Soft sensors are mathematical models employed to estimate hard-to-measure variables from
available easy-to-measure variables. These sensors are typically developed using either …

Deep learning-based soft sensor has been a hot topic for quality variable prediction in
modern industrial processes. Feature representation with deep learning is the key step to …

This paper proposes an adaptive soft sensing method based on selective ensemble of local
partial least squares models, referring to as the SELPLS, for quality prediction of nonlinear …

Due to time-varying virtual drift in multimode processes, the performance of soft sensors will
degrade after online deployment. Traditional adaptive mechanisms have been developed to …

Data‐driven soft sensing approaches have been a hot research field for decades and are
increasingly used in industrial processes due to their advantages of easy implementation …

Wind power has become an important part of clean energy. Reliable wind power forecasting
is the key to performing optimal scheduling of wind energy. However, it is challenging to …

The goal of this paper is to design and implementation of a new data-driven soft sensor that
uses state dependent parameter (SDP) models to improve product quality monitoring. The …

The use of soft sensors for the prediction of Nitric Oxides (NOx) emissions to meet quality
regulations has become increasingly attractive from the economic point of view. However …

In semiconductor manufacturing processes, virtual metrology (VM) has been investigated as
a promising tool to predict important characteristics of products. Although partial least …

Batch processes are characterized by inherent nonlinearity, multiple phases and time-
varying behavior that pose great challenges for accurate state estimation. A multiphase just …