Helsinki University of Technology

About: Helsinki University of Technology is a based out in . It is known for research contribution in the topics: Thin film & Vortex. The organization has 8962 authors who have published 20136 publications receiving 723787 citations. The organization is also known as: TKK & Teknillinen korkeakoulu.

Components of functional-structural tree models

Abstract: Cet article a pour objet de faire une synthese des approches realisees dans le cadre de la modelisation fonctionnelle et structurale des arbres (FSM). Ces modeles resultent du couplage entre la modelisation du fonctionnement ecophysiologique d'arbres, d'une part, et la modelisation des processus morphologiques, d'autre part. Apres une breve presentation des approches existantes, nous introduisons la notion «d'unite elementaire ideale» (IEU) qui peut etre consideree comme la composante fondamentale des FSM au regard de la souplesse qu'elle confere dans l'articulation des processus. La distribution des metabolites et la croissance sont ensuite abordees comme etant les processus a resoudre de facon prioritaire dans le developpement des FSM, et les differentes approches pouvant etre mises a contribution dans la construction de ces modeles sont discutees. Enfin nous analysons les besoins en programmation des FSM, discutons des avancees necessaires et evaluons leur adequation a la resolution d'objectifs divers.

On optimal population size of genetic algorithms

Abstract: A description is given of the results of experiments to find the optimum population size for genetic algorithms as a function of problem complexity. It seems that for moderate problem complexity the optimal population size for problems coded as bitstrings is approximately the length of the string in bits for sequential machines. This result is also consistent with earlier experimentation. In parallel architectures the optimal population size is larger than in the corresponding sequential cases, but the exact figures seem to be sensitive to implementation details. >

Approximation errors and model reduction with an application in optical diffusion tomography

Abstract: Model reduction is often required in several applications, typically due to limited available time, computer memory or other restrictions. In problems that are related to partial differential equations, this often means that we are bound to use sparse meshes in the model for the forward problem. Conversely, if we are given more and more accurate measurements, we have to employ increasingly accurate forward problem solvers in order to exploit the information in the measurements. Optical diffusion tomography (ODT) is an example in which the typical required accuracy for the forward problem solver leads to computational times that may be unacceptable both in biomedical and industrial end applications. In this paper we review the approximation error theory and investigate the interplay between the mesh density and measurement accuracy in the case of optical diffusion tomography. We show that if the approximation errors are estimated and employed, it is possible to use mesh densities that would be unacceptable with a conventional measurement model.