scholarly journals Quantitative interpretation of impedance spectroscopy data on porous LSM electrodes using X-ray computed tomography and Bayesian model-based analysis

2017 ◽  
Vol 19 (37) ◽  
pp. 25334-25345 ◽  
Author(s):  
Giuseppe F. Brunello ◽  
William K. Epting ◽  
Juwana de Silva ◽  
Paul A. Salvador ◽  
Shawn Litster ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Impedance Spectroscopy ◽  
Temperature Dependence ◽  
Bayesian Model ◽  
Quantitative Interpretation ◽  
Spectroscopy Data ◽  
Bayesian Calibration ◽  
X Ray ◽  
X Ray Computed ◽  
Model Based Analysis

With Bayesian calibration the uncertainty of the temperature dependence of the TPR current can be obtained.

scholarly journals Technical Note. The Concept of a Computer System for Interpretation of Tight Rocks Using X-Ray Computed Tomography Results

2017 ◽  
Vol 39 (1) ◽  
pp. 101-107 ◽  
Author(s):  
Magdalena Habrat ◽  
Paulina Krakowska ◽  
Edyta Puskarczyk ◽  
Mariusz Jędrychowski ◽  
Paweł Madejski
Keyword(s):  
Computed Tomography ◽  
Computer System ◽  
Oil And Gas ◽  
Technical Note ◽  
Quantitative Interpretation ◽  
X Ray ◽  
Qualitative And Quantitative ◽  
Rock Structure ◽  
X Ray Computed ◽  
Tight Rocks

Abstract The article presents the concept of a computer system for interpreting unconventional oil and gas deposits with the use of X-ray computed tomography results. The functional principles of the solution proposed are presented in the article. The main goal is to design a product which is a complex and useful tool in a form of a specialist computer software for qualitative and quantitative interpretation of images obtained from X-ray computed tomography. It is devoted to the issues of prospecting and identification of unconventional hydrocarbon deposits. The article focuses on the idea of X-ray computed tomography use as a basis for the analysis of tight rocks, considering especially functional principles of the system, which will be developed by the authors. The functional principles include the issues of graphical visualization of rock structure, qualitative and quantitative interpretation of model for visualizing rock samples, interpretation and a description of the parameters within realizing the module of quantitative interpretation.

X-Ray Computed Tomography of Ultralightweight Metals

1999 ◽  
Vol 11 (1) ◽  
pp. 199-211
Author(s):  
J. M. Winter ◽  
R. E. Green ◽  
A. M. Waters ◽  
W. H. Green
Keyword(s):  
Computed Tomography ◽  
X Ray ◽  
X Ray Computed

scholarly journals Nano- scale X-ray Computed Tomography: Morphological Analysis of Fuel Cell Electrodes

2013 ◽  
Vol 19 (S2) ◽  
pp. 630-631
Author(s):  
P. Mandal ◽  
W.K. Epting ◽  
S. Litster
Keyword(s):  
Computed Tomography ◽  
Fuel Cell ◽  
Morphological Analysis ◽  
Fuel Cell Electrodes ◽  
X Ray ◽  
Nano Scale ◽  
X Ray Computed

Extended abstract of a paper presented at Microscopy and Microanalysis 2013 in Indianapolis, Indiana, USA, August 4 – August 8, 2013.

scholarly journals Ant Colony-Based Hyperparameter Optimisation in Total Variation Reconstruction in X-ray Computed Tomography

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 591
Author(s):  
Manasavee Lohvithee ◽  
Wenjuan Sun ◽  
Stephane Chretien ◽  
Manuchehr Soleimani
Keyword(s):  
Computed Tomography ◽  
Total Variation ◽  
Cross Validation ◽  
Ant Colony ◽  
Reference Image ◽  
X Ray ◽  
X Ray Computed ◽  
Hyperparameter Selection ◽  
The Cross ◽  
Optimal Set

In this paper, a computer-aided training method for hyperparameter selection of limited data X-ray computed tomography (XCT) reconstruction was proposed. The proposed method employed the ant colony optimisation (ACO) approach to assist in hyperparameter selection for the adaptive-weighted projection-controlled steepest descent (AwPCSD) algorithm, which is a total-variation (TV) based regularisation algorithm. During the implementation, there was a colony of artificial ants that swarm through the AwPCSD algorithm. Each ant chose a set of hyperparameters required for its iterative CT reconstruction and the correlation coefficient (CC) score was given for reconstructed images compared to the reference image. A colony of ants in one generation left a pheromone through its chosen path representing a choice of hyperparameters. Higher score means stronger pheromones/probabilities to attract more ants in the next generations. At the end of the implementation, the hyperparameter configuration with the highest score was chosen as an optimal set of hyperparameters. In the experimental results section, the reconstruction using hyperparameters from the proposed method was compared with results from three other cases: the conjugate gradient least square (CGLS), the AwPCSD algorithm using the set of arbitrary hyperparameters and the cross-validation method.The experiments showed that the results from the proposed method were superior to those of the CGLS algorithm and the AwPCSD algorithm using the set of arbitrary hyperparameters. Although the results of the ACO algorithm were slightly inferior to those of the cross-validation method as measured by the quantitative metrics, the ACO algorithm was over 10 times faster than cross—Validation. The optimal set of hyperparameters from the proposed method was also robust against an increase of noise in the data and can be applicable to different imaging samples with similar context. The ACO approach in the proposed method was able to identify optimal values of hyperparameters for a dataset and, as a result, produced a good quality reconstructed image from limited number of projection data. The proposed method in this work successfully solves a problem of hyperparameters selection, which is a major challenge in an implementation of TV based reconstruction algorithms.

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