LGECO INSA de Strasbourg

Strasbourg, France

LGECO INSA de Strasbourg

Strasbourg, France
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Yan W.,University of Strasbourg | Zanni-Merk C.,University of Strasbourg | Rousselot F.,University of Strasbourg | Cavallucci D.,LGECO INSA de Strasbourg | Collet P.,University of Strasbourg
Advanced Materials Research | Year: 2014

A growing number of industries feel the need of formalizing their innovation approaches. Modern innovation theories and methods use different knowledge sources for solving inventive design problems. These sources are generally about similar notions, but the level of detail of their description can be very different. We are interested in finding semantic links among these sources and developing an intelligent way of managing this knowledge, with the goal of assisting the inventive design expert during his activities. This paper explores a short text semantic similarity approach to search potential links among these sources. These links available could facilitate the retrieval for the heuristic solutions of inventive problems for TRIZ users. © (2014) Trans Tech Publications, Switzerland.


Latour B.,LGECO INSA de Strasbourg | Harmand S.,University of Lille Nord de France | Harmand S.,University of Valenciennes and Hainaut‑Cambresis
Quantitative InfraRed Thermography Journal | Year: 2013

In this study, the local convective heat transfer from a disc was evaluated using an infrared thermographic experimental set-up. Solving the inverse conduction heat transfer problem allows the local convective heat transfer coefficient to be identified. We used an inverse method, based on spatial regularisation, in order to take radial and azimuthal conduction into account. This model was tested by using a crossflow with a Reynolds number ranging from 11,350 to 39,600, corresponding to the turbulent flow domain. In this paper, the local convective heat transfer distribution on the disc surface allows to study the boundary layer development with upstream Reynolds number as well as the horseshoe vortex and its impact on heat transfer around and downstream of the cylinder. © 2013 Copyright Taylor and Francis Group, LLC.


Bouche P.,LGeCO INSA de Strasbourg | Zanni-Merk C.,LGeCO INSA de Strasbourg
Simulation | Year: 2011

In our increasingly competitive world, nowadays companies implement improvement strategies in every department and, in particular, in their manufacturing systems. This paper discusses the use of a global method, based on a knowledge-based approach, aiming at the development of a software tool for modeling and analysis of production flows. The main goal is the improvement of the performance of the production line. This method is based on data-processing and data-mining techniques and will help the acquisition of the meta-knowledge that is needed for finding correlations among different events in the line. Different techniques will be used: a graphical representation of the production, identification of specific behavior and research of correlations among events in the production line. Most of these techniques are based on statistical and probabilistic analyses. Events are expressed in the form of phenomena. To carry out high-level analyses, a stochastic approach will be used to identify breakdown models, which are the expression of specific correlations between phenomena. Breakdowns models will be the basis for, finally, defining action plans to improve the performance of the manufacturing lines. © 2010 The Author(s).


Bouche P.,LGECO INSA de Strasbourg | Gartiser N.,LGECO INSA de Strasbourg | Zanni-Merk C.,LGECO INSA de Strasbourg
Smart Innovation, Systems and Technologies | Year: 2011

This article describes the research project MAEOS. MAEOS is a project about the modelling of the support to the organizational and strategic development of SMEs. The main objective of MAEOS is to improve the efficiency and performance of business advice to SMEs. To achieve this objective, a multi-disciplinary team was created. Two main research areas are represented: artificial intelligence and management sciences. This work aims at establishing a set of methods and software tools for analysis and diagnosis of SMEs. We address three main questions: how to extract knowledge from experts but also practical knowledge from consultants, how to formalize it and how to use it to help a consultant or an entrepreneur. © Springer-Verlag Berlin Heidelberg 2011.


Rousselot F.,LGECO INSA de Strasbourg | Zanni-Merk C.,LGECO INSA de Strasbourg | Cavallucci D.,LGECO INSA de Strasbourg
Global Product Development - Proceedings of the 20th CIRP Design Conference | Year: 2011

In this chapter, we present an approach to knowledge acquisition from patents based on our own inventive design methodology. This methodology, based on TRIZ, extends its practice to the resolution of complex problems. We have proposed an ontology of all the concepts and models used in our approach. An operative process easing knowledge acquisition, useful to the experts practicing inventive design, is based on this ontology. © Springer-Verlag Berlin Heidelberg 2011.


Gartiser N.,LGeCo INSA de Strasbourg | Zanni-Merk C.,ICube INSA de Strasbourg | Boullosa L.,National University of Rosario | Casali A.,National University of Rosario
Procedia Computer Science | Year: 2014

This article describes the research project MAEOS, whose purpose is to model the organizational and strategic development of SMEs. The main objective of this project is to improve the efficiency and performance of business advice given to this kind of companies by establishing a set of methods and software tools for analysis and diagnosis. In order to achieve this, a multi-disciplinary team was created in which two main research areas are represented: artificial intelligence and management science. In this work several key questions of the knowledge engineering field are addressed by the team: how to extract theoretical knowledge (e.g. from scientific works in management science) and practical one (e.g. from consultants); how to formalize it and use it to assist consultants in their daily work. © 2014 The Authors. Published by Elsevier B.V.

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