Soft sensors have been extensively developed and applied in the process industry. One of
the main challenges of the data-driven soft sensors is the lack of labeled data and the need …

In analyzing and assessing the condition of dynamical systems, it is necessary to account for
nonlinearity. Recent advances in computation have rendered previously computationally …

While unsupervised variational autoencoders (VAE) have become a powerful tool in
neuroimage analysis, their application to supervised learning is under-explored. We aim to …

Dimensional sentiment analysis (DSA) aims to compute real-valued sentiment scores of
texts in multiple dimensions such as valence and arousal. Existing methods for DSA are …

Just like humans, robots can improve their performance by practicing, ie by performing the
desired behavior many times and updating the underlying skill representation using the …

Garbage recycling is becoming an urgent need for the people as the rapid development of
human society is producing colossal amount of waste every year. However, current machine …

Numerical simulations for spatiotemporal processes involving material, geometrical and
contact nonlinearities might be computationally prohibitive for many-evaluation applications …

To discover and learn interpretable factors behind the visual data, many approaches use
extra regularization terms in learning disentangled representations, which lead to poor …

Non-linear analysis is of increasing importance in wind energy engineering as a result of
their exposure in extreme conditions and the ever-increasing size and slenderness of wind …

Data-driven models have shown broad application prospects in soft sensor modeling.
However, numerous challenges persist. On the one hand, data-driven soft sensor methods …