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SaaS service charges in different ways: some can use the number of charges, and some can use the number and timing of charges, some charge by the amount of data processing.
D Cost reduction The cost of SaaS software is calculated by the number of services.
E Available everywhere The data of SaaS software is existed on servers in the Internet, and it is a virtual storage location, or even the specific physical location users do not know.
The SaaS model solution requires the renter put all the relevant data in the platform provided by the service provider, so it faces greater security risks of the enterprise data’s specificity, reliability, and security, and the most important problem is the data security, it has been restricted to a certain extent the further promotion and application of SaaS model.
Research and design of SAAS application data model.

An explanation for the observed influence of structure and dispersion factors on the melting parameters has been proposed on the basis of X-ray diffraction data, electron microscopy, and model calculations.
From the data in Fig. 2, b, the melting parameters of the submicron Cu particles are indirectly related to their size: the smaller the size of Cu particles, the more amount of oxide they contain, and the greater deviation of the value DfusHsp from the standard one is observed.
Obviously, it is of fundamental importance to analyze in detail the influence of the very thin superficial films, possible impurities, and substrates/matrices on the properties when interpreting unusual experimental data for nanomaterials.
The model calculations, which took into account the effect of the oxide shell thickness on the value of the specific heat of fusion, were performed; the results of calculations were compared with experimental data.
Zhai, Size-dependent melting properties of Sn nanoparticles by chemical reduction synthesis, Trans.

Figure 1 reports the weight gain data over time.
(Full markers) experimental data; (open markers) data corrected with Cr-evaporation; (solid line) calculated values from kinetic model.
The parabolic rate constant obtained from the weight gain data is 9.42 ×10-14 g2cm-4s-1 and agrees with those calculated from literature data [6].
Calculated data.
Corrected data.

IF Frequency Transmitter Intermediate frequency voltage trasducer System Characteristic Analysis Figure 2 Induction heating furnace temperature optimal control structure Induction heating temperature control hierarchical optimization software realization Data acquisition software Data acquisition software based on Siemens PPI protocol [3], at the same time collecting up to four PLC for real-time data and collecting cycle is 200ms.
Data acquisition software will transfer the collected PLC real-time data into the database.
Data processing module in daily 12:00 (configurable) shall be responsible for the data processing for the day before.
When the data processing module complete preprocessing work, clustering /classification module according to different initial temperature and temperature rise will pretreatment of data classification.
Monitoring: monitoring is based on preprocessed data, displays a single TMP2 / TMP3 / VGD / VOUT / IOUT/ VCTRL.

In order to validate the proposed model, we employed experimental data from the HDS setup.
However, it is well-known, ANN and SVM are not capable to model uncertainty and fuzziness of the data.
The powerful of FL lies in its ability to handle uncertainty and to extract rules from the given data.
Table 2 demonstrates the statistical analysis of the experimental data.
The symbol yi represents the experimental value while ӯi refers to the simulated/predicated value of a specific data point i

Second, the improved locally linear embedding algorithm is used to reduce data dimensionality.
Using Euclidean distance to find nearest neighbors for each sample data .
Selecting the proportion of 50% from each type of data as the training set, the rest as the test set.
Experimental steps are as follows: Using the SVM algorithm to train and test the original data; Using LLE algorithm to reduce dimension for the original data, the neighbor number=3,4,5,,15, and the dimension value is the every value of dimension of the data set.
Then Using SVM algorithm to train and test the data; Using the method in paper to reduce dimension for the original data, other process similar to step 2.

Thus deformation of a web I and flanges II considered as rolling of rectangular strips on a flat body of rolls, and believed, that reduction of flanges occurs only vertical roll.
The calculated data were compared with the results of experimental studies [19, 20], estimating convergence of calculation and experimental data by the formula ∆P=Pcal-PexpPexp, %.
As you can see, the results of the calculations have a satisfactory convergence with the experimental data.
Calculations and experimental data in the H-beam rolling in universal stand US-2 of "NTMK" Number of pass Thickness [mm] Reduction ratio λ Experimental data of forces [MN] Calculation data of forces [MN] Convergence of calculation and experimental data [%] Web Flange d' d a' a Ph Pv Ph Pv ΔPh ΔPv 35B1 1 9.9 8.3 15.6 12.8 1.193 2.98 1.38 2.18 1.26 -26.9 -8.7 2 8.3 7.1 12.8 10.8 1.169 2.62 1.5 1.95 1.12 -25.3 -25.5 3 7.1 6.2 10.8 9.3 1.145 2.35 1.46 1.74 0.99 -25.9 -32.5 40K2 1 18.7 16.6 29.8 25.8 1.126 2.34 1.7 2.3 2.35 -1.7 38.6 2 16.6 14.9 25.8 22.7 1.114 2.14 2.2 2.12 2.14 -1.2 -2.6 3 14.9 13.6 22.7 20.3 1.096 1.86 1.95 1.8 1.84 -1.2 -5.6 45B2 1 12.9 11.4 20 17.4 1.132 2.71 1.43 2.95 1.40 8.9 -1.8 2 11.4 10.2 17.4 15.4 1.118 2.47 1.74 2.89 1.37 17 -21.3 3 10.2 9.2 15.4 13.9 1.109 2.25 1.65 2.75 1.29 22.2 -21.8 Conclusions 1.

Mechanical properties are around 188 GPa for Young's modulus, 254 MPa 0.2% proof strength, 453 MPa ultimate tensile strength, 26 % elongation, and 45 % reduction of area.
Δa data, appear significant difference.
When JIR curves are available, the fracture resistance KIR curves in plan strain state can be then determined using the equation below IR2 IR 1 J E K ν− = (2) By the test P-∆a data in Fig. 2, resistance JIR-∆a and KIR-∆a curves are given in Fig. 4.
From the data, the specimen scales have few of differences to measure directly the fracture roughness KIQ values in plan strain state.
The fracture roughness, KIC,P-C, at arbitrary P-C levels can be evaluated using formula ( ) ( )[ ]( )rmsIc s1 avIc lg1 lg IC, 10 KntZK CP CP K −+− − − = (3) where lgKIC,av and lgKIC, rms are average value and standard deviation of the test logarithms of fracture roughness KICi data; i=1, 2, …, ns; ns is number of the KIC data.

Firstly we get historical traffic data of related roads from the government department, and traffic investigation to some key roads is carried out.
Analysis and amend survey data, then we obtain the road capacity, zero-flow time and the actual traffic flows with no control measure.
The data are shown in Table 1.
Only a few of the data are listed here due to Length limit.
As operable control measures are involved with one-way lane, no left or no right turn, exceptive lane, bus lane, close area, data of capacities will be translated to these measures.

Many companies can’t use the data in the right way or cannot work out the efficiency of projects.
However, there are some practical analyzing tools, such as line charts and E-P analysis diagram, can be used to do with data [8].
According to the data, optimization of the technique could be suggested.
It was shown in Fig.6 that the fixed water consumption was 2687.8 in Phase II, less than the data in phase I, indicating some large-scale economy after the plant enlarging.
The effects of plant enlarging and cleaning cleaner production programs can be shown by the unit consumption of electricity and fresh water; and some abnormal data were proved by the administration.