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Experiments of the examined lateral extrusion process provide data for the verification of the investigated process simulation models.
Still a stress calculation inside the BEM domain is possible based on the boundary data.
CAD data of tool and workpiece design of the lateral extrusion process was used to create the geometry for the investigated process simulation models.
To achieve the model requirements, the imported geometry data was further processed.
The comparison with experimental data proved the validity of the FEM/BEM model and the calculated results.

E-mail: jinliguo@hrbust.edu.cn Keywords:Grapheme/platinum; EHD; in-situ reduction; counter electrode Abstract.
Graphene/platinum composite gel was prepared with chloroplatinic acid and graphene oxide (GO) as precursors by in-situ reduction method.
Platinum has high catalytic activity toward I3- reduction and is sufficiently corrosion-resistant to iodine compound present in the electrolyte [5].
The overall Raman peak intensities are diminished after reduction treatment, suggesting loss of carbon during reduction [12].
Fig.3 Raman spectra of GO and RGO Electrical Properties Analysis Fig. 4(a) shows the representative current density-voltage characteristics with the numerical data listed in Table 1.

As part of this work the analysis of the impact relative rolling reduction at the connection area of bimetallic plate after the rolling process was carried out.
Bimetal plates Al99.8-M1E were rolled with relative rolling reduction e = 10%, 15% and 20%.
The microscope is connected to the computer on which is installed the NIS-Elements software for data acquisition and analysis.
The range of possible magnifications, along with fully automatic table provides not only to collect data from one area.
There was however observed, that the connection area after rolling shows a reduction of waviness.

Regarding this study, a head dummy was used for the 3D scanning process for the data acquisition.
From the point cloud data, horizontal plane was used to obtain sections for the head area.
The cloud data then transfer to CATIA for 3D modeling for the padding as shown in figure 1.
A section curve from the cloud data is use as an example for this study.
It was constructed from tangent curves with a low deviation from the original data.

Hydrogeology and Groundwater Hydrology Drilling data analysis.
Groundwater usage and groundwater level observation data.
Firstly, steady state simulation is conducted, and various hydrological parameters, and other input data, of the model are adjusted by the groundwater level observation data of the eight self-recording wells around the area.
As shown in Figure 5, the simulation results are fairly consistent with the groundwater level observation data.
The annual groundwater level of the STSP is 6 m from the historical data.

Then, Reduce reduction operation is performed to process these data in multi-nodes to produce the dataset results.
The users of data mining agent can access Meta data to generate local data model, which is extracted from the data.
Users of data mining agent can access Meta data to generate local data model that is extracted from the data.
Both local data mining algorithm and global data mining algorithms can perform calculations on different data platform of data mining
The new data model of cloud management n data [J].

The developed data processing algorithm for the multi-element array system was tested in the MatLab software package.
The cost of devices that allow receiving and processing data from the array is very high, since it requires processing and transmission of large amounts of data simultaneously.
The realization of the hardware of partial data processing can significantly reduce the amount of information transmitted to a personal computer or display device, as well as reduce data processing time.
Each cycle of the ADT startup block increases the value of the row counter, which is an address for the data memory.
MATLAB software package is selected for data processing and constructing an image.

The results show that our model can both enhance learning performance and classification accuracy. 1 Feature Reduction based on Manifold Learning Since the original dimensionality of the feature space gathered from the primary image data is usually very large, which will seriously affect the performance and results of classification, dimensionality reduction for the original feature space is thus not a negligible phase.
Linear dimensionality reduction will usually satisfy the tasks of linear distributed reduction, but in nonlinear cases it will lose certain efficiency and accuracy.
The methods of nonlinear dimensionality reduction are hence widely introduced for such nonlinear reduction situations.
Its basic idea is that the overall information served by overlapping the local neighbors maintains the original topology structure of the primary image data, using local linear approximation to the overall linear to the global and meanwhile keeping the local geometry structures unchanged.
[9] Belkin M and Niyogi P, “Laplacian Eigenmaps for Dimensionality Reduction and Data Representation[J],” Neural Computation, 2003,15(6), pp. 1373–1396.

An approach to the problem is to apply dimensionality reduction to the data for the object of classification and identification.
Learning the manifold underlying the fault data is important because the manifold reveals the intrinsic structure of the data and the essential relations in them.
In Section 4, experiments are performed on the benchmark data and real data sets to verify the effectiveness of the proposed method.
In Step 2, the local geometry in the neighborhood of each data point is characterized by the linear coefficients that best reconstruct the data point from its neighbors.
From the experimental results, it can be concluded that the proposed CSSLLE has an ability of extracting the most discriminating data with the best generalization among all the used dimensionality reduction methods.

The calculated data show that the improvement measures, e.g. reduction of dolomite consumption and energy consumption, in Chinese Pidgeon process led to 23% decrease of the GWP for the primary magnesium production in 2009 compared with 2005.
Data Collection.
The input data of materials and energy consumption were based on investigation to China magnesium factories.
An upper bound, average and lower bound data of Pidgeon process were summarized in Table 1.
These values were compared with published data in Table 3 in order to estimate the influence of uncertainties on these impacts.