nuclide emission rate estimation method based on gamma dose rate measurement for nuclear emergency management

Measured surface-atmosphere fluxes of energy (sensible heat, H, and latent heat, LE) and CO 2 ( FCO 2) represent the “true” flux plus or minus potential random and systematic measurement errors. Here, we use data from seven sites in the AmeriFlux network, including five forested sites (two of which include “tall tower” instrumentation), one grassland site, and one agricultural site, to conduct a cross-site analysis of random flux error. Quantification of this uncertainty is a prerequisite to model-data synthesis (data assimilation) and for defining confidence intervals on annual sums of net ecosystem exchange or making statistically valid comparisons between measurements and model predictions. We differenced paired observations (separated by exactly 24 h, under similar environmental conditions) to infer the characteristics of the random error in measured fluxes. Random flux error more closely follows a double-exponential (Laplace), rather than a normal (Gaussian), distribution, and increase as a linear function of the magnitude of the flux for all three scalar fluxes. Across sites, variation in the random error follows consistent and robust patterns in relation to environmental variables. For example, seasonal differences in the random error for H are small, in contrast to both LE and FCO 2, for which the random errors are roughly three-fold larger at the peak of the growing season compared to the dormant season. Random errors also generally scale with R n ( H and LE) and PPFD ( FCO 2). For FCO 2 (but not H or LE), the random error decreases with increasing wind speed. Data from two sites suggest that FCO 2 random error may be slightly smaller when a closed-path, rather than open-path, gas analyzer is used.

This paper investigates the task of tracking and strain estimation of arteries in intravascular ultrasound images. A tracking method is proposed to extract the inner and the outer contours of the vessel wall (lumen/intima-media and intima-media/adventitia interfaces, respectively), and the deformations along them. This estimation is carried out by a non parametric sequential Bayesian method. The Bayesian modeling holds three ingredients: the prior, which is given by a manually defined segmentation of the contours on the first image; the transition, which is assumed to follow a Markovian random walk; and the likelihood, which is a distance between patches distributed along the contours. The underlying Bayesian posterior distribution is approximated using a sequential Monte Carlo approach. Experiments on three PVA-C phantoms present direct readings of the deformations along the lumen/intima-media contour.

The kinetic compensation effect states that there is a linear relationship between Arrhenius parameters ln A and E for a family of related processes. It is a widely observed phenomenon in many areas of science, notably heterogeneous catalysis. This paper explores one of the mathematical, rather than physicochemical, explanations for the compensation effect and for the isokinetic relationship. It is demonstrated, both theoretically and by numerical simulations, that random errors in kinetic data generate an apparent compensation effect (sometimes termed the statistical compensation effect) when the true Arrhenius parameters are constant. Expressions for the gradient of data points on a plot of ln A against E are derived when experimental kinetic data are analysed by linear regression, by non-linear regression and by weighted linear regression. It is shown that the most appropriate analysis technique depends critically on the error structure of the kinetic data. Whenever data points on a plot of ln A against E are in a straight line with a gradient close to 1/RT, then confidence ellipses should be calculated for each data point to investigate whether the apparent compensation effect arises from random errors in the kinetic measurements or has some other origin.

This paper addresses a speech recognition problem in non-stationary noise environments: the estimation of noise sequences. To solve this problem, we present a particle filter-based sequential noise estimation method for the front-end processing of speech recognition. In the proposed method, the particle filter is defined by a dynamical system based on Polyak averaging and feedback. We also introduce a switching dynamical system into the particle filter to cope with the state transition characteristics of non-stationary noise. In the evaluation results, we observed that the proposed method improves speech recognition accuracy in the results of non-stationary noise environments by a noise compensation method with stationary noise assumptions

Comparative study of intermittent spray cooling in single and two phase regimes