Diagnosis of aerospace structure defects by a HPC implemented soft computing algorithm

Abstract

This study concerns with the diagnosis of aerospace structure defects by applying a HPC parallel implementation of a novel learning algorithm, named U-BRAIN. The Soft Computing approach allows advanced multi-parameter data processing in composite materials testing. The HPC parallel implementation overcomes the limits due to the great amount of data and the complexity of data processing. Our experimental results illustrate the effectiveness of the U-BRAIN parallel implementation as defect classifier in aerospace structures. The resulting system is implemented on a Linux-based cluster with multi-core architecture.

5 pages, IEEE International Workshop on Metrology for Aerospace

References in corpus (1)

• Towards a HPC-oriented parallel implementation of a learning algorithm for bioinformatics applications

Cited by in corpus (2)

• Feature Extraction and Soft Computing Methods for Aerospace Structure Defect Classification
• Shape-based defect classification for Non Destructive Testing