Stratégie de désassemblage d'un modèle CAO basée sur le concept de « sous-assemblage »

De nos jours, suite aux exigences croissantes des clients, les produits mécaniques deviennent de plus en plus complexes. Les outils de CAO doivent être capables de suivre cette évolution et offrir par conséquent les fonctionnalités requises pour assister les concepteurs dans les choix et la validation de ces produits. Les outils de calcul par la méthode des éléments finis, les outils de simulation des mouvements sont parmi les outils d'assitance au concepteur et totalement intégrés à la CAO. Dans cet article, une méthode de génération des séquences de désassemblage (SD) des produits mécaniques est proposée afin d'aider le concepteur durant le cycle de vie des produits. Cette méthode est basée sur le concept de sous-assemblage. Ainsi, une SD du produit en des sous-assemblages est proposée. Ensuite, à chaque sous-assemblage, une SD est générée. Pour mener à bien les propositions, une solution de co-simulation Solidworks-Matlab est détaillée. Un exemple concret est présenté afin de mettre en évidence la faisabilité et l'efficacité de l'approche proposée

PROMASC track report of provisioning and management of service oriented architecture and cloud computing

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Reuse and cooperation in E-Learning systems

International audienceThe aim of our research is to build a cooperative e-learning system adapted to different learners' profiles (knowledge levels, pedagogical preferences, goals, etc.). In this paper, we propose to enrich the e-learning systems by the concepts of learners' experiences capitalization and reuse. The system is used to build a large memory of learning situations supporting the reuse

E-Learning System Adapted to Learner Profile

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A Meta-model for context-aware adaptive Business Process as a Service in collaborative cloud environment

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Potential energy curves determination and relative properties of NaSr+ molecular ion for the ground and several excited states

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Energy saving in WSN using monitoring values prediction

8pagesInternational audienceThe Wireless Sensor Networks (WSNs) deployment introduces many issues and challenges mainly in terms of energy independence. In this context, we adopted the IBM control loop which is composed of four steps (Monitor, Analyze, Plan and Execute) to manage Quality of Service (QoS) 1. This paper focuses on the first step which consists in monitoring and sending periodically QoS values such as the value of power remaining in the battery of each sensor. We notice that the transmission process is very costly in terms of energy and reduces the battery lifetime. In this work, we propose a probabilistic approach that estimates a part of these QoS monitoring values and therefore economizes their transmission energy and extends the sensor battery lifetime. Our approach is based on the hidden Markov chain and the fuzzy logic. It is composed of two steps: (i) learning which allows apprehending the WSNs behavior and (ii) prediction which estimates QoS monitoring values. A WSN application deployed in a datacenter is studied as an illustration. The carried out experiments over AZEM WSN simulator show that the gain varies from 25% to 75% of the battery energy

Energy saving in WSN using monitoring values prediction

8pagesInternational audienceThe Wireless Sensor Networks (WSNs) deployment introduces many issues and challenges mainly in terms of energy independence. In this context, we adopted the IBM control loop which is composed of four steps (Monitor, Analyze, Plan and Execute) to manage Quality of Service (QoS) 1. This paper focuses on the first step which consists in monitoring and sending periodically QoS values such as the value of power remaining in the battery of each sensor. We notice that the transmission process is very costly in terms of energy and reduces the battery lifetime. In this work, we propose a probabilistic approach that estimates a part of these QoS monitoring values and therefore economizes their transmission energy and extends the sensor battery lifetime. Our approach is based on the hidden Markov chain and the fuzzy logic. It is composed of two steps: (i) learning which allows apprehending the WSNs behavior and (ii) prediction which estimates QoS monitoring values. A WSN application deployed in a datacenter is studied as an illustration. The carried out experiments over AZEM WSN simulator show that the gain varies from 25% to 75% of the battery energy