Semi-supervised data are quite common in the process industry, which has caught much
attention in recent years. The semi-supervised feature of process data not only has a great …

Voltammetric electronic tongue (VE-tongue) is a promising technology for advanced sensing
and measurement applications. The review examines the measurement principles, working …

Industrial process plants are instrumented with a large number of redundant sensors and the
measured variables are often contaminated by random noises. Thus, it is significant to …

Missing data values and differing sampling rates, particularly for important parameters such
as particle size and stream composition, are a common problem in minerals processing …

This paper presents a variational autoencoder-based just-in-time (JIT) learning framework
for soft sensor modeling. Just-in-Time learning is often applied for soft sensor modeling in …

Missing data imputation and automatic fault detection of wastewater treatment plant (WWTP)
sensors are crucial for energy conservation and environmental protection. Given the …

Soft sensor technique has become a promising solution to enable real-time estimations of
difficult-to-measure quality variables in industrial processes. However, traditional soft sensor …

The polymerization process produces industrially important products; hence, its monitoring
and control are of paramount importance. However, the nonavailability of real-time (on …

Data-based soft sensors have been widely applied in industrial processes for enabling
online prediction of difficult-to-measure variables. However, there exists a common …

Industrial data are often high-dimensional, nonlinear and multiple-modal. This paper
develops a soft sensor model based on Gaussian mixture Variational Autoencoder (GMVAE) …