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The implemented quantification algorithm and Hadoop platform are also introduced here.
The best heterogeneity quantification algorithm is the one with high HDP (mean slope) and low DDP.
Subsequently the effect of noise on a PNN based uncertainty quantification algorithm is explored in this study.
Subsequently the influence of noise is investigated on radial basis function based uncertainty quantification algorithm to account for the inevitable variability for practical field applications.
It has been demonstrated here that the accuracy and efficiency of the Bayesian quantification algorithm increases if damage localization is carried out a-priori.
In summary, by combining the aggregation function and proposed quantification algorithm, the feature generation module produces high-level features in the second stage of our proposed framework.
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In many ways, the detail of the process is like a black box, with very little published information on quantification algorithms and applied weightings.
With the damage number determined, the localization and quantification algorithms are then developed.
As a case study, we shall focus on binary quantification problems, introducing ensembles versions for two well-known quantification algorithms.
Prior to quantification algorithms implementation, it is important to properly prepare the supports on which algorithms will be tested.
Standard segmentation and morphology quantification algorithms were applied as well as finite element (FE) simulation based on segmented and gray value images.
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