Characterising the information requests of aerospace engineering designers

During product development, engineering designers raise several information requests that make them search through human and documentary sources. This paper reports research to characterise, in detail, these requests for designers working in a major aerospace engineering company. The research found that at a high level, a distinction can be made between requests to acquire information and to process information. The former are raised to access design and domain information. The latter, instead, are formed to define designs. For researchers, this study extends existing knowledge of information requests by characterising key differences in their nature and explaining how they are used in the design process. For practitioners, these findings can be used as a basis to understand the diverseness of information requests and how to channel efforts to support designers in information seeking. In particular, the research indicates that a strategy to support designers should enable the development of engineering communities that share information effectively and the introduction of techniques that facilitate the documentation of information. © 2012 Springer-Verlag London Limited.

Bayesian separation and recovery of convolutively mixed autoregressive sources

In this paper we address the problem of the separation and recovery of convolutively mixed autoregressive processes in a Bayesian framework. Solving this problem requires the ability to solve integration and/or optimization problems of complicated posterior distributions. We thus propose efficient stochastic algorithms based on Markov chain Monte Carlo (MCMC) methods. We present three algorithms. The first one is a classical Gibbs sampler that generates samples from the posterior distribution. The two other algorithms are stochastic optimization algorithms that allow to optimize either the marginal distribution of the sources, or the marginal distribution of the parameters of the sources and mixing filters, conditional upon the observation. Simulations are presented.