Application of Laguerre based adaptive predictive control to Shape Memory Alloy (SMA) actuators

This paper discusses the use of an existing adaptive predictive controller to control some Shape Memory Alloy (SMA) linear actuators. The model consists in a truncated linear combination of Laguerre filters identified online. The controller stability is studied in details. It is proven that the tracking error is asymptotically stable under some conditions on the modelling error. Moreover, the tracking error converge toward zero for step references, even if the identified model is inaccurate. Experimentalcresults obtained on two different kind of actuator validate the proposed control. They also show that it is robust with regard to input constraints.ANR MAFESM

The Hydraulic Jump in Liquid Helium

We present the results of some experiments on the circular hydraulic jump in normal and superfluid liquid helium. The radius of the jump and the depth of the liquid outside the jump are measured through optical means. Although the scale of the apparatus is rather small, the location of the jump is found to be consistent with the assumption that the jump can be treated as a shock, if the surface tension is taken into account. The radius of the jump does not change when going down in temperature through the lambda point; we think that the flow is supercritical. A remarkable feature of the experiment is the observation of stationary ripples within the jump when the liquid is superfluid.Comment: Submitted to the proceedings of the 24th International Conference on Low Temperature Physics. 2 figure

Influence properties of partial least squares regression.

Regression; Partial least squares; Least-squares; Squares; Squares regression;

The multivariate least trimmed squares estimator.

In this paper we introduce the least trimmed squares estimator for multivariate regression. We give three equivalent formulations of the estimator and obtain its breakdown point. A fast algorithm for its computation is proposed. We prove Fisher-consistency at the multivariate regression model with elliptically symmetric error distribution and derive the influence function. Simulations investigate the finite-sample efficiency and robustness of the estimator. To increase the efficiency of the estimator, we also consider a one-step reweighted version, as well as multivariate generalizations of one-step GM-estimators.Model; Data; Distribution; Simulation;

Influence properties of partial squares regression.

In this paper, we compute the influence function for partial least squares regression. Thereunto, we design two alternative algorithms, according to the PLS algorithm used. One algorithm for the computation of the influence function is based on the Helland PLS algorithm, whilst the other is compatible with SIMPLS.The calculation of the influence function leads to new influence diagnostic plots for PLS. An alternative to the well known Cook distance plot is proposed, as well as a variant which is sample specific.Moreover, a novel estimate of prediction variance is deduced. The validity of the latter is corroborated by dint of a Monte Carlo simulation.Influence function; Design; Algorithms; Simulation;

Scaling and tuning of EW and Higgs observables

We study deformations of the SM via higher dimensional operators. In particular, we explicitly calculate the one-loop anomalous dimension matrix for 13 bosonic dimension-6 operators relevant for electroweak and Higgs physics. These scaling equations allow us to derive RG-induced bounds, stronger than the direct constraints, on a universal shift of the Higgs couplings and some anomalous triple gauge couplings by assuming no tuning at the scale of new physics, i.e. by requiring that their individual contributions to the running of other severely constrained observables, like the electroweak oblique parameters or $\Gamma(h \rightarrow \gamma\gamma)$, do not exceed their experimental direct bounds. We also study operators involving the Higgs and gluon fields.Comment: v2: 41 pages, 12 tables, 4 figures. Plots of the RG-induced bounds from S and T added, presentation of our approach in sections 2 and 4 improved, a few typos fixed, references added, conclusions and analysis unchanged. Version to appear in JHE

A novel, nondestructive, dried blood spot-based hematocrit prediction method using noncontact diffuse reflectance spectroscopy

Dried blood spot (DBS) sampling is recognized as a valuable alternative sampling strategy both in research and in clinical routine. Although many advantages are associated with DBS sampling, its more widespread use is hampered by several issues, of which the hematocrit effect on DBS-based quantitation remains undoubtedly the most widely discussed one. Previously, we developed a method to derive the approximate hematocrit from a nonvolumetrically applied DBS based on its potassium content. Although this method yielded good results and was straightforward to perform, it was also destructive and required sample preparation. Therefore, we now developed a nondestructive method which allows to predict the hematocrit of a DBS based on its hemoglobin content, measured via noncontact diffuse reflectance spectroscopy. The developed method was thoroughly validated. A linear calibration curve was established after log/log transformation. The bias, intraday and interday imprecision of quality controls at three hematocrit levels and at the lower and upper limit of quantitation (0.20 and 0.67, respectively) were less than 11%. In addition, the influence of storage and the volume spotted was evaluated, as well as DBS homogeneity. Application of the method to venous DBSs prepared from whole blood patient samples (n = 233) revealed a good correlation between the actual and the predicted hematocrit. Limits of agreement obtained after Bland and Altman analysis were -0.076 and. +0.018. Incurred sample reanalysis demonstrated good method reproducibility. In conclusion, mere scanning of a DBS suffices to derive its approximate hematocrit, one of the most important variables in DBS analysis

Nonparametric vs parametric binary choice models: An empirical investigation

Research Methods/ Statistical Methods,