Most cited paper on Journal of Process Control published by SDSC faculty

Professor S. Joe QIN and Assistant Professor Yining DONG at the School of Data Science published the paper “A novel dynamic PCA algorithm for dynamic data modeling and process monitoring” in “Journal of Process Control” in 2018.  Since publication, the paper has been ranked the most downloaded papers for the past 90 days, and the most cited paper for three consecutive years.  In the paper, a novel dynamic PCA (DiPCA) algorithm is developed to extract explicitly a set of dynamic latent variables with descending order of predictability to capture the dynamic variations in high-dimensional time series data. In the past three years, the algorithm has been successfully applied for prediction, process monitoring and fault diagnosis in many real industrial applications.

Link of the publication:  https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85020414335&origin=inward

Website of Journal of Process Control:  https://www.journals.elsevier.com/journal-of-process-control