The slow scale problem corresponds to common engineering practices by smearing out fine-scale microstructures.
Further the method of multiple scales is used to obtain the equations at a slow scale.
Within each pair of equations, onedependent variable evolves on a fast time scale and one on a slow scale.
To this end, we proposed low complexity power control and MS algorithms that rely on slow scale CSI.
This model performs well in short-term forecasting of wind speed and wind power owing to the slow scale of meteorological changes.
The basic idea is to separate time into two scales, a slow scale for tribological phenomena and a fast scale for dynamics.
Xu (Xu et al. 2009) employed the generalized variational principles to decompose a boundary value problem with random microstructure into a slow scale deterministic problem and a fast scale stochastic one using a Green's function based multiscale method.
In this study, by employing the generalized variational principles, a Green-function-based multiscale method is formulated to decompose a boundary value problem with random microstructure into a slow scale deterministic problem and a fast scale stochastic one.
To overcome this instability, we inject a high frequency sinusoidal signal into the system that forces the system to remain stable while at the same time retaining its basic slow scale properties (like the steady state error).
