Artículo de publicación ISIOptical flow approaches calculate vector fields
which determine the apparent velocities of objects in timevarying
image sequences. They have been analyzed extensively
in computer science using both natural and synthetic
video sequences. In life sciences, there is an increasing
need to extract kinetic information from temporal image
sequences which reveals the interplay between form and
function of microscopic biological structures. In this work,
we test different variational optical flow techniques to quantify
the displacements of biological objects in 2D fluorescent
image sequences. The accuracy of the vector fields is
tested for defined displacements of fluorescent point sources
in synthetic image series which mimic protein traffic in neuronal dendrites, and for GABABR1 receptor subunits in
dendrites of hippocampal neurons. Our results reveal that
optical flow fields predict the movement of fluorescent point
sources within an error of 3% for a maximum displacement
of 160 nm. Displacement of agglomerated GABABR1
receptor subunits can be predicted correctly for maximum
displacements of 640 nm. Based on these results, we introduce
a criteria to derive the optimum parameter combinations
for the calculation of the optical flow fields in experimental
images. From these results, temporal sampling frequencies
for image acquisition can be derived to guarantee correct
motion estimation for biological objects.J. Delpiano and J. Jara are funded by a PhD scholarship from CONICYT
(Chile). O. A. Ramírez is funded by FONDECYT (3110157). Research
in SCIAN-Lab (S. Härtel) is funded by FONDECYT (1090246) and
FONDEF (D07I1019). SCIAN-Lab is member of the German-Chilean
Center of Excellence Initiative for Medical Informatics (DAAD), BNI
(ICM P09-015-F), and the Advanced Imaging & Bioinformatics Initiative
AI·BI (http://www.aibi.cl)
The application of artificial intelligence-based techniques has covered a wide range of applications related to electric power systems (EPS). Particularly, a metaheuristic technique known as Particle Swarm Optimization (PSO) has been chosen for the tuning of parameters for Power System Stabilizers (PSS) with success for relatively small systems. This article proposes a tuning methodology for PSSs based on the use of PSO that works for systems with ten or even more machines. Our new methodology was implemented using the source language of the commercial simulation software DigSilent PowerFactory. Therefore, it can be translated into current practice directly. Our methodology was applied to different test systems showing the effectiveness and potential of the proposed technique.
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