The calculation of forces in the kinematic pairs of mechanisms by inverse dynamics is usually performed without friction considerations. In practice, when examination of articulated mechanisms takes into account friction, the solution of the inverse dynamics results in a complex procedure. If a modular approach for the inverse dynamics is used, then exact solutions are available, but not necess...

We explore robust methods of automatically quantifying constriction location, constriction degree and gestural kinematics of Italian short and long consonants using direct image analysis techniques applied to rtMRI data. Articulatory kinematics are estimated from correlated regional changes in pixel intensity. We demonstrate that these methods are capable of quantifying differences in constrict...

The robot kinematic mapping x = f() generally has multiple distinct solution branches fjf() = x d g for a given end{eeector location x d , where each branch can have a nontrivial manifold structure (as in the case of a redundant manipulator). Learning techniques which exploit known topological properties of the mapping are used to determine the number and nature of these branches. Speciically, ...

In this paper the kinematics, (position, velocities and accelerations) and the dynamics of a 6-RUS parallel manipulator by using the natural coordinates are analysed. One numerical example of kinematics and inverse dynamics is presented.

in this paper, we show that inverse data envelopment analysis (dea) models can be used to estimate output with fuzzy data for a decision making unit (dmu) when some or all inputs are increased and deficiency level of the unit remains unchanged.

 Traditional regression analyses assume normality of observations and independence of mean and variance. However, there are many examples in science and Technology where the observations come from a skewed distribution and moreover there is a functional dependence between variance and mean. In this article, we propose a method for regression analysis under Inverse Gaussian model when th...