Metal matrix composites (MMCs) are regarded to be one of the most principal classifications in composite materials. The thermal characterization of hybrid MMCs has become increasingly important in a wide range of applications. Thermal conductivity is one of the most important properties of MMCs. Since nearly all MMCs are used in various temperature ranges, measurement of thermal conductivity as a function of temperature is necessary in order to know the behavior of the material. In the present research, evaluation of thermal conductivity has been accomplished for aluminum alloy (Al) 6061, silicon carbide (SiC) and graphite (Gr) hybrid MMCs from room temperature to [Formula: see text]C. Al-based composites reinforced with SiC and Gr particles have been prepared by stir casting technique. The thermal conductivity behavior of hybrid composites with different percentage compositions of reinforcements has been investigated using laser flash technique. The results have indicated that the thermal conductivity of the different compositions of hybrid MMCs decreases by the addition of Gr with SiC and Al 6061. Few empirical models have been validated concerning with the evaluation of thermal conductivity of composites. Using the experimental values namely density, thermal conductivity, specific heat capacity and enthalpy at varying temperature ranges, computational investigation has been carried out to evaluate the thermal gradient and thermal flux.
Although texture is important for many vision-related tasks, it is not used in most salience models. As a consequence, there are images where all existing salience algorithms fail. We introduce a novel set of texture features built on top of a fast model of complex cells in striate cortex, i.e., visual area V1. The texture at each position is characterised by the two-dimensional local power spectrum obtained from Gabor filters which are tuned to many scales and orientations. We then apply a parametric model and describe the local spectrum by the combination of two one-dimensional Gaussian approximations: the scale and orientation distributions. The scale distribution indicates whether the texture has a dominant frequency and what frequency it is. Likewise, the orientation distribution attests the degree of anisotropy. We evaluate the features in combination with the state-of-the-art VOCUS2 salience algorithm. We found that using our novel texture features in addition to colour improves AUC by 3.8% on the PASCAL-S dataset when compared to the colour-only baseline, and by 62% on a novel texture-based dataset.
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