The objective of the work reported in this paper is to make decisions on the current state of a
dynamical system for pattern classification and anomaly/fault detection, which is often …

The article presents a mixed deep neural network (DNN) approach for detecting micron-
scale fatigue damage in high-strength polycrystalline aluminum alloys. Fatigue testing is …

Fatigue damage is one of the most common causes of failure in aerospace structural
components. While numerical modeling and laboratory-scale experimentation provide much …

Internet of things memiliki kemampuan untuk menghubungkan obyek pintar dan
memungkinkan mereka untuk berinteraksi dengan lingkungan dan peralatan komputasi …

The paper presents a coupled machine learning and pattern recognition algorithm to enable
early-stage fatigue damage detection in aerospace-grade aluminum alloys. U-and V …

Fatigue damage detection and its classification in metallic materials are persistently
challenging the structural health monitoring community. The mechanics of fatigue damage is …

Transfer learning (TL) is a machine learning (ML) tool where the knowledge, acquired from a
source domain, is “transferred” to perform a task in a target domain that has (to some extent) …

Anomaly detection is an essential step in the task of automating complex processes,
allowing the control algorithm to identify any undesirable operation and take preventive or …

Internet of things memiliki kemampuan untuk menghubungkan obyek pintar dan
memungkinkan mereka untuk berinteraksi dengan lingkungan dan peralatan komputasi …

The paper presents a coupled machine learning and pattern recognition algorithm to enable
early-stage fatigue damage detection in aerospace-grade aluminum alloys. U-and V …