Extremal basic frequency of non-homogeneous plates

In this paper we propose two numerical algorithms to derive the extremal principal eigenvalue of the bi-Laplacian operator under Navier boundary conditions or Dirichlet boundary conditions. Consider a non-homogeneous hinged or clamped plate $\Omega$, the algorithms converge to the density functions on $\Omega$ which they yield the maximum or minimum basic frequency of the plate

Introducing the sequential linear programming level-set method for topology optimization

The authors would like to thank Numerical Analysis Group at the Rutherford Appleton Laboratory for their FORTRAN HSL packages (HSL, a collection of Fortran codes for large-scale scientific computation. See http://www.hsl.rl.ac.uk/). Dr H Alicia Kim acknowledges the support from Engineering and Physical Sciences Research Council, grant number EP/M002322/1Peer reviewedPublisher PD

A topology optimization method based on the level set method incorporating a fictitious interface energy

This paper proposes a new topology optimization method, which can adjust the geometrical complexity of optimal configurations, using the level set method and incorporating a fictitious interface energy derived from the phase field method. First, a topology optimization problem is formulated based on the level set method, and the method of regularizing the optimization problem by introducing fictitious interface energy is explained. Next, the reaction–diffusion equation that updates the level set function is derived and an optimization algorithm is then constructed, which uses the finite element method to solve the equilibrium equations and the reaction–diffusion equation when updating the level set function. Finally, several optimum design examples are shown to confirm the validity and utility of the proposed topology optimization method

Regularization properties of Mumford–Shah-type functionals with perimeter and norm constraints for linear ill-posed problems

In this paper we consider the simultaneous reconstruction and segmentation of a function f from measurements g = Kf, where K is a linear operator. Assuming that the inversion of K is illposed, regularization methods have to be used for the inversion process in case of inexact data. We propose using a Mumford–Shah-type functional for the stabilization of the inversion. Restricting our analysis to the recovery of piecewise constant functions, we investigate the existence of minimizers, their stability, and the regularization properties of our approach. Finally, we present a numerical example from single photon emission computed tomography (SPECT).FWF, T 529-N18, Mumford-Shah models for tomography I

ALGORYTMY TOPOLOGICZNE DO ROZWIĄZYWANIA ZAGADNIENIA ODWROTNEGO W TOMOGRAFII ELEKTRYCZNEJ

In this paper, there were investigated topological algorithms to solve the inverse problem in electrical tomography. The level set method, material derivative, shape derivative and topological derivative are based on shape and topology optimization approach to electrical impedance tomography problems with piecewise constant conductivities. The cost of the numerical algorithm is enough good, because the shape is captured on a fixed grid. The proposed solution is initialized by using topological sensitivity analysis. Shape derivative and material derivative (or topological derivative) have been incorporated with level set methods to investigate shape optimization problems.W artykule przedstawiono algorytmy topologiczne do rozwiązania problemu odwrotnego w tomografii elektrycznej. Metoda zbiorów poziomicowych, pochodna materialna, pochodna kształtu i pochodna topologiczna zostały oparte na topologii optymalizacji kształtu do rozwiązania odwrotnego w elektrycznej tomografii impedancyjnej. Koszt algorytmu numerycznego jest wystarczająco dobry, ponieważ kształt jest osadzony na stałej siatce. Proponowany algorytm inicjowano za pomocą topologicznej analizy wrażliwościowej. Pochodna kształtu, pochodna materialna (lub pochodna topologiczna) zostały połączone z metodą zbiorów poziomicowych do badania problemów optymalizacji kształtu

Reconstruction of Binary Functions and Shapes from Incomplete Frequency Information

The characterization of a binary function by partial frequency information is considered. We show that it is possible to reconstruct binary signals from incomplete frequency measurements via the solution of a simple linear optimization problem. We further prove that if a binary function is spatially structured (e.g. a general black-white image or an indicator function of a shape), then it can be recovered from very few low frequency measurements in general. These results would lead to efficient methods of sensing, characterizing and recovering a binary signal or a shape as well as other applications like deconvolution of binary functions blurred by a low-pass filter. Numerical results are provided to demonstrate the theoretical arguments.Comment: IEEE Transactions on Information Theory, 201