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The starting microstructures for the simulations were generated from experimental data taken using electron backscatter pattern analysis.
Although the EBSP data is already discretized in a form suitable for input into a Monte Carlo Potts model, as a result of noise in the experimental data some pre-processing of the data is also necessary.
For starting microstructures with higher cube-fractions ( ≈ 65%) , the evolution more closely matches the experimental data.
In accordance with a large amount of experimental data, the energy of the grain boundaries in the system was defined in each case according to the Read-Shockley equation, with a minimum value set at 0.1 [7]: ( ) ( ) [ ]    −θθ =γ 1 ]ln1[/,1.0max m m θ/θ m m θ>θ θ≤θ : : (3) Less experimental data exists for the variation of boundary mobility with misorientation.
Only a small advantage (Λ=1.02, or cube grain surface energies ≈ 2% lower those of other grains) is necessary to give a noticable cube-texture enhancement to levels comparable with the experimental data.

Within this context, evaluate the reduction of iron ore pellets using the dilatometer technique constitutes a promising approach for optimizing this process.
The results indicate the sintering progress of the particles that comprise the pellets as well as reduction the porosity.
Densification of powder compacts during sintering under constant heating rate has been studied and methods of analysing the densification kinetic data have been suggested [2].
Data obtained in the shrinkage or swelling of a pellet from dilatometer can be usually generalized as behavior macroscopic of a porous body.
Model Kinetic The kinetic data dilatometer analysis were used to evaluate the kinetics of densification of iron ore pellet.

The S-N data shows continuous reduction tendency.
It was found that with the increase of test relative humidity, the fatigue properties of the alloy is decreased, the fatigue limit in 95% RH was nearly 20% reduction of that in 15% RH, only 67% of its yield strength.
Meanwhile, the S-N data is much more scattered than it in lower relative humidity.
Humidity caused the decrease in fatigue strength of the alloy and scattered the S-N data obviously.

Fig. 2 also shows that a decrease in strain rate from 4x10-3 to 1x10-4 s-1 leads to a fatigue life reduction of 48 %.
In air, the reduction of fatigue life is around 41 % for a decrease of strain rate from 4x10-3 to 1x10-4 s-1.
Similar data for a polished surface finish from a previous study [6-7] are also included.
By integrating the data of Fig. 5 for a crack depth ranging between 45 µm and 500 µm, it is shown that this stage corresponds to about one third of the fatigue life, regardless of the strain rate.
In particular, the reduction of fatigue life is more important than in vacuum and in air

In this paper, the methods of extracting minerals weight information are studied based on hyperion hyperspectral remote sensing data, taking the region of Gannan area in Jiangxi as an example.
Data source and preprocessing The data source of this study is EO-1′s Hyperion hyperspectral data and the range of study area is longitude114°45′-114°59′, latitude 25°19′-26°15′.
However, image data doesn't conform to statistical distribution because of the complexity of the actual surface features, and there are mixed pixels, data noise and not interested features and other reason, which cause the detecting algorithm generates false alarm, finally DN value of gray scale image may output negative value or the value of greater than 1.
[4] Boardman J W,Kruse F A,Green R O.Mapping Target Signatures Via Partial Unmixing of AVIRIS data:in Summaries[J] ,Fifth JPL Airborne Earth Science Workshop. 1995,1:23-26
Hyperspectral Image Classification and Dimensionality Reduction:An Orthogonal Subspace Projection Approach[J].

The uniform sampling method can be applied to simplify cloud data, which can extract n data from the number of i data point through a certain compression radio.
The data acquisition step captures the surface data of a part by a 3D laser-scanning device, the quality of raw point data determines the quality of reconstructing surfaces.
The amount of point data can be simplified by using chord-angle deviation method under the base of data accuracy after reduction.
Point cloud data reduction methods of octree-based coding and neighborhood search.
Data Reduction Methods for Reverse Engineering.

Chemical transformation graphene oxide reduction method has been called “GO”.
The reduction of graphene can concluded through chemical conversion.
The reduction of graphene is shown in Figure 2.
Fig. 2 Preparation schematic of reduction of graphene Figure 2 is structure of reduction of graphene.
In order to study the conductivity and service life characteristics of the composite membrane, this paper gets the data sheet which is shown in Table 1 through experiments eventually.

This data is in agreement with the results of Tomimura et al.[5], obtained by induction magnetization.
In 301LN a 60% cold reduction was required to produce the nearly fully martensitic structure while in 301 the cold reduction of 70% was able to induce only about 80% martensite.
XRD data.
However, XRD data in Fig. 1a confirms that the microstructure became mainly austenitic within 100 s even at 700°C.
Kyröläinen: Outokumpu Stainless Oy, Tornio, Finland, process data (2005)

Efficiency of Steel Casting Production by Aluminothermic Metal Reduction Igor G.
An exothermic oxidation-reduction reaction, in which iron reduction from waste occurs, serves as a basis for this technological process [6].
From experimental data the volume of reduced metal amounts to 50-55 % of the initial feeder volume [10].
The lower range value is conditioned by experimental data for thermite mixtures with various contents of the reducer at loose filling [12].
Komarov, Mathematical Model of Iron Reduction with Aluminothermic Method, Advanced Materials Research. 1040 (2014) 484-488

Moreover, the reduction is the percentage of reducing the thickness of the strip.
The experimental data has been arranged in the best way to analyze the effect of each processing parameter individually.
To analyze the reduction effect on the surface roughness, the values of surface roughness (Ra) have been plotted against the reduction values as shown in Fig. 3.
Fig. 3 Effect of reduction on surface roughness By analyzing the trends in Fig. 3, it can be noted the obvious linear decrease of the surface roughness found as a result of increasing the reduction.
It is known as explained above the effect of reduction which is in summary with increasing the reduction the surface roughness decreases.