Setting up a comprehensive DBN is, however, a non-trivial undertaking so, in the situation for Santorini where speed was important, we implemented a trade-off solution: 'a time-stepping BBN', giving us modelling inference capabilities somewhere between those of a static BBN and those of a DBN.
We implemented a fixed point iteration solution technique, i.e., the system is solved by calculating (5) A (k + 1 ) g, i (x g ) = A (k ), bare g, i + ∑ l Z (k ) g, i, l ∫ R d l d d l y F l (y, x g, { A (k ) }, { A model } ), from the previous dressing functions A (k ) g, i.
In our proposed solution, we implemented a cross-layer based stable and energy-efficient routing technique in which QoS monitoring agents collect and calculate the link reliability metrics such as LET, PLRT, LPER and LRSS.
Following the classical formulation according to Kass et al. [ 11], we implemented a new, computationally more efficient numerical solution scheme [ 12].
We implement a hybrid solution for situation comprehension and projection that combines data-driven approach, by using temporal extension of Fuzzy Formal Concept Analysis, and goal-driven approach, by using Fuzzy Cognitive Maps.
Here, we implement a partial solution by decoupling the encoding of semantic relationships from the querying and utilization of those relationships.
We implement a closed-form solution for the EM algorithm to estimate the population genetic parameters of QTL segregating in a bird population and the simplex algorithm to estimate the curve parameters describing the power of bird in response to different flight speeds.
Once we identified the problem, we worked quickly to implement a solution and attempted to restore all lost data.
Do we investigate them and implement a solution?
