Search results

A strong correlation among the increasing of the N concentration at the SiO2/SiC interface, the reduction of the interface state density located near the conduction band and the improvement of the MOSFETs performance was obtained.
The main effect of the N implantation is the reduction of the electron interface trap density, Dit, located near the conduction band [4], with a consequent increase of the electron channel conductivity of the transistor [3].
However, a N pile-up near the surface due to a segregation coefficient between SiC and SiO2 greater than unity [8] should make the reported data on N concentration at the SiO2/SiC interface slightly underestimated.
Therefore, the transversal electric field dependence of the field effect mobility reported in Fig. 2 suggests a reduction of the interface trap concentration related with the N presence at the SiO2/SiC interface.
Furthermore, the decrease of Vth as the Nint increases is easy explained with the reduction of the negative charge at the interface due to the reduced Dit.

After received the low resolution representation of spatial data, the users can browse and analysis the data.
When network is busy, the progressive transmission of spatial data can significantly reduce the time that users wait for data.
Progressive transmission of vector data over the internet 2.
Fig.5 is data view of the client.
（a）Rough data view (b) Intermediate data view (c) Final data view Figure.5.

Data processing examples show that the model's practicality and reliability.
The main characteristic of grey system theory is the research about small data and uncertainty, and the basic tool is grey generation.
Behavioral data of the system may be chaotic and complex, but there is always some kind of law among them.
Grey generation is to find the law from these behavioral data, and establish grey model according to the law, further predict the system by solving the model [1].
Example There are the fatigue experimental data (Mpa) in [9]: = [ 560,540,l523,500,475] Corresponds temperature (oC): t = [100, 150,200,250,300] The number corresponding to the temperature: k = [1,2,3,4,5] The model was obtained by using this method proposed in this paper: The fitting value of the data: =[560.6246,540.2185,520.5551,501.6074,,475] The relative error: e(%)=[-0.11154,-0.040461,0.46748,-0.32149,0] The mean of the relative error is 0.18819%.

The research presents the results of data analysis on degassing of wheel grades of steels in a tank degasser with a capacity of 120 tons, operated at the JSC “Ural Steel”.
The Purpose and Object of the Study The study of the laws of degassing of wheel steel grades was carried out on the basis of the production data on the results of vacuum metal treatment at the installation vacuum tank degasser (VTD) SIEMENS-VAI with a capacity of 120 tons, operated since 2012 in an electric steel shop of the JSC “Ural Steel”.
The Basic Data To study the patterns of steel degassing at the VTD, an analysis of the production data on the smelting of wheel steel grades for November-December 2019 was carried out.
The averaged data on process parameters and the results of steel degassing of the studied steels are given in Table 1.
Therefore, for further processing from the initial production data, the dropouts of melting was screened with a freeboard value of more than 600 mm, and the influence of the slag layer thickness, visually estimated, was not taken into account.

Models are validated with supersonic nozzle data and wall impinging jet mass transfer data from literature.
The adjusted model was tested with NASA supersonic jet data [6,7].
Fabritius: A mathematical model for the reduction stage of the AOD process.
Fabritius: A mathematical model for the reduction stage of the AOD process.

The numerically gained results are validated by experimental data.
This paper shows an evaluation of possible methods to simulate the cutting operation in a commercial available simulation program and the validation of the results by experimental data.
Therefore the experimental data is measured using the microhardness measurement system Fischerscope® HM2000.
Preliminary results show a good match of experimental and simulative data.
By validating a suitable element removal threshold with experimental data it is possible to analyze different tool designs for a given material combination.

Currently, in order to using rough sets to get weight, firstly, we should do the attribute reduction on the basis of data sets, delete redundant attributes, then using attributes sets after reduction as the new index system, acquiring the weight last[2-6].
This model improved the attributes reduction approach and divided attributes into two categories by attributes reduction definition.
Above all, demonstrate the improvement method’s feasibility and effective from both data and theory.
Healthy-dwelling sound environment data is shown in Table 1.
Get the decision attribute through the classic k-means clustering algorithm.The original number of data is 706 totally and it reduce to 627 after removing the noise data.

They significantly contribute to improvement of forgings quality and production costs reduction.
The crucial points of the numerical simulations are material input data and implemented material models.
The paper is dealing with overview of methods for the input data measurement.
Introducion The FEM simulation results are as good as the input data and thus care must be paid to use appropriate material data and models describing considered material behavior under service loading conditions.The influence of strain, strain rate, stresses and temperatures have to be addressed.
Example of data measured for fitting of Jonson/Cook model can be seen in Fig. 1.

On the other hand, if the request is about fetch, then the supervision and management daemon access Sarp Table and get the information of meta-data (Table1), system obtain data from corresponding GS through the meta-data.
Through the meta-data shown in Table 2, a load data F can be located by a vector [I, s, L], where I behalf of the index number of GS, s behalf of the index number of start sector block of data in GS, L behalf of block size.
In order to quantify the load on GS, HGS will note the processing time named t and the amount of data named d for each request.
Suppose at time T, one GS have processed N requests, consuming time of ,and the amount of data is , then the load on the GS can be expressed as ,Where i∈(1,...
Overload-Reduction rate represented by .

The data did not suggest any local texture but was dominated by spottiness, particularly in the region not disturbed by the friction stir-weld tool.
The DIC strain data were averaged longitudinally over a distance of 50 mm in the area around the HEDXD line scans.
This was confirmed when the full-field DIC data was viewed and angled fronts of localised plasticity could be seen traversing the specimen.
The stresses are from HEDXD data and deformation strains from the DIC data.
Figure 6 shows the plastic strain distribution across the specimen combining the data from HEDXD, Fig. 3 (the elastic strain reduction, that is residual stress reduction/E, must first be calculated from this data, shown by the HEDXD arrow), and DIC, Fig. 4.