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Comparing the calculation result and the actual data, the precision of the rolling force calculation is high.
(4) inoutin hhhr /)( −= (5) Rolling forces in elastic deformation zones: )()( 1 3 2 2 out in m out in out m out e in ee hhRWk hh h k E FFF −'− − − =+= ξ ν (6) Where: p F is the rolling force of plastic deformation zone, e F is the rolling force of elastic deformation zones. in e F is the rolling force of elastic entry deformation zone, out e F is the rolling force of elastic recovery deformation zone, mk is the average deformation resistance. int is the back tension, outt is the front tension, α is the influence coefficient of back tension, β is the influence coefficient of front tension, W is the strip width, R ' is the roll flatten radius, inh is the entry thickness, outh is the delivery thickness, r is the reduction
( )       ∆⋅ − +=' eqhW F E RR π ν 2116 1 (7) Where equivalent reduction: ( )ine p out e eq hhhh ∆+∆+∆=∆ (8) ( )outout out out e tkh E h − − =∆ 2 1 ν (9) ( )outoutout out in in e p tkh E hhhh − − +−=∆+∆ 2 1 ν (10) Where R ' is the roll flatten radius, R is the original roll radius, F is the rolling force.
The calculation results are compared with the actual data as shown in Table 1.
Table 1: Model calculation data and actual data comparing table Stand No. 1 2 3 4 5 Work roll radius[mm] 272.0 238.2 239.7 264.0 249.7 Deformation resistance [MPa] 474.51 653.48 759.77 827.41 880.36 Friction coefficient [-] 0.0996 0.0388 0.0304 0.0266 0.0223 Roll flatten radius [mm] 341.2 294.1 346.4 495.8 588.7 Model calculation data Rolling force [kN] 7927.6 7199.9 6829.3 7732.7 8137.1 Deformation resistance [MPa] 466.07 640.22 757.11 865.27 894.23 Friction coefficient [-] 0.0946 0.0359 0.0300 0.0310 0.0234 Roll flatten radius [mm] 341.7 295.0 346.8 495.3 588.8 Actual data Rolling force [kN] 7898.0 7009.6 6804.5 8179.8 8533.5 Set R' to the original roll radius Calculate rolling force F with Eq. 1 Calculate roll flatten radius R' with Eq. 7 mineRR ≤−' Set RR '= Start End Yes No Set RR =' Fig.2.

PC monitoring center of harmful gas mainly receives data which is collected by gas sensors and temperature & humidity sensor and stores data according to the data structure.
The module of data acquisition system adopts integral point to collect.
Take to collect data once one hour for example, 24 times for a whole day.
It can also transfer the collected data to the upper computer for displaying in combination with CAN bus transmission technology and provide some information of reference for the user.
The Design of Real-time Data Acquisition Program Based On VB.

The RVM is used to learn the feature data and implement the classification work when new measured data are available.
Then the latter is used as input data for training the RVMs.
To obtain the highest separation rate for the test data sets, the best parameters are established for each SVM using the validation data.
Four conclusions can be drawn from the study. (1) The wavelet packet node energies of the structural vibration signal are useful as the damage feature and are easy to obtain. (2) Dimension reduction is necessary for the feature data, and the NMF technique can be used for dimension reduction.
Piersol, Random data: Analysis and measurement procedures.

Statistical method is employed to cope with gradient data-gradient projection in the horizontal and vertical direction to compute the average gradient value.
Multi-level B-Spline interpolation is employed to smooth gradient data.
In the first stage, a noise reduction process is performed.
At last, three sub-arraies, which contain the gradient data of every pair of edges, are chosen.
It is immune to all inverse factors and protects edge data to maximum.

First, Hummers' method is used to prepare GO, which is then reduced by hydrothermal reduction method to prepare rGO.
Therefore, it can be basically confirmed that GO has been reduced to rGO via the hydrothermal reduction method.
This also proves that rGO is successfully prepared based on GO in the hydrothermal reduction process.
At the same time, to verify the comprehensiveness and reliability of the test data, the paraffin that is used as the substrate material in the composite absorber is also tested for electromagnetic parameters.
According to the data in Fig. 5 (e), paraffin that is used as a substrate material has no significant dissipation and storage capacity for incident electromagnetic wave energy.

In order to overcome the actual physical limitation of the hydroforming facilities, a Finite Element (FE) model of the WHF process was also created implementing experimental data (flow stress curves and FLCs) and tuned using data from preliminary WHF tests.
In this regard, Schultz et al. presented that mass reduction, obtained by replacing steel with aluminium, can be as high as 40–60%; in addiction, a substantial reduction of bodyweight (almost 10%) could improve the fuel efficiency by 6–8% [1-2].
Experimental results were interpolated using an approximating response surface based on a second order polynomial formulation; the models were characterized by a correlation coefficient (R2) equal to about 95%, indicating their effectiveness in fitting the input data.
Preliminary HF tests used for investigating the material anisotropy were used for tuning the numerical model: in particular, results (in terms of both TR% and F%) from FE simulations changing the Coefficient Of Friction (COF), were compared to the correspondent experimental data in order find the best fit.
The preliminary experimental activity showed that holding the process temperature leads to an increase of the mechanical properties (due to the ageing phenomenon) together with a ductility reduction; in addition, anisotropy effect revealed to be negligible.

Data in this research indicated that composting can reduce the activity of heavy metals to some extent to achieve the agricultural use.
Composting process aimed at: (1) destruction of pathogenic organism; (2) stabilization of organic matter; (3) drying to reduction; and (4) production of materials environmental friendly.
As shown in Fig. 2, the concentration of exchangeable, reduction and oxidation species of copper and nickel were decreased with the increasing of composting time.
As for copper, the residual species was found in the range of 56.5 – 64.8 %, and the exchangeable and reduction species were all less than 7 % (Fig. 2).
But for nickel, the exchangeable and reduction species were significantly higher than copper, and the ratios of the concentration of two kinds of species to the total metals were 15 – 22 % and 12 – 16 % (Fig. 2).

Data Validation.
Enhance review of the mining and exploration data by visual assessment of spatial data.
Some data anomalies are identified easily.
There is a substantial reduction in learning and assessment process time compared to conventional approaches.
Only some drilling data formats, ventilation network data formats and some logging data formats are similar to those of the international standards, the other data mining formats are different.

The technical hurdle of collecting internal cavity surface data is solved.
Acquisition Data of Valve Body Surface Duplicating Internal Cavity.
Surface Data collection: Data collection of prototype surface spot is usually carried out by contact measuring equipment or non-contact measuring equipment[7].
The data of spot-cloud can be processed by many methods of spot-cloud reduction introduced in reference[10,11].
It means that the data of spot-cloud would be processed twice.

Using the determined mass and mole data of CO2 and n-decane the viscosity and the density of pendant drop (HD) may be calculated from the Eq. 3 [15] and Eq. 4 [7, 16, 17] respectively
Table 1: Data from CO2 solubility experiment.
Table 3 shows the data of the rate of CO2 mass transfer (effective diffusion coefficient) into n-decane from different environmental fluids consisting of CO2 (CNF (0.5 g/l), and CW) for the pressure range of 10-70 bar, at 25oC and 45oC.
Table 3: Diffusion coefficient data for CNF-decane, CW-decane, and CO2-decane for a pressure range of 10-70 bar, at 25oC and 45oC.
Journal of Chemical & Engineering Data, 2014. 60(1): p. 171-180