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Also, under this methodology, the problem is solved independently for each subsystem such that the state variables to be considered are the energy storage and energy net interchange of the subsystem.
In the proposed methodology, the problem of modeling crowd behaviors is formulated as a symbolic regression problem and the self-learning gene expression programming is utilized to solve the problem and automatically obtain behavioral rules that match data.
Therefore, as described in the solution procedure in "Methodology", the problem was solved for every mode and the features of the electricity produced from wind power plants and the price of wind power were determined with the newly added wind power generation capacity taken into account.
When applying the theory of elastic shells to derive the equations that conform to the proposed methodology, the problem of determining the mechanical response of semi-circular arches treated with the theory of thin and thick formulations has been re-visited.
But against this methodology, the problem discussed earlier arises (Sect. 4): an account of fruitfulness is required that licenses deviation from intuitive content for normative concepts.
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As an alternative, designers have recently adopted optimization and inverse methodologies to solve design problems involving radiative transfer; the optimization methodology solves the inverse problem implicitly by transforming it into a multivariable minimization problem, while the inverse design methodology solves the problem explicitly using regularization.
The methodology breaks the problem down into four different tasks, which have then to be treated separately.
This methodology solves the problem of nonlinearities and load changes of TFLM drives.
Then, Sect. 3 presents the methodology, where the problem is formally posed along with the improvements, and the proposed optimization algorithm to calculate the paths.
In spite of the intricacy of the investigated problem, the adopted solution methodology of the problem enables one to obtain a closed-form solution of eigenfrequency predictions.
The proposed methodology divides the problem into smaller sub-problems, whose feasible regions vary over time achieving an agreement to solve the global problem.
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