Interdigitated Electrode for Electrical Characterization of Commercial Pseudo-Binary Biodiesel-Diesel Blends.

Non-standard diesel blends can be harmful to the environment and human health. In this context, a simple analytical method to estimate the biodiesel mixture ratio in diesel was developed based on impedance spectroscopy (IS) associated with interdigitated sensors. In this article, four different interdigitated sensors with varied comb spacing (G) were simulated using the COMSOL Multiphysics software.

Autofocus Entropy Repositioning Method Bioinspired in the Magnetic Field Memory of the Bees Applied to Pollination.

In this paper, a bioinspired method in the magnetic field memory of the bees, applied in a rover of precision pollination, is presented. The method calculates sharpness features by entropy and variance of the Laplacian of images segmented by color in the HSV system in real-time. A complementary positioning method based on area feature extraction between active markers was developed, analyzing color characteristics, noise, and vibrations of the probe in time and frequency, through the lateral image of the probe.

Online discrete-time LQR controller design with integral action for bulk Bucket Wheel Reclaimer operational processes via Action-Dependent Heuristic Dynamic Programming.

In this paper, a novel approach for online design of optimal control systems applied to the bulk resumption process by bucket wheel reclaimer (BWR) is presented. This approach is based on reinforcement learning paradigms, more specifically Action Dependent Heuristic Dynamic Programming (ADHDP), that learn online in real-time the Discrete Linear Quadratic Regulator (DLQR) optimal control solution with integral action. Due to the geometric irregularities of the storage yard stacks and variation in physical and chemical characteristics of the stacked material, the flow control of solid bulks by bucket wheel reclaimer requires methods that are suitable with the high degree of imprecision of process variables and environment uncertainties.

Neural-genetic synthesis for state-space controllers based on linear quadratic regulator design for eigenstructure assignment.

Toward the synthesis of state-space controllers, a neural-genetic model based on the linear quadratic regulator design for the eigenstructure assignment of multivariable dynamic systems is presented. The neural-genetic model represents a fusion of a genetic algorithm and a recurrent neural network (RNN) to perform the selection of the weighting matrices and the algebraic Riccati equation solution, respectively. A fourth-order electric circuit model is used to evaluate the convergence of the computational intelligence paradigms and the control design method performance.