51234 Investigation of the possibilities of X-ray computed tomography for determining the structural parameters of carbon-reinforced plastics

1994 ◽  
Vol 27 (2) ◽  
pp. 106
Author(s):  
V.I. Barakhov ◽  
V.D. Protasov ◽  
A.V. Sukhanov
Keyword(s):  
Computed Tomography ◽  
Structural Parameters ◽  
X Ray ◽  
Reinforced Plastics ◽  
X Ray Computed

Nature of the clay - cation bond affects soil structure as verified by X-ray computed tomography

Soil Research ◽  
2012 ◽  
Vol 50 (8) ◽  
pp. 638 ◽  
Author(s):  
Alla Marchuk ◽  
Pichu Rengasamy ◽  
Ann McNeill ◽  
Anupama Kumar
Keyword(s):  
Computed Tomography ◽  
Hydraulic Conductivity ◽  
Zeta Potential ◽  
Structural Stability ◽  
Structural Parameters ◽  
Pore Connectivity ◽  
X Ray ◽  
Clay Dispersion ◽  
X Ray Computed ◽  
Cation Ratio

Non-destructive X-ray computed tomography (µCT) scanning was used to characterise changes in pore architecture as influenced by the proportion of cations (Na, K, Mg, or Ca) bonded to soil particles. These observed changes were correlated with measured saturated hydraulic conductivity, clay dispersion, and zeta potential, as well as cation ratio of structural stability (CROSS) and exchangeable cation ratio. Pore architectural parameters such as total porosity, closed porosity, and pore connectivity, as characterised from µCT scans, were influenced by the valence of the cation and the extent it dominated in the soil. Soils with a dominance of Ca or Mg exhibited a well-developed pore structure and pore interconnectedness, whereas in soil dominated by Na or K there were a large number of isolated pore clusters surrounded by solid matrix where the pores were filled with dispersed clay particles. Saturated hydraulic conductivities of cationic soils dominated by a single cation were dependent on the observed pore structural parameters, and were significantly correlated with active porosity (R2 = 0.76) and pore connectivity (R2 = 0.97). Hydraulic conductivity of cation-treated soils decreased in the order Ca > Mg > K > Na, while clay dispersion, as measured by turbidity and the negative charge of the dispersed clays from these soils, measured as zeta potential, decreased in the order Na > K > Mg > Ca. The results of the study confirm that structural changes during soil–water interaction depend on the ionicity of clay–cation bonding. All of the structural parameters studied were highly correlated with the ionicity indices of dominant cations. The degree of ionicity of an individual cation also explains the different effects caused by cations within a monovalent or divalent category. While sodium adsorption ratio as a measure of soil structural stability is only applicable to sodium-dominant soils, CROSS derived from the ionicity of clay–cation bonds is better suited to soils containing multiple cations in various proportions.

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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