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Spectral analysis as a dimension reduction method, it aims to embed the data into a lower dimension.
And it is difficult to verify the accuracy of the correspondence results by random data, consequently a trade-off evaluation system used to assess the accuracy of result is urgent need.

The Case study The data of this case study is derived from the US weapon acquisition for the 2012 fiscal year [8], of which 60 key technologies are selected.
One is transforming the belief degrees into the Basic Probability Mass (BMP), which derives the matching degree of the assessment goal on all the combinations/rules by clustering the matching degrees of the input data on all the combinations/rules; the other is synthesizing the result of the four combinations/rules and obtaining the belief of achieving the kth level in the result, denoted as ; and the incomplete information, denoted as .
There is still much successive work needs to be done, e. g. the technology redundancy reduction, the tradeoff analysis among varied resources, the reallocation of funding budgets as well as technology groups and et al.

Along with the strife for cost reduction in device production, this results in the demand of increasing uniformities of epitaxial layers at larger wafer diameters with low intra-run wafer-to-wafer and run-to-run deviations and higher yields.
The data represent values for our standard epitaxial layers with Fig. 4: Distribution of layer thickness and doping concentration uniformity of more than 2500 100 mm wafers.
These data are derived from over 2500 processed wafers.

Thewritten in polynomial form: (5) The next stage will be the main reduction discrete tracks, split into nine small segment, this 10 node has an advance anglecorresponds to a time t, the advance angleandterminal time as a variable to be optimized, and each time point t can be obtained by the following equation: (6) Since the direction of the angle is not exceeded, because once over the satellite will be accelerated, consume more fuel, so: (7) For the terminal time, according to Tsiolkovsky formula and the main deceleration phase initial conditions can be estimated:
Numerical Simulation and Analysis Assuming that the data indicators and each relevant parameters we set in the Table 1 and Table 2 below, the use of improved ant colony optimization algorithm:(Where,is Hohmann transfer orbit radius in recent months,is Hohmann transfer orbit radius Apolune,is the average radius of the Moon, Na is number of ants, Nc is cycles).
Thus a series of typical error values can be obtained as shown in Table 4: Table 4 Typical error value propulsion angle The initial position deviation(m) The initial speed deviation(m/s) Propulsion deviation(N) Position measurement error(m) Location scale factor error 100 25 150 100 0.2% From the data in Table 4 can be drawn, the initial position deviation propulsion deviation magnitude, initial speed deviation in magnitude.

SiC wafers grown by physical vapor transport (PVT) have improved in crystal quality, with significant reductions in crystal dislocations.
The un-decorated wafer shows two peaks around 527 and 675 nm in the yellow region while the decorated wafer shows a single and dominant peak at 537 nm (PLATO data).
Data collection and analysis by courtesy of EtaMax. 36-08 and 71-10 were processed at process D and A, respectively.

From the data given in Table 4 and 5, it is clear that after brazing and subsequent heat treatment, the YS of the inner shell material of the gas generator i.e.
Also there is no reduction in UTS or % elongation even with coarser grain of ASTM1.
From the above data it is clear that after brazing and subsequent heat treatment, the mechanical properties of the outer shell material of the gas generator i.e.

Table 1 The common mechanical properties of 7075-T6 aluminum alloy Young’s module Poisson ratio Yield strength Tensile strength Elongation Area reduction Fracture toughness 72GPa 0.33 559MPa 607MPa 9.7% 20.4% Experiment Setup.
Fig.4 The load history obtained from the SHPB loading three-point bending specimen test Fig.5 The dynamic stress intensity for the specimen calculated by the load data However, there were many other factors influencing the calculating results, such as the contact state between the incident bar and specimen, or the specimen’s dimension.
Fig.6 The load history obtained from the experiment without specimen Fig.7 The dynamic stress intensity measured by the load data obtained by the experiment without specimen Summary The effect of wedge end on the determination of dynamic fracture toughness is analyzed by the three-point bending specimen loaded by SHPB and three incident bars with different dimensions of wedge end were used.

Introduction Since the life cycle of a product is getting shorter due to the rapid industrial development and customers diverse needs, the reduction of the new product development time should be the significant issue [1].
Polyamide powder is sintered through such a process and the data scanned by CAD files for the SLS process were converted into the STL file format and sliced in 100µm unit against the z axis.
[2] Park, S.M., Advanced Data Exchange for Solid Freeform Fabrication, The University of Texas at Austin, Ph.

Under this condition, if the analysis method for experimental data was still used traditional method, the evaluated results would not agree with practical results.
Experiments The Hopkinson bar used in the present investigation consists of pressure gun, striker bar, loading bar and data acquisition system (Fig.1).
Fig.1 Schematic of the Hopkinson bar Fig.2 Closer-up view of the contact situation, including loading bar, specimen and supports The tested material in this work is low-alloy steel with yield strength, elongation and area reduction of ~479MPa, ~21% and ~77%, respectively.

The resulting strength distribution is further validated by applying it to experimental data of elementary flax fiber strength at several gauge lengths.
Elementary fiber strength and modulus correlates with the average fiber diameter in that increase in fiber diameter is related to reduction of its stiffness and strength [5, 7].
Finally, fiber strength distribution due to geometrical variability is given by ( ) ( ) ∫ ∫ ∞ − ∞               −               −−=            = 0 0 0 1 00 2 0 exp exp1 dt Sc t Sc tm tl l dt t c t c p t PP S S S m m m S m S nd δ δ σ σ σ δ (3) where the analytical form of distributions ( )xPδ , ( )xPS , applied for fitting of experimental data as shown in Figs. 2 and 3, is substituted in the integrand in the right hand side of the equation.