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The communication protocol of RS232 interface has one start bit (0), 8-bit data (LSB first), 1 stop bit (1), and has no parity, 4800 baud rate.
A real-time resistance collection program was developed by using visual C++ programming language, which can be used to receive the data from the SD2002/5 constantly, and the data were finally stored in computer in text format.
Fig. 3 represents the data of piezoresistivity of MRE with different nickel content proportions (the weight fraction of nickel are 60%, 50%, 40%).
Fig. 4 shows the data of piezoresistivity of MRE with the same proportion (the weight proportion of nickel and iron are both 60%) but different soft magnetic materials.
Fig. 5 shows the data of magnetoresistivity of MRE.

Fig. 1: (a) Extracted oxide thicknesses from the CV data and comparison with XRR data.
A systematic overestimation of dOx from the CV data is consistent with what was found in Ref. [6] and indicates a lower real effective oxide dielectric constant of εr,eff ~3.66.
We also fitted both data using the Deal-Grove model, obtaining the following fitted parameters (CV: B = 58.8 nm2/hr, B/A = 3.72 nm/hr; XRR: B = 54.3 nm2/hr, B/A = 3.69 nm/hr) for the CV and XRR data, respectively.
Fig. 3: (a) Gp-V data at 1MHz.
(b) Density of interface state traps extracted from Gp-V data.

Introduction Relative to the satellites and manned aircraft, the biggest advantage of UAV is rapid and timely access to data.
It allows fitting of scanned data, filling of surface holes, and remeshing of existing models.
Many implicit surface fitting methods segment the data into regions for local fitting, and further combine these local approximations using blending functions.
In contrast, Poisson reconstruction is a global solution that considers all the data at once, without resorting to heuristic partitioning or blending.
Thus, like radial basis function (RBF) approaches, Poisson reconstruction creates very smooth surfaces that robustly approximate noisy data.

At present, there were two generally methods of the damage identification of beam, one was using by the inherent frequency and vibration type of structure, but the overall data sometimes was not very sensitive to the damage of structure[1], because injury was a local phenomenon typical; the other was through the evolution of modal strain modal [2,3] to locate the damage, if direct application of the strain mode, the observation of mode orders and freedom degrees requirements were relatively high [4], it was difficult to contact the model and the actual.
So the damage degree can be used to stiffness reduction, such as type: Where is damage degree,is elastic modulus after damage,-no damage modulus. 2．Numerical simulation 2.0 Computational model As shown in Figure 1 for non-prestressed reinforced concrete beam, the length of beam is 10m, the cross-sectional area is, the elastic modulus is 3.0e10Pa, the density is 2500, the Poisson's ratio is 0.2, divided into one thousand elements using by beam3.
Figure 1 2.1 Single point of damage (case 1) Respectively damage in 251st, 501st, 751st element, damage degreeis 0.05, 0.1, 0.15, calculating the first 3 order strain modal data by ANSYS, compare to no damage strain modal data, can be obtained the difference curve of the comparison, as shown in Figure 2. 2.2 Multiple damage (case 2) Respectively damage in 251st, 501st, 751st element, Respective damage degree is 0.05, 0.1, 0.15,calculating the first 3 order strain modal data by ANSYS, compare to no damage strain modal data, can be obtained the difference curve of the comparison, as shown in Figure 3. 2.3 Large area of damage (case 3) Damage in 426st-576st element, damage degree is 0.15 calculating the first 3 order strain modal data by ANSYS.
Comparing to no damage strain modal data, can be obtained the difference curve of the comparison, as shown in Figure 4.

The essence is observing data from different angles and looking for the optimum pursuit method which can reflect the data characteristic at utmost and dig data information sufficiently.
The projection pursuit method is an effective dimensionality reduction technology which is applied to analysis and deal with the higher dimensional, nonlinear and abnormal problems.
In essential, p dimensional data is integrated into as the value in projected direction.
Through solving the maximum value of projection index function, we can obtain the optimal direction of projection, and fully reveal certain structure features of high dimensional data.
Table 1 Network information processing system of the original data Indicators System 1 System 2 System 2 receive the imlet 12 11 10 Coverage/km 600 600 500 Intelligence source dynamic configuration ability 0.6 0.7 0.5 To deal with time delay/s 1 0.5 0.1 Target associated error rate/% 10 6 12 Mobile detection capability 0.7 0.8 0.8 Anti-interference ability 0.8 0.9 0.8 Position prediction error/m 100 90 120 Speed prediction error/% 12 10 8 Heading prediction error/% 10 10 20 Refers to the calibration error/° 0.4 0.3 0.3 The correction precision of site 0.7 0.8 0.8 Output density 26 28 30 Degree of sharing 0.6 0.8 0.7 Take 0.1 density threshold, calculated each evaluation index weight coefficients are shown in table 2.

Data processing.
The test data using SPSS13.0 data analysis software to carry on the regression analysis, and the analysis method to analyze the interaction between two factors in response to Matlab7.0.
According to the test arrangement and test data, using the two orthogonal revolving combination design, the equation variables to values, calculated by regression equation: (1) The equation (1) variance analysis of regression equation results as shown in Table 3.
The importance of factors analysis method for processing data [5], influence factor contribution rate were four factors, X1((NH4) 2SO4) =1.73, X2(KH2PO4) =1.72, X3 (MgSO4, 7H2O) =1.09, and X4 (CaCl2) =0.89, the degree of influence from large to small the sequence is: X1, X3, X2, X4.
Based on the analysis of test results, the regression equation is found between the ethanol yield and four kinds of nutrients, through the analysis of interaction of dimension reduction analysis of regression equation and double factors, discussed the different nutrient level scope in alcohol fermentation.

So it`s really hard to calculate the total carbon footprint because of the large amount of data required.
The data in production and processing stage is as the follows.
Finished product transportation and using stage, the energy consumption of products transport (Ept) is 0.0108424tce/t[9，10，11].The data in waste and recycling stage is as the follows.
With some processing, we get the data in Table 2. as follows.
So the greatest reductions in the carbon footprint of PP-R pipe can be achieved in the raw material production and recycling transportation processes.

The construction process has ended, even if there is a lot of irreparable cost overspends and cost reduction is necessary in follow-up construction process.
In data output, management team should collect monthly cost data, output monthly cost result or total cost result.
In addition, the system can save information and historical cost data as basis and reference for preparing planned construction cost in the future.
There are many cost data to process in time during construction.
Broadly speaking, the system has four functions such as planned management, actual cost management, comparing and analyzing cost and cost data management.

Department of Defense design guidelines for conventionally constructed steel stud walls use acceptability criteria that are much more conservative than wood stud walls due to the lack of data available.
Composite behavior between the veneers and the steel studs has not been considered to be a valid assumption; the laboratory test data presented in this paper indicate the apparent effect of veneers.
Objectives Building from previous research, a main objective of the current study is to create an analytical methodology—validated against test data—that can accurately predict response limit states for various types of steel stud wall assemblies.
Nonetheless, data collected from load tree tests show that the overall response of the system is more greatly affected by the presence of sheathing than by the strength of the sheathing materials used.
Also, test data have shown that sheathing has a substantial effect on the resistance of steel stud wall systems, suggesting that current design methods may require modification to allow for composite wall behavior consideration.

Traditional collaborative filtering recommendation system put emphasis on data but ignores the users.
Introduction With the development of information technology, Internet data is becoming increasingly huge.
As a result, in the case of a very sparse data, there will be missing mistakes when generating user nearest neighbor set.
The quality of system recommendation depends largely on the historical data sets.
The user similarity study tend to find similarity among several users from the point of view of macro data, while ignoring the fact that each user is unique .In web2.0 era, which is people-oriented, user-centered, users in the use of the Internet will leave a lot of personal data.