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Most scholars study in the pilot study stage, heavy workload, cost expensive, the resulting data often have significant limitations.
Thus, according to the example of the original test data, using empirical formula to calculate the experience of the three cutting force values were: Fz equal to 3774.62N, Fy equal to 1509.85N, Fx equal to 1132.39N.
The minimum stress along the flank, in contact with the end of the stress, a total reduction of only 20% to 30%, in a distance equal to the length of exposure, the stress drop of the rake face 3/5 -2/3 times.

Introduction Systemic and topical capsaicin administration produce a reversible anti-inflammatory action by the inactivation of the neuropeptide release from peripheral nerve endings, therefore these mechanisms account for reduction of neurogenic inflammation.
All formula showed the newtonian flow (data not shown).
When these solutions contacted with phosphate buffer pH 6.8 the sol state suddently transformed into turbid gel at the interface (data not shown).

Table 1 Initial parameters of the numerical simulation Parameters Data Blank material Al 5052 Internal diameter of the billet /mm 20 External diameter of the billet /mm 80 Speed of punch /mm·s-1 3 Simulation friction coefficient 0.1 Modulus of elasticity of the blank 68900 Poisson's ratio of the Blank 0.33 Extrusion process and simulation results and analysis Analysis of material flowing velocity distribution throughout the extrusion process Velocity vector diagram from different positions depressed (Figure 4) can be analyzed: （1）Velocity distribution curve along the horizontal exit (cross-section under the foul line): the left is fast and the right side is slow at the same level, the left is earlier than the right side to reach this speed at the same speed （2）Analyze the speed difference between the left and the right: export at the same level, the data from chart we can see the punch more closer to the export, the greater the difference between left and right speed, bending the
The third stage, bending forming stage, the extrusion pressure fluctuation in a process of change, in this stage, owing to the speed difference between the punches, the reduction difference between the stocks on both sides , and the two sides of the punch force status is more complex , the stress of the punch on both sides is non-uniform with the extrusion so it is in a volatile state Fig.5 Load strike curve Equivalent stress field Figure 6 shows the equivalent stress field scattergram in different stages of extrusion process.

The torque M and the corresponding angle θ that spring tube suffered can be given by the Eq.5 and Eq.6: (5) (6) Fitting and calculating all measurement data by using the least squares method and we can get the stiffness of the spring tube for K: (7) L is the effective arm length , the units of Fi is mN; the units of Si is μm; the units of L is cm.
The system includes a motor and motor drive circuits, the reduction mechanism and motion conversion mechanism.
The computer system used to control the automatic loading, processing and calculating the force and displacement data it collected, outputing measurements and curves.

The abductive network is then applied to synthesize the data sets obtained from the numerical simulation, and a prediction model is established ultimately.
Fig.1 show the load predicted by the FEM is closer to the experimental data than the prediction by upper-bond method of Choi et al [1].
The abductive network is then applied to synthesize the data sets obtained from the numerical simulation, and a prediction model is established ultimately.
Base on the training database regarding to process parameters, ln(Pmax) and w0.5, shown in Table 1, the abductive networks with a criterion of minimum square error can be developed for predicting ln(Pmax) and w0.5 under a suitable range of process 0.0 10.0 20.0 30.0 40.0 Height Reduction [ % ] 0.0 20.0 40.0 60.0 80.0 100.0 120.0 Load [ KN * 10 ] Current simulation Experimental data of Choi et. al [7] Theoretical results of Choi et. al [7] Simulation results of Yang [12] parameters.
Fig.3 The effect of the module on Fig.4 The effect of the module on Fig.5 The effect of the number of teeth the maximum forging force final face width on the maximum forging force Fig.6 The effect of the number of Fig.7 The effect of the ratio of height Fig.8 The effect of the ratio of teeth on the final face width to diameter of the billet on the height to diameter of the billet maximum forging force on the final face width In order to validate the accuracy of the prediction model, another 8 data sets of the suitable range are tested for the maximum forging force and the final face width predictions.

To find out the necessary diffusion parameters, the data were fitted with numerical optimisation.
It clearly indicates that the fit describes well the experimental data.
This ferromagnetic phase fraction can be measured and the corresponding data is shown in Table 3.
The experimental data were fitted by the Arrhenius equation (Eq. 6).
Table 4 summarises the calculated data for the investigated material states.

According to the empirical data [4-7], various carbon emission coefficients of carbon sources as in Table 1.
statistical standards, the textile carbon emission in Henan Province can not be directly compare with other provinces, but can compare the energy consumption (Million tons standard coal), then you can know the probably situation of carbon emission, each provincial energy consumption statistics data basically from 2007, therefore, this article only compare the two years data from 2007 to 2008.
The energy consumption intensity comparison in industry of Henan in 2008 Data: Henan Statistical Yearbook (2009), where the added value is industrial enterprises above designated size.
Comparison Characteristics in Henan The result in Fig. 1 show that, compared with the other three provinces, the energy consumption of textile in Henan in 2007 was less than Hunan and Jiangxi, however, the data in 2008 was 3 times in Jiangxi.
Journal of Safety and Envionment, 6, 88--91(2006) [7] Gang Wang, Xiao Feng.: CO2 emission reduction through energy integration.

Fixed several discharge conditions discharge, discharge data obtained, and then change the fixed discharge conditions, a series of data are then used as training samples to train the neural network.
First of all, all the data obtained are used to train the network, and the error is into the flat area after training network about 100.
After training completely network, the data during validating data extracting the pulse process parameters is entered into the network to obtain the output predicting value, so it is referred as predicting SOC. 1.
Comparing the predicted and experimental values Since the data are more, the maximum absolute error is less than 5%.
CHEN Shi at School of Chemical Engineering and Environment, Beijing Institute of Technology who supported the experiment data and case.

The RFID transponder saves data in the chip, keeping away from stain and damage.
The data is password-protected and difficult to be falsified, which ensure its security.
The RFID reader will then read and decode the data, and send it to the higher level system through the communication interfaces[7].
When the battery compartment is in the induction area of the RFID reader, the reader will read ID information on the tag, communicate data with upper-level management unit, upload the collected ID data, execute the command issued by higher level management unit, and finally upload the ID information of the battery compartment to the monitoring system.
The uploaded data is the basis of unified battery compartment management.

Large straightness error may result in the life reduction wear and rotational run-out.
Practical examples For comparative purposes, the measured data available in the literature [7] are selected.
Table 1 (a) and (b) show the measured data of the planar straightness error of a linear stage.
It is observed from table 3 that, although there is a disparity in the choice of the initial region, the evaluation results is not much difference for the same data.
For calculating convenience, take two endpoints of the measured data as the initial reference point and the initial regionis more than two times estimation error value of the measured.