12 citations · 27 across the 10 of their papers we have counts for
10 papers
Quantum Safe-Set Bayesian Optimization for Quality Improvement in Fuselage Assembly
Jiayu Liu, Chong Liu, Trevor Rhone +1
Recent efforts in smart manufacturing have enhanced aerospace fuselage assembly processes, particularly by innovating shape adjustment techniques to minimize dimensional gaps betwe…
Uni-3DAD: GAN-Inversion Aided Universal 3D Anomaly Detection on Model-free Products
Jiayu Liu, Shancong Mou, Nathan Gaw +1
Anomaly detection is a long-standing challenge in manufacturing systems. Traditionally, anomaly detection has relied on human inspectors. However, 3D point clouds have gained atten…
Spatiotemporal Predictions of Toxic Urban Plumes Using Deep Learning
Yinan Wang, M. Giselle Fernández-Godino, Nipun Gunawardena +2
Industrial accidents, chemical spills, and structural fires can release large amounts of harmful materials that disperse into urban atmospheres and impact populated areas. Computer…
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…
An Intelligent End-to-End Neural Architecture Search Framework for Electricity Forecasting Model Development
Jin Yang, Guangxin Jiang, Yinan Wang +1
Recent years have witnessed exponential growth in developing deep learning (DL) models for time-series electricity forecasting in power systems. However, most of the proposed model…
Coverage Path Planning for Robotic Quality Inspection with Control on Measurement Uncertainty
Yinhua Liu, Wenzheng Zhao, Hongpeng Liu +2
The optical scanning gauges mounted on the robots are commonly used in quality inspection, such as verifying the dimensional specification of sheet structures. Coverage path planni…
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…
NP-ODE: Neural Process Aided Ordinary Differential Equations for Uncertainty Quantification of Finite Element Analysis
Yinan Wang, Kaiwen Wang, Wenjun Cai +1
Finite element analysis (FEA) has been widely used to generate simulations of complex and nonlinear systems. Despite its strength and accuracy, the limitations of FEA can be summar…
StressNet: Deep Learning to Predict Stress With Fracture Propagation in Brittle Materials
Yinan Wang, Diane Oyen, Weihong +7
Catastrophic failure in brittle materials is often due to the rapid growth and coalescence of cracks aided by high internal stresses. Hence, accurate prediction of maximum internal…
Tensor decomposition to Compress Convolutional Layers in Deep Learning
Yinan Wang, Weihong "Grace" Guo, Xiaowei Yue
Feature extraction for tensor data serves as an important step in many tasks such as anomaly detection, process monitoring, image classification, and quality control. Although many…