![]() ![]() įurthermore, research indicates that some HEAs have considerably better strength-to-weight ratios, with a higher degree of fracture resistance, tensile strength, and corrosion and oxidation resistance than conventional alloys. These alloys are currently the focus of significant attention in materials science and engineering because they have potentially desirable properties. ![]() Some alternative names, such as multi-component alloys, compositionally complex alloys and multi-principal-element alloys are also suggested by other researchers. The term "high-entropy alloys" was coined by Taiwanese scientist Jien-Wei Yeh because the entropy increase of mixing is substantially higher when there is a larger number of elements in the mix, and their proportions are more nearly equal. Hence, high-entropy alloys are a novel class of materials. ![]() For example, additional elements can be added to iron to improve its properties, thereby creating an iron-based alloy, but typically in fairly low proportions, such as the proportions of carbon, manganese, and others in various steels. Prior to the synthesis of these substances, typical metal alloys comprised one or two major components with smaller amounts of other elements. High-entropy alloys ( HEAs) are alloys that are formed by mixing equal or relatively large proportions of (usually) five or more elements. It's a fascinating and complex subject which really can't be summarised in one post.Alloys with high proportions of several metals Atomic structure model of fcc CoCrFeMnNi A vector with relatively 'high' entropy is a vector with relatively high information content. A vector with relatively 'low' entropy is a vector with relatively low information content. A component with low entropy is more homogenous than a component with high entropy, which they use in combination with the smoothness criterion to classify the components.Īnother way of looking at entropy is to view it as the measure of information content. In the context of the paper low entropy (H(s_m) means low disorder, low variance within the component m. So what does this mean? In image processing entropy might be used to classify textures, a certain texture might have a certain entropy as certain patterns repeat themselves in approximately certain ways. The probability density p_n is calculated using the gray level histogram, that is the reason why the sum runs from 1 to 256. Here is the probability that outcome s_m happens. H(s_m) is the entropy of the random variable s_m. As the level of disorder rises, the entropy rises and events become less predictable.īack to the definition of entropy in the paper: Entropy can serve as a measure of 'disorder'. One way to view entropy is to relate it to the amount of uncertainty about an event associated with a given probability distribution. They are talking about Shannon's entropy. The target component is a tumor and the paper reads: "the tumor related component with "almost" constant values is expected to have the lowest value of entropy."īut what does low entropy mean in this context? What does each bin represent? What does a vector with low entropy look like? But I'm failing to understand what entropy is in this case.Īnd they say that '' are probabilities associated with the bins of the histogram of '' In the paper I'm reading, the authors wish to select a component m for which matches certain smoothness and entropy criteria. T is the total number of pixels in the image, is the value of the source component (/signal/object) i at pixel j ![]() The output of the algorithm is a matrix, which represents a segmentation of an image into M components. I'm reading an image segmentation paper in which the problem is approached using the paradigm "signal separation", the idea that a signal (in this case, an image) is composed of several signals (objects in the image) as well as noise, and the task is to separate out the signals (segment the image). ![]()
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