The Supernumerary Robotic 3rd Thumb for Skilled Music Tasks

Wearable robotics bring the opportunity to augment human capability and performance, be it through prosthetics, exoskeletons, or supernumerary robotic limbs. The latter concept allows enhancing human performance and assisting them in daily tasks. An important research question is, however, whether the use of such devices can lead to their eventual cognitive embodiment, allowing the user to adapt to them and use them seamlessly as any other limb of their own. This paper describes the creation of a platform to investigate this. Our supernumerary robotic 3rd thumb was created to augment piano playing, allowing a pianist to press piano keys beyond their natural hand-span; thus leading to functional augmentation of their skills and the technical feasibility to play with 11 fingers. The robotic finger employs sensors, motors, and a human interfacing algorithm to control its movement in real-time. A proof of concept validation experiment has been conducted to show the effectiveness of the robotic finger in playing musical pieces on a grand piano, showing that naive users were able to use it for 11 finger play within a few hours

Stochastic multiscale analysis in hydrodynamic lubrication

A stochastic multiscale analysis framework is developed for hydrodynamic lubrication problems with random surface roughness. The approach is based on a multi-resolution computational strategy wherein the deterministic solution of the multiscale problem for each random surface realization is achieved through a coarse-scale analysis with a local upscaling that is achieved through homogenization theory. The stochastic nature of this solution because of the underlying randomness is then characterized through local and global quantities of interest, accompanied by a detailed discussion regarding suitable choices of the numerical parameters in order to achieve a desired stochastic predictive capability while ensuring numerical efficiency. Finally, models of the stochastic interface response are constructed, and their performance is demonstrated for representative problem settings. Overall, the developed approach offers a computational framework, which can essentially predict the significant influence of interface heterogeneity in the absence of a strict scale separation. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd

Stress-gradient materials: an analytical exploration

International audienceA new stress-gradient elasticity theory has recently been proposed by Forest & Sab (Mech. Res. Comm. 40:16--25, 2012), and further analyzed by Forest, Legoll & Sab (J. Elas., submitted). This new model makes the assumption that the complementary energy is a function of the local stress and (the deviatoric part of) its first gradient. Its derivation (including boundary conditions) relies on a rigorous variational approach. However, the resulting set of equations is rather complex in general. In order to better understand this model, it is proposed as a first step to consider a subclass of stress-gradient materials, assuming material isotropy and restricting to only one internal material length.In this talk, we propose an analytical exploration of this simplified model. We will produce the closed-form solution to Eshelby's spherical inhomogeneity problem. From the analysis of the corresponding local fields, we will show that stress- and strain-gradient elasticity theories are not in duality, as one would be tempted to think.This elementary solution will then be used to extend the micromechanical model of Mori and Tanaka to stress gradient materials. In particular, we will show that (contrary to strain-gradient materials) a decrease of the internal material lenth tends to stiffen the material

Non-Parametric Approximations for Anisotropy Estimation in Two-dimensional Differentiable Gaussian Random Fields

Spatially referenced data often have autocovariance functions with elliptical isolevel contours, a property known as geometric anisotropy. The anisotropy parameters include the tilt of the ellipse (orientation angle) with respect to a reference axis and the aspect ratio of the principal correlation lengths. Since these parameters are unknown a priori, sample estimates are needed to define suitable spatial models for the interpolation of incomplete data. The distribution of the anisotropy statistics is determined by a non-Gaussian sampling joint probability density. By means of analytical calculations, we derive an explicit expression for the joint probability density function of the anisotropy statistics for Gaussian, stationary and differentiable random fields. Based on this expression, we obtain an approximate joint density which we use to formulate a statistical test for isotropy. The approximate joint density is independent of the autocovariance function and provides conservative probability and confidence regions for the anisotropy parameters. We validate the theoretical analysis by means of simulations using synthetic data, and we illustrate the detection of anisotropy changes with a case study involving background radiation exposure data. The approximate joint density provides (i) a stand-alone approximate estimate of the anisotropy statistics distribution (ii) informed initial values for maximum likelihood estimation, and (iii) a useful prior for Bayesian anisotropy inference.Comment: 39 pages; 8 figure

Hyperresolution information and hyperresolution ignorance in modelling the hydrology of the land surface

There is a strong drive towards hyperresolution earth system models in order to resolve finer scales of motion in the atmosphere. The problem of obtaining more realistic representation of terrestrial fluxes of heat and water, however, is not just a problem of moving to hyperresolution grid scales. It is much more a question of a lack of knowledge about the parameterisation of processes at whatever grid scale is being used for a wider modelling problem. Hyperresolution grid scales cannot alone solve the problem of this hyperresolution ignorance. This paper discusses these issues in more detail with specific reference to land surface parameterisations and flood inundation models. The importance of making local hyperresolution model predictions available for evaluation by local stakeholders is stressed. It is expected that this will be a major driving force for improving model performance in the future. Keith BEVEN, Hannah CLOKE, Florian PAPPENBERGER, Rob LAMB, Neil HUNTE

Genetic-Background Modulation of Core and Variable Autistic-Like Symptoms in Fmr1 Knock-Out Mice

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Stochastic modeling of a class of stored energy functions for incompressible hyperelastic materials with uncertainties

International audienceIn this Note, we address the construction of a class of stochastic Ogden's stored energy functions associated with incompressible hyperelastic materials. The methodology relies on the maximum entropy principle, which is formulated under constraints arising in part from existence theorems in nonlinear elasticity. More specifically, constraints related to both polyconvexity and consistency with linearized elasticity are considered and potentially coupled with a constraint on the mean function. Two parametric probabilistic models are thus derived for the isotropic case and rely in part on a conditioning with respect to the random shear modulus. Monte Carlo simulations involving classical (e.g. Neo-Hookean or Mooney-Rivlin) stored energy functions are then performed in order to illustrate some capabilities of the probabilistic models. An inverse calibration involving experimental results is finally presented

STOCHASTIC MODEL AND GENERATOR FOR RANDOM FIELDS WITH SYMMETRY PROPERTIES: APPLICATION TO THE MESOSCOPIC MODELING OF ELASTIC RANDOM MEDIA ∗

Abstract. This paper is concerned with the construction of a new class of generalized nonparametric probabilistic models for matrix-valued non-Gaussian random fields. More specifically, we consider the case where the random field may take its values in some subset of the set of real symmetric positive-definite matrices presenting sparsity and invariance with respect to given orthogonal transformations. Within the context of linear elasticity, this situation is typically faced in the multiscale analysis of heterogeneous microstructures, where the constitutive elasticity matrices may exhibit some material symmetry properties and may then belong to a given subset M sym n (R) of the set of symmetric positive-definite real matrices. First of all, we present an overall methodology relying on the framework of information theory and define a particular algebraic form for the random field. The representation involves two independent sources of uncertainties, namely one preserving almost surely the topological structure in M sym n (R) and the other one acting as a fully anisotropic stochastic germ. Such a parametrization does offer some flexibility for forward simulations and inverse identification by uncoupling the level of statistical fluctuations of the random field and the leve

Itô SDE-based generator for a class of non-Gaussian vector-valued random fields in uncertainty quantification

International audienceThis paper is concerned with the derivation of a generic sampling technique for a class of non-Gaussian vector-valued random fields. Such an issue typically arises in uncertainty quantification for complex systems, where the input coefficients associated with the elliptic operators must be identified by solving statistical inverse problems. Specifically, we consider the case of non-Gaussian random fields with values in some arbitrary bounded or semi-bounded subsets of R^n. The approach involves two main features. The first one is the construction of a family of random fields converging, at a user-controlled rate, towards the target random field. Each of these auxialiary random fields can be subsequently simulated by solving a family of Itô stochastic differential equations. The second ingredient is the definition of an adaptive discretization algorithm. The latter allows refining the integration step on-the-fly and prevents the scheme from diverging. The proposed strategy is finally exemplified on three examples, each of which serving as a benchmark, either for the adaptivity procedure or for the convergence of the diffusions

Stochastic hyperelastic constitutive laws and identification procedure for soft biological tissues with intrinsic variability

International audienceIn this work, we address the constitutive modeling, in a probabilistic framework, of the hyperelastic response of soft biological tissues. The aim is on the one hand to mimic the mean behavior and variability that are typically encountered in the experimental characterization of such materials, and on the other hand to derive mathematical models that are almost surely consistent with the theory of nonlinear elasticity. Towards this goal, we invoke information theory and discuss a stochastic model relying on a low-dimensional parametrization. We subsequently propose a two-step methodology allowing for the calibration of the model using standard data, such as mean and standard deviation values along a given loading path. The framework is finally applied and benchmarked on three experimental databases proposed elsewhere in the literature. It is shown that the stochastic model allows experiments to be accurately reproduced, regardless of the tissue under consideration