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Algorithm Works (algorithm + work)

Distribution by Scientific Domains

Engineering	50%

Selected Abstracts

Performance analysis of IDEAL algorithm for three-dimensional incompressible fluid flow and heat transfer problems

INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN FLUIDS, Issue 10 2009
Dong-Liang Sun
Abstract Recently, an efficient segregated algorithm for incompressible fluid flow and heat transfer problems, called inner doubly iterative efficient algorithm for linked equations (IDEAL), has been proposed by the present authors. In the algorithm there exist inner doubly iterative processes for pressure equation at each iteration level, which almost completely overcome two approximations in SIMPLE algorithm. Thus, the coupling between velocity and pressure is fully guaranteed, greatly enhancing the convergence rate and stability of solution process. However, validations have only been conducted for two-dimensional cases. In the present paper the performance of the IDEAL algorithm for three-dimensional incompressible fluid flow and heat transfer problems is analyzed and a systemic comparison is made between the algorithm and three other most widely used algorithms (SIMPLER, SIMPLEC and PISO). By the comparison of five application examples, it is found that the IDEAL algorithm is the most robust and the most efficient one among the four algorithms compared. For the five three-dimensional cases studied, when each algorithm works at its own optimal under-relaxation factor, the IDEAL algorithm can reduce the computation time by 12.9,52.7% over SIMPLER algorithm, by 45.3,73.4% over SIMPLEC algorithm and by 10.7,53.1% over PISO algorithm. Copyright © 2009 John Wiley & Sons, Ltd. [source]

Rapid haplotype reconstruction in pedigrees with dense marker maps

JOURNAL OF ANIMAL BREEDING AND GENETICS, Issue 1 2004
J. J. Windig
Summary Reconstruction of marker phases is not straightforward when parents are untyped. In these cases information from other relatives has to be used. In dense marker maps, however, the space of possible haplotype configurations tends to be too large for procedures such as Monte Carlo Markov chains (MCMC) to be finished within a reasonable time. We developed an algorithm that is fast and generally finds the most probable haplotype. The basic idea is to use, the smallest informative marker brackets in offspring, for each marker interval. By using only information from the offspring and analysing each marker interval separately, the lengthy analysis of large numbers of different haplotype configurations is avoided. Nevertheless the most probable haplotype can be found quickly provided the marker map is dense and enough offspring are available. Simulations are provided to indicate how well the algorithm works at different combinations of marker density, number of offspring and number of alleles per marker. In situations where the algorithm reconstruction of the most probable haplotype is not guaranteed, the algorithm may still provide a haplotype close to the optimum, i.e. a suitable starting point for numeric optimization algorithms. Zusammenfassung Die Rekonstruktion der Kopplungsphasen von Markern ist nicht unkompliziert, wenn die Typisierung der Eltern fehlt. In derartigen Fällen müssen Informationen von Verwandten genutzt werden. In dichten Markerkarten tendiert der Bereich für mögliche Haplotypenkonfigurationen jedoch dazu, zu groß zu werden, um Verfahren wie Monte Carlo Markov Chains (MCMC) in einem angemessenen Zeitrahmen anzuwenden. Wir entwickelten einen Algorithmus, der schnell ist und im Allgemeinen die wahrscheinlichsten Haplotypen findet. Die grundlegende Idee dabei bestand darin, für jeden Markerintervall erstfolgende informative Markern am linker und rechter Zeite in den Nachkommen zu nutzen. Durch die ausschließliche Nutzung von Nachkommeninformationen und durch die separate Analyse von Markerintervallen, wird die langatmige Analyse großer Anzahlen unterschiedlicher Haplotypenkonfigurationen umgangen. Dennoch kann der wahrscheinlichste Haplotyp schnell gefunden werden, vorausgesetzt die Markerkarte ist dicht und ausreichend Nachkommen sind verfügbar. Simulationen werden zur Verfügung gestellt, um zu zeigen wie gut der Algorithmus bei unterschiedlichen Kombinationen von Markerdichte, Anzahl von Nachkommen und Allelen pro Marker arbeitet. In Situationen, wo die algorithmische Rekonstruktion des wahrscheinlichsten Haplotypen nicht garantiert werden kann, kann der Algorithmus dennoch einen Haplotypen nahe des Optimums bereitstellen, z.B. einen geeigneten Startpunkt für numerische Optimierungsalgorithmen. [source]

Potential-based path planning for robot manipulators

JOURNAL OF FIELD ROBOTICS (FORMERLY JOURNAL OF ROBOTIC SYSTEMS), Issue 6 2005
Chien-Chou Lin
In this paper, a potential-based path-planning algorithm for a high DOF robot manipulator is proposed. Unlike some c-space-based approaches, which often require expensive preprocessing for the construction of the c-space, the proposed approach uses the workspace information directly. The approach computes, similar to that done in electrostatics, repulsive force and torque between objects in the workspace. A collision-free path of a manipulator will then be obtained by locally adjusting the manipulator configuration to search for minimum potential configurations using that force and torque. The proposed approach is efficient because these potential gradients are analytically tractable. Simulation results show that the proposed algorithm works well, in terms of computation time and collision avoidance, for manipulators up to 9 degrees of freedom (DOF). © 2005 Wiley Periodicals, Inc. [source]

Maximum Likelihood Estimation in Dynamical Models of HIV

BIOMETRICS, Issue 4 2007
J. Guedj
Summary The study of dynamical models of HIV infection, based on a system of nonlinear ordinary differential equations (ODE), has considerably improved the knowledge of its pathogenesis. While the first models used simplified ODE systems and analyzed each patient separately, recent works dealt with inference in non-simplified models borrowing strength from the whole sample. The complexity of these models leads to great difficulties for inference and only the Bayesian approach has been attempted by now. We propose a full likelihood inference, adapting a Newton-like algorithm for these particular models. We consider a relatively complex ODE model for HIV infection and a model for the observations including the issue of detection limits. We apply this approach to the analysis of a clinical trial of antiretroviral therapy (ALBI ANRS 070) and we show that the whole algorithm works well in a simulation study. [source]