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This problem can be resolved by combining a Gaussian-pyramid-based adaptive scale selection method [6] or a multi-scale convolution method [12] with the proposed method; however, this design usually requires lots of computations and decreases the computational efficiency of the entire enhancement process.
The decimation decreases the computational load on the analysis module.
The construction of this model allows VB inference, which extremely decreases the computational burden.
Thus, the geometric algebra-based method significantly decreases the computational burden.
However, this method decreases the computational efficiency and impedes its applicability in bulk power systems.
This diagonal formulation is favorable since it decreases the computational complexity during implementation.
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Previous studies have mostly relied on parallelization and grid computing to decrease the computational time of MCSs.
In this case, the likelihood function is based on local neighborhoods computations thus dramatically decreasing the computational load of the algorithm.
In order to decrease the computational cost, only the neighbor measurements are considered for density computation.
The algorithm can decrease the computational time in consequence of eliminating the number of infeasible solutions.
The restriction in model size was applied in order to decrease the computational cost.
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