66,073 research outputs found
Noether's Theorem for Fractional Optimal Control Problems
We begin by reporting on some recent results of the authors (Frederico and
Torres, 2006), concerning the use of the fractional Euler-Lagrange notion to
prove a Noether-like theorem for the problems of the calculus of variations
with fractional derivatives. We then obtain, following the Lagrange multiplier
technique used in (Agrawal, 2004), a new version of Noether's theorem to
fractional optimal control systems.Comment: To be presented at FDA'06 - 2nd IFAC Workshop on Fractional
Differentiation and its Applications, 19-21 July 2006, Porto, Portugal.
Accepted (07-March-2006) for the Conference Proceeding
Fractional Noether's theorem in the Riesz-Caputo sense
We prove a Noether's theorem for fractional variational problems with
Riesz-Caputo derivatives. Both Lagrangian and Hamiltonian formulations are
obtained. Illustrative examples in the fractional context of the calculus of
variations and optimal control are given.Comment: Accepted (25/Jan/2010) for publication in Applied Mathematics and
Computatio
Accurate Pulmonary Nodule Detection in Computed Tomography Images Using Deep Convolutional Neural Networks
Early detection of pulmonary cancer is the most promising way to enhance a
patient's chance for survival. Accurate pulmonary nodule detection in computed
tomography (CT) images is a crucial step in diagnosing pulmonary cancer. In
this paper, inspired by the successful use of deep convolutional neural
networks (DCNNs) in natural image recognition, we propose a novel pulmonary
nodule detection approach based on DCNNs. We first introduce a deconvolutional
structure to Faster Region-based Convolutional Neural Network (Faster R-CNN)
for candidate detection on axial slices. Then, a three-dimensional DCNN is
presented for the subsequent false positive reduction. Experimental results of
the LUng Nodule Analysis 2016 (LUNA16) Challenge demonstrate the superior
detection performance of the proposed approach on nodule detection(average
FROC-score of 0.891, ranking the 1st place over all submitted results).Comment: MICCAI 2017 accepte
A Dual Mesh Method for a Non-Local Thermistor Problem
We use a dual mesh numerical method to study a non-local parabolic problem
arising from the well-known thermistor problem.Comment: Published in SIGMA (Symmetry, Integrability and Geometry: Methods and
Applications) at http://www.emis.de/journals/SIGMA
Numerical analysis of a nonlocal parabolic problem resulting from thermistor problem
We analyze the spatially semidiscrete piecewise linear finite element method
for a nonlocal parabolic equation resulting from thermistor problem. Our
approach is based on the properties of the elliptic projection defined by the
bilinear form associated with the variational formulation of the finite element
method. We assume minimal regularity of the exact solution that yields optimal
order error estimate. The full discrete backward Euler method and the
Crank-Nicolson-Galerkin scheme are also considered. Finally, a simple algorithm
for solving the fully discrete problem is proposed
A minimal HIV-AIDS infection model with general incidence rate and application to Morocco data
We study the global dynamics of a SICA infection model with general incidence
rate. The proposed model is calibrated with cumulative cases of infection by
HIV-AIDS in Morocco from 1986 to 2015. We first prove that our model is
biologically and mathematically well-posed. Stability analysis of different
steady states is performed and threshold parameters are identified where the
model exhibits clearance of infection or maintenance of a chronic infection.
Furthermore, we examine the robustness of the model to some parameter values by
examining the sensitivity of the basic reproduction number. Finally, using
numerical simulations with real data from Morocco, we show that the model
predicts well such reality.Comment: This is a preprint of a paper whose final and definite form is with
'Statistics Opt. Inform. Comput.', Vol. 7, No 2 (2019). See
[http://www.IAPress.org]. Submitted 16/Sept/2018; Revised 10 & 15/Dec/2018;
Accepted 15/Dec/201
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