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With the increase of spinning cake quality, the reduction of working frequency is faster than that of natural frequency.
With the increase of spinning cake quality, reduction range of revolving speed is larger than reduction range for natural frequency of take-up winder, so that its working frequency is gradually close to sixth natural frequency and vibration response increases too.
Soon afterwards, with further reduction of working frequency, stress response reduces correspondingly.
Fig.3 The first model of take-up winder Fig.4 The maximum stress of support sleeve Fatigue life evaluations In the following, fatigue life of take-up winder will be predicted according to ultra high cycle experimental data.

Besides, reliable thermal data for this material are therefore indispensable for practical purposes[2].
Mass reduction of the sample prepared by mechanical grinding method was 3.86% more than that prepared by static melting method, mainly because the sample prepared by static melting method had been heated well during its preparation process, which had a positive impact on the removal of moisture.
Wherein the sample’s mass reduction prepared by static melting method was 3.71%, a little better than that from mechanical grinding method, whose mass reduction was up to 4.48%. 3.3 DSC curves of cycle experiments The sample of optimum proportion referring to the Phase Diagram was selected to be tested for cycle experiments, in which the samples would be heated from 160 degree Celsius to 770 degree Celsius for 7 times.
Fig.5 TG curves in static melting method Fig.6 TG curves in mechanical grinding method We can see from the Fig. 5, the quality of the sample from static melting method tended to be stable during the six times cycles, remaining at between 95.8% and 95.4%,and the sample reduction was less than 0.5%.

The numerical models for pollutant dispersion were validated against wind tunnel experiment of Concentration Data of Street Canyons (CODASC) [13].
Same goes to the pollutant dispersion with all the heating wall condition except along the windward wall, where the heating showed overall reduction of pollutant concentration (Table 2).
For pollutant dispersion profiles, there are overall reductions of concentration in all thermal cases, except for the windward wall heating condition (Table 2) due to the reduction of overall wind speed in street canyon and caused the accumulation of pollutant (Table 2).
Other, there is a significant overall velocity reduction within the street canyon when the windward wall was heated due to the upward motion close to the windward wall, which impede the main flow to enter the street canyon at roof level.

It aims at finding a subset of the input data, which are lower dimensional and less redundant.
Given -dimensional input data, PCA assumes that the first-order and second-order statistics of these data are known or can be estimated, and there exist cross-correlations between elements, which mean that the input data could be compressed without much loss of information.
Usually, , which shows that PCA could reduce data dimension.
In the back propagation process, the weights are adjusted to better model the training data [4].
Given N sampling data , the following algorithm could recognize gestures.

The data shown indicates that this property varies significantly depending on which deposition chemistry is used.
B concentration (cm-3) The data shows that the gradient is the steepest for the widest spike (2.7 s).
Data for 950°C is shown for comparison and one can see that the gradient increases in going from 950°C to 1050°C (for the reasons mentioned above).
The data taken together with the data of Fig. 8 show that this is because tail movement during the low and intermediate parts of a spike-anneal profile set a minimum to junction depth which can only be approached at the expense of reduced abruptness.
Data are shown for various combinations of these two temperatures.

Fig. 3 The input data.
The network learn by the learning data sample included input data and output data, in this learning process, each connection weight and the output value of each unit are changed to improve the accuracy of the model.
The BP NN learning data is derived from the Xuzhou GCL PV power Co., LTD.
The number of input data is shown in Fig. 3, and the Fig. 3 is explained in Table 1.
Input data Table 2.

The energy consumption, costs and budget data was obtained from engineers in the said industry.
ER1 Data.
Data from energy meters including bills should be entered here.
The data will be read into other tabs for analytical purposes.
This worksheet will read the data from the data tab and display some trends.

Most research work is limited to experimental results, but, also, the modelling of hard turning can provide useful data to better understanding the process.
Furthermore, experimental data must be on hand prior to the construction of the model in order to determine the chip geometry.
Furthermore, it includes a wide database of workpiece and tool materials commonly used in cutting operations, offering all the required data for effective material modelling.
The proposed oblique cutting model for dry high speed hard turning can provide, as in the case of 2D modelling, much useful data.
The proposed oblique cutting models are in a position of providing the same data as the 2D models, but also some additional information, e.g., for the 3D formation of the chip.

XPS analysis showed little to no carbon present on the surface after the solvent clean (data not shown).
A two-layer model consisting of TiN and SiO2 on Si was used to fit the ellipsometry data.
The two-layer model did not fit the data for the film deposited at 623 K and the variation increased to about 7 Å in a film that is nominally 17 Å thick.
Above 623 K, the XPS data shows that other surface reactions occur at similar rates.
This is shown by the reduction in Ti coverage detected at 623 K.

Main geometrical data incremental forming As above described, deformation mechanics is basically stretching, i.e. it causes a corresponding thickness reduction according to material incompressibility [15].
The measurements were repeated on different sections on all the walls of the specimen in order to check the data coherency.
The lowest step size leads to a thinning reduction and a more homogeneous thickness distribution.
A certain variability in the experimental data was in fact observed, due to both measurements accuracy and probably also to other phenomena; in particular the role of planar anisotropy deserves, according to the authors's opinion, a more detailed investigation.
These problems involved also relevant difficulties in the prediction of thinning: only a very careful and continuously monitored simulation permitted to obtain predicted thickness values closed enough to the experimental data.