TY  - JOUR
AU  - Uddin, Ming
AU  - Karimi, Elena
TI  - Artificial Neural Networks Applied to Graphene-Based Composites
JO  - Open Access Journal of Engineering Applications
PY  - 2020
VL  - 44
IS  - 1
SP  - 489
EP  - 506
AB  - Recent failures in telecommunication infrastructure have highlighted the need for a more rigorous treatment of graphene-based composites. Measurements are triangulated with artificial neural networks to reduce the influence of instrument error. Measured and simulated results differed by less than 24% across the full operating range. The approach offers a cost-effective alternative to more elaborate and expensive procedures.
KW  - graphene-based composites
KW  - artificial neural networks
KW  - renewable energy
KW  - heavy metal removal
SN  - 3105-7004
DO  - 10.55555/oajea.2020.4401.025
UR  - https://journal.bsaps.com/article/9692
ER  -