• Citer

Schematics Retrieval Using Whole-Graph Embedding Similarity

par Almasri, Feras ;Debeir, Olivier
Référence Electronics (Switzerland), 13, 7, 1176
Publication Publié, 2024-04

Article révisé par les pairs

• Accès en ligne
• Détails
• Contenu
• Statistiques

Résumé :

This paper addresses the pressing environmental concern of plastic waste, particularly in the biopharmaceutical production sector, where single-use assemblies (SUAs) significantly contribute to this issue. To address and mitigate this problem, we propose a unique approach centered around the standardization and optimization of SUA drawings through digitization and structured representation. Leveraging the non-Euclidean properties of SUA drawings, we employ a graph-based representation, utilizing graph convolutional networks (GCNs) to capture complex structural relationships. Introducing a novel weakly supervised method for the similarity-based retrieval of SUA graph networks, we optimize graph embeddings in a low-dimensional Euclidean space. Our method demonstrates effectiveness in retrieving similar graphs that share the same functionality, offering a promising solution to reduce plastic waste in pharmaceutical assembly processes.