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The results reveal more pronounced effects for smaller sample dimensions and also suggest that most of the evolving dislocation structure is formed prior to the occurrence of large strain bursts.
In this paper an alternative procedure to NORMAL 07-81 is proposed [18], using slightly smaller sample dimensions (2x2x2 cm), and there are presented first experimental data on three different marble stones, characterised by X-ray powder diffraction (XRD).
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It can be seen when the sample dimension is 1, due to the small sample dimension, the compression sampling signal cannot form the distance-preserving map with the original high-dimensional space.
Small samples with dimensions 30×5 mm or 60×20 mm can be characterized.
The effect of sample dimensions and dislocation source density were investigated and compared to small scale mechanical tests conducted on titanium and zirconium.
The impact on work due to dry eye dimension was both included and excluded from factor analyses due to smaller sample sizes for the work-related items.
Due to the small-sample-high-dimension nature of the microarray dataset, it is not difficult to find highly discriminative gene subsets of small size.
This paper presents a novel machine learning method, termed support vector machine (SVM), to obtain a global optimization model in conditions of large project dimensions, small sample sizes and non-linearity.
However, microarray data are known to have some features, such as high dimension, small sample, high noise, high redundancy, and skewed class distribution which is called class imbalance problem.
Moreover, we report size dependency and strain dependency in the hardening behavior at these dimensions, where higher hardening is observed for smaller samples and at lower strains.
An ablation cell was developed to host large or several small samples (up to dimensions 230 mm × 34 mm × 16 mm, L × W × D) for high spatial resolution analysis.
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