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  • Prediction of state of health and remaining useful life of lithium-ion battery using graph convolutional network with dual attention mechanisms
Title: Prediction of state of health and remaining useful life of lithium-ion battery using graph convolutional network with dual attention mechanisms
Award ID(s):
2131619
PAR ID:
10428780
Author(s) / Creator(s):
;
Date Published:
Journal Name:
Reliability Engineering & System Safety
Volume:
230
Issue:
C
ISSN:
0951-8320
Page Range / eLocation ID:
108947
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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    A unified approach to the determination of eigenvalues and eigenvectors of specific matrices associated with directed graphs is presented. Matrices studied include the new distance matrix, with natural extensions to the distance Laplacian and distance signless Laplacian, in addition to the new adjacency matrix, with natural extensions to the Laplacian and signless Laplacian. Various sums of Kronecker products of nonnegative matrices are introduced to model the Cartesian and lexicographic products of digraphs. The Jordan canonical form is applied extensively to the analysis of spectra and eigenvectors. The analysis shows that Cartesian products provide a method for building infinite families of transmission regular digraphs with few distinct distance eigenvalues. 
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