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Moreover, magnesium alloys have high specific strength and specific stiffness, heat conduction performance, machining property, electromagnetic shielding ability and liquid forming performance, and also doing well in damping vibration reduction of noise reduction, which makes AZ31B magnesium of great use value and broad application prospect.
And the experimental data indicates that the AZ31B magnesium alloy material meets the safety requirements of the material of the high-speed train luggage rack.
And the experimental data indicates that the magnesium alloy material meets the safety requirements of the material of the high-speed train luggage rack.
(a) The profile ahead (b) The profile behind Fig.4 Equivalent (von-mises) stress cloud of the luggage rack profile A B According to the experimental data provided by a company, the actual measured value of the equivalent stress of the aluminum profile is 68Mpa.

Compared with the past research with acoustic emission sensor, this formula reduces the bandwidth of sensor and the data quantity, simplifies the signal processing circuit, makes it possible to perform real-time monitor .
(3) and is the highest and lowest frequency respectively, and (4) The Logarithm energy of each triangle filter group output is: (5) MFCC coefficient with cosine transform: (6) ，The MFCC of each scale is showed in Fig. 2(b); Carrying out MFCC characteristic extracting of normal signal and failure signal respectively, each scale of wavelet packet decomposition contains 12 eigenvalues, 8 scales contain 96 eigenvalues. the characteristic of signal of 3 sensors is integrated to be a 288 dimensions eigenvector used for failure monitor.( Red dotted line represents MFCC of normal signal , blue solid line represents MFCC of failure signal) MFCC The eigenvector has 288 dimensions, making it difficult to extract the characteristics and the data
Assuming the eigenvector is a vector of n dimensions, then the PCA is:（1）Constrcting sample matrix with original eigenvector, one row of the matrix represents a observed sample, one column of the matrix represents a one dimension data.
It shows that reducing dimensions does not reduce efficient characteristics, so it is feasible to improve signal processing speed and fault diagnosis Fig.3 Comparison of MFCC feature vectors before and after dimensionality reduction by PCA Table 1 Recognition rate comparison of various feature vectors Source of characteristicsdimensions Upper sensor 96 Middle sensor 96 Inferior sensor 96 The combination of 3 sensors 288 Reduced dimensions with PCA 9 Identification rate 96% 96% 94% 100% 100% is not effected, that is of great significance to realize real time processing with algorithm.
Evaluation of PCA dimensionality reduction techniques in imaging spectroscopy for foreign object detection[C].

The trained and the predicted transmission are compared with the experimental data.
A new predicted data at 10% H2 concentration has been introduced using proposed ANN model.
The experimental data coincides with the proposed ANN model (line is the ANN model and symbol is experimental data).
The result of ANN model showed a good agreement with the experimental data.
The predicted values of the transmission variation with the wavelength at 10% hydrogen concentration The ANN model not only simulated the data but also predicted the data using the above examined model.

The PSD data is given in Fig. 1.
Here, the data represents average value from 5 measurements.
The Dynamic Young’s modulus data is presented in Table 8.
MIP data measured for 28 days cured samples is given in Fig. 3.
MIP data – cumulative curve.

Calcinations resulted in crystal growth and agglomeration and subsequently, reduction in the effective surface area of tin oxide powders.
Both XRD (Fig. 1) and FTIR data of the precipitated phase indicated formation of hydrous tin oxide phase from the homogeneous precipitation [14].
In addition, comparison of equivalent spherical diameter, esd, values (obtained from the BET results) with crystallite sizes (obtained from the XRD data) reveals possible agglomeration during the calcination.
However, crystal growth and agglomeration and subsequently, reduction in the effective surface area of tin oxide powders occurred during calcination.
Acarbas: unpublished data [15] B.

Test data and analysis Residual water test data.By the residue water test of AC-16 and OGFC-16 , we get the variation of the residual water of two gradation with time, as shown in Table 2 and Table 3.
The test data of water stability.
With the further reduction of residual water, the bond strength between asphalt and aggregate begins to be restored, making the splitting tensile strength increase.
With the further reduction of residual water, the bond strength between asphalt and aggregate begins to be restored.

For the parametric method, GFRFs are calculated directly by nonlinear parametric models such as NARX (Nonlinear AutoRegressive with eXogenous input) model [6], which can be identified from measured input-output data.
This method leads to significant reductions in both the computational requirements and the mathematical tractability comparing to traditional Volterra model.
When the expected discrete frequency resolution and the maximum input frequency are given, the least data length N should be taken as 4M to avoid aliasing.
By using the equivalent kernel, the size of is reduced from to , which implies the significant reduction of computational requirements and store spaces in solving Eq.10.
By using multi-tone inputs and the higher order spectrum of input-output data, the generalization ability and the robustness of obtained model can be guaranteed.

Results and discussion Analysis of the obtained X-ray powder diffraction data of composites, presented in Table 1, has shown the presence of three phases: cubic crystal βphase (Al(Cu,Fe)), icosahedral quasicrystal iphase and monoclinic crystal λphase (Al13Fe4).
The dspaces and intensity of reflections correspond to data presented in Ref. [4].
X-ray phase analysis data.
It can be deduced that the reduction of fibres discontinuity and incoherence of composites obtained with 0.02 mm/min. occur.
Reduction of the fibres density at lower range of presented rates may be deduced.

The applications of the two methods for industry plants have been reported [8-12], and the reductions in liquid discharge were achieved.
The limiting data provided in Tab. 1 are based on the experience of the factory and tested by the site measurement.
The necessary limiting data of the process includes the limiting inlet and outlet contaminant concentration (ppm), and the contaminant mass load (kg/h) through the unit.
Tab. 1 Limiting Data of Water Inlet and Outlet Unit Ci,inlim (mg/L) Ci,outlim (mg/L) Filim (t/h) ∆mi (kg/h) Unit Ci,inlim (mg/L) Ci,outlim (mg/L) Filim (t/h) ∆mi (kg/h) 1 0 1 49 0.049 5 10 75 22.7 1.475 2 0 1 35.7 0.0357 6 20 75 22.7 1.475 3 0 24 40.3 0.9672 7 100 1000 45.4 44.1 4 0 50 13 0.65 8 500 1000 26.6 13.3 In Tab. 1, Filim, the limiting flow rate, is the freshwater requirement and ∆mi is the mass load of contaminant.
The 117.4 t/h freshwater corresponds to 45.97% reduction in freshwater requirement was obtained.

A future trend seems to be a reduction of the product dimensions while keeping their product capabilities.
Fig. 4 Theoretical assumption of the roughness change when machined at 1000 DPI 1 – beam path at 500 DPI, 2 – beam path at 1000 DPI, 3 – hatched final profile of the machined surface Results and discussion Listed below, there are arithmetical averages of the measured data for the transverse roughness Ra at 500 DPI and 1000 DPI ( Tab.
To get the maximal information of the measured data, 3D graphs in Matlab software were created.
According to the obtained data (groove depth, mean arithmetical deviation and maximal profile height) an optimum configuration of power and feed setting resulting in achieving of the desired values for surfaces machined with laser feed at 500 DPI and 1000 DPI.
This implicates worsening of accuracy of size as well as the reduction of surface quality especially at lower value of feed [5, 6, 7, 8].