In recent years, modern spectral analysis techniques, such as ultraviolet–visible (UV-vis)
spectroscopy, mid-infrared (MIR) spectroscopy, near-infrared (NIR) spectroscopy, Raman …

Virtual sensing technology is crucial for high product quality and productivity in any industry.
This review aims to clarify the trend of research and application of virtual sensing technology …

Uncertainty quantification (UQ) tasks, such as model calibration, uncertainty propagation,
and optimization under uncertainty, typically require several thousand evaluations of the …

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 …

Data-driven soft sensors have been widely used in industrial processes. Traditional soft
sensors are mostly shallow networks, which cannot easily describe the complicated process …

Deep learning is an important and effective tool for process soft sensor modeling in
industrial artificial intelligence. Traditional deep learning methods like stacked autoencoder …

To realize stable operation of the ironmaking process, it is important to predict hot metal
temperature (HMT) in a blast furnace. Recently, deep learning is emerging as a highly active …

Probabilistic principal component regression (PPCR) has been introduced for soft sensor
modeling as a probabilistic projection regression method, which is effective in handling data …

Laser-induced breakdown spectroscopy (LIBS) is a promising atomic emission
spectroscopic technique for multi-elemental analysis and has the advantages of real-time …

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