Deep Neural Networks (DNNs) trained on one dataset (source domain) do not perform well
on another set of data (target domain), which is different but has similar properties as the …

With the ChemCam instrument, laser-induced breakdown spectroscopy (LIBS) has
successively contributed to Mars exploration by determining the elemental compositions of …

Fault diagnosis is very important to the modern manufacturing system. As a powerful data‐
driven method, machine learning (ML) has been widely used for fault diagnosis. However, a …

The mutual incompatibility of distinct spectroscopic systems is among the most limiting
factors in laser-induced breakdown spectroscopy (LIBS). The cost related to setting up a …

We develop a family of techniques to align word embeddings which are derived from
different source datasets or created using different mechanisms (eg, GloVe or word2vec) …

Manifold alignment is very prevalent in machine learning for extracting common latent space
from multiple datasets. These algorithms generally aim to achieve higher alignment …

Background Laser-induced breakdown spectroscopy (LIBS) is extensively utilized a range of
scientific and industrial detection applications owing to its capability for rapid, in-situ …

A large number of certified samples are usually required to build models in the quantitative
analysis of complicated matrices in laser-induced-breakdown spectroscopy (LIBS). Because …

Manifold learning is a class of machine learning methods that exploits the observation that
high-dimensional data tend to lie on a smooth lower-dimensional manifold. Manifold …

Industrial robots play an increasingly important role in a growing number of fields. For
example, robotics is used to increase productivity while reducing costs in various aspects of …