			Home About us Contact

Minimization Process (minimization + process)

Distribution by Scientific Domains

Engineering	60%

Selected Abstracts

A finite element algorithm for parameter identification of material models for fluid saturated porous media

INTERNATIONAL JOURNAL FOR NUMERICAL AND ANALYTICAL METHODS IN GEOMECHANICS, Issue 5 2001
R. Mahnken
Abstract In this contribution an algorithm for parameter identification of geometrically linear Terzaghi,Biot-type fluid-saturated porous media is proposed, in which non-uniform distributions of the state variables such as stresses, strains and fluid pore pressure are taken into account. To this end a least-squares functional consisting of experimental data and simulated data is minimized, whereby the latter are obtained with the finite element method. This strategy allows parameter identification based on in situ experiments. In order to improve the efficiency of the minimization process, a gradient-based optimization algorithm is applied, and therefore the corresponding sensitivity analysis for the coupled two-phase problem is described in a systematic manner. For illustrative purpose, the performance of the algorithm is demonstrated for a slope stability problem, in which a quadratic Drucker,Prager plasticity model for the solid and a linear Darcy law for the fluid are combined. Copyright © 2001 John Wiley & Sons, Ltd. [source]

Model-based shape from shading for microelectronics applications

INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, Issue 2 2006
A. Nissenboim
Abstract Model-based shape from shading (SFS) is a promising paradigm introduced by Atick et al. [Neural Comput 8 (1996), 1321,1340] in 1996 for solving inverse problems when we happen to have a lot of prior information on the depth profiles to be recovered. In the present work we adopt this approach to address the problem of recovering wafer profiles from images taken using a scanning electron microscope (SEM). This problem arises naturally in the microelectronics inspection industry. A low-dimensional model, based on our prior knowledge on the types of depth profiles of wafer surfaces, has been developed, and based on it the SFS problem becomes an optimal parameter estimation. Wavelet techniques were then employed to calculate a good initial guess to be used in a minimization process that yields the desired profile parametrization. A Levenberg,Marguardt (LM) optimization procedure has been adopted to address ill-posedness of the SFS problem and to ensure stable numerical convergence. The proposed algorithm has been tested on synthetic images, using both Lambertian and SEM imaging models. © 2006 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 16, 65,76, 2006 [source]

Incomplete sensitivities and cost function reformulation leading to multi-criteria investigation of inverse problems

OPTIMAL CONTROL APPLICATIONS AND METHODS, Issue 2 2003
A. Cabot
Abstract This paper deals with the application of typical minimization methods based on dynamical systems to the solution of a characteristic inverse problem. The state equation is based on the Burgers equation. The control is meant to achieve a prescribed state distribution and a given shock location. We show how to use incomplete sensitivities during the minimization process. We also show through a redefinition of the cost function that a multi-criteria problem needs to be considered in inverse problems. This example shows that a correct definition of the minimization problem is crucial and needs to be studied before a direct application of brute force minimization approaches. Copyright © 2003 John Wiley & Sons, Ltd. [source]

The ECMWF operational implementation of four-dimensional variational assimilation.

THE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 564 2000
II: Experimental results with improved physics
Abstract A comprehensive set of physical parametrizations has been linearized for use in the European Centre for Medium-Range Weather Forecasts (ECMWF's) incremental four-dimensional variational (4D-Var) system described in Part I. The following processes are represented: vertical diffusion, subgrid-scale orographic effects, large-scale precipitation, deep moist convection and long-wave radiation. The tangent-linear approximation is examined for finite-size perturbations. Significant improvements are illustrated for surface wind and specific humidity with respect to a simplified vertical diffusion scheme. Singular vectors computed over 6 hours (compatible with the 4D-Var assimilation window) have lower amplification rates when the improved physical package is included, due to a more realistic description of dissipative processes, even though latent-heat release contributes to amplify the potential energy of perturbations in rainy areas. A direct consequence is a larger value of the observation term of the cost-function at the end of the minimization process when improved physics is included in 4D-Var. However, the larger departure of the analysis state from observations in the lower-resolution inner-loop is in better agreement with the behaviour of the full nonlinear model at high resolution. More precisely, the improved physics produces smaller discontinuities in the value of the cost-function when going from low to high resolution. In order to reduce the computational cost of the linear physics, a new configuration of the incremental 4D-Var system using two outer-loops is defined. In a first outer-loop, a minimization is performed at low resolution with simplified physics (50 iterations), while in the second loop a second minimization is performed with improved physics (20 iterations) after an update of the model trajectory at high resolution. In this configuration the extra cost of the physics is only 25%, and results from a 2-week assimilation period show positive impacts in terms of quality of the forecasts in the Tropics (reduced spin-down of precipitation, lower root-mean-square errors in wind scores). This 4D-Var configuration with improved physics and two outer-loops was implemented operationally at ECMWF in November 1997. [source]

MINIMAL VALID AUTOMATA OF SAMPLE SEQUENCES FOR DISCRETE EVENT SYSTEMS

ASIAN JOURNAL OF CONTROL, Issue 2 2004
Sheng-Luen Chung
ABSTRACT Minimal valid automata (MVA) refer to valid automata models that fit a given input-output sequence sample from a Mealy machine model. They are minimal in the sense that the number of states in these automata is minimal. Critical to system identification problems of discrete event systems, MVA can be considered as a special case of the minimization problem for incompletely specified sequential machine (ISSM). While the minimization of ISSM in general is an NP-complete problem, various approaches have been proposed to alleviate computational requirement by taking special structural properties of the ISSM at hand. In essence, MVA is to find the minimal realization of an ISSM where each state only has one subsequent state transition defined. This paper presents an algorithm that divides the minimization process into two phases: first to give a reduced machine for the equivalent sequential machine, and then to minimize the reduced machine into minimal realization solutions. An example with comprehensive coverage on how the associated minimal valid automata are derived is also included. [source]