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The present method uses linear triangular elements and handles a low number of optimization variables.
The proposed method uses Linear Time Varying (LTV) approximations of the nonlinear model to design the controller.
The proposed method uses linear matrix inequality technique to find the common positive definite covariance matrix and feedback gains for each rule of the T S fuzzy models.
This method uses linear amplification to eliminate the errors introduced during the early cycles of PCR.
The automatic method uses linear regression as model for scale variables [ 44].
This method uses linear amplification to eliminate the errors introduced during the early cycles of polymerase chain reaction.
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A finite-difference (FD) method is used to investigate the method using linear and fan-shaped receiver spreads.
The enthalpy equation is discretised using a semi-implicit Galerkin finite element method using linear basis functions.
Then, an implicit time integration method using linear approximations is developed built on the extended state-space formalism.
The incremental finite element method using linear Timoshenko beam elements is formulated by the total Lagrangian approach for the superelastic, large deformation analysis of SMA helical springs.
We present the design of a high-resolution Petrov Galerkin (HRPG) method using linear finite elements for the problem defined by the residualR≔∂ϕ∂t+u∂ϕ∂x−k∂2ϕ∂x2+sϕ−fwhere k,s⩾0.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com