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., [1-3], which requires special physical simulation techniques to create the material data for modelling [2].
Experimental For modelling the transient behaviour of flow stress in ferrite, compression tests were carried out on a plain carbon steel (0.092C-0.19Si-0.45Mn-0.026Al-0.008V) in a Gleeble 1500 thermomechanical simulator to generate the flow stress data for ferrite at 700°C both at constant and varying strain rate conditions.
To establish the model, the SRX kinetics and Qrex of Armco Iron and 39 steels were determined from the stress relaxation data.
The validity of the model was later assessed with the new data obtained for several other steels and special steel compositions, such as extra low or low carbon bainitic steels, high-Si dual phase (DP) and TRIP steels and Cr-Mo steels [13].
CF, wherein the experimentally measured data for some special steel grades have also been shown along with the model line (Eq. 10).

All data was fitted with a Lorentz model between 2 and 5 oscillators depending on the sample.
Data was taken at each of the two or three voltage values without moving the sample of voltage probe to eliminate the positioning effects.
ScN thin film shows a spectral shift above 4eV and Extinction coefficient data shows a spectral shift above 4eV and intensity decrease through the entire photon energy (wavelength) range from 1eV to 5eV.
(b-1) and (b-2) show a non-point-to-point (PTP) fit of the tested data.
(c-1) and (c-2) show a PTP fit of the tested data.

In Fig. 4 data is also shown for the higher solute 3%Mg alloy that originally had a more defined lamellar HAGB structure with higher average grain aspect ratios.
In order to alleviate this problem, and give some indication of the direct continuity of the evolving structure on annealing, the EBSD map in Fig. 2 was used as input data for a 2D Monte Carlo-Potts (MCP) model, using the same boundary energies and mobilites described above.
Fig. 11 2D Monte CarloPotts (MCP) model, of a typical severe deformation structure, produced by 90° rotation of the billet between each ECAE pass, using orientation data from the ESD map in Fig. 2 , to define the starting structure; (a) t =0, (b) t = 7, (c) t = 22 (d) t = 40, relative model time.
From the EBSD data shown above it is clear that this is not the case (Fig. 10b).
Summary The annealing behavior of alloys containing submicron grain fragments produced by severe deformation has been discussed based on high resolution EBSD data and MCP simulations.

Furthermore, it was equally discovered that artificial intelligent for specific application are based on the data obtained from such application.
De Luca & Gallo [5], studies show that with the use of Artificial Neural Network, the estimated data of road traffic in urban space can be estimated using existing data from other road links in the same urban space.
Artificial neural network can also be used in determining the mechanical properties of a material by training it with appropriate data [6].
The use of Artificial Neural Networks for extending road traffic monitoring data spatially: an application to the neighbourhoods of Benevento. 
On big data, artificial intelligence and smart cities.

Principal component analysis is a dimension reduction method which can divided the original variables into a few principal components.
Firstly, make principal component analysis, then re-use the extracted principal components as a new integrated variable, the principal component score matrix as the new integrated variable data to make cluster analysis.
To avoid the impact of the dimension of values, before PCA, carry on the standardization of original data and build up a standardized matrix.
Using SPSS13.0 statistical software to analyse the standardized data, find the principal components in accordance with eigenvalue>1, the cumulative contribution rate>85%.
They can adequately represent and explain the original data to accurately reflect the development level of tourism central city centrality.

-Preprocess the data: The data is cleaned and transformed into an appropriate format to be mined.
Data preprocessing allows the original data to be transformed into a suitable shape to be used by a particular data mining algorithm or framework.
This is normally a manual process including a number of general data preprocessing tasks such as data cleaning, user identification, session identification, data transformation and enrichment, data integration, data Database Apply data mining techniques Preprocess data Interpret/Evaluate Figure 1.
Mining CMS data Collect usage data reduction.
Although the amount of work required in data preparation is less, the following tasks also need to be done: Select data: It is necessary to choose much more useful data for mining.

Thermo-physical properties were obtained by parametric fitting of time-temperature data obtained from cooling curve.
We can conclude that the driving force for grain growth is reduction in grain boundary area.
Ni amount Thermal Diffusivity (mm2.s-1) Specific Heat Capacity (J.kg-1.K-1) Density (kg.m-3) Thermal Conductivity (W.m-2.K-1) 0% 20.45 452 7897 73 20% 5.20 460 7933 19 40% 2.66 460 8169 10 2% 17.60* 453* 7900* 63* *Values have been computed from the regression of table values data.

The magnitude of true stress when a specimen fractured has been precisely determined from the measured data of a drawing force and the cross sectional area of the draw-bent specimen after fracture.
It causes serious thickness reduction to sheet metals and very often leads to fracture.
A potentiometer is attached to the hydraulic cylinder A, and sends displacement data to a computer.

Among the different test methods to characterize the fibre/matrix interfacial shear strength, the fragmentation test is one of the most simple in terms of experimental setup and the amount of data that can be extracted from one single test.
Fragment lengths After the fragment lengths were measured it became clear that the observed fragmentation lengths did not in all cases correspond to the true saturation limit, and that accordingly a careful data discrimination process was needed.
Based on the experimental observations the fragments in the length range of 8-15mm were located in zones where saturation was not achieved, and consequently these data were dismissed from the analysis.
Detailed statistical fitting tests were performed on the remaining fragment length data using the Kolmogorov-Smirnov and Chi-square method.
The observed steel-polyester fragment length data showed a good fit with the extreme statistical distributions.

This article uses cellular automaton to simulate basic road sections, considering two modes ofvehicle network safety applications may affect the future traffic flow, through the simulation, analysisthe basic traffic flow data, conclude how the future vehicle network safety applications impact on traffic flow.
After the road simulation, we collect data, and analysis, come up with the conclusions.
This paper is structured as follows: first talk about the traffic cellular automaton model, and analysis of the experimental data, the final display conclusions.
The model simulation and result  In this paper, useMatlab to build the simulation model, establish a period of three lanes , road length is 1000 meters , a Cellular length is 7.5 meters, open boundary, the max speed is 5,the three models run separately.Every model run 2000 steps and collect the data, then discuss the impact.
But the data shows if drivers know the vehicle densities on front road, it still can cause the long queue length.