A multivariate density forecast model based on deep learning is designed in this paper to forecast the joint cumulative distribution functions (JCDFs) of multiple security margins in power systems.
Improving the image resolution and acquisition speed of magnetic resonance imaging (MRI) is a challenging problem.
The nonlinear manifold is designed to characterize the temporal correlation of dynamic signals.
However, the selection of the parameters of L+S is empirical, and the acceleration rate is limited, which are common failings of iterative compressed sensing MR imaging (CS-MRI) reconstruction methods.
The deep learning methods have achieved attractive performance in dynamic MR cine imaging.
Meanwhile, it builds a neighborhood structure on the set of local minimum via two appropriate perturbation moves and integrates two combinatorial optimization methods, Tabu Search and Iterated Local Search, to systematically search for good local minima.