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Fall 2023
The problem of missing data is omnipresent in a wide range of real-world datasets. When learning and predicting on this data with neural networks, the typical strategy is to fill-in or complete these missing values in the dataset, called impute-then-regress. Much less common is to attempt to...
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Robust Generalized Weighted Probabilistic Principal Component Regression with Application in Data-driven Optimization
DownloadSpring 2022
The operations of the plant may deviate from the initial design due to the uncertainties and changes in the several conditions as a result of market demand, operation conditions, and safety regulations over time. To maintain productivity, safety, and efficiency, operators should ensure the...
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Fall 2020
Data-driven modeling approaches have been widely studied and applied to the process industries for inferential sensor development, process monitoring and fault detection and early warnings, etc. Essential information of process, like dynamic and relationships between process variables are buried...