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The future challenges for SiC device technology are cost reduction and increased reliability.
Introduction As the market share of SiC devices in power electronic application grows continuously, cost reduction and reliability improvement of SiC power devices becomes essential.
The defect density data show that the new epitaxy process results continually in a BPD to TED conversion rate ranging from 99.995 to 100% with an edge exclusion of 2.5 mm.
Summary Cost reduction and increased reliability are the main future challenges for SiC devices and the epitaxial layer growth is a key process in order to accomplish this.
Yield increase during epitaxial growth is achieved by the reduction of structural defects such as basal plane dislocations and triangular defects and the increase of doping and thickness uniformities.

Many ideas were put forward to solve this problem, weight reduction seems to be the most feasible solution [1, 2].
Material replacement is the most direct way to realize weight reduction, which uses light metals like Al to take place heavy materials like steel.
Engineering stress-strain curves were got after tensile tests, then transformed engineering stress-strain data into true stress-strain data by calculating.
Table 2 RMSEs between fitting result data and experiment data Strain rates [s-1] 0.0003 0.003 0.03 Temperature [℃] 360 430 500 360 430 500 360 430 500 RMSE 1.70 0.23 0.10 2.48 0.44 0.15 7.41 3.15 0.26 Conclusions In order to investigating flow stress behavior of material Al A5005 at high temperature, twelve hot tensile tests at temperatures 360℃, 430℃, 500℃ and strain rates 0.0003s-1, 0.003s-1, 0.03s-1 were set up.
A constitutive model and data for materials subjected to large strains, high strain rates, and high temperatures[J].

The main factor of plasticity for optimal warm temperature selection from examined temperature interval is value of reduction of area that was determined by tensile test.
On the basis of thermal course of plasticity characteristics (reduction of area Z, ductility A) we are able to observe reduction of area decline at the temperature 750 °C.
Fig. 3 Courses of graphic relations of parameters resulted from the tensile test For the purpose of optimal warm temperature selection from examined temperature interval the crucial indicator of steel 16MnCr5 plasticity is value of reduction of area Z.
As reduction of area Z achieves its maximum value at the temperature 700 °C, the same will be recommended as optimal temperature of steel 16MnCr5 for warm forming.
For starting a simulation of spur gear it is necessary to properly define the input data – these data were determined as follows: · process - closed die forging · material of billet DIN 17210 (1.7131) · material of the tool ASTM A 681 (H13) · temperature of billet 700 °C · temperature of the tool 250 °C Fig. 4 Closed die model and correct material flow in closed die cavity Computer simulation results of warm forging at the recommended temperature 700 °C describes fig. 4. where it is possible to see correct plastic flow and flawless filling of closed die cavity.

The crystalline phases were identified by the JCPDS data bank.
Fig. 1 shows the XRD patterns of samples prepared at reduction temperatures of 45 °C and 50 °C respectively.
These peaks, according to JCPDS data bank (06-0344), are the fingerprints of CuCl.
At the reduction temperature of 50 °C, the prepared CuCl powder starts to sinter (Fig. 2b). 
When the reduction temperature is increased, the surface area and pore volumes become smaller.

The data collection is to recognize and calculate the performance of FR.
The required weather data are maximum wind velocity and temperature.
Tolerance calculation Average Avg. error Result Temperature (°C) 27.95 7% 27.95 ± 7% Wind velocity (m/s) 4.63 43% 4.63 ± 43% Data Averaging and Tolerance Data averaging is variable in calculating the spin ratio and other FR calculations after collecting the wind velocity and temperature data.
The calculation will combine all the weather data from any station.
Moreover, the tolerance calculation estimates the error of the weather data.

The study provides experimental data that could be used for the design and development of more efficient exchangers for refrigeration conditioning, heat pump and some other systems.
Experimental facility and data reduction Fig. 1 shows a schematic of the experiment facility and test section.
All the experimental signals are collected and processed by Agilent 34970A data acquisition system.
Comparisons of the experimental data with existing correlations were made for 96 experimental data, as shown in Fig. 2.
The following hold for the straight tube: in the laminar range, fc=64/Re, and fc=0.3164/Re0.25 in the turbulent range, Meyer [7] experimental data for the straight tube was plotted as well.

Data obtained from the FE model included die-work piece contact pressure, effective stress and strain and material deformation velocity.
The correlation between the calculated and FEA data was obtained in this research.
Also the stress-strain data can be plotted [8].
The peak temperature occurs at the surface of the work piece because of plastic deformation and frictional heating; also it is immediately after the radial reduction zone of the die.
That is because; (1) The material that is heated by dissipative processes in the reduction zone will cool by conduction as the material progresses through the post-reduction zone. (2) Frictional heating is largest in the reduction zone because of the larger values of shear stress in that zone.

Data Source Emissions allowances markets in European Union have existed two stages: the pilot phase (2005-2007), and the Kyoto phase (2008-2012).
Data samples are from the most liquid and promising spot and futures exchange platforms under the EU ETS.
Considered the continuity and availability of numerical samples, we select the data samples cover the period from April 8, 2008 to December 20, 2010 in the Kyoto phrase.
Take an example for dynamic hedge ratios, the maximum hedge ratio is 0.9854, the minimum hedge ratio is 0.9050, and the average hedge ratio is 0.9424 in the observation period of data samples.
Compared with the risk reduction of unhedged portfolio of futures assets, market participants can achieve significant risk reduction in assets portfolio of futures contracts with different maturities by using one-factor and two-factor hedging policy.

At present, based on the current functions such as data collection, monitoring, energy supply and demand balance analysis, energy assessment and management, EMS will develop and tap its own capabilities to realize the real-time forecast, allocation optimization and smart scheduling against energy to meet the actual needs.
Main functions of Energy Management System The functions of EMS can be divided into three levels generally: firstly, the control of energy management equipments and data acquisition from energy equipment; secondly, energy monitoring and scheduling system; last, energy analysis and management.
(1)Data acquisition function.
Collect the production data required for the purpose of monitoring, data calculation, statistics and analysis to achieve energy management in the works
Collect energy consumption data and establish a database according to it and gather the purchase and consumption of all energy and the product and process energy consumption to make up a table of actual energy balance

Modeling 1.1 Data preparation and selection of input factors In this paper, 50 sets of data, collected from a textile mill in Shandong province, was used for prediction, which includes 5 combed and carded yarns of 60S, 40S, 32S, 20S and 16S.
To eliminate the effects of different original variables dimension, the 60 sets of data need to be standardized.
（1） 1.2 Principal components analysis Principal component analysis is an effective dimension reduction technology for data, by which the high correlated variables could be compressed and converted to several new compositive substitutes for the original.
New variables, which were independent from each other, could not only contain the most information of the original data and reduce the number of the input factor, but also simplified the complexity of the subsequent prediction model.
The relative predicted value error (RE) and root mean squared error (RMSE) of 10 sets of validation data is shown in Table-6, and the target fitting chart and epochs of 4 prediction models is given in Figure-3.