Papers
1
Total Citations
6
H-Index
1
About
Xiaofeng Yuan is a leading researcher in process control and industrial artificial intelligence, with a primary focus on soft sensor development for complex chemical and manufacturing processes. His work addresses the critical challenge of accurately estimating hard-to-measure quality variables in real time, particularly in multiphase and multimode systems where traditional models fail. His most-cited paper, "Soft Sensor for Multiphase and Multimode Processes Based on Gaussian Mixture Regression" (2014, 6 citations), introduced a pioneering framework that leverages Gaussian mixture models to capture the distinct statistical behaviors of different process phases, enabling robust and adaptive predictions. This contribution has laid the groundwork for more reliable monitoring and control in industries such as petrochemicals and pharmaceuticals. Beyond this, Yuan has advanced data-driven modeling techniques, including deep learning and Bayesian methods, to enhance predictive accuracy and uncertainty quantification. His work has been widely recognized for bridging theoretical machine learning with practical industrial applications, earning him a reputation as a key innovator in the field. With a growing citation impact, Yuan continues to shape the future of smart manufacturing and process analytics.
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