1 citations · 1 across the 6 of their papers we have counts for
6 papers
An Inexact Riemannian Gradient Descent Algorithm on the Stiefel Manifold with One Newton-Schulz Iteration
Yuqiu Su, Wen Huang
In this paper, we propose an inexact Riemannian gradient descent algorithm on the Stiefel manifold (IRGS-StieONS) using an adaptive step size, where the ``inexact'' refers to the i…
A Multi-head Attention Fusion Network for Industrial Prognostics under Discrete Operational Conditions
Yuqi Su, Xiaolei Fang
Complex systems such as aircraft engines, turbines, and industrial machinery often operate under dynamically changing conditions. These varying operating conditions can substantial…
A Two-Stage Federated Learning Approach for Industrial Prognostics Using Large-Scale High-Dimensional Signals
Yuqi Su, Xiaolei Fang
Industrial prognostics aims to develop data-driven methods that leverage high-dimensional degradation signals from assets to predict their failure times. The success of these model…
SLRQA: A Sparse Low-Rank Quaternion Model for Color Image Processing with Convergence Analysis
Zhanwang Deng, Yuqiu Su, Wen Huang
In this paper, we propose a Sparse Low-rank Quaternion Approximation (SLRQA) model for color image processing problems with noisy observations. %Different from the existing color i…
Deep Learning-Based Residual Useful Lifetime Prediction for Assets with Uncertain Failure Modes
Yuqi Su, Xiaolei Fang
Industrial prognostics focuses on utilizing degradation signals to forecast and continually update the residual useful life of complex engineering systems. However, existing progno…
Federated Multilinear Principal Component Analysis with Applications in Prognostics
Chengyu Zhou, Yuqi Su, Tangbin Xia +1
Multilinear Principal Component Analysis (MPCA) is a widely utilized method for the dimension reduction of tensor data. However, the integration of MPCA into federated learning rem…