In the DSSN model, the time-varying delay is characterized by introducing a Bernoulli stochastic variable.
The phenomenon of PDT, existing for the designed estimator of underlying switched neural networks are characterized by introducing a Bernoulli distributed white sequence.
Next, the critical issue of semantic interoperability is addressed, and further characterized by introducing a level of detail of the physical modeling.
Functionally graded (FG) porous structures are a novel class of weight-efficient engineering materials and characterized by introducing graded non-uniform porosities.
In addition, the phenomena of the multiple missing measurements are characterized by introducing a set of mutually independent random variables, which reflect that each sensor could have individual missing probability.
Precisely, the topology of density is characterized by introducing the corresponding gradient vector field ∇ r ρ(r) given by a bundle of trajectories as curves r = r(s) parametrized by some parameter s and satisfying the equation dr(s)/ds = ∇ r ρ(r( s )).
