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Principal component analysis (PCA) is an unsupervised pattern recognition method, which is the classic feature extraction and dimensionality reduction technique.
But when the multidimensional data in the principal component projection subspaces are very close between different classes, it will easily result in the failure of classification modeling.
Data and Calculations Data.
All of the SELDI－TOF MS data comes from Clinical Proteomics Program, National Cancer Institute (NCI), U.
Each spectrum measured is composed of 15154 data points.

Data recorded by borehole arrays with sensors installed at surface and geologic layers provide critical information for studies of local site amplification effects.
The Classification of Earthquake, City and Disaster Reduction (2003) [5] Ghosh S K.

At first, the designers look for files and design data, compare the designing reducer and the existing reducers, then give out a design scheme combined with design experiences, and check the components.
Two methods usually are used to exchange data between Pro/E and ANSYS.
One is IGES, and the other is the data change interface in ANSYS [5].
Wu: Research on model data conversion based on Pro/E and ANSYS, Manufacturing Automation, Vol. 33(2011), pp.119-122 [6] J.
Amit: Trade-off Analysis in Minimum Volume Design of multi-stage Spur Gear Reduction Units, Mechanism and Machine Theory, Vol.35(2000), pp.609-627

The experimental curve and the data When the metal is in the plastic deformation, there is a certain functional relation among its deformation resistance, the deformation degree, the strain rate and the distortion temperature.
(1) In this formula: In order to decide the coefficients, the following data are needed: When, ; When, ; When, ; Formula (1) needs to solve 6 equations, after the simplification, we can get: (2) In the formula: ; ; ; ; ; ; ; ; In order to decide the value of under any conditions, i.e. , we need to integral , , , and .
But the calculated results indicate that under the condition of, according to formula (3) we get the value which satisfies is different from the experimental data.
So when we decide the data according to formula (3), under any conditions, i.e. , we must take reference to the values of , and .
When, the curve of is in down cross along with the reduction of ().

Solar cars are different from traditional cars, and the domestic data related to solar cars is relative less.
In view of the actual average speed is 80 km/h [1, 2] and the fact that power solely rely on solar panels and batteries, the reduction of wind resistance coefficient of solar cars will determine the result of the competition.
Fig. 2.1 The CAD design of the solar car prototype Tab. 2.1 The contour data of the solar car prototype Length of contour（mm） Width of contour（mm） Hight of contour（mm） Track width （mm） Wheelbase（mm） 4800 1800 980 1335 1965 3 The Numerical Analysis of Body Flow With full digitalization design getting mature in the computer application, the flaws of the test vehicle in the wind tunnel are effectively solved.
This paper will use computer software to describe the application of air dynamics in car body theoretically and carry on a computer simulation test based on theoretical data to analyse the results.
In spite of the wind resistance coefficient is not as small as the highest level solar car prototype in the world, the data is relatively good.

There was no comparison data available for phosphate bonded bricks at elevated temperatures.
Manufacturers publish only cold modulus of rupture data for bricks but it can be deduced (see Table 2) that those published numbers would not lead to higher hot strengths than shown in Fig.4.
Table 2 below represents data from various publications and compares different materials based on fracture toughness, work of fracture and modulus of rupture.
All data shown was evaluated at ambient temperature.
Table 4: Furnace operation data Furnace Capacity 90 t Production Rate 180 t / day Fluxing Salt per day 14.5 t Circulation through Archways 13.6 t/min.

The numerical results and the flow details are concluded and compared with published experimental data, .Results displayed in good agreement with theoretical analysis results.
According to the method that literature [6] provided, using powerful data processing function of excel to programe transform the 2d coordinate to 3d data points.
Afterwards, import the data to CFD processor Gambit software, using spline curve to fit values at each section point so as to establish a smooth three-dimensional propeller surface.
Table 3 Calculated results compared with test data J KT 10KQ η0 Test Calculated Error Test Calculated Error Test Calculated Error 0.1 0.3036 0.2900 4.48 0.2679 0.2847 6.30 0.1839 0.1720 6.47 0.2 0.2526 0.2485 1.59 0.2585 0.2600 0.59 0.3406 0.3042 2.23 0.3 0.2193 0.2160 1.48 0.2450 0.2530 3.26 0.4496 0.4077 1.64 0.4 0.1723 0.1652 4.11 0.2204 0.2225 0.98 0.5248 0.4727 5.09 0.5 0.1193 0.1158 2.93 0.1920 0.1978 3.02 0.5171 0.4658 5.83 0.6 0.0423 0.0392 7.32 0.1526 0.1521 0.30 0.3749 0.3599 4.00 0.7 -0.0135 -0.0142 4.82 0.1097 0.1029 3.44 -0.1555 -0.1492 8.51 Calculated value and test value Curve fitting comparison is shown in figure 3.
The numerical results are compared with published experimental data and the flow details are concluded, as a whole it can meet the needs of the engineering design.

Deflection results from the numerical simulations were compared to data collected in the field.
As shown in Figure 2, the obtained data follow a non-linear decreasing trend line for all normalized embedment lengths considered in the analysis.
The rate of reduction of dh,max decreases significantly with increasing system stiffness.
This result is consistent with the data presented by [3].
The data represent the difference between readings recorded after preloading of the second and the third anchor levels.

We expect that our data might be eventually used to improve golf performance.
The infrared camera was used to acquire kinematic data from 39 optical markers.
The kinematic and kinetic data were synchronized using a VICON460 system.
Also, 50 test data were used for validation, excluding the learning data obtained at random.
Fig. 3 shows the validation results of 50 obtained test data, with an x axis of 50 test data for the validation portion- 'slice swing' from 1 to 25 and 'normal orbit swing' from 26 to 50.

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.