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Engineering creep tests carried out at constant load are aimed at determination of the creep strength or creep fracture strength, i.e. the data needed for design.
The effectiveness of a constitutive equation to model primary, secondary and tertiary creep deformation can vary with material characteristics and source data distribution.
Further, the values of P91 steel correspond to published data on the creep behaviour of precipitation strengthened steels [1,5].
This means a substantial reduction of testing time.
The constant load tests correspond to the application conditions and the data are needed for design.

Qualitative method has a direct relationship with the expert experience, and quantitative methods influenced by the training data.
Based on these data, the reservoir water affected area will be divided into four intervals: 60m-110m, 110m-253.91m, 253.91m-365.58m,> 365.58m.
Information value calculation by Information model According to the statistical landslide of Wan Zhou data, create landslide data layer, which overlay lithology, slope structure, slope gradient and reservoir graderespectively.
Thenstandardize each data, which normalize between 0 and 1, detailsare shown in Table 2on the right.
The results Select 20% of past landslidedataas validated sample, the rest participate used as the training data.

Multistage Deep-Drawing with Alternating Blank Draw-In for Springback Reduction in Transfer and Progressive Dies David Briesenick1,a*and Mathias Liewald1,b 1Institute for Metal Forming Technology, University of Stuttgart, Germany adavid.briesenick@ifu.uni-stuttgart.de, bmathias.liewald@ifu.uni-stuttgart.de *corresponding author Keywords: High-Strength Steel, Multistage Metal Forming, Springback Compensation.
Fig. 7 shows the results of the surface comparison between original CAD data and measured springback geometry, scanned with optical 3D measuring device GOM ATOS 5M after each forming operation.
Altan, "Prediction and reduction of springback in 3D hat shape forming of AHSS", Procedia Manuf., vol. 15, pp. 660–667, 2018
Tekkaya, "Springback prediction and reduction in deep drawing under influence of unloading modulus degradation", Int.
Liewald, "Forming with alternating blank draw-in as a new approach for springback reduction", 5th Int.

With the production water injection pump unit consumption data for training data, prediction results show that this method can achieve good prediction effect.
Introduction Today in the oilfield of energy saving and emission reduction work further, oilfield energy-saving management position have understandable, and energy saving target forecast and become the most important energy saving management.
This paper presents prediction method that based on BP neural network and QPSO, It effective use of the BP neural network mobility advantage, And the method to solve the BP neural network itself are easy to fall into the local minimum problem and the problem of slow convergence speed, It realize both advantages coexist, It reached the ideal test results, This method through a production water injection pump unit consumption indicators for training data, It proved the feasibility of this method.
QPSO optimized BP neural network chart Experimental Result This method has been used to in a energy saving production information system, Verify the actual data to the first production ore key energy consumption equipment water injection pump unit consumption.
Select 60 set of data samples as test object, including 40 group as the training sample data, and 20 set of data as a test sample.

(2) Data transformation processing Data transformation processing principle is used to unqualified sequence.
After the data transformation processing under the cover, it is able to get high precision of GM (1, 1) model
(6) The use of model prediction After setting up GM (1, 1) model by residual test proof enough accuracy, the data can be forecasted and we can get forecast point after several adjacent period accumulated unfinished project cost Combine GM (1, 1) prediction and network technology to propose control scheme The accumulative total cost data obtained by the GM (1, 1) and network technology data are compared.
We find out: , If n=3, the contact does not satisfy the coverage, it needs the original sequence for data processing
(2) Data transformation processing Using root to process data, It can fit, the sequence can be made into GM (1, 1) modeling

The simulation on the real measured data is carried on.
Filtered gyro signal of azimuth and elevation (solid: before filtering, dashed: after filtering) After filtering the azimuth gyro’s random error is , elevation gyro random error is .It can be seen that large number of mutations in the output voltage data reduction, data changes more continuous, which is more realistic process; contrast random error size before and after filtering, the Kalman filter designed in this paper can reduce random errors included in the output voltage of azimuth, elevation gyro, increase the accuracy of the gyro output, thereby improving the ship’s shaking isolation.

Based on the data analysis using Brunauer-Emmert-Teller (BET), the surface area for carbon sorbent is higher than organic sorbent.
The data from the research papers were then extracted based on to the electrospinning parameters and characteristics of the sorbents used.
The organic solvent data were divided into two parts.
Results and Discussions The analysis and discussion of the data obtained from the previous research papers were explained in this section.
The analyzed data obtained show a variation in the formation of MgO nanofibers.

Many scholars of domestic and foreign use different satellite image data and a variety of methods to do the recognition research of the water body area.
Feng Zhongkui and Li Xiaohui [4] chose Landsat-5 TM satellite image data of the United States from 1986 to 2005, using supervised classification method to track and monitor research on the Qinghai Lake area up to 20 years; Li Wenbo etc. use ETM+ image as a data source, using 2 kinds normalization index to quantitatively extract water area from respectively remote sensing data, and comparing the result of the extraction with supervision classification extraction results; Yu Jin-Kang et al[6] use decision tree methods to extract water area information from SPOT remote sensing image.
DATA methods Data sources Data of the Environment Mitigation Satellite used in this study are from the Environmental Satellite Data products Sharing Service website (http://www.secmep.cn).
Finally, using water bodies identified model to extract the vector of Qinghai Lake area, in ArcMap software, loading image data and the extracted vector data, and through interactive editing process, you can get the area data of Qinghai Lake.
Results and Analysis The inter-annual variation of Qinghai Lake area ​​Selecting the November data of 2008-2011 in comparison inter-annual of Qinghai Lake area .Data analysis indicated that, during 2008-2011, Qinghai Lake area was continuously increasing trend, and in 2011 compared to the same period in 2008, 2009 and 2010, respectively, increased by 44.48 km2, 42.46 km2 and 37.32 km2, which indicated that the increase of Qinghai Lake area was significantly in 2011 (Figure 1).

Reduction of Metal Consumption in View of Mining Cutting Tools Operation Sergey A.
According to the results of the in situ tests of picks conducted by LLC "Gorniy Instrument" in coal faces of Kuzbass mines the following data on the causes for picks failure were received (Table 1) [10].

The results from this simulation can provide some useful information for choosing reasonable cutting edge and machining data to improve surface integrity and prolong cutting tool life in micro-milling operation.
Micro-milling Cutter and Machining Data Micro Milling Cutter In the micro-milling, the diameter of micro milling cutter with two cutting edges is usually between tenths of a millimeter to a few millimeters.
Since there is difference in essence between micro-milling and the conventional machining, the choice of machining data should be take consideration again.
The machining data of the micro-milling cutting process simulated is as follows: Speed 50.265 m/min, Feed Rate 0.12 mm / r, Cutting Depth 0.1mm.
Since manufacturing of micro milling cutter is much more difficult than conventional, so a reasonable choice of machining data to improve tool life has a practical significance for micro machining.