most citedSelf-scalable Tanh (Stan): Faster Convergence and Better Generalization in Physics-informed Neural Networks

14 citations · 31 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG20223 cited

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…

cs.LG20224 cited

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…

stat.AP20221 cited

A Novel Sparse Bayesian Learning and Its Application to Fault Diagnosis for Multistation Assembly Systems

Jihoon Chung, Bo Shen, Zhenyu +1

This paper addresses the problem of fault diagnosis in multistation assembly systems. Fault diagnosis is to identify process faults that cause the excessive dimensional variation o…

cs.LG202214 cited

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…

cs.CV20229 cited

Smooth Robust Tensor Completion for Background/Foreground Separation with Missing Pixels: Novel Algorithm with Convergence Guarantee

Bo Shen, Weijun Xie, Zhenyu Kong

The objective of this study is to address the problem of background/foreground separation with missing pixels by combining the video acquisition, video recovery, background/foregro…