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The unknown values in equations 3 and 4 can be obtained by experimental data fitting.
Assuming that n is taken as 0.5~4.0 in Equation 4，series of and m can be obtained from linear regression with experimental data in Table 1.
The scatter data in Fig. 10 showed that the value of was minimum as n was between 0.7 and 1.0.
The experimental data corresponding to scatter data and calculation results corresponding to solid lines were plotted in Fig. 11.
It can be seen that predicted and experimental data have a good correlation.

First, relativity of the estimation data of motion vector (MV) needs to be removed.
The data accessing module loads pixel data from off-chip SDRAM and save them into embedded memory of search window, and pixel data of reference frame is also loaded by this module.
Data relativity analysis of adjacent MB.
So most of the pixel data located in the overlap region is mutually used.
How to improve the utility rate of the data in this region is of great importance for reduction of on chip memory area and SDRAM access cycles.

Then collect the data of the temperatures inside and outside the models.
The collection of experimental data is mainly realized by manual reading means (the error arising there shall be analyzed during the data processing), and the readings were recorded every half an hour.
Totally 3816 groups of data were collected in 106 hours from October, 2010 to March, 2011.
Qualitative calculation will be made on the temperature data.
So it is required to make the secondary correction on the fabrication error of basic data and finally obtain more accurate data.

But it does not take into account the nonlinear contact influence, different reduction algorithms cause that technological exchanges and the vehicle's performance evaluation results are inconsistent and it is necessary to improve and simplify the description.
At the same time, data comparsion the of two algorithms are shown in figure 6-figure 8: Fig.4.

The problems of the tooth root bending stress increase,distortion, the life of the gear reduction and other issues existing in the gear heat treatment process, In order to solve these problems, functionally gradient material (FGM) was used on gear working surface. the analysis of heat treated gear performance and common gear performance comparison analysis has maded; A functionally gradient material cylindrical involute gear model and a common cylindrical involute gear model were created, Then finite element calculation of bending stress and analysis of the two models has maded.
common cylindrical involute gear model： ①Select unit :Select 10 node 92 units.②Define the material:Gear of Q235 material selection, its elastic modulus E=200GPa, poisson's ratioμ=0.3.③The parameters of the gear:Modulus m=6mm, number of teeth z=28, pressure Angle a=20°, the rest are standard parameters.④Modeling:Tooth profile of involute gear, spur gear model is established in ANSYS as shown in figure 1a.In order to calculate more quickly, the interception of these three teeth, as shown in figure 1b.⑤Cell division:Before planar graph drawing to segment involute gear with Booleans operation is broken into three parts, and to prepare for after loading.Grid as shown in figure 1c.⑥Ioading: Because if the load is loaded into the gear mesh line, the maximum stress appears in the end of the line of action and the gear junction.While root point also produce stress concentration, but only by its color, roughly calculated the severe stress value, is not conducive to the concrete stress value for data

For cost reduction and quality improvement of machining products, optimum output machining parameters such as material removal rate, tool wear ratio and surface roughness is very essential.
For all parameters 5 levels of data using CCD is used (Table 1).

(i.e., data is being communicated from the sensor but the data are wrong).
PCA can be defined as a linear transformation of the original correlated data into a new set of uncorrelated data.
Figure1 shows the results of dimensioned data based on two principal components for 2000 normal samples and 500 abnormal samples. “*” represent normal data while “o” represent abnormal data.
It is clearly to see normal data are centralized and abnormal data are far away.
At the same time we detect fault data with SPE.

AMI is constituted of HAN (Home Area Networks), smart meters, data concentrators and MDMS (Meter Data Management System).
The attacker can intercept the data and break off the data transmission.
The main communication load types in AMI include: (1) Periodic data from smart meters to MDMS via the data concentrator.
The data must be transmitted in set time.
Av is the data type transmitted in AMI, .

MSC.ADAMS/View supports a variety of data interfaces, such as Parasolid, DWG, DXF, IGES and other formats.
ADAMS can better identify the Parasolid format which will establish the UG environment data model into ADAMS environment.
In the Model Name box, enter the English name; click OK to complete the import data model; (3) Import model, right-click each component in the modify body dialog box to change its quality, material, and moment of inertia, the initial setting; Pair Constraint and Point Drive Added.
The job is only to achieve a parallel mechanism with three-dimensional data model in UG data exchange between ADAMS software and communications.
In addition, the drive also considering the choice of the singularity, moment of inertia and the reduction ratio and the operational and other factors.

Because the interpolation requires a huge amount of data, the management mechanism for the storage area and reference pixels can be very complex if all interpolation is implemented by hardware only.
The parameters and pixel data required by the filter are stored separately into the parameters cache and data buffer cache through the coprocessor interface by CPU.
The control unit controls the arithmetic unit to perform filtering operation with the parameters, and the filtered results will be written back to the data cache.
The hardware unit contains the data cache and the parameters cache, which store data and parameters for computing.
Single-ISA heterogeneous multicore architectures: The potential for processor power reduction.