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Conventional GM (1, 1) model for long-term data to predict accuracy is not high, but metabolism GM (1, 1) model can be made up for conventional GM (1, 1) model of this one defect [1, 2].
Consider continuous differential equation, we can get the cloud forecasting model of system, (3) The solution of this differential equation is: (4) So the reduction solution (discrete form) of the single factor system cloud gray SCGM (1, 1) model is: (5) In this paper, we predicted the stress and camber of bridge by establishing the SCGM (1, 1) model with equal dimension and new information.
Set a SCGM (1, 1) model, after accumulating x(0)and we can get this sequence: X(1)= (0.868, 1.840, 2.792, 3.732, 4.719), then we can get the reduction solution R(0)(k)=30.709709×(1-e-0.029434)× e0.029434(k-1), R(0)=( 0.891, 0.917, 0.945, 0.973, 1.002, 1.032), so predict stress value is Z(6) = R(0)(k)Y(6) =1.032×10.34=9.79MPa, The result error is e=-4.406%.
For control the elevation of the box girder top surface in the construction process, we take the ratio of the measured data and theoretical camber value as raw data sequence in building model, to predict the camber of next section.
In order to ensure the accuracy of the measurement data, we should pay attention to measuring method and the selection of measurement instruments in construction control.

Application of Pt modified Al diffusion coating to CMSX-4 single crystals resulted in fatigue life reduction at 500 °C and in fatigue life increase at 900 °C [4].
More thorough data on fatigue behavior of the untreated material are reported elsewhere [7,8].
Experimental data were fitted by the Manson-Coffin law log 2Nf = (1/c) log εap - (1/c) log fε,
Experimental data are fitted by the Basquin law Fig. 1.
Experimental data were approximated by the power law log σa = log K´ + n´ log εap

An increased value of h corresponds to a reduction in emitter width or doping, and reduces the amount of stored charge near that junction.
As well as comparison with experimental data, finite element simulations have been used to validate physical models used in the diode electro-thermal model as well as to investigate dynamic device behavior.
It can be seen that the simulation output closely matches experimental data, though a discrepancy remains in that the final current tail is not captured by the model.
Finite element simulation results have indicated that this is due to the reduction in carrier drift velocity after most of the charge is removed from the base region, resulting in a longer transit time and thus the current tail characteristic at turn-off.
This model has resulted in improved matching of device power losses whilst reducing the amount of empirical data employed for simulations, thus facilitating efficient and accurate prediction of device power losses in power electronics circuit applications.

The determination of the particle size of each catalyst was carried out using XRD-Powder Diffraction and XPowder® software for data analysis.
Therefore, the data confirmed that both nickel(II) compounds have been successfully synthesized.
The catalyst was further characterized using powder XRD and based on the data, the NiO particle size was determined by Scherrer method according to Eq. 1 using XPowder® software.
The detail of nickel(II)oxide peaks was identified using JCPDS standard data no. 22-1189 and the result are presented on Table 2.
Data 55(9) (2010) 3410-3417

Anisotropic effects are measured by analysing tensile tests data for the specimens from 0o, 45o and 90o rolling directions of thin sheet of 0.8 mm thickness.
Reduction in test speeds at room temperature results in a significant variation in the deformation behaviour of an alloy, Fig. 4(a).
It is also observed that reduction in test speeds at room temperature results in a significant variation in the deformation behaviour of magnesium alloy and at higher temperature, 200oC, no significant change in deformation behaviour is observed at two different test speeds.

These results are compared with the experimental data measured in the same test conditions of another SiC power device already introduced to the market.
As can be seen, in this case the improved channel conductivity modulation fine balances the carrier mobility reduction up to a VDS of about 0.3 V.
This barrier is lowered by increasing the value of VDS as well as increasing the temperature as an effect of the reduction of the built-in potential at the gate-channel junction.

According to this phenomenon, this paper compared the two different bearing floating slab noise data, studied their dynamic characteristics under train loads.
As the lines with a large number of steel spring FST structure are in operation, there are low frequency noise like "gu long gu long" in the carriage when the train runs over the steel spring floating slab area, which affects passenger’s comfort, and also cause the more and more attention of researchers[1]-[3]. 2 Noise testing data in carriage when train runs over different track area Aiming at the problem, noise data from different floating slab areas was measured in the carriage.
One group of data (Fig.1-Fig.3) is the train run from common slab track to steel spring FST area.
The data from Fig.6 shows: noise in the vehicle rises unapparent when the train moves over high damp floating slab.
The test data in Fig.5 verified this method is feasible.

The control method based on the data-driven is a new control algorithm born in recent years[6].
The reduction of rules is great.
Through data processing and fuzzy clustering algorithm, the collected data are divided into five fuzzy states from low to high.
Using the nearest neighbor clustering to deal with the data roughly, and then finding out the data clustering center, and the data clustering membership matrix by using FCM.
Tsimring: Data-based control trajectory planning for nonlinear systems [J].

Introduction The SLM process is an additive process used to create metal parts with complexes shapes from CAD data.
The consequences are the reduction of fatigue life of the part or the initiation of cracks.
Table 1 Material composition of maraging steel Elements Fe Ni Co Mo Ti Al Cr C Mn, Si P,S [wt%] 64,6-69,35 17-19 8,5-9,5 4,5-5,2 0,6-0,8 0,05-0,15 0,5 0,03 < 0,1 < 0,01 The table shape support is design to allow the bonding of transducers with their wires for the data processing (in Fig. 2).
The instrumented support is clamped by bolts on the plate link to the moving bed of the machine and the transducer wires are connected to a FRONTDAQ data acquisition device.
Table 2 : Experimental data Layer thickness Cooling time between layers Manufacturing height Support 1 40 [µm] 8 [s] 10 [mm] Support 2 20 [µm] 34 [s] 5 [mm] Support 3 40 [µm] 34 [s] 10 [mm] The well-known theory of the removal layer method is used [7] and modified to determine the residual stresses in the part and support during the layer addition with the measured strains.

The DIC displacement profile data was first normalised by dividing the displacement data by the load increment with respect to the "reference" frame to give the unit load-deflection for the specimen.
Results were in excellent agreement with the published manufacturer's data, which gives a value for elastic modulus of 3.1 GPa.
The flexural strength data for the graphite specimens is plotted against density in Figure 7.
Figure 8 shows the corresponding DIC-derived modulus data obtained from the same series of tests.
The DIC-derived modulus data were systematically lower than the dynamic modulus values, in the ratio of ~0.92.