English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The alternating current potential drop (ACPD) is a nondestructive technique that is widely used to detect and size defects in conductive material. This paper describes a combined ACPD and support vector machine (SVM) approach to accurately recognize typical defects on the bottom surface of a metal plate, i.e., pits and cracks. We first conducted a simulation study, and then, based on ACPD, measured five voltage ratios between the test region and reference region. The analysis of finite simulation data enables the binary classification of two kinds of defects. To obtain an accurate separating hyperplane, key parameters of the SVM classifier were optimized using a genetic algorithm with training data from the simulations. Based on the optimized SVM classifier, reliable estimates of the defects in a metal plate were then obtained. The recognition results of the simulation dataset shows that the trained and optimized SVM model has a high classification accuracy, and the metal plate experiment also indicates that the model has good precision in actual defect classification

An SVM approach with alternating current potential drop technique to classify pits and cracks on the bottom of a metal plate