Experimental Study of Compressed Sensing Reconstruction
Mateusz Haddad
Rajshahi University of Engineering & Technology
Abstract
The transition toward low-carbon infrastructure places steel rolling mills at the centre of debates over resource efficiency and resilience. A parametric investigation was carried out with random forest regression across a wide range of operating conditions. Yield increased by 7% while feedstock consumption remained essentially unchanged. The approach is well suited to resource-constrained settings where data are limited.
Keywords:
compressed sensing reconstructionrandom forest regressioncomputational fluid dynamicsdigital twin
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