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It indicated that, 400 degrees increase in air temperature causes a 37.1% reduction in flame length.
It was observed that, adding H2 results reduction in flame length.
Data acquisition was used; the data were corrected for radiation and convection errors.
This reduction in the flame temperature in axial direction is nearly constant.
The reduction in the flame length is due to increases the chemical reaction rate which reduces the time of combustion.

The principal component analysis[1,2] is a classical data dimension reduction algorithm, using less as possible of the independent data representation of the original data main information, so PCA algorithm not only can reduce the dimension data, but also remove the correlation of the data, the major message of the original data has also been retained.
First use SVM-RFE algorithm to get the best feature subset of data set, and then get the dimension reduction processing of the best subset through the PCA, dimension reduction of the data set as the SVM classifier experimental training set and test set, the experimental results show that the method in improving the SVM classifier experimental classification and recognition rate was significantly shorten the training and test of time.
Fig. 1 PCA geometric explanation To avoid data dimension and the effects of different amplitude, the first step to a set of data is normalized.
The experimental results and analysis Because many category data set can turn to several two category data set one to one, in this paper we take two category data set as subjects to test the actual resultsof the new characteristic choosing method.
Use VS2010 programming to realise characteristic chosing, take the 5 data set in the UCI data bank[8] as subjects, as Table 1 shows.

All parameters of this model can be easily identified from available experimental data.
This function represents experimental data very well [5].
An own subroutine was developed, which exports the material data of the damaged composite to ABAQUS software, where the effects of stress wave spreading are simulated utilizing the explicit dynamics module.
The goal was to study the wave propagation with and without the influence of stiffness reduction in the composite booster part caused by damage.
Results show that stiffness reduction has significant influence on the wave propagation in the composite structure.

With the aid of OST, the interference to channel estimation form data symbols can be eliminated completely.
Moreover, data symbols can serve as pseudo-pilots to enhance the estimation performance.
A well-designed fully pilot based method, i.e., a pilot inserting-scheme assists us to trace the channel changing timely with little bandwidth reduction (rate loss) and few data distortion.
Define as input vector, as data vector, as training vector.
With the initial estimation, we can detect the data vector using Kalman Detector.

Many works have been published regarding the definition of the CZM parameters and a few data reduction techniques are currently available (e.g. the property determination technique, the direct method and the inverse method) that enclose varying degrees of complexity and expected accuracy of the results.
The inverse method consists on the estimation of the CZM parameters by trial and error fitting analyses between FEM data and the test results.
The CZM laws were derived by a direct method that used Prony-series to the Gn/Gs vs. dn/ds data.
The values introduced in ABAQUS® for the adhesive layer damage laws, defined from average values of the test data, are as follows: Young’s modulus, E=1.85 GPa, shear modulus, G=0.56 GPa, tn0=21.63 MPa, ts0=17.9 MPa, Gnc=0.43 N/mm and Gsc=4.70 N/mm.
Fig. 5 b) corresponds to Gsc and pictures a significant difference to the data of Fig. 5 a) (Gnc), as Pm/Pm0 varies nearly proportionally with LO for under predictions of Gsc.

Examples of industrial data and real time monitoring of hot band microstructure evolution using online non-destructive technique are presented confirming the efficiency of thermomechanical processing in ensuring the proper quality of AHSS sheet products.
Thicker hot bands have more homogeneous microstructure and are less prone gauge defect after cold rolling, which is confirmed by the data in Fig. 2.
The sensors are linked to the HSM data acquisition system to guarantee proper synchronization of the signals with the process flow as well as easy real time collection and analysis of permeability measurement data.
First, during hot rolling of the selected series of hot bands the online magnetic permeability measurement data were collected.
Using these two sets of measurement data, the effects of HSM processing parameters on Ld were quantified.

Turn on power supply of the laser device and the measuring system, and record measured data of the instrument.
Start calculating the measuring result 5 minutes after turning on the instrument, and save measured data every 30 seconds.
-0.1345 -0.1345 111-120 -0.1345 -0.1345 -0.1345 -0.1345 -0.1345 -0.1345 -0.1345 -0.1345 -0.1345 -0.1345 From Table 2, we can see that the system data becomes stable after 84 times.
Please refer to Table 3 for experimental result. 12:00 pm 10:00 pm (measure the system raising by 0.04mm) N result N result 1-5 0.0945-0.0872-0.1107-0.1000.102327 1-5 0.1013-0.0999-0.1013-0.1012-0.0949 6-10 0.0975-0.1008-0.1012-0.1015-0.0989 6-10 0.0956-0.1075-0.1009-0.0981-0.1041 Table 3 Repeatability experiment data Unit (mm) From the experimental data, we can see that there is slight difference as to standard deviation of repeatability experiment data in the daytime and at night.
Make real-time recording of the accumulated height difference for each measurement by system software, save the measured data, and display the accumulated height difference curve on the user interface.

The article considers the dependent process steps with attributes data.
However, the multivariate control charts dealing with discrete data are lacking.
Therefore, this research will focus on the dependent process with attributes data.
Design of the VSSI Charts We considered a process with the two dependent steps of attributes data.
Base on this, we used VSSI scheme to monitor the dependent process steps with attributes data.

Figure 2: TD-GCMS data from TENAX tubes (left) and from wafer desorption (right).
The GCMS data from desorbed silicon wafers showed that higher total organic contaminations were found in all four experiments where heating was applied.
The review of ICPMS data (Figure 3) showed no significant differences between the eight experiments.
The ToF-SIMS data show an organic contamination distribution with higher contamination close to the heating device which illustrates the influence of heat in the process.
The data for metal and ionic contamination showed that there is no additional contamination which is caused by the FOUP conditioning process or the test bench.

Slabs were removed from the annealed material, which were hot rolled at 50% (MCSR50) and 72% (MCSR72) thickness reduction ratio that correspond to areas reductions of about 20% and 67%, respectively.
Fig. 2 shows the results of transverse rupture strength tests made in the present work, in comparison to published data for AISI M2 high-speed steel
Results of transverse rupture strength testing (TRS) of evaluated materials, in comparison to data obtained by others researchers after heat treatment.
In this case, the analysis was based mainly on data obtained with cutting speed of 34 m/min.
Compatible results were come across proceeding a similar analysis, based on values of crater wears verified during machining tests, and also when specific Taylor's equation for each material is predicted from data of the tests.