Diagnosis of aerospace structure defects by a HPC implemented soft computing algorithm
This addresses defect diagnosis in aerospace structures, which is domain-specific and incremental as it applies a new implementation to an existing problem.
The study tackled the problem of diagnosing aerospace structure defects by applying a high-performance computing (HPC) parallel implementation of a novel learning algorithm called U-BRAIN, achieving effectiveness as a defect classifier in aerospace structures.
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.