no code implementations • 20 Oct 2023 • Jihoon Chung, Zhenyu Kong
This article proposes a novel fault diagnosis method: clustering spatially correlated sparse Bayesian learning (CSSBL), and explicitly demonstrates its applicability in a multistation assembly system that is vulnerable to the above challenges.
1 code implementation • 13 Mar 2023 • Chenyang Li, Jihoon Chung, Biao Cai, Haimin Wang, Xianlian Zhou, Bo Shen
This paper focuses on two model compression techniques: low-rank approximation and weight pruning in neural networks, which are very popular nowadays.
no code implementations • 28 Oct 2022 • Jihoon Chung, Bo Shen, Zhenyu, Kong
It is beneficial to generate effective artificial sample data for the abnormal states to make a more balanced training set.
no code implementations • 28 Oct 2022 • Jihoon Chung, Bo Shen, Andrew Chung Chee Law, Zhenyu, Kong
Since AM typically fabricates a small number of customized products, this paper aims to create an online learning-based strategy to mitigate the new defects in AM process while minimizing the number of samples needed.
no code implementations • 28 Oct 2022 • Jihoon Chung, Bo Shen, Zhenyu, Kong
Fault diagnosis is to identify process faults that cause the excessive dimensional variation of the product using dimensional measurements.
no code implementations • 26 Apr 2022 • Raghav Gnanasambandam, Bo Shen, Jihoon Chung, Xubo Yue, Zhenyu, Kong
To address this, a Self-scalable tanh (Stan) activation function is proposed for the PINNs.
no code implementations • ICCV 2021 • Jihoon Chung, Cheng-hsin Wuu, Hsuan-ru Yang, Yu-Wing Tai, Chi-Keung Tang
We contribute HAA500, a manually annotated human-centric atomic action dataset for action recognition on 500 classes with over 591K labeled frames.
Ranked #1 on Action Recognition on HAA500
2 code implementations • CVPR 2020 • Ho Kei Cheng, Jihoon Chung, Yu-Wing Tai, Chi-Keung Tang
In this paper, we propose a novel approach to address the high-resolution segmentation problem without using any high-resolution training data.
Ranked #1 on Semantic Segmentation on BIG (using extra training data)