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Title:

Biomechanical Computer Models

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Publisher:

InTech

Year of Publication:

2011-11-25

Source:

http://www.intechopen.com/download/pdf/pdfs_id/22188

http://www.intechopen.com/download/pdf/pdfs_id/22188 Minimize

Document Type:

05

Language:

en

Subjects:

Theoretical Biomechanics

Theoretical Biomechanics Minimize

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ISBN:978-953-307-851-9

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Title:

Experiments on the perpendicular giant magnetoresistance in magnetic multilayers

Year of Publication:

1999

Document Type:

Text

Language:

en

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Title:

An Elementary Introduction to Atomic Force Microscopy and Related Methods

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2015-03-03

Source:

http://www.uni-saarland.de/fak7/hartmann/files/docs/pdf/download/IntroductionAfm.pdf

http://www.uni-saarland.de/fak7/hartmann/files/docs/pdf/download/IntroductionAfm.pdf Minimize

Document Type:

text

Language:

en

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Metadata may be used without restrictions as long as the oai identifier remains attached to it.

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Title:

p-FEM for finite deformation powder compaction

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The simulation of powder compaction problems (die-compaction and cold isostatic pressing) is considered herein by an implicit highorder (p-version) finite element method. In this class of problems use is made of a finite strain viscoplasticity model with evolution equations for internal variables developed for the highly compressible behavior in...

The simulation of powder compaction problems (die-compaction and cold isostatic pressing) is considered herein by an implicit highorder (p-version) finite element method. In this class of problems use is made of a finite strain viscoplasticity model with evolution equations for internal variables developed for the highly compressible behavior in powder compaction processes. The classical approach of implicit finite elements applies the combination of Backward-Euler integration scheme and the Multilevel-Newton algorithm to solve the system of differential-algebraic equations resulting from the space-discretized weak formulation by means of p-version finite elements. This approach requires on Gauss-point level a robust stress-algorithm. The challenging investigations are the incorporation of the applied highly non-linear viscoplasticity model into a p-version finite element formulation using follower load applications. Several axisymmetric numerical examples show the feasibility and good performance of this p-version approach. Minimize

Publisher:

Universitätsbibliothek der Technischen Universität München ; University library of the Munich University of Technology

Year of Publication:

2014-03-27

Document Type:

article

Subjects:

info:eu-repo/classification/ddc:620 ; p-Version FEM; Finite strain; Viscoplasticity; Axisymmetry; Die-compaction; Cold isostatic pressing; Metal powder compaction

info:eu-repo/classification/ddc:620 ; p-Version FEM; Finite strain; Viscoplasticity; Axisymmetry; Die-compaction; Cold isostatic pressing; Metal powder compaction Minimize

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info:eu-repo/semantics/openAccess

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info:eu-repo/grantAgreement/EC/FP7/

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Title:

On volumetric locking-free behavior of p-version finite elements under finite deformations.

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We demonstrate the locking-free properties of the displacement formulation of p-finite elements when applied to nearly incompressible Neo-Hookean material under finite deformations. For an axisymmetric model problem we provide semi-analytical solutions for a nearly incompressible Neo-Hookean material exploited to investigate the robustness of p-...

We demonstrate the locking-free properties of the displacement formulation of p-finite elements when applied to nearly incompressible Neo-Hookean material under finite deformations. For an axisymmetric model problem we provide semi-analytical solutions for a nearly incompressible Neo-Hookean material exploited to investigate the robustness of p-FEM with respect to volumetric locking. An analytical solution for the incompressible case is also derived to demonstrate the convergence of the compressible numerical solution towards the incompressible case when the compression modulus is increased. Minimize

Publisher:

Universitätsbibliothek der Technischen Universität München ; University library of the Munich University of Technology

Year of Publication:

2014-03-27

Document Type:

article

Subjects:

info:eu-repo/classification/ddc:620 ; p-FEM;locking-free;nearly incompressible Neo-Hooke material;finite strains;hyperelasticity; axisymmetry

info:eu-repo/classification/ddc:620 ; p-FEM;locking-free;nearly incompressible Neo-Hooke material;finite strains;hyperelasticity; axisymmetry Minimize

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info:eu-repo/semantics/openAccess

info:eu-repo/semantics/openAccess Minimize

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Title:

Improved tissue modelling and fast solver methods for high resolution FE-modelling in EEG/MEG-source localization

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this paper we will outline how individualized high resolution finite element (FE) models, exploiting multimodal MR-imaging protocols, are automatically constructed. We present an improved segmentation of the skull through a combination of T1- and PD-MRI. The structural information about the white matter fibre directions are won through an MR-dif...

this paper we will outline how individualized high resolution finite element (FE) models, exploiting multimodal MR-imaging protocols, are automatically constructed. We present an improved segmentation of the skull through a combination of T1- and PD-MRI. The structural information about the white matter fibre directions are won through an MR-diffusion tensor imaging protocol [1] . The use of fast techniques to solve the large sparse systems of linear equations arising from the 3D FE method is necessary in order to have more acceptable solution times with high resolution anisotropic models and inverse source localization. Preconditioned Krylov-subspace-methods are among the most attractive iterative methods. We will compare incomplete threshold-factorization- with multigrid- preconditioners, the latter is known to be an optimal method with respect to the operation count and memory. Since the geometric construction of a grid-hierarchy is difficult (we only have tensor measurements for the finest level), we use a pure algebraic multigrid (AMG) preconditioner. 2 Methods Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-04-15

Source:

http://www.cns.mpg.de/Publications/Documents/biomag2000.pdf

http://www.cns.mpg.de/Publications/Documents/biomag2000.pdf Minimize

Document Type:

text

Language:

en

DDC:

518 Numerical analysis *(computed)*

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Metadata may be used without restrictions as long as the oai identifier remains attached to it.

Metadata may be used without restrictions as long as the oai identifier remains attached to it. Minimize

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Electronic transport in a series of multiple arbitrary tunnel junctions

Electronic transport in a series of multiple arbitrary tunnel junctions Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2012-11-19

Source:

http://arxiv.org/pdf/cond-mat/9706137v1.pdf

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Document Type:

text

Language:

en

Subjects:

Coulomb blockade ; Coulomb staircase ; Single electron tunneling ; semiclassical model ; multiple tunnel junctions ; I-V characteristics Typeset using REVTEX

Coulomb blockade ; Coulomb staircase ; Single electron tunneling ; semiclassical model ; multiple tunnel junctions ; I-V characteristics Typeset using REVTEX Minimize

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Metadata may be used without restrictions as long as the oai identifier remains attached to it.

Metadata may be used without restrictions as long as the oai identifier remains attached to it. Minimize

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Title:

Optimizing Logical Proofs with Connectionist Networks

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We describe a connectionist. In this paper we examine the performance of three different types of connectionist networks for this task. The networks showed an interesting trade-off between the exactness of reproduction of a learned proof strategy and the ability to generalize suitably to new proofs.

We describe a connectionist. In this paper we examine the performance of three different types of connectionist networks for this task. The networks showed an interesting trade-off between the exactness of reproduction of a learned proof strategy and the ability to generalize suitably to new proofs. Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-04-11

Source:

http://ls11-www.informatik.uni-dortmund.de/people/mam/publications/./ann91.ps.gz

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Document Type:

text

Language:

en

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Title:

Optimizing logical proofs with connectionist networks

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Publisher:

NorthHolland

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2008-07-01

Source:

http://www.martinwrangel.de/uni/publications/ann91.pdf

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Document Type:

text

Language:

en

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Title:

Learning Of Control Knowledge For Symbolic Proofs With Backpropagation Networks

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This paper presents the application of a connectionist network to optimize symbolic proofs. By symbolic proofs we understand proofs in first order logic. Prolog interpreters are an implementation of theorem provers for special first order formulas, called horn clauses, based on the resolution principle [Robinson 65]. The usage of Prolog interpre...

This paper presents the application of a connectionist network to optimize symbolic proofs. By symbolic proofs we understand proofs in first order logic. Prolog interpreters are an implementation of theorem provers for special first order formulas, called horn clauses, based on the resolution principle [Robinson 65]. The usage of Prolog interpreters for symbolic proofs implies a certain proof strategy: first, selection of the leftmost partial goal in the resolvent as goal to be solved and second, selection of the clauses in written order for the resolution of the selected partial goal. In case of failure of a partial goal, the interpreter backtracks systematically to the last choice made without analyzing the cause of failure. Even for simple programs, this implicit control strategy is not sufficient to obtain efficient computations [Naish 84]. Explicit control by using control constructs like cut is, however, contradictory to Prolog as a declarative programming language. The need for the distinction of logic and control has therefore long been recognized [Kowalski 75]. We describe an approach to learn and store control knowledge in a connectionist network. The system utilizes a three-layer backpropagation network. A meta-interpreter generates training patterns encoding successful Prolog proofs. Trained with these examples of proofs the network generalizes a control strategy to select clauses. Another metainterpreter uses the network to compute optimized proofs. 2. TRAINING THE SYSTEM Fig.1a shows the generation of the training data. A generating metainterpreter (GMI) containing the Prolog program is asked to prove a goal. PROLOG GMI database Backpropagation NETWORK encoder & decoder query program answer PROLOG OMI database Backpropagation NETWORK query program answer. Minimize

Publisher:

Elsevier

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-04-11

Source:

http://nats-www.informatik.uni-hamburg.de/~weber/Paper/ICNC90/icnc90.ps.gz

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Document Type:

text

Language:

en

DDC:

004 Data processing & computer science *(computed)*

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