This paper presents a Petri net (PN) analysis technique which extend the analysis capabilities of T-invariants. This technique is based on the conversion of the PN into formulas of linear logic and on the introduction of the marking into P-invariants. It allows to test if T-invariants are pertinent related to the application's constraints of marking. Our technique determines for each valid sequ...

Many statistical problems involve the learning of an importance/effect of a variable for predicting an outcome of interest based on observing a sample of n independent and identically distributed observations on a list of input variables and an outcome. For example, though prediction/machine learning is, in principle, concerned with learning the optimal unknown mapping from input variables to a...

We present an integrated design and marketing approach to facilitate the generation of an optimal robust set of product design alternatives to carry forward to the prototyping stage. The approach considers variability in both (i) engineering design domain, and (ii) customer preferences in marketing domain. In the design domain, the approach evaluates performance and robustness of a design alter...

One of the key challenges in robotic bipedal locomotion is finding gait parameters that optimize a desired performance metric, such as robustness or energy efficiency. Typically, gait optimization requires extensive robot experiments and specific expert knowledge. Instead, we propose to apply data-driven machine learning to automate and speed up the process of gait optimization. In particular, ...