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Polytopic uncertainties identification for electrically stimulated lower limbs
(2019-01-01)
Functional electrical stimulation (FES) has been used for a wide range of applications in individuals with spinal cord injury. Spinal cord injury rehabilitation modalities based on FES include gait training programs, ...
Monte carlo simulation to consider uncertainty in the reliability analysis of dynamic positioning systems
(2020-01-01)
Nowadays, Dynamically Positioned (DP) units are responsible for most offshore oil exploitation operations, including drilling and maintenance campaigns. Due to the large congestion of the oil fields, keeping the vessel ...
Use Of Emulator Methodology For Uncertainty Reduction Quantification
(Society of Petroleum Engineers (SPE), 2014)
Optimal reactive power dispatch using stochastic chance-constrained programming
(2012-11-27)
Deterministic Optimal Reactive Power Dispatch problem has been extensively studied, such that the demand power and the availability of shunt reactive power compensators are known and fixed. Give this background, a two-stage ...
Geometric and reflectance signature characterization of complex canopies using hyperspectral stereoscopic images from uav and terrestrial platforms
(2016-01-01)
Light-weight hyperspectral frame cameras represent novel developments in remote sensing technology. With frame camera technology, when capturing images with stereoscopic overlaps, it is possible to derive 3D hyperspectral ...
A neural network approach for robust nonlinear parameter estimation in presence of unknown-but-bounded errors
(Elsevier B.V., 2000-01-01)
Systems based on artificial neural networks have high computational rates due to the use of a massive number of simple processing elements and the high degree of connectivity between these elements. This paper presents a ...
A neural system to robust Nonlinear optimization subject to disjoint and constrained sets
(Int Inst Informatics & Systemics, 2001-01-01)
The ability of neural networks to realize some complex nonlinear function makes them attractive for system identification. This paper describes a novel method using artificial neural networks to solve robust parameter ...
H ∞ state feedback control of discrete-time Markov jump linear systems through linear matrix inequalities
(2011-12-01)
This paper addresses the H ∞ state-feedback control design problem of discretetime Markov jump linear systems. First, under the assumption that the Markov parameter is measured, the main contribution is on the LMI ...