A rich supply of data and innovative algorithms have made data-driven modeling a popular
technique in modern industry. Among various data-driven methods, latent variable models …

Detection techniques for massive multiple-input multiple-output (MIMO) have gained a lot of
attention in both academia and industry. Detection techniques have a significant impact on …

Industrial process data are naturally complex time series with high nonlinearities and
dynamics. To model nonlinear dynamic processes, a long short-term memory (LSTM) …

The growth of data collection in industrial processes has led to a renewed emphasis on the
development of data-driven soft sensors. A key step in building an accurate, reliable soft …

Abstract Stacked auto-encoder (SAE)-based deep learning has been introduced for fault
classification in recent years, which has the potential to extract deep abstract features from …

Soft sensors have been extensively used to predict the difficult-to-measure key quality
variables. The robust soft sensors should be able to sufficiently extract the local dynamic and …

The low degree of enterprise digitization and the existence of personalized customization
and small batch production manufacturing modes lead to the characteristics of small …

Accurate and fast fault detection in transmission lines is of high importance to maintain the
reliability of power systems. Most of the existing methods suffer from false detection of high …

Deep transfer network (DTN) has been widely used for classification tasks, which introduces
maximum mean discrepancy (MMD) based loss function to extract similar latent features and …

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 …