Application of reciprocity for facilitation of wave field visualization and defect detection

The motion visualization in a structural component was studied for defect detection. Elastic motions were excited by hammer impacts at multiple points and received by an accelerometer at a fixed point. Reciprocity in elastodynamics is only valid under certain conditions. Its validity under given experimental conditions was derived from the elastodynamic reciprocity theorem. Based on this, the dynamic motion of the structural component was obtained for fixed-point excitation from measurements performed using multipoint excitations. In the visualized eigenmodes, significant additional deformation was observed at the wall thinning inserted as an artificial defect. To prevent the dependence of defect detection on its position within the mode shape, another approach was proposed based on the extraction of guided wave modes immediately after impact excitation. It is shown that this maximum intensity projection method works well in detecting defects.

Transfer Learning with Human Corneal Tissues: An Analysis of Optimal Cut-Off Layer

Transfer learning is a powerful tool to adapt trained neural networks to new tasks. Depending on the similarity of the original task to the new task, the selection of the cut-off layer is critical. For medical applications like tissue classification, the last layers of an object classification network might not be optimal. We found that on real data of human corneal tissues the best feature representation can be found in the middle layers of the Inception-v3 and in the rear layers of the VGG-19 architecture.