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Register a new userThreat assessment of a possible Vehicle-Born Improvised Explosive Device using DSmT
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Added by
Jean Dezert
Publication date
2010
fields
Informatics Engineering
and research's language is
English
Authors
Jean Dezert
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This paper presents the solution about the threat of a VBIED (Vehicle-Born Improvised Explosive Device) obtained with the DSmT (Dezert-Smarandache Theory). This problem has been proposed recently to the authors by Simon Maskell and John Lavery as a typical illustrative example to try to compare the different approaches for dealing with uncertainty for decision-making support. The purpose of this paper is to show in details how a solid justified solution can be obtained from DSmT approach and its fusion rules thanks to a proper modeling of the belief functions involved in this problem.
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The management and combination of uncertain, imprecise, fuzzy and even paradoxical or high conflicting sources of information has always been, and still remains today, of primal importance for the development of reliable modern information systems involving artificial reasoning. In this introduction, we present a survey of our recent theory of plausible and paradoxical reasoning, known as Dezert-Smarandache Theory (DSmT), developed for dealing with imprecise, uncertain and conflicting sources of information. We focus our presentation on the foundations of DSmT and on its most important rules of combination, rather than on browsing specific applications of DSmT available in literature. Several simple examples are given throughout this presentation to show the efficiency and the generality of this new approach.
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Multitasking optimization is a recently introduced paradigm, focused on the simultaneous solving of multiple optimization problem instances (tasks). The goal of multitasking environments is to dynamically exploit existing complementarities and synergies among tasks, helping each other through the transfer of genetic material. More concretely, Evolutionary Multitasking (EM) regards to the resolution of multitasking scenarios using concepts inherited from Evolutionary Computation. EM approaches such as the well-known Multifactorial Evolutionary Algorithm (MFEA) are lately gaining a notable research momentum when facing with multiple optimization problems. This work is focused on the application of the recently proposed Multifactorial Cellular Genetic Algorithm (MFCGA) to the well-known Capacitated Vehicle Routing Problem (CVRP). In overall, 11 different multitasking setups have been built using 12 datasets. The contribution of this research is twofold. On the one hand, it is the first application of the MFCGA to the Vehicle Routing Problem family of problems. On the other hand, equally interesting is the second contribution, which is focused on the quantitative analysis of the positive genetic transferability among the problem instances. To do that, we provide an empirical demonstration of the synergies arisen between the different optimization tasks.
In this paper we perform an assessment of the 2880 Earth impact risk for asteroid (29075) 1950 DA. To obtain reliable predictions we analyze the contribution of the observational dataset and the astrometric treatment, the numerical error in the long-term integration, and the different accelerations acting on the asteroid. The main source of uncertainty is the Yarkovsky effect, which we statistically model starting from 1950 DAs available physical characterization, astrometry, and dynamical properties. Before the release of 2012 radar data, this modeling suggests that 1950 DA has 99% likelihood of being a retrograde rotator. By using a 7-dimensional Monte Carlo sampling we map 1950 DAs uncertainty region to the 2880 close approach b-plane and find a 5 x 10^-4 impact probability. With the recently released 2012 radar observations, the direct rotation is definitely ruled out and the impact probability decreases to 2.5 x 10^-4.
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