In [9], a fast pre-processing is proposed to generate estimations of the RD costs.
The six generated estimation functions were tested.
If many scene changes occur, the proposed complexity estimation method generates estimation errors as a result of reduced similarity between neighboring frames.
The neighbor-joining method was not used because the estimated distance matrix using the JTT substitution model (implemented in PHYLIP 3.65 [ 106]) generated estimation errors due to too many substitutions.
According to the authors, this is a purely endogenous form of proactive control: The internally generated estimation of the fixation-cross duration provides the conflict-predicting information, not the exogenous presentation of the fixation-cross per se.
Comparisons of the resulting incidences were accomplished via generation of an RR ratio and its corresponding 95% CI; P-values were generated using Generalizing Estimation Equation methodology.
A Swarm Intelligence (SI) based algorithm, which is known as Particle Swarm Optimization (PSO), has been used to generate error estimation functions.
A Luenberger observer is designed to generate the estimation of system states, and the integral sliding surface is constructed to guarantee exponential stability of networked semi-Markovian switching systems in the mean square sense.
To overcome this obstacle, in this paper, a state observer is designed to generate the estimation of system states, based on which a sliding mode controller is designed to stabilize the resulting closed-loop system.
For porosity, the simple kriging, the ordinary kriging and the Sigsm methods could generate similar estimation results.
