Detecting a community in a network is a matter of discerning the distinct features and
connections of a group of members that are different from those in other communities. The …

Deep learning, which is a subfield of machine learning, has opened a new era for the
development of neural networks. The auto-encoder is a key component of deep structure …

Digital twin is a significant way to achieve smart manufacturing, and provides a new
paradigm for fault diagnosis. Traditional data-based fault diagnosis methods mostly assume …

As communities represent similar opinions, similar functions, similar purposes, etc.,
community detection is an important and extremely useful tool in both scientific inquiry and …

In recent years, deep learning (DL), a rebranding of neural networks (NNs), has risen to the
top in numerous areas, namely computer vision (CV), speech recognition, and natural …

Increased life expectancy coupled with declining birth rates is leading to an aging
population structure. Aging-caused changes, such as physical or cognitive decline, could …

Intelligent fault diagnosis is a promising tool to deal with mechanical big data due to its
ability in rapidly and efficiently processing collected signals and providing accurate …

Deconvolution methods (DMs) which can adaptively design the filter for the feature
extraction is the most effective tool to counteract the effect of the transmission path …

This paper introduces the use of single-layer and deep convolutional networks for remote
sensing data analysis. Direct application to multi-and hyperspectral imagery of supervised …

Remote sensing scene understanding is a highly challenging task, and has gradually
emerged as a research hotspot in the field of intelligent interpretation of remote sensing …