Optical constants of gold blacks: Fractal network models and experimental data

2002 ◽  
Vol 65 (24) ◽  
Author(s):  
Juan A. Sotelo ◽  
Vitaly N. Pustovit ◽  
Gunnar A. Niklasson
Keyword(s):  
Experimental Data ◽  
Optical Constants ◽  
Network Models ◽  
Fractal Network

Comparisons of Support Vector Regression and Neural Network in Modelling the Hydraulic Damper

2011 ◽  
Vol 403-408 ◽  
pp. 3805-3812 ◽  
Author(s):  
Kong Hui Guo ◽  
Xian Yun Wang
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Support Vector Regression ◽  
Network Models ◽  
Support Vector ◽  
Neural Network Models ◽  
Hydraulic Damper ◽  
Nonparametric Models ◽  
Force Velocity ◽  
Bpnn Model

Nonparametric models of hydraulic damper based on support vector regression (SVR) are developed. Then these models are compared with two kinds neural network models. One is backpropagation neural network (BPNN) model; another is radial basis function neural network (RBFNN) model. Comparisons are carried out both on virtual damper and actual damper. The force-velocity relation of a virtual damper is obtained based on a rheological model. Then these data are used to identify the characteristics of the virtual damper. The dynamometer measurements of an actual displacement-dependent damper are obtained by experiment. And these data are used to identify the characteristics of this actual damper. The comparisons show that BPNN model is best at identifying the characteristics of the virtual damper, but SVR model is best at identifying the characteristics of the actual damper. The reason is that all experimental data include noise more or less. When the amplitude of the noise is smaller than the parameter of SVR, the noise can not affect the construction of the resulting model. So when training a model based on the experimental data, SVR is superior to other neural networks methods.

Review of Pc-Sw Data for Network Model Validation

2010 ◽  
Vol 2 ◽  
pp. 15-30
Author(s):  
O.A. Olafuyi
Keyword(s):  
Experimental Data ◽  
Transport Properties ◽  
Laboratory Experiments ◽  
Network Models ◽  
Micro Ct ◽  
Pore Scale ◽  
Predictive Capability ◽  
Flow Transport ◽  
Scale Network ◽  
Pore Scale Network

Advances in micro-CT imaging of porous materials provide the opportunity to extract representative networks from the images. This improves the predictive capability of pore scale network models to predict multiphase flow transport properties. However, all these predictions need to be validated with laboratory experimental data. The experimental data for such validation may either be from the literature or newly conducted laboratory experiments on same outcrops. This paper presents the review of some of the available Pc – Sw experimental data available in the literature for validating the predictions made by network models.

Confidence Interval Simulation for Systems of Random Variables

2005 ◽  
Vol 129 (3) ◽  
pp. 836-842 ◽  
Author(s):  
Thomas A. Cruse ◽  
Jeffrey M. Brown
Keyword(s):  
Experimental Data ◽  
Confidence Interval ◽  
Bayesian Network ◽  
Upper Bound ◽  
Random Variables ◽  
Network Models ◽  
Probability Of Failure ◽  
Cumulative Distribution ◽  
Probabilistic Design ◽  
Design Assessment

Bayesian network models are seen as important tools in probabilistic design assessment for complex systems. Such network models for system reliability analysis provide a single probability of failure value whether the experimental data used to model the random variables in the problem are perfectly known or derive from limited experimental data. The values of the probability of failure for each of those two cases are not the same, of course, but the point is that there is no way to derive a Bayesian type of confidence interval from such reliability network models. Bayesian confidence (or belief) intervals for a probability of failure are needed for complex system problems in order to extract information on which random variables are dominant, not just for the expected probability of failure but also for some upper bound, such as for a 95% confidence upper bound. We believe that such confidence bounds on the probability of failure will be needed for certifying turbine engine components and systems based on probabilistic design methods. This paper reports on a proposed use of a two-step Bayesian network modeling strategy that provides a full cumulative distribution function for the probability of failure, conditioned by the experimental evidence for the selected random variables. The example is based on a hypothetical high-cycle fatigue design problem for a transport aircraft engine application.

Applications of the Theory of Optical Spectroscopy to Numerical Simulations

1993 ◽  
Vol 47 (5) ◽  
pp. 566-574 ◽  
Author(s):  
M. Milosevic ◽  
S. L. Berets
Keyword(s):  
Experimental Data ◽  
Optical Constants ◽  
Incident Angle ◽  
Spectroscopic Technique ◽  
Sample Thickness ◽  
Spectroscopic Techniques ◽  
Spectral Features ◽  
Reflection Spectra ◽  
Experimental Conditions ◽  
Potential Applications

A numerical simulation has been developed to extract the optical constants from experimental spectra. In particular, transmission, internal reflection, and external reflection spectra can be simulated for any incident angle, polarization, and sample thickness. The simulation is used here to determine the optical constants of two materials and to illustrate differences in spectral features that arise from variations in experimental conditions. Other potential applications of this method include determining film thicknesses from experimental data, selecting the best spectroscopic technique for a particular sample, and cross-referencing spectroscopic techniques.

scholarly journals The anisotropy in the optical constants of quartz crystals for soft X-rays

2021 ◽  
Vol 54 (2) ◽  
Author(s):  
A. Andrle ◽  
P. Hönicke ◽  
J. Vinson ◽  
R. Quintanilha ◽  
Q. Saadeh ◽  
...  
Keyword(s):  
Crystal Structure ◽  
Experimental Data ◽  
Refractive Index ◽  
Optical Constants ◽  
Quartz Crystal ◽  
Crystal Surface ◽  
X Rays ◽  
Data Set ◽  
Quartz Crystals ◽  
X Ray

The refractive index of a y-cut SiO2 crystal surface is reconstructed from orientation-dependent soft X-ray reflectometry measurements in the energy range from 45 to 620 eV. Owing to the anisotropy of the crystal structure in the (100) and (001) directions, a significant deviation of the measured reflectance at the Si L 2,3 and O K absorption edges is observed. The anisotropy in the optical constants reconstructed from these data is also confirmed by ab initio Bethe–Salpeter equation calculations for the O K edge. This new experimental data set expands the existing literature data for quartz crystal optical constants significantly, particularly in the near-edge regions.

scholarly journals Grid cells show field-to-field variability and this explains the aperiodic response of inhibitory interneurons

2017 ◽  
Author(s):  
Benjamin Dunn ◽  
Daniel Wennberg ◽  
Ziwei Huang ◽  
Yasser Roudi
Keyword(s):  
Experimental Data ◽  
Firing Rate ◽  
Grid Cell ◽  
Network Models ◽  
Theoretical Models ◽  
Inhibitory Neurons ◽  
Inhibitory Interneurons ◽  
Grid Cells ◽  
External Stimuli ◽  
Individual Grid

AbstractResearch on network mechanisms and coding properties of grid cells assume that the firing rate of a grid cell in each of its fields is the same. Furthermore, proposed network models predict spatial regularities in the firing of inhibitory interneurons that are inconsistent with experimental data. In this paper, by analyzing the response of grid cells recorded from rats during free navigation, we first show that there are strong variations in the mean firing rate of the fields of individual grid cells and thus show that the data is inconsistent with the theoretical models that predict similar peak magnitudes. We then build a two population excitatory-inhibitory network model in which sparse spatially selective input to the excitatory cells, presumed to arise from e.g. salient external stimuli, hippocampus or a combination of both, leads to the variability in the firing field amplitudes of grid cells. We show that, when combined with appropriate connectivity between the excitatory and inhibitory neurons, the variability in the firing field amplitudes of grid cells results in inhibitory neurons that do not exhibit regular spatial firing, consistent with experimental data. Finally, we show that even if the spatial positions of the fields are maintained, variations in the firing rates of the fields of grid cells are enough to cause remapping of hippocampal cells.

scholarly journals Novel Lattice Models for Porous Media

2012 ◽  
Vol 1475 ◽  
Author(s):  
Andrey P. Jivkov ◽  
Joseph E. Olele
Keyword(s):  
Experimental Data ◽  
Porous Media ◽  
Coordination Number ◽  
Support Systems ◽  
Lattice Models ◽  
Network Models ◽  
Constitutive Laws ◽  
Average Coordination Number ◽  
Engineering Applications ◽  
Internal Damage

ABSTRACTNetwork models of porous media are beneficial for predicting evolution of macroscopic permeability. This work proposes novel models based on truncated octahedral support. Systems with different pore coordination spectra for a given average coordination number can be constructed to match experimental data. This feature, and the allowed pore coordination of 14, make the proposed models more realistic and flexible than existing models with cubic support. Experimental data for two sandstones with substantially different properties are used to demonstrate the models’ ability to predict permeability. A strategy for calculating its evolution with internal damage is also described and results are presented. Developments of this strategy are suggested for deriving mechanism-based constitutive laws for engineering applications.

scholarly journals Convergence of a Belief Propagation Algorithm for Biological Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-16
Author(s):  
Sang-Mok Choo ◽  
Young-Hee Kim
Keyword(s):  
Experimental Data ◽  
Biological Networks ◽  
Belief Propagation ◽  
Iterative Scheme ◽  
Biological Systems ◽  
Network Models ◽  
Stochastic Approach ◽  
Factor Graph ◽  
Process Step ◽  
Whole Process

Constructing network models of biological systems is important for effective understanding and control of the biological systems. For the construction of biological networks, a stochastic approach for link weights has been recently developed by using experimental data and belief propagation on a factor graph. The link weights were variable nodes of the factor graph and determined from their marginal probability mass functions which were approximated by using an iterative scheme. However, there is no convergence analysis of the iterative scheme. In this paper, at first, we present a detailed explanation of the complicated multistep process step by step with a network of small size and artificial experimental data, and then we show a sufficient condition for the convergence of the iterative scheme. Numerical examples are given to illustrate the whole process and to verify our result.

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