This brief develops a novel just-in-time (JIT) learning-based soft sensor for modeling of
industrial processes. The recorded data is assumed to exhibit non-Gaussian signal …

Just-in-time learning (JITL) has recently been used for online soft sensor modeling. Unlike
traditional global approaches, the JITL-based method employs a local model built from …

Soft sensors have been extensively applied for predicting difficult-to-measure quality
variables. However, industrial processes are often characterized with the nonlinearity and …

This paper presents a new semi-supervised probabilistic density-based regression
approach, called Semi-supervised Weighted Gaussian Regression (SWGR), for the soft …

Real time estimation of target quality variables using soft sensor relevant to time varying
process conditions will be a significant step forward in effective implementation of Industry …

Softsensors are widely used for estimating product quality or other key variables when on-
line analyzers are not available. However, their estimation performance deteriorates when …

Just-in-time learning (JITL), which can deal with both process nonlinearities and time-
varying characteristics, has become a widely used tool for industrial soft sensing. High …

Just-in-time learning (JITL) is one of the most widely used strategies for soft sensor modeling
in nonlinear processes. However, traditional JITL methods have difficulty in dealing with …

In modern industrial process control, just-in-time learning (JITL)-based soft sensors have
been widely applied. An accurate similarity measure is crucial in JITL-based soft sensor …

Just-in-time learning (JITL) is a commonly used technique for industrial soft sensing of
nonlinear processes. However, traditional JITL approaches mainly focus on equal sample …