267 research outputs found
Singular arcs in the generalized Goddard's Problem
We investigate variants of Goddard's problems for nonvertical trajectories.
The control is the thrust force, and the objective is to maximize a certain
final cost, typically, the final mass. In this report, performing an analysis
based on the Pontryagin Maximum Principle, we prove that optimal trajectories
may involve singular arcs (along which the norm of the thrust is neither zero
nor maximal), that are computed and characterized. Numerical simulations are
carried out, both with direct and indirect methods, demonstrating the relevance
of taking into account singular arcs in the control strategy. The indirect
method we use is based on our previous theoretical analysis and consists in
combining a shooting method with an homotopic method. The homotopic approach
leads to a quadratic regularization of the problem and is a way to tackle with
the problem of nonsmoothness of the optimal control
GCP: Gossip-based Code Propagation for Large-scale Mobile Wireless Sensor Networks
Wireless sensor networks (WSN) have recently received an increasing interest.
They are now expected to be deployed for long periods of time, thus requiring
software updates. Updating the software code automatically on a huge number of
sensors is a tremendous task, as ''by hand'' updates can obviously not be
considered, especially when all participating sensors are embedded on mobile
entities. In this paper, we investigate an approach to automatically update
software in mobile sensor-based application when no localization mechanism is
available. We leverage the peer-to-peer cooperation paradigm to achieve a good
trade-off between reliability and scalability of code propagation. More
specifically, we present the design and evaluation of GCP ({\emph Gossip-based
Code Propagation}), a distributed software update algorithm for mobile wireless
sensor networks. GCP relies on two different mechanisms (piggy-backing and
forwarding control) to improve significantly the load balance without
sacrificing on the propagation speed. We compare GCP against traditional
dissemination approaches. Simulation results based on both synthetic and
realistic workloads show that GCP achieves a good convergence speed while
balancing the load evenly between sensors
Communication Efficiency in Self-stabilizing Silent Protocols
Self-stabilization is a general paradigm to provide forward recovery
capabilities to distributed systems and networks. Intuitively, a protocol is
self-stabilizing if it is able to recover without external intervention from
any catastrophic transient failure. In this paper, our focus is to lower the
communication complexity of self-stabilizing protocols \emph{below} the need of
checking every neighbor forever. In more details, the contribution of the paper
is threefold: (i) We provide new complexity measures for communication
efficiency of self-stabilizing protocols, especially in the stabilized phase or
when there are no faults, (ii) On the negative side, we show that for
non-trivial problems such as coloring, maximal matching, and maximal
independent set, it is impossible to get (deterministic or probabilistic)
self-stabilizing solutions where every participant communicates with less than
every neighbor in the stabilized phase, and (iii) On the positive side, we
present protocols for coloring, maximal matching, and maximal independent set
such that a fraction of the participants communicates with exactly one neighbor
in the stabilized phase
A Unified Model for Evolutionary Multiobjective Optimization and its Implementation in a General Purpose Software Framework: ParadisEO-MOEO
This paper gives a concise overview of evolutionary algorithms for
multiobjective optimization. A substantial number of evolutionary computation
methods for multiobjective problem solving has been proposed so far, and an
attempt of unifying existing approaches is here presented. Based on a
fine-grained decomposition and following the main issues of fitness assignment,
diversity preservation and elitism, a conceptual global model is proposed and
is validated by regarding a number of state-of-the-art algorithms as simple
variants of the same structure. The presented model is then incorporated into a
general-purpose software framework dedicated to the design and the
implementation of evolutionary multiobjective optimization techniques:
ParadisEO-MOEO. This package has proven its validity and flexibility by
enabling the resolution of many real-world and hard multiobjective optimization
problems
Particle approximation of the intensity measures of a spatial branching point process arising in multi-target tracking
The aim of this paper is two-fold. First we analyze the sequence of intensity
measures of a spatial branching point process arising in a multiple target
tracking context. We study its stability properties, characterize its long time
behavior and provide a series of weak Lipschitz type functional contraction
inequalities. Second we design and analyze an original particle scheme to
approximate numerically these intensity measures. Under appropriate regularity
conditions, we obtain uniform and non asymptotic estimates and a functional
central limit theorem. To the best of our knowledge, these are the first sharp
theoretical results available for this class of spatial branching point
processes.Comment: Revised version Technical report INRIA HAL-INRIA RR-723
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