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Normalization is an annealing process applied to ferrous alloys to give the material a uniform fine-grained structure and to avoid excess softening in steel. It involves heating the steel to 20–50 °C above its upper critical point, soaking it for a short period at that temperature and then allowing it to cool in air.
In another usage in statistics, normalization refers to the creation of shifted and scaled versions of statistics, where the intention is that these normalized values allow the comparison of corresponding normalized values for different datasets in a way that eliminates the effects of certain gross influences, as in an anomaly time series. Some ...
Iron-carbon phase diagram, showing the conditions under which austenite (γ) is stable in carbon steel. Allotropes of iron; alpha iron and gamma iron. Austenite, also known as gamma-phase iron (γ-Fe), is a metallic, non-magnetic allotrope of iron or a solid solution of iron with an alloying element. [1]
Heat treating furnace at 1,800 °F (980 °C) Heat treating (or heat treatment) is a group of industrial, thermal and metalworking processes used to alter the physical, and sometimes chemical, properties of a material.
Normalization model, used in visual neuroscience; Normalization in quantum mechanics, see Wave function § Normalization condition and normalized solution; Normalization (sociology) or social normalization, the process through which ideas and behaviors that may fall outside of social norms come to be regarded as "normal"
In fluid dynamics, normalized root mean square deviation (NRMSD), coefficient of variation (CV), and percent RMS are used to quantify the uniformity of flow behavior such as velocity profile, temperature distribution, or gas species concentration. The value is compared to industry standards to optimize the design of flow and thermal equipment ...
The Lagrange constraints that () is properly normalized and has the specified mean and variance are satisfied if and only if , , and are chosen so that = (). The entropy of a normal distribution X ∼ N ( μ , σ 2 ) {\textstyle X\sim N(\mu ,\sigma ^{2})} is equal to H ( X ) = 1 2 ( 1 + ln 2 σ 2 π ) , {\displaystyle H(X)={\tfrac {1}{2}}(1 ...
To quantile normalize two or more distributions to each other, without a reference distribution, sort as before, then set to the average (usually, arithmetic mean) of the distributions. So the highest value in all cases becomes the mean of the highest values, the second highest value becomes the mean of the second highest values, and so on.