The existing deep transfer learning-based intelligent fault diagnosis studies for machinery
mainly consider steady speed scenarios, and there exists a problem of low diagnosis …

Existing researches about unsupervised cross-domain bearing fault diagnosis mostly
consider global alignment of feature distributions in various domains, and focus on relatively …

This paper presents a novel method for learning the cyclic contents of stochastic time series:
the deep time-growing neural network (DTGNN). The DTGNN combines supervised and …

Sparse Bayesian learning (SBL) is a popular machine learning approach with a superior
generalization capability due to the sparsity of its adopted model. However, it entails a matrix …

Cyber-physical systems and data-driven techniques have potentials to facilitate the
prediction and control of product quality, which is one of the two most important issues in …

The flourish of the Internet of Things (IoT) and data-driven techniques provide new ideas for
enhancing agricultural production, where evapotranspiration estimation is a crucial issue in …

Quality Diversity (QD) algorithms such as MAP-Elites are a class of optimisation techniques
that attempt to find many high performing points that all behave differently according to a …

In recent years, the major geological hazard of landslides has greatly impact normal human
life. Deep belief networks (DBN) is a commonly used deep learning model, and the DBN …

An accelerated failure time (AFT) model has been widely used for the analysis of censored
survival or failure time data. However, the AFT imposes the restrictive log-linear relation …

Nonlinear device characteristics present a severe performance-bottleneck for several
upcoming next-generation wireless communication systems and prevent them from …