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The experimental modal data, i.e. the eigenfrequencies � � �and mode shapes � � �, are obtained from measurements.
The modal data are identified before and after applying the damage.
Experimental modal data.
Conclusions A FE model updating method using modal data is presented.
Furthermore, a good correlation between the experimental and the updated numerical modal data is obtained.

Introduction Face-gear drives are widely used in the aviation power transmission because of the advantages of face-gear in torque split and reduction of weight and known as "The hope of rotorcraft transmission in 21st Century" [1].
Face-gear Lubrication characteristic analysis Based on the above analysis steps, EHL analysis can be carried out according to the gear data and lubricant properties in table 1.
Gear data and lubricant properties Face-gear torque,T 600N×M Pinion tooth number, N1 18 Shaper tooth number, Ns 20 Face-gear tooth number, N2 100 Module, m 2.5mm Pressure angle,a0 25° Face-gear angular velocity w 200rad/s Young’s modulus E1,2 206800Mpa Poisson's ratio g1,2 0.29 Viscosity pressure coefficient a 2.2×10-8pa-1 Initial viscosity h0 0.026838 pa×s Initial density r0 870 kg/m3 Calculatting load distribution and entrainment velocity in single-cycle, the results are shown in Figure 5: Fig. 5 Load percentage and entrainment velocity curve in Single-tooth meshing cycle of face-gear It can be seen from Figure 5, At the meshing entry point A which is at the tooth root，there are two pairs of teeth in action, with the face-gear running, the carrying capacity of one pair of teeth gradually increases, and two pairs contacting change into one pair contacting at the meshing mid-point B, one pair of teeth carrying capacity reaches its maximum, and later two pairs of teeth contacting, then
On this basis, the lubrication equations of face-gear were established according to EHL theory, film thickness and pressure in the process of face-gear drives were calculated by using multi-grid algorithm, the design datum mark of face-gear lubrication was got, which can provide a reference for designer.

Data Process of Material Management System Data Process.
Design of Data Bank of the System [3,5,6,7,8,9].
In an information system, data bank is often used to store and manage a tremendous amount of data.
Data bank system not only describes data itself but also depicts the correlations between them using structured models, and this system adopts relational data bank model.
Structural Design of Data Bank Concept.

Some high-throughput tests combining nanoindentation tests with EPMA technology to test alloys have proven to be efficient, and a large number of compositions and performance data can be obtained just by testing a small number of materials [5, 8].
At the same time, some scholars use statistical methods such as principal component analysis [10, 11] to analyze the test data of the obtained materials, which has sped up the new material research.
Then the principal component analysis method and the alloy equivalent method are used to perform a dimensionality reduction analysis on the composition at different indentation respectively, integrating multiple composition data into specific parameters.
As shown in Table 4, the first two components contain 87.654% (>85%) of original data and corresponding eigenvalues 4.075 and 1.184 are greater than 1, indicating that the two components actually cover most information of these six elements content.
In summary, the data of composition and performance at multiple indentations can be used to establish some atlases through the method of principal component analysis, thus helping optimizing alloy composition to obtain higher or lower E and H values.

As per the data received, the inputs were incorporated and accordingly the results are interpreted.
A discussion was made about open and closed loop pressure control for leakage reduction of pipes [13].
Reynolds, Open and closed loop pressure control for leakage reduction, J.

Based on 1947-1974 years’ data of U.S., Kraft.J and Kraft.A(1978) use Granger causality test and come to the conclusion that economic growth and energy consumption is in the presence of unidirectional causality; Cheng and Lai(1997) conclude there is only unidirectional causality from GDP to energy consumption through applying Granger test and co-integration analysis based on data of China Taiwan in 1955-1993;Sun Yiqing, Wang Zilong(2011) use co-integration theory and Granger causality test, and get a one-way causal relationship between energy consumption and economic growth in Jiangsu.
We take advantage of the statistical methods of correlation analysis and regression analysis to make supplementary and prediction on the basis of datum.
Data selection.
Therefore, we use GDP and energy consumption data of Liaoning in 2012 to predict and test the regression equation of the model.
Energy consumption and economic growth in Asian economies: a more comprehensive analysis using panel data [J].

As measurement noise will interfere with the test data, test data is get by the theoretical calculations with the addition of random noise: .
Where l is the standard gauss distributing random data, and e is level of random noise.
There are a total of 96 groups training data.
Some training data are presented in Table 3.
And other 100 groups testing data will be used for reviewing the validity of this method.

The result can provide data reference to improve environmental-friendly for manufacturing process [3].
(2) Data processing of scatter degree combination evaluation method The evaluation conclusions of types of evaluation methods are regarded as the value of indexes.
Combined with the evaluation system of resource and environmental index in manufacturing process, the input and output data table of valve body production process can be established based on IPO analysis and the data analysis results are listed, as shown in Table 1.
Based on the data above, data envelopment analysis [6], analytic hierarchy process [7] and grey comprehensive evaluation [8] are respectively applied to make an evaluation of resources and environment attribute of the valve body’s manufacturing process.
In the meanwhile, some other methods, such as data envelopment analysis, analytic hierarchy process and grey comprehensive evaluation, are respectively applied to make the evaluation.

The average values of TWR for each parameter at levels 1, 2 and 3 for S/N data are plotted in figure 1 and raw data is plotted in figure 2.
Table 3 and 4 shows the pooled ANOVA table for S/N data and raw data respectively.
Table 5 and 6 shows the Taguchi response table for S/N data and raw data respectively.
) Figure 2 Effect of input on TWR (raw data) From figure 1 and 2 it can be seen that to get minimized value of tool wear rate the optimized value of tool electrical conductivity is 26316 S/m, gap current is 8 A and pulse on time is 63 µs.
Table 3 Pooled ANOVA for TWR (S/N data) Table 4 Pooled ANOVA for TWR (raw data) Table 5 Response table for TWR (S/N data) Table 6 Response table for TWR (raw data) Based on optimal set of parameters confirmatory experiments have been performed to validate the obtained results.

Network packet capture is the first step in the process of network forensics, and then the preservation and analysis of captured network data streams in which the network packets are displayed in transmission order and organized to establish connection in the transport layer between two hosts, which is called “Sessionizing”.
The correlation of network flow -removing irrelevant data with filter as capturing network flow in certain circumstances, the integrality of data-demanding data streams continuously monitored rather than retransmitted with extravagant hopes for network forensics tools, the rate of packet capture, the above are the primary factor considered in network forensics and analysis.
Firstly, electronic evidence can be preserved according to the requirement of secrecy of data on the server of Intrusion Tolerant System, which guarantees its legitimacy.
However, this system is imperfect in mechanism of simultaneous collection of host data and network data; in addition, it lacks connection with access control, access authentication, data encryption and some other network security mechanisms.
Removing meaningless and useless characteristic points or noise by the discovery of point characteristics of information behavior contributes to the reduction of information storage volume,the enhancement of veracity of detection,the exaltation of computing speed.