8,839 research outputs found
Sensitivity of inferences in forensic genetics to assumptions about founding genes
Many forensic genetics problems can be handled using structured systems of
discrete variables, for which Bayesian networks offer an appealing practical
modeling framework, and allow inferences to be computed by probability
propagation methods. However, when standard assumptions are violated--for
example, when allele frequencies are unknown, there is identity by descent or
the population is heterogeneous--dependence is generated among founding genes,
that makes exact calculation of conditional probabilities by propagation
methods less straightforward. Here we illustrate different methodologies for
assessing sensitivity to assumptions about founders in forensic genetics
problems. These include constrained steepest descent, linear fractional
programming and representing dependence by structure. We illustrate these
methods on several forensic genetics examples involving criminal
identification, simple and complex disputed paternity and DNA mixtures.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS235 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Optimisation of the weighting functions of an H<sub>â</sub> controller using genetic algorithms and structured genetic algorithms
In this paper the optimisation of the weighting functions for an H<sub>â</sub> controller using genetic algorithms and structured genetic algorithms is considered. The choice of the weighting functions is one of the key steps in the design of an H<sub>â</sub> controller. The performance of the controller depends on these weighting functions since poorly chosen weighting functions will provide a poor controller. One approach that can solve this problem is the use of evolutionary techniques to tune the weighting parameters. The paper presents the improved performance of structured genetic algorithms over conventional genetic algorithms and how this technique can assist with the identification of appropriate weighting functions' orders
Optimal design of a three-phase AFPM for in-wheel electrical traction
Sinusoidally fed permanent magnet synchronous motors (PMSM) fulfill the special features required for traction
motors to be applied in electric vehicles (EV). Among them, axial flux permanent magnet (AFPM) synchronous motors are
especially suited for in-wheel applications. Electric motors used
in such applications must meet two main requirements, i.e. high power density and fault tolerance. This paper deals with the
optimal design of an AFPM for in-wheel applications used to drive an electrical scooter. The single-objective optimization
process carried out in this paper is based on designing the AFPM to obtain an optimized power density while ensuring appropriate fault tolerance requirements. For this purpose a set of analytical
equations are applied to obtain the geometrical, electric and mechanical parameters of the optimized AFPM and several design restrictions are applied to ensure fault tolerance capability. The optimization process is based on a genetic
algorithm and two more constrained nonlinear optimization algorithms in which the objective function is the power density.
Comparisons with available data found in the technical bibliography show the appropriateness of the approach
developed in this work.Postprint (published version
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