Student's-t Mixture Regression-Based Robust Soft Sensor Development for Multimode Industrial Processes. - Jingbo Wang 004; Weiming Shao; Zhihuan Song (Sensors, 2018)

Robust Soft Sensing for Multi-mode Processes Based on Bayesian Regularized Student's-t Mixture Regression. - Jingbo Wang 004; Weiming Shao; Zhihuan Song (ASCC, 2019)

Robust inferential sensor development based on variational Bayesian Student's-t mixture regression. - Jingbo Wang 004; Weiming Shao; Zhihuan Song (Neurocomputing, 2019)

Semisupervised Robust Modeling of Multimode Industrial Processes for Quality Variable Prediction Based on Student's t Mixture Model. - Weiming Shao; Zhiqiang Ge; Zhihuan Song; Jingbo Wang 004 (IEEE Trans. Ind. Informatics, 2020)

Dynamic Variational Bayesian Student's T Mixture Regression With Hidden Variables Propagation for Industrial Inferential Sensor Development. - Jingbo Wang 004; Weiming Shao; Xinmin Zhang; Zhihuan Song (IEEE Trans. Ind. Informatics, 2021)

Identification of important factors influencing nonlinear counting systems. - Xinmin Zhang; Jingbo Wang 004; Chihang Wei; Zhihuan Song (Frontiers Inf. Technol. Electron. Eng., 2022)