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To address these issues, fuzzy random variables are implemented and two developments are made in this paper.
In each case, the calculated changes in design variables are implemented on the initial structure, and the changes in eigenvalues are computed using FE method.
In other deterministic optimization approaches, the expected values are used, which most likely leads to violations of the constraints when the decision variables are implemented on site.
When stability in the data has been reached, two or more independent variables are implemented.
First, although the independent variables are implemented in the same condition, they cannot be simultaneously in effect.
Four tests for investigating linear and non-linear dependencies between variables are implemented: Pearson correlation, Spearman's rank, Distance correlation (Szekely and Rizzo, 2009) and the recently described Maximal Information Coefficient (MIC) (Reshef et al., 2011).
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A sensitivity analysis among the state variables and some auxiliary variables were implemented to identify their internal relationships.
Kalman filter, which is used for mapping neural activities to kinematic variables, is implemented both on PC (MATLAB-based) and FPGA.
The idea of production feedback control using production KPIs as referenced controlled variables was implemented on a procedural model of a production process for a polymerisation plant.
Sequential backward and forward selections of variables were implemented to reduce the number of variables while maximising the accuracies, resulting in increased accuracies of κ: 0.72 and κ: 0.83 for the associations and aggregated categories using six and three variables respectively.
Several variables were implemented in the model considering: road design (combinations of horizontal/vertical road elements), vehicle features (e.g. wheelbase, front track, height of the center of gravity), vehicle dynamics (acceleration, deceleration) and environmental conditions.
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