The role of parametric assumptions in adaptive Bayesian estimation.

Variants of adaptive Bayesian procedures for estimating the 5% point on a psychometric function were studied by simulation. Bias and standard error were the criteria to evaluate performance. The results indicated a superiority of (a) uniform priors, (b) model likelihood functions that are odd symmetric about threshold and that have parameter values larger than their counterparts in the psychometric function, (c) stimulus placement at the prior mean, and (d) estimates defined as the posterior mean. Unbiasedness arises in only 10 trials, and 20 trials ensure constant standard errors. The standard error of the estimates equals 0.617 times the inverse of the square root of the number of trials. Other variants yielded bias and larger standard errors.

Linking Parametric CAD with Adjoint Surface Sensitivities

The goal of this work is to present an efficient CAD-based adjoint process chain for calculating parametric sensitivities (derivatives of the objective function with respect to the CAD parameters) in timescales acceptable for industrial design processes. The idea is based on linking parametric design velocities (geometric sensitivities computed from the CAD model) with adjoint surface sensitivities. A CAD-based design velocity computation method has been implemented based on distances between discrete representations of perturbed geometries. This approach differs from other methods due to the fact that it works with existing commercial CAD packages (unlike most analytical approaches) and it can cope with the changes in CAD model topology and face labeling. Use of the proposed method allows computation of parametric sensitivities using adjoint data at a computational cost which scales with the number of objective functions being considered, while it is essentially independent of the number of design variables. The gradient computation is demonstrated on test cases for a Nozzle Guide Vane (NGV) model and a Turbine Rotor Blade model. The results are validated against finite difference values and good agreement is shown. This gradient information can be passed to an optimization algorithm, which will use it to update the CAD model parameters.

Testing Wavefunction Collapse Models using Parametric Heating of a Trapped Nanosphere

We propose a mechanism for testing the theory of collapse models such as continuous spontaneous localization (CSL) by examining the parametric heating rate of a trapped nanosphere. The random localizations of the center-of-mass for a given particle predicted by the CSL model can be understood as a stochastic force embodying a source of heating for the nanosphere. We show that by utilising a Paul trap to levitate the particle and optical cooling, it is possible to reduce environmental decoher- ence to such a level that CSL dominates the dynamics and contributes the main source of heating. We show that this approach allows measurements to be made on the timescale of seconds, and that the free parameter λcsl which characterises the model ought to be testable to values as low as 10^{−12} Hz.

Parametric Analysis of Concrete Solar Collectors

Concrete solar collectors offer a type of solar collector with structural, aesthetic and economic advantages over current populartechnologies. This study examines the influential parameters of concrete solar collectors. In addition to the external conditions,the performance of a concrete solar collector is influenced by the thermal properties of the concrete matrix, piping network andfluid. Geometric and fluid flow parameters also influence the performance of the concrete solar collector. A literature review ofconcrete solar collectors is conducted in order to define the benchmark parameters from which individual parameters are thencompared. The numerical model consists of a 1D pipe flow network coupled with the heat transfer in a 3D concrete domain. Thispaper is concerned with the physical parameters that define the concrete solar collector, thus a constant surface temperature isused as the exposed surface boundary condition with all other surfaces being insulated. Results show that, of the parametersinvestigated, the pipe spacing, ps, concrete conductivity, kc, and the pipe embedment depth, demb, are among those parameterswhich have greatest effect on the collector’s performance. The optimum balance between these parameters is presented withrespect to the thermal performance and discussed with reference to practical development issues.

CAUSAL INFERENCE WITH A CONTINUOUS TREATMENT AND OUTCOME: ALTERNATIVE ESTIMATORS FOR PARAMETRIC DOSE-RESPONSE FUNCTIONS WITH APPLICATIONS.

Causal inference with a continuous treatment is a relatively under-explored problem. In this dissertation, we adopt the potential outcomes framework. Potential outcomes are responses that would be seen for a unit under all possible treatments. In an observational study where the treatment is continuous, the potential outcomes are an uncountably infinite set indexed by treatment dose. We parameterize this unobservable set as a linear combination of a finite number of basis functions whose coefficients vary across units. This leads to new techniques for estimating the population average dose-response function (ADRF). Some techniques require a model for the treatment assignment given covariates, some require a model for predicting the potential outcomes from covariates, and some require both. We develop these techniques using a framework of estimating functions, compare them to existing methods for continuous treatments, and simulate their performance in a population where the ADRF is linear and the models for the treatment and/or outcomes may be misspecified. We also extend the comparisons to a data set of lottery winners in Massachusetts. Next, we describe the methods and functions in the R package causaldrf using data from the National Medical Expenditure Survey (NMES) and Infant Health and Development Program (IHDP) as examples. Additionally, we analyze the National Growth and Health Study (NGHS) data set and deal with the issue of missing data. Lastly, we discuss future research goals and possible extensions.

Nonparametric Evaluation of Quantitative Traits in Population-Based Association Studies when the Genetic Model is Unknown

Statistical association between a single nucleotide polymorphism (SNP) genotype and a quantitative trait in genome-wide association studies is usually assessed using a linear regression model, or, in the case of non-normally distributed trait values, using the Kruskal-Wallis test. While linear regression models assume an additive mode of inheritance via equi-distant genotype scores, Kruskal-Wallis test merely tests global differences in trait values associated with the three genotype groups. Both approaches thus exhibit suboptimal power when the underlying inheritance mode is dominant or recessive. Furthermore, these tests do not perform well in the common situations when only a few trait values are available in a rare genotype category (disbalance), or when the values associated with the three genotype categories exhibit unequal variance (variance heterogeneity). We propose a maximum test based on Marcus-type multiple contrast test for relative effect sizes. This test allows model-specific testing of either dominant, additive or recessive mode of inheritance, and it is robust against variance heterogeneity. We show how to obtain mode-specific simultaneous confidence intervals for the relative effect sizes to aid in interpreting the biological relevance of the results. Further, we discuss the use of a related all-pairwise comparisons contrast test with range preserving confidence intervals as an alternative to Kruskal-Wallis heterogeneity test. We applied the proposed maximum test to the Bogalusa Heart Study dataset, and gained a remarkable increase in the power to detect association, particularly for rare genotypes. Our simulation study also demonstrated that the proposed non-parametric tests control family-wise error rate in the presence of non-normality and variance heterogeneity contrary to the standard parametric approaches. We provide a publicly available R library nparcomp that can be used to estimate simultaneous confidence intervals or compatible multiplicity-adjusted p-values associated with the proposed maximum test.

Robust CDM and pole placement PID based thrust controllers for multirotor motor-rotor simplified model

Positioning and orientation precision of a multirotor aerial robot can be increased by using additional control loops for each of the driving units. As a result, one can eliminate lack of balance between true thrust forces. A control performance comparison of two proposed thrust controllers, namely robust controller designed with coefficient diagram method (CDM) and proportional, integral and derivative (PID) controller tuned with pole-placement law, is presented in the paper. The research has been conducted with respect to model/plant matching uncertainty and with the use of antiwindup compensators for a simple motor-rotor model approximated by first-order inertia plus delay. From the obtained simulation results one concludes that appropriate choice of AWC compensator improves tracking performance and increases robustness against parametric uncertainty.

Aerodynamic parametric analysis of an unconventional joined-wing aircraft configuration

In this paper, the experimental results of an unconventional joined-wing aircraft configuration are presented. The test model uses two different wings, forward and rear, both joined in tandem and forming diamond shapes both in plant and front views. The wings are joined in such a way that it is possible to change the rear wing dihedral angle values and the rear wing sweep angle values in 25 different positions that modify the relative distance and the relative height between the wings. To measure the system aerodynamic coefficients itis necessary to perform wind tunnel tests. The datapresented corresponds to the lift, drag and induced drag aerodynamic coefficients, as well as the aerodynamic efficiency and the parameter for minimum required power, from the calculated values of the lift and drag time series measured by a 6-axis force and torque sensor. The results show the influence on the aerodynamic coefficients of the rear wing sweep and dihedral angles parameters. As a main result, it can be concluded that, in general terms, the lift and induced drag aerodynamic coefficients values decrease as both the distance and height between the wings increase, on the other hand, the total drag aerodynamic coefficient decreases if the distance between the wings increases, but nevertheless shows a slight tendency to increase if the height of the rear wing increases, whereas the aerodynamic efficiency and the parameter for minimum required power increase if the distance between the wings increases

An approximate method for the solution of an influence function foil rolling model

Fleck and Johnson (Int. J. Mech. Sci. 29 (1987) 507) and Fleck et al. (Proc. Inst. Mech. Eng. 206 (1992) 119) have developed foil rolling models which allow for large deformations in the roll profile, including the possibility that the rolls flatten completely. However, these models require computationally expensive iterative solution techniques. A new approach to the approximate solution of the Fleck et al. (1992) Influence Function Model has been developed using both analytic and approximation techniques. The numerical difficulties arising from solving an integral equation in the flattened region have been reduced by applying an Inverse Hilbert Transform to get an analytic expression for the pressure. The method described in this paper is applicable to cases where there is or there is not a flat region.