Abstract
Information entropy was applied to size class selection for the unfolding of grain size distributions and the results were compared with traditional forward and inverse methods using arbitrarily selected size classes. This comparison shows that information entropy provides a better representation of the data.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have