Laboratoire d’Ingénierie des Systèmes Physiques Et Numériques (LISPEN)

 

Le LISPEN met en synergie les compétences d’enseignants-chercheurs des campus d’Aix-en- Provence de Cluny (Institut Image à Chalon sur Saône) et de Lille autour de la maîtrise du développement et l’exploitation des systèmes dynamiques multi-physiques et virtuels pour l’Industrie du Futur. Les thématiques de recherche traitent de l’Ingénierie Système & Maquette numérique, la Simulation et le Contrôle des systèmes, l’Interaction Homme-Système et l’Aide à la décision.
http://lispen.ensam.eu/

Recent Submissions

  • On the Use of Quality Metrics to Characterize Structured Light-based Point Cloud Acquisitions 
    Article dans une revue avec comité de lecture
    LI, Tingcheng; RUDING, Lou; POLETTE, Arnaud; DOMINIQUE, NOZAIS; ZILONG, SHAO; PERNOT, JEAN-PHILIPPE (Computer-Aided Design & Applications, 2023-01-01)
    Even if 3D acquisition systems are nowadays more and more e cient, the resulting point clouds nevertheless contain quality defects that must be taken into account beforehand, in order to better anticipate and control ...
  • On the use of quality metrics to characterize structed light-based point cloud acquisitions 
    Communication avec acte
    LOU, Rudin; ccARNAUD, POLETTE; ZILONG, SHAO; DOMINIQUE, NOZAIS; PERNOT, JEAN-PHILIPPE (2022-07-11)
    Accurately transferring the real world to the virtual one through reverse engineering is of utmost importance in Industry 4.0 applications. Indeed, acquiring good quality 3D representations of existing physical objects ...
  • Towards a Novel Comparison Framework of Digital Maturity Assessment Models 
    Ouvrage scientifique
    COGNET, B.; PERNOT, J.-P.; RIVEST, L.; DANJOU, C.; WUEST, T.; KÄRKKÄINEN, H.; LAFLEUR, M. (Springer International Publishing, 2020-02-28)
    The fourth industrial revolution is forcing companies to rethink their status quo – creating a need to assess their digital maturity as a basis for improvements. As a result, there is a variety of maturity models available ...
  • Sensitivity Analysis for Simulated Annealing-Based Fitting of CAD Models to Point Clouds of Digitized Parts 
    Ouvrage scientifique
    POLETTE, Arnaud; PERNOT, Jean-Philippe; GIANNINI, Franca; MONTI, Marina (Springer International Publishing, 2021-04-22)
    AbstractThis paper addresses the way a simulated annealing-based fitting strategy can be enhanced by leveraging a sensitivity analysis able to characterize the impact of the variations in the parameters of a CAD model on ...
  • Automatic CAD Assemblies Generation by Linkage Graph Overlay for Machine Learning Applications 
    Communication avec acte
    VERGEZ, Lucas; POLETTE, Arnaud; PERNOT, Jean-Phillipe (CAD, 2021-07-05)
    Enlarging 3D model databases by shape synthesis is a large field of research. Indeed, the use of machine learning techniques requires a huge amount of labeled CAD models, and it is therefore crucial to rely on large ...
  • MULTI-SOURCE POINT CLOUD SEMANTIC SEGMENTATION USING NEURAL NETWORK 
    Article dans une revue avec comité de lecture
    MONTLAHUC, J.; POLETTE, A.; TAHAN, A.; PERNOT, J.-P.; RIVEST, L. (Copernicus GmbH, 2022-05-30)
    Abstract. The purpose of this study is to enhance point cloud semantic segmentation by using point clouds from multiple distinct technologies on the same capture location and to determine whether employing various technologies ...
  • H-BIM and Artificial Intelligence: Classification of Architectural Heritage for Semi-Automatic Scan-to-BIM Reconstruction 
    Article dans une revue avec comité de lecture
    CROCE, Valeria; CAROTI, Gabriella; PIEMONTE, Andrea; DE LUCA, Livio; VÉRON, Philippe (MDPI AG, 2023-02-23)
    We propose a semi-automatic Scan-to-BIM reconstruction approach, making the most of Artificial Intelligence (AI) techniques, for the classification of digital architectural heritage data. Nowadays, Heritage- or Historic-Building ...

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