POM Mechanical Properties

Thanks to its high degree of crystallinity, polyoxymethylene (POM) homopolymer exhibits high mechanical properties of strength, stiff ness and creep. In the case of POM copolymer, the strength and stiff ness are slightly lower because of its lower degree of crystallinity. Furthermore, the use domain ranges from -30°C to 150°C which allows various applications. More specifi cally to the POM, friction properties are excellent due to oxygen contained in the POM monomer. Th e major drawback of POM homopolymer is its brittleness at RT compare to polyolefi ns, typically nominal strain at break values are close to 20%. To improve mechanical properties at failure, diff erent strategies can be adopted as POM copolymer have lower crystallinity degree (strain at break close 70%) or added polyurethane in POM matrix (100ST from DuPont for instance). However, we will see that these strategies lead to a decrease in the material’s stiff ness. Lastly, another possible way is the use of specific processing conditions to induce oriented morphology. We will review all these aspects. This chapter is divided in two major parts: the first part is devoted to shortterm mechanical properties as elastic properties as a function of temperature and morphology aspects, failure properties and the diff erent strategies to improve them. The second part aims to present long-term properties such as creep behavior and modifi cations induced by aging, leading to an embrittlement of the POM limiting its lifetime

Séparation des niveaux de vigilance, à partir d'un signal EEG par les cartes auto-organisatrices de Kohonen

Colloque avec actes et comité de lecture. internationale.International audiencePlusieurs études ont déjà été menées pour tenter de discriminer, à l'aide de réseaux de neurones artificiels, les différents états de vigilance d'un sujet humain. Dans ce papier, nous présentons en détail une méthode de séparation des niveaux de vigilance, à partir d'un signal EEG par les cartes auto-organisatrices de Kohonen. Nous y avons associé dès le début des médecins dont l'expertise nous a été précieuse pour le recueil des données et la mise au point de notre modèle

Analyse et classification des états de vigilance par réseaux de neurones

Plusieurs études ont déjà été menées pour tenter de discriminer, à l'aide de réseaux de neurones artificiels, les différents états de vigilance d'un sujet humain. Dans ce rapport, nous rappelons ces études et nous présentons en détail les travaux que nous menons actuellement dans ce même domaine. Notre travail est original sur trois points. Tout d'abord nous avons mené une étude plus large et exhaustive sur les modèles neuronaux utilisés, sur leurs caractéristiques et sur leurs performances. Ensuite, nous y avons associé dès le début des médecins, dont l'expertise nous a été précieuse pour le recueil des données et la mise au point fine de nos modèles. Enfin, et surtout, notre étude a été orientée de manière à pouvoir obtenir un système léger, utilisable sans entrave par un sujet humain. Nous nous sommes en particulier attachés à limiter les besoins de calcul et de mémoire, ainsi que les accès aux données. Cette approche devrait donner lieu prochainement à la réalisation d'un système électronique portable

Micromechanical characterization of the interphase layer in semi-crystalline polyethylene

The interphase layer in semi-crystalline polyethylene is the least known constituent, compared to the amorphous and crystalline phases, in terms of mechanical properties. In this study, the Monte Carlo molecular simulation results for the interlamellar domain (i.e. amorphous+ interphases), reported in (Macromolecules 2006, 39, 439–447) are employed. The amorphous elastic properties are adopted from the literature and then two distinct micromechanical homogenization approaches are utilized to dissociate the interphase stiffness from that of the interlamellar region. The results of the two micromechanical approaches match perfectly. Interestingly, the dissociated interphase stiffness lacks the common feature of positive definiteness, which is attributed to its nature as a transitional domain between two coexisting phases. The sensitivity analyses reveal that this property is insensitive to the non-orthotropic components of the interlamellar stiffness and the uncertainties existing in the interlamellar and amorphous stiffnesses. Finally, using the dissociated interphase stiffness, its effective Young's modulus is calculated, which compares well with the effective interlamellar Young's modulus for highly crystalline polyethylene, reported in an experimental study. This satisfactory agreement along with the identical results produced by the two micromechanical approaches confirms the validity of the new information about the interphase elastic properties in addition to making the proposed dissociation methodology quite reliable when applied to similar problems

Fascismos, dictaduras y populismo en Europa y América Latina (años 30 y 40)

Cartel y programa del Encuentro celebrado en la Facultad de Comunicación de la Universidad de Sevilla, en el marco de las II Jornadas Americanistas de Otoño organizadas por la Escuela de Estudios Hispano-Americanos del Consejo Superior de Investigaciones Científicas. Sevilla, 2-4 de noviembre de 2004.Peer reviewe

Experimental evidence of size effect in nano‐reinforced polymers: Case of silica reinforced PMMA

International audienceno abstrac

Physiological Signal Classification with Artificial Neural Networks

Colloque avec actes et comité de lecture. nationale.National audienceA connectionist tool to help diagnose physiological signal, the Somatosensory Evoked Potentials (SEP), is presented. It is designed to be used in functional neurophysiological exploration. The tool should decide whether the signal is normal or pathological according to the inferior limbs SEP recording of an adult population. The obtained results (with a rate of success of 84%) allow us to think how to enlarge the application field of our tool to other evoked potentials and mainly to enlarge the data by the acquisition of SEP at different recording sites to locate the pathology

Artificial Neural Networks to extract Knowledge from EEG

EEG signals are very difficult to interpret because they are dynamic, non-linear and non-stationary signals. Human expertise also indicates that multi-level analysis must be performed to integrate various sources of knowledge. In this paper, we review these difficulties and propose that artificial neural networks could be good candidates to handle such a difficult problem