52 citations · 63 across the 8 of their papers we have counts for
8 papers
An Unsupervised Time Series Anomaly Detection Approach for Efficient Online Process Monitoring of Additive Manufacturing
Frida Cantu, Salomon Ibarra, Arturo Gonzales +3
Online sensing plays an important role in advancing modern manufacturing. The real-time sensor signals, which can be stored as high-resolution time series data, contain rich inform…
ADs: Active Data-sharing for Data Quality Assurance in Advanced Manufacturing Systems
Yue Zhao, Yuxuan Li, Chenang Liu +1
Machine learning (ML) methods are widely used in industrial applications, which usually require a large amount of training data. However, data collection needs extensive time costs…
Advancing Additive Manufacturing through Deep Learning: A Comprehensive Review of Current Progress and Future Challenges
Amirul Islam Saimon, Emmanuel Yangue, Xiaowei Yue +2
This paper presents the first comprehensive literature review of deep learning (DL) applications in additive manufacturing (AM). It addresses the need for a thorough analysis in th…
Pseudo Replay-based Class Continual Learning for Online New Category Anomaly Detection in Advanced Manufacturing
Yuxuan Li, Tianxin Xie, Chenang Liu +1
The incorporation of advanced sensors and machine learning techniques has enabled modern manufacturing enterprises to perform data-driven classification-based anomaly detection bas…
Attention-stacked Generative Adversarial Network (AS-GAN)-empowered Sensor Data Augmentation for Online Monitoring of Manufacturing System
Yuxuan Li, Chenang Liu
Machine learning (ML) has been extensively adopted for the online sensing-based monitoring in advanced manufacturing systems. However, the sensor data collected under abnormal stat…
Knowledge Distillation-based Information Sharing for Online Process Monitoring in Decentralized Manufacturing System
Zhangyue Shi, Yuxuan Li, Chenang Liu
In advanced manufacturing, the incorporation of sensing technology provides an opportunity to achieve efficient in-situ process monitoring using machine learning methods. Meanwhile…
Model-Informed Generative Adversarial Network (MI-GAN) for Learning Optimal Power Flow
Yuxuan Li, Chaoyue Zhao, Chenang Liu
The optimal power flow (OPF) problem, as a critical component of power system operations, becomes increasingly difficult to solve due to the variability, intermittency, and unpredi…
A Generative Adversarial Network-based Selective Ensemble Characteristic-to-Expression Synthesis (SE-CTES) Approach and Its Applications in Healthcare
Yuxuan Li, Ying Lin, Chenang Liu
Investigating the causal relationships between characteristics and expressions plays a critical role in healthcare analytics. Effective synthesis for expressions using given charac…