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Finally, we analyze the data, and put forward the government subsidy to promote the development of new energy sedans based on the consumer demand.
According to the official data, the oil consumption per hundred kilometers of F3 is 6.5 liters, and F3DM is 2.7 liters.
We can confirm the total life cycle cost for three kinds of sedans based on the above, and specific data is as shown in table 1.
Figure 1 Three cars’ total cost variation trend graph without government subsidy in 5 years Figure 2 Three Cars’ Total Cost Variation Trend Graph with the Government Subsidy in 5 Years Conclusions Based on the data analysis above, in order to promote the development of new energy vehicles as soon as possible in recent years, our country may take the following measures.
Through the above data extrapolation, on one hand, our government should encourage the independent research and development, and strengthen the training of technical personnel; encourage and support the combination of industry, school, and research institution, solving the key technology of new energy sedan jointly.

The critical data of various indexes are different at the same class in Table 1,translate them into comparable data of columns in order to eliminate the influence and each index is dimensionless ,with the first class being the standard item, separately the various index is divided by the first index {(1,2,3,4 index score)/(the first index score)}.Quantitative score of Table 1 is proceeded with dimensionless and then get the critical whitenzation number of non-dimensional classification that can be seen in Table 2.
The sample value of original data non-dimensioned will be substituted into whitenzation function .
Data used in the paper are obtained in the way of experts’ consultant.
The process of original data is shown in Table 4 while Table 5 is the dimensionless processing of raw data.
Table 4 Original data samles Evaluating index Max score Case I Case II Score of Case I Score of Case II Table 1 Max score Dimensionless in Case I Dimensionless in Case II CS 12 10 19 83.33 75 100 0.833 0.750 CWP 20 14 16 70 80 100 0.700 0.800 AP 12 8 10 66.67 83.33 80 0.833 1.042 ECR 12 10 10 83.33 83.33 100 0.833 0.833 IUE 12 8 8 66.67 66.67 100 0.667 0.667 WRC 16 11 13 68.75 81.25 100 0.688 0.813 IWU 16 10 10 62.5 62.5 100 0.625 0.625 MS 16 10 9 62.5 56.25 80 0.625 0.563 UGM 24 15.5 15 62.71 62.5 80 0.784 0.781 CT 16 10 13 62.5 81.25 100 0.625 0.813 EM 20 15 16 75 80 100 0.750 0.800 Table 5 Non-dimensional processing of original data Clustering index Project number 1 CS 2 CWP 3 AP 4 ECR 5 IUE 6 WRC 7 IWU 8 MS 9 UGM 10 CT 11 EM Project 1 0.833 0.700 0.833 0.833 0.667 0.688 0.625 0.625 0.784 0.625 0.750 Project 2 0.750 0.800 1.042 0.833 0.667 0.813 0.625 0.563 0.781 0.813 0.800 From the two outcomes above, it can be seen that both Case I and Case II belongs to the

DSC analysis data are shown in Table 1.
Table 1 The DSC data analysis of inorganic materials Phase Change Material Phase Transition Point [°C] Phase Transition Enthalpy [J/g] CaF2·4H2O 18.5 231.0 CaCl2·6H2O 29.7 171.0 Single Organic Material.
Some organic materials were tested using differential scanning calorimetry, and the DSC test data are shown in Table 3.
The research data of fatty acids and esters are shown in Table 4.
The research data are shown in Table 5.

It is an approach to outsource data with the aim of decreasing the data storage and reducing the management issues [21].
Main benefits of cloud computing implementation are: less infrastructure investment, convenience, flexibility, enhanced performance and cost reduction [5].
Cloud Computing System Architecture within the Precast Construction Industry Cloud computing technology sends and retrieves the data and various applications via the utilisation of internet and central remote servers including the application servers and the database server.
The integration of cloud computing, mobile clients (such as the smart mobile devices including the smartphones and tablets), servers and data center [4-6,9,19,23] and logistics management [24] could be applied for the precast supply chain management.
Furthermore, the cloud computing implementation within the precast construction industry, will deliver significant opportunities for improving the effectiveness and enhancing the appropriate information flow along with accessing to data, information and services.

These methods are Live Text Data, Live Cell Data and Live Graph Data
· Live Text Data: Display and record all live parameter of battery at a time set by the user
· Live Cell Data: Display and record voltage parameters on each cell in the battery pack
· Live Graph Data: Maximum only 4 parameters can be chosen to be displayed and recorded simultaneously in graph form over a range of time set by the user.
Fig. 3: Live graph data for battery charging process in 1 hour On Road Testing.

However, when the loading data is unknown, we should detect the variation of crack closure point by local strain.
In this study the acceleration and deceleration were obsereved depending on the conditions of overload or underload, in comparison with baseline data.
However, for the purpose of detecting a hazardous load when stress data is unknown, the strain functions were determined in this study.
It is shown in the present study that the detection can be carried out without the information of stress data.
Instead of stress data, we can use the data of strain function h1.

For the remaining life assessment of the analyzed material after 60.000 hours and after was made the rehabilitate was used the expert system iRiS-Power [1] with the following modules: - iRiS- Material: data base with physical and mechanical characteristics of the materials including 16Mo5 steel, - iRiS-Creep: for the creep evaluation of the technical resistance for duration, - iRiS-Fatigue: for the resistance evaluation to fatigue at 773 K, - iRiS-TRD for the exhaustion creep evaluation to fatigue and remaining life.
For the low-cycle fatigue assessment in isotherm regime at 773 K from the data base of the iRiS-Material was utilized experimental data on the same kind of steel in different laboratories.
The remaining life assessment and creep-fatigue analyze - the final creep data - step3.
The failure conclusions to the remaining life - the final creep data - step3, on the creep tests results are presented in comparison with the TRD curves at 758 K, the creep fracture breaking curves experimentally determined on 34 samples from the reactor (ring 2 and 3) (figures 16 and 17).
[8] * * * ASTM E 739-06: Statistical analysis of linear or linearized stress-life (S-N) and strain-life (ε-N) fatigue data.

Whereas support vector machine (SVM) is based on structural risk minimization principle and has very good generalization with few data samples.
Fig. 1 Plane graph of surface main buildings Measurement of vibration data In order to control the effects of blast vibration in Beijing East Road No.1 tunnel excavations, data measurement operations were carried out with seismographs device “UBOX-5016” made by Top Measurement and Control Technology Co., LTD.
If C is too small then insufficient stress will be placed on fitting the training data.
If C is too large then the algorithm will overfit the training data.
But value is too large, reduction data points fit precision.

This includes network attacks against vulnerable services, data driven attacks on applications, host based attacks such as privilege escalation, unauthorized logins and access to sensitive files, and malaria [3].
The data used in the experiment is KDD CUP 99 data sets.
This data set provides 9 week's network connection data that gathered from on a simulation local area network.
Each record in the data set has contains 41 characteristics and 1 marking of kind.
The record in the data set has been divided into 5 kinds, namely normal connection (Normal) denial of service (DoS), the unauthorized remote service to login (R2L), the unauthorized access to the local super user's privilege, scanning and probing (Probing).

The software we designed using the wavelet transform for signal noise reduction, and using cross-correlation method for time delay estimation and leak point location.
The data acquisition software is based on a dual-channel independent soundcard, and the wavelet denoising is implemented by LabVIEW and MATLAB mixing programming.
The sound velocity we measured in the experimental environment was about 1470m/s, this value was used in processing the following experimental data.
To study the impacts of different wavelet basis and decomposition levels on location accuracy, we randomly selected 100 data with mutations removed under the test state of 20m ~ 40m, and the results shown as in Table 2.
For comparing the location accuracy difference when we use or without use wavelet denosing, 10 group data (every group has 100 data) were selected under three test states.