Just-in-time Learning (JITL) is a soft measurement method commonly used in industrial
processes, which can update local models in real-time to solve the problem of inaccurate …

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 …

Accurate soft sensing modeling of complex industrial processes can provide operation
guidance for improving the product quality. However, most modeling methods cannot mine …

Background Data-driven soft sensor technology is currently an important means for
industrial data prediction, addressing the challenge of predicting key quality variables in …

Abstract Just-in-time (JIT) learning based soft sensors have been widely used for predicting
product quality variables of nonlinear processes. They dynamically build online local models …

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 …

Just‐in‐time learning (JITL) technique has been widely used for adaptive soft sensing of
nonlinear processes. It builds online local model with the most relevant samples from …

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 …

Industrial processes are often characterized with high nonlinearities and dynamics. For soft
sensor modelling, it is important to model the nonlinear and dynamic relationship between …

Industrial soft sensing models have found extensive application in predicting key process
variables that are challenging to directly measure. However, the effectiveness of …