14 citations · 32 across the 7 of their papers we have counts for
5 papers · 1 filter
A Sparse Bayesian Learning for Diagnosis of Nonstationary and Spatially Correlated Faults with Application to Multistation Assembly Systems
Jihoon Chung, Zhenyu Kong
Sensor technology developments provide a basis for effective fault diagnosis in manufacturing systems. However, the limited number of sensors due to physical constraints or undue c…
Anomaly Detection in Additive Manufacturing Processes using Supervised Classification with Imbalanced Sensor Data based on Generative Adversarial Network
Jihoon Chung, Bo Shen, Zhenyu +1
Supervised classification methods have been widely utilized for the quality assurance of the advanced manufacturing process, such as additive manufacturing (AM) for anomaly (defect…
Reinforcement Learning-based Defect Mitigation for Quality Assurance of Additive Manufacturing
Jihoon Chung, Bo Shen, Andrew Chung Chee Law +2
Additive Manufacturing (AM) is a powerful technology that produces complex 3D geometries using various materials in a layer-by-layer fashion. However, quality assurance is the main…
Self-scalable Tanh (Stan): Faster Convergence and Better Generalization in Physics-informed Neural Networks
Raghav Gnanasambandam, Bo Shen, Jihoon Chung +3
Physics-informed Neural Networks (PINNs) are gaining attention in the engineering and scientific literature for solving a range of differential equations with applications in weath…
WOOD: Wasserstein-based Out-of-Distribution Detection
Yinan Wang, Wenbo Sun, Jionghua "Judy" Jin +2
The training and test data for deep-neural-network-based classifiers are usually assumed to be sampled from the same distribution. When part of the test samples are drawn from a di…