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The destruction of the node can be evidently seen from Figure 6, and drawn from the data of Table 1 U type flashboard node is larger bearing capacity than Groove type flashboard node in line with engineering practice .
Thus affecting the bearing capacity of the entire tower, causing a major security risk. 2 Through this data we can see that the arc length method is basically correct to calculate the model results, and which reflectes broadly in line with the actual. 3 The flashboard is connected with the branch pipe and board that is the weak parts of the component.
Width to thickness ratio of strut stable strength reduction [J].

As a potential structural material for high temperature applications, the tribological performance of Ti3AlC2 at high temperatures is very important data but is still less known.
All XPS data were collected on a PHI-5702 multifunctional photoelectron spectrometer, using Al-Ka X-ray as the excitation source at a pass energy of 29.4 eV and a resolution of ±0.2 eV, The peak of adventitious carbon (C 1s: 284.8 eV) was used for calibration.
An obvious reduction in wear rates was found from 400°C to 600°C.

Table 1 The result of the comparison experiment result sample extracting times extraction time (min) extracting rate (%) total flavonoid content g/100g A1 1 25 33.6 3.03 A2 1 25 13.3 2.98 B1 2 60×2 29.0 2.53 B2 2 60×2 11.0 2.30 C1 1 30 34.1 3.12 C2 1 30 13.9 3.01 D1 2 90×2 30.2 2.50 D2 2 90×2 11.2 2.31 According to the data in the table, the conclusion is clear that the ultrasonic extraction tech. is better than decoction method.
(2) Through a large number of experimental researches on various plants medicine extraction, statistical data show that ultrasonic extraction process is very significant and considerable than decoction extraction process.
(3) The advantage of energy conservation (over 60%) and emission reduction (low carbon) is gotten with the ultrasonic extraction technology.

Its expression formula is: (2.6) Using the same conclusion, and reduction can get the following equation: (2.7) The left side of the equation means risk reward , E(rM)-rF means the whole market risk reward, βi means the securities i contribution of risk for the market portfolio.
0.000715 0.001004 0.712113 0.006190073 600328 S4 0.001291 0.001004 1.285524 0.015625435 600707 S5 0.000241 0.001004 0.239878 -0.00136116 600696 S6 0.010214 0.001004 10.1733 0.636040023 600452 S7 0.000154 0.001004 0.153327 0.00938665 600005 S8 0.000854 0.001004 0.850688 0.002934658 600272 S9 0.000675 0.001004 0.672434 0.006677273 600354 S10 0.000736 0.001004 0.732848 0.010802466 600805 S11 0.001091 0.001004 1.086765 0.023133779 601005 S12 0.000658 0.001004 0.655846 0.001436169 600810 S13 0.000669 0.001004 0.666522 0.011574123 600612 S14 0.001101 0.001004 1.096616 0.000678981 600000 S15 0.000915 0.001004 0.911667 0.000328257 691628 S16 0.000849 0.001004 0.84527 -0.003152332 601857 S17 0.000707 0.001004 0.704428 0.003779229 600019 S18 0.000821 0.001004 0.818046 0.004763123 600679 S19 0.000542 0.001004 0.539527 0.017509525 600975 S20 0.000727 0.001004 0.724099 0.008381313 Identified β coefficient can be used to make regression test and the corresponding inspection .Half of the cross section data
The risk-free rate can be calculated by deposit interest rate of half time , since there be upward trend of deposit rates , some data can be calculated for the mean .Then useing the BJS method to test: Firstly, according to the number of return , 20 stocks in accordance with the β coefficients are divided into four groups [4], as shown in the following table: Table 2 The Sample packets The first group The second group The third group The fourth group S1 S3 S2 S1 S5 S6 S10 S8 S7 S11 S12 S15 S9 S13 S17 S16 S19 S14 S20 S18 In order to improve the validity of β coefficient, portfolio return in the first period would be estimated to get β, which would be tested for the relation with the combination rate of return in the second period .

Wu introduce effective uses of Gerschgorin radii of the unitary transformed covariance matrix for source number estimation[3], many papers tried to modify Gerschgorin radii to improve its performance .Martin Haardt[1] presented Unitary ESPRIT which achieved a substantial reduction of the computational complexity by real-valued algorithm.
Jn is permutation matrix,In is unit matrix of order n, We defining XS=[X(T1), X(T2), X(T3), …,X(TL)] (L is the number of snapshot) as the sample at the moment [T1, T2, T3, …,TL] ,and matrix XC as the matrix removing the last row of XS (the data matrix received after removing the last element from the array), then we can construct （M-1）×2L matrix ZC=[XCJM-1XC*JL].
Then defining Rv=XSXSH/L, which is the covariance matrix averaging the snapshot data, and we can also achieve (M × M) forward-backward smoothing matrix R’fb of matrix Rv .

Introduction Gadolinium in nanocrystalline state shows significant modifications in magnetic properties comparing with coarse-grained: for example, substantial reduction in the effective atomic magnetic moment and an increase of the coercivity [1], the Curie temperature shift to lower temperatures [2], strong induced magnetic anisotropy in the crystallites [3].
The resistance data were normalized to the zero field value [R(H=0)] for simplicity.
There is a discrepancy between the MR and magnetization data for all samples (Fig. 5 a,b).

This operations and maintenance costs, renewal costs, and the cumulative support can be resulted a consequential effect on total cost reduction of ownership.
This institute is primary federal entity for gathering, analyzing, and presenting data related to education building facilities in the United States and nations [2].
NCES method provides a framework for decision maker, schools facilities managers, and the public to identify a basic set of schools facilities data, including definitions that will meet their information requirements [2].

Dimension reduction increases rates of skin detection a,but easily leads to duplication of color and non-color pixels ,meanwhile threshold setting brings a certain error[5].
Section IV experimental results and performance analysis data processing.
(a ) color distribution of CrCb plane (b) before median filtering (c) after median filtering Figure 2.2 The color distribution of CrCb plane and boundary extraction Direct least squares ellipse fitting skin model In computer vision research,there are two ways on Ellipse fitting based on known data[7], one is Hough transform .Time and space complexity of the algorithm increases greatly When the parameters is more than 2[8].Another method is least square fitting, its calculation is simple but the robustness is weak[9].

Algorithm Principle Benford Model A statistical law called Benford’s Law [5, 6], was introduced by Frank Benford in 1938 and predicts the frequency of appearance of the MSD for a broad range of natural and artificial data.
Algorithm flow is shown in Figure 2, Algorithm procedure is as follows: (1)We first make the Gaussian blur and dimensionality reduction processing for test images, then make 8×8 not-repeated block discrete cosine transformation of images’ RGB three channels for getting 8×8 block DCT coefficient matrix
Experimental Results and Discussion Experimental Results We test by using Matlab R2011b, the experimental data are from Columbia University Image Database [10].

Assume the discrete data of signal ( )x t is 12, , , nx x x� with certain sampling frequency, the time-domain features are extracted as follows: Skewness: 3 1 1 N i i x N α = = ∑ (1) Waveform index: 1 / | | | | N rms f rms i i X S N X x X = = = × ∑ (2) Kurtosis index: 4 4 4 1 1 / N i rms i rms K x X X N β = = = ∑ (3) Autocorrelation coefficient: ( ) ( ) / (0) x x x n t R n t R ρ ∆ = ∆ (4) Where maxX is the max amplitude of signal, rmsX is the root mean square amplitude, N is the length of discrete data and 1 1 ( ) ( ) ( ) N n x n R n t x r x r n N n − = ∆ = + − ∑ , 0,1,2 , ( ) n M M N = � � (5) Then add peak values and the peak frequencies in the specific frequency intervals as the frequency-domain
And through the above description, the training process can be seen as a dimension reduction and clustering to high dimensional features and the diagnosis is a matching process using this lowdimensional information.