Numerical modeling of polycrystalline thin-film solar cell serves as an imperative procedure to test the suitability of proposed physical clarification and to anticipate the effect of physical changes on cell performance. All in all, this must be conducted with only partial knowledge of input parameters. In this paper, we evaluated the numerical simulation of CdS/CIGS tandem multi junction sola...

A full three-dimensional, single phase computational fluid dynamics model of a proton exchange membrane fuel cell (PEMFC) with both the gas distribution flow channels and the Membrane Electrode Assembly (MEA) has been developed. A single set of conservation equations which are valid for the flow channels, gas-diffusion electrodes, catalyst layers, and the membrane region are developed and numer...

We study the properties of a future singularity encountered by a perfect fluid observer in tilting spatially homogeneous Bianchi cos-mologies. We derive the boost formulae for the Weyl tensor to establish that, for two observers that are asymptotically null with respect to each other, their respective Weyl parameters generally both tend to zero, constant, or infinity together. We examine three ...

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, ...