Company Filing History:
Years Active: 2020
Title: XuDong Shi: Innovator in Soft Sensing Technologies
Introduction
XuDong Shi is a prominent inventor based in Shanghai, China. He has made significant contributions to the field of soft sensing technologies, particularly in industrial processes. His innovative approach addresses the challenges of limited labeled data samples in nonlinear and multimode processes.
Latest Patents
XuDong Shi holds a patent titled "Soft sensing of a nonlinear and multimode processes based on semi-supervised weighted Gaussian regression." This patent discloses methods for soft sensing of nonlinear and multimode industrial processes using a semi-supervised probabilistic density-based regression approach known as Semi-supervised Weighted Gaussian Regression (SWGR). In this method, different weights are assigned to each training sample based on their similarities to a query sample. A local weighted Gaussian density is constructed to capture the joint probability of historical samples around the query sample. The training process incorporates both labeled and unlabeled data samples through a maximum likelihood estimation algorithm. This approach allows the soft sensor model to approximate the nonlinear mechanics of input and output variables, effectively addressing the insufficiency of labeled samples. The output prediction and the uncertainty of prediction can be obtained through the conditional distribution.
Career Highlights
XuDong Shi is affiliated with King Abdulaziz University, where he continues to advance his research in soft sensing technologies. His work has garnered attention for its practical applications in various industrial settings.
Collaborations
Some of his notable coworkers include Yusuf Abdulaziz Al-Turki and Abdullah Abusorrah. Their collaborative efforts contribute to the ongoing development of innovative solutions in the field.
Conclusion
XuDong Shi's contributions to soft sensing technologies exemplify the impact of innovative thinking in addressing complex industrial challenges. His work not only enhances the understanding of nonlinear processes but also paves the way for future advancements in the field.