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Main part of preparation work is simplification of 3D-CAD data to decrease mesh scale and without impacting the solution accuracy.
In this study, automatic model preparation method is developed by using of geometrical and topological information of 3D-CAD data.
Several thousand bumps can exist in manufacturing 3D-CAD data.
We can easily detect it using geometrical data which is described as conic or cylindrical face in CAD data.
The purpose of this study is reduction of period to create CAE model from 3D-CAD data.

For an optimal reduction of the size of the model the CMS with reduction of interface dof is used.
This coupling methodological approach is used on one hand to reduce the size of the model and on the other hand to analyze the structures where the data on the uncertainties are limited enough.
Convex models which are adapted to the structures where the data on the uncertainties are limited enough are defined as the distance between the nominal values of the parameters and what we wish to determine, the uncertainties, to satisfy a given criterion of conception.
SS(3) SS(2) SS(1) Fig. 2 : Assembled plane plates The following data is considered: Plate 1 and 2: dimensions 1m*2m, thickness e1 = e2 = 0.02m Plate 3: dimensions 1m*1m, thickness e3 = 0.05m The vector of uncertainties parameters u is selected here to be the thicknesses of the three plates
These reduction procedures lead to a large reduction of the CPU time.

The constitutive relationship of alloy GH706 has been established by linear regression analysis of the experimental data taken from the Arrhenius equations as a model.
During the compression, the load-stroke data were collected through both displacement sensor and pressure sensor and then were used to extrapolate the allowable reduction of the trail ingot.
The material constants of the constitutive equation can be evaluated from the experimental stress-strain data obtained from hot compression tests.
By using the flow stress data for the experiments as shown in Fig.2, at the peak strain, the values ofn1 and β are obtained as 4.9322 and 0.03286, respectively.
By taking the natural logarithm of Eq. (3), lnsinhασ=QRn1T+1nlnε-1nlnA (6) the parameters A, n and Q are easy to be determined from the experimental stress-strain data obtained at different strain rates and temperatures by linear fitting method.

Unfortunately, in data management scenarios today it is rarely the case that all the data can be fit nicely into a conventional relational DBMS, or into any other single data model or system.
Most data management scenarios today rarely have a situation in which all the data can be not nicely into a conventional relational DBMS, or into any other single data model or system.
Unfortunately, in data management scenarios today it is rarely the case that all the data can be fit nicely into a conventional relational DBMS, or into any other single data model or system.
The data space is a collection of data[2] which is associated with the main body and its relationship to the body, all of the data in the data space can be controlled.
Therefore according to uncertain data, lots of data model and data management system are proposed such as TRIO, MayBMS and MystiQ and so on.

From this data, the reduction potentials were determined for each metal, finding the values of -0.0024, -1.1274 and -0.5892 V vs calomel for Cu, Ni and Zn, respectively.
Displacement in the reduction potential with the increase of the metal concentration in the leaching solution was observed.
Concentrations were calculated for each metal in order to produce the data needed in the thermodynamic study using Pourbaix type diagrams, by the Hydra/Medusa software [13] and to evaluate the possibility for the selective metal recovery by electrochemical methods.
From the above diagrams, it was possible to observe the influence of the concentration of each metal on the reduction potential.
Reduction potential values determined for the electrodeposition of leached metals.

This agreement is a strong argument for the consistency of the simulation and the data reduction scheme.
This agreement is a strong argument for the consistency of the simulation and the data reduction scheme.
The data reduction with the essential work method does not work in this case, as Fig. 7 shows.
Therefore, these two points are not used for data reduction even though they lie on the regression line.
The data reduction of these curves results in Fig. 8.

Dosage in the 50 ~ 100mg/l to meet the water when the wellbore is less than 25% of the wax viscosity and single-tube process requirements gathering, can be compared to the multi-port wells, monitoring data before and after dosing showed that the crude oil after dosing freezing point decreased 5.6 ℃, the average rate of 87.4% viscosity reduction, the average rate of 91.5% paraffin.
Faced with this situation provides a well technician DRA(Drag reduction agent ) oil soluble formulation and preparation method.
Dosage in the 50 ~ 100mg/l to meet the water when the wellbore is less than 25% of the wax viscosity and single-tube process requirements gathering, can be compared to the multi-port wells, monitoring data before and after dosing showed that the crude oil after dosing freezing point decreased 5.6 ℃, the average rate of 87.4% viscosity reduction, the average rate of 91.5% paraffin.
Conclusion (1)After dosing oil solidifying point, viscosity, and a significant reduction in the amount of wax, can be compared by multi-port wells, monitoring data before and after dosing showed that the freezing point of crude oil after dosing decreased 5.6 ℃, the average rate of 87.4% viscosity reduction, The average rate of 91.5% paraffin.
(2) The oil-soluble DRA can be provided with the double functions of the drag reduction in single-tube gathering process and pipeline back pressure reduced

In this paper, we give a fuzzy decision tree (simply FDT) induction algorithm, named FDTAmbig, to handle the classification with discrete attributes through the uncertainty reduction.
FDTAmbig selects the attribute which will cause the further reduction of uncertainty as the expanded attribute for each decision node.
We can regard this instance as a noise data.
However, FDTAmbig with q=0.95 can recognize this noise data effectively and terminates the growth opportunely when no uncertainty reduction occurs.
Liu, Multi-valued attribute and multi-labeled data decision tree algorithm, International Journal of Machine Learning and Cybernetics 2 (2) (2011) 67-74

The high volume of data resulting from these observations presents many challenges, particularly in the areas of data transmission, data storage and assimilation [4].
Suppose our dataset consists of l spectra of n radiances into an l by n data matrix R.
All the channels are completely clear data and none of the eigenvectors correspond to cloud signals.
Performance tests indicate that the WRF 4D-Var minimization requires 21% less computer resources (elapsed CPU time) when 20 PC scores are used instead of 119 AIRS radiance-achieving an 6 fold reduction in data volume and 21% reduction in the overall cost of assimilation.
The assimilation of AIRS radiance data at ECMWF.

Data on physicochemical parameters of the choline chloride-urea system are provided in work [15].
This data allows predicting MV2+ behavior as mediator for the cobalt electrolyte.
The data obtained after potentiostatic reduction at -0.8 V (potential of methylviologen reduction) are presented in fig. 4.
Difference between peaks c and d is approximately 150 mV that is in good agreement with data obtained above via investigation of cobalt reduction process.
These data allows concluding that in the system containing cobalt sulfate and methylviologen dichloride reduction of cobalt is carried out in electrode region at potential of methylviologen reduction.