As key equipment in modern industry, it is important to diagnose and predict the health
status of bearings. Data-driven methods for remaining useful life (RUL) prognostics have …

In recent years, great achievements have been made in graph convolutional network (GCN)
for non-Euclidean spatial data feature extraction, especially the skeleton-based feature …

The exponential increase in network traffic leads to considerable stress in 5G
communication. The ultra-high reliability and low latency communication in 5G provides an …

Deep Neural Networks have shown tremendous success in the area of object recognition,
image classification and natural language processing. However, designing optimal Neural …

For an automated driving system to be robust, it needs to recognize not only fixed signals
such as traffic signs and traffic lights, but also gestures used by traffic police. With the aim to …

Dynamic graphs are extensively employed for detecting anomalous behavior in nodes
within the Internet of Things (IoT). Graph generative models are often used to address the …

As Machine Learning becomes more prominent in the military, we are faced with a different
take on the old problem of how to collect data relevant to some military mission need. We …

Welcome back to my series on Graph Neural Networks. Today I am going to introduce you to
the basic theory underneath one of the greatest Graph Neural Network frameworks: the …

Agriculture is experiencing a crisis due to economical, social, political, and climatic factors,
causing a dropping in manpower in farming. Accordingly, research in robotics has been …