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The measured incomplete {111}, {220}, and {200} pole figure data were used to calculate the orientation distribution function (ODF) by the WIMB program [17].
The specimens for EBSD were annealed in a salt bath at 195℃ for 1 h to obtain good image-quality data.
The average grain size and the fraction of high-angle boundaries of these specimens obtained from their EBSD data are shown in Fig. 3.
Different reductions in last pass.
Fig. 5 shows the average grain size and the fraction of high-angle boundaries obtained from the EBSD data for the sheets asymmetrically rolled by the three different rolling schedules in Fig. 4.

The emission rules forced the car brands to start new researches to find new solutions for mass reduction.
Our investigations are focusing mainly on aluminum, because of its high mass reduction potential.
These parts also have high mass reduction potential.
The possible mass savings of different parts [1]: · Engine and transmission parts 30-60% · Chassis and suspension parts 10-50% · Hang-on parts 30-55% · Wheel rims 10-50% · Bumper systems 30-50% Analyzing the data of new cars we can see that the mass is increasing, though car manufacturers are using high amount of HSS, UHSS, and aluminum alloys.
FLC curves at different temperatures The force vs time diagram includes useful data about the forming process (Figure 7).

According to the experiment data, the on road testing results (particle reductions) are approximately equal to geometrical mean of filter media efficiency and filter total efficiency.
In this section, the tested vehicle with the same cabin filter represents a typical up to data passenger car.
Moreover, filter media’s efficiencies are higher than other two experiments’ data.
According to the experiment data, the on road testing results (particle reductions) are approximately equal to geometrical mean of filter media efficiency and filter total efficiency.
According to the experiment data, the on road testing results (particle reductions) are approximately equal to the geometrical mean of filter media efficiency and filter total efficiency. 3.

These ratings are employed to reduce the number of the derived design options, and thereby used as input data to a neural network.
Unsupervised NNs usually perform some kind of data compression, such as dimensionality reduction or clustering.
(i) Motor and Gear Reduction.
The input data is presented to the NN and the NN will compute an output value that approximates the desired result.
However, for successful training , large volume of training data is used and computer time consumption is high.

The Si steel was multi-passed rolled at 800oC to a various reductions up to 50%.
The 0.2% proof strength (a) tended to increase with the increasing of the reduction and also strain rate.
The total elongation (b) tended to increase with the increase of the reduction by the rolling when the data were compared at the same strain rate.
The threshold strain rate increased with the increasing of the reduction.
The threshold strain rate is higher with increasing of the rolling reduction

The required data used for training and testing the ANN is obtained by conducting trial runs in wire cut electric discharge machine.
Proposed algorithm paves reduction in time for fixing the values for the process parameters and thus reduces the production time along with reduction in cost of machining processes and thereby increases the production as well as the efficiency.
Mathematical models developed by ANN using the trial data providing towards the selection of the optimum process parameters and process conditions without conducting trials or experiments [5].
As the outcome of training, the trial or experimental values were obtained and the result is validated by comparing the actual data with the experimental or trial data.
All the parameters were normalized by using matlab and this is the requirement to make the data flow on neural network.

After these samples were cold-rolled with 20% and 50% reduction ratios, they were heated to 600°C.
The equivalent strain (ε) converted from the reduction ratio R by the equation ε=-(4/3) 0.5ln(1-R).
The grain stucture was similar between the as-rolled and the heated sample in the reduction ratio of 50%.
Figure 8 shows (100) pole figure generated from the EBSP data of Fig. 7, revealing the strong (130)[3-12] texture component of the heated-at-400°C sample compared with the (213)[36-4] ] texture component of the of the as-rolled sampled.
Fig.8 �111 pole figures of Al-Fe-Ni alloy (a) as-rolled and (b) annealed at 400°C (generated from the EBSP data shown in Fig. 7).

Model data agreed well with experimental data.
The preparation methods of (R)-HPBE included resolution of the corresponding racemate [2-4], asymmetric reduction of the prochiral ethyl-2-oxy-4-phenylbutyrate and its derivates [5-9], enzymatic esterification of 2-hydroxy-4-phenylbutanoic acid, and chemical multi-step synthesis [10,11].
Preparation of racemic HPBE. racemic HPBE was prepared by chemical reduction. 1.0095 g ethyl-2-oxo-4-phenylbutyrate dissolved in 30 ml of anhydrous ethyl alcohol was added in 500 ml flask containing 0.054 g NaBH4, 130.0 ml anhydrous ethanol and 8.0 ml distilled water and stirred constantly for 4 h.
Kinetic model can predict the actual experimental reactions Fig. 6 Comparisons model data and experimental data (initial substrate concentration was 0.022 mol/L) Fig. 8 Comparisons model data and experimental data (initial substrate concentration was 0.036 mol/L) Fig. 7 Comparisons model data and experimental data (initial substrate concentration was 0.028 mol/L) Fig. 9 Comparisons model data and experimental data (initial substrate concentration was 0.048 mol/L) Fig. 10 Comparisons model datas and experimental datas (initial substrate concentration was 0.074 mol/L) Conclusions Kinetic resolution of ethyl 2-hydroxy-4-phenylbutyrate by lipase AK as catalyst was investigated.
Radhika, Enantio- and regiospecific reduction of ethyl 4-phenyl-2,4-dioxobutyrate with baker’s yeast: preparation of (R)-HPB ester, Tetrahedron: Asymmetry 15 (2004) 3443-3447

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.

Using crash data in the determination of road safety status can be categorized as reactive measures.
Most basic procedure of reactive actions is generally based on crash data of the road since this data has numerous crash information such as location of crash (by kilometer of the road) and time of crash (time, day and month).
The first phase of collecting road environment data was completed using drive-through method.
A data reduction technique called principal component analysis was applied to cluster the road environment indicators in several groups hence determined the characteristics of each group.
Fig. 3 : Number of Crash Data along the Study Area.