A health status estimation method based on interpretable neural network observer for HVs.

Estimating the health status is a crucial step in learning about the health of hypersonic vehicles beforehand. The estimation results can be used to detect abnormal states and provide data reference for fault diagnosis. However, certain conventional neural network-based estimate techniques rely heavily on data and have limited model interpretability, which challenges the accuracy of the estimation results.

A coalitional game-based joint monitoring mechanism for combating COVID-19.

In the absence of effective treatment for COVID-19, disease prevention and control have become a top priority across the world. However, the general lack of effective cooperation between communities makes it difficult to suppress the community spread of the global pandemic; hence repeated outbreaks of COVID-19 have become the norm. To address this problem, this paper considers community cooperation in disease monitoring and designs a joint epidemic monitoring mechanism, in which adjacent communities cooperate to enhance their monitoring capability.

Periodic analysis on gas path fault diagnosis of gas turbines.

The gas path fault diagnosis is considered widely to ensure the economy, safety and practicability of gas turbines. Traditional gas path diagnosis methods are vulnerable to various uncertainties, resulting in a deviation between the diagnostic results and the real states, which brings huge potential safety hazard to industrial production. Periodic analysis can suppress the uncertainty interference and extract accurately the features of performance parameters to improve the accuracy of health evaluation.

Dynamic-Sensitive centrality of nodes in temporal networks.

Locating influential nodes in temporal networks has attracted a lot of attention as data driven and diverse applications. Classic works either looked at analysing static networks or placed too much emphasis on the topological information but rarely highlighted the dynamics. In this paper, we take account the network dynamics and extend the concept of Dynamic-Sensitive centrality to temporal network.