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In order to determine the reasonable and balanced treatment parameters that ensure the required level of nitrogen content in the above steel grades, an analysis of production data for the period of November-December 2016 has been carried out.
Table 1 shows the average data of the results of degassing of the above steel grades.
According to the data of Table 1, the operating parameters of the degassing process vary within the sufficiently wide limits, which causes significant fluctuations in the content of hydrogen and nitrogen in steel.
Therefore, for the sake of further processing, the heats with a freeboard exceeding 500mm were rejected from the initial production data, and the effect of the thickness of the slag layer, assessed visually, was not taken into account.
To assess the cumulative quantitative effect of the main parameters of degassing on the nitrogen removal, there has been carried out a regression analysis of production data.

The goodness-of-fit has been verified by computing the variance in the predicted results versus the real data, the probability that the fitted model has no useful terms, and the significance of the regression.
R-squared indicates the variance in the predicted results versus the real data.
This is the proportion of total variability in the data which is explained by the regression model, a score of "1" indicating a perfect fit.
Range-to-variance ratio indicates how well the model predicts values at the data points.
The results shown in figures 3-7 indicate that the regression model for the selected strategy, DOE Screening, D-Optimal design, Interactions, matches the test data very well.

By using XRD data crystallite size of V2O5 was estimated to 33.98nm and crystallite size of TiO2 was estimated to 35.04nm.
After doing number of adjustment on the obtained pattern of XRD we get XRD pattern data.
That data gives us the structural and diffraction information about nanoparticles.
Ti m.mm 0 0 0 2a O m.2m 0.327 0.327 0 4f The profile of X-ray diffraction data is fitted with the Rietveld Refinement and obtained spectra of V2O5/TiO2 is shown in figure.
The rietveld refinement of the sample is done by Fullprof Software and there is good agreement between the data and also provides low value of R-Factor.

After the system reached a steady state, the test data were collected and stored.
Combining the data from Table 2-4, a comparison between FE and TFE with the same cooling capacity can be obtained in Table 5.
Working data of cooling media water in FE and TFE 1 2 3 4 5 6 7 Avg.
Data comparison between FE and TFE.
The results suggest that pool boiling heat transfer coefficient data increase with , and .

The experimental thermal data collected by thermocouples positioned along the casting length were used as input information into an Inverse Heat Transfer Code implemented in this work in order to determine the hi variation in time.
The transient hi profile has a typical drastic reduction from a high initial value due to the development of an air gap, followed by a recovery to an essentially constant value.
The success of the applied simulation technique depends on accurate data of heat transfer coefficient at the solder/substrate interface and on the thermophysical properties of the solder alloy and substrate.
The other method is to conduct temperature measurements in the casting and in the chill at several designated locations and use this information as input data in an inverse method to derive the heat transfer coefficient [8-12].

The statistical property of the number can be gained when the time series modeling method is employed to analyze the dynamic data, the statistical property can often reflect some characteristics of the system producing the dynamic data.
The damage is implemented through elastic modulus reduction.
The detailed experimental setup can be found in literature [9] The acceleration responses in Y direction on node 2, node 6, node 10 and node 14 are selected as the research data, only the 2500 data between 5s and 10s is employed to establish the AR model.
Because the experiment data are limited, the network is trained with structural damage characteristic vector gained from numerical simulation, and the excitation is extracted from experiment record.
The structural damage characteristic vectors gained from experiment data are used as test specimen, the results are shown in table 2.

Data observed are transformed to model parameters, through a neural network that is trained with this kind of data.
The input matrices provide the input data for neural network and the target matrices provide the target data.
The first step in a neural network design is to configure the input and target data that will be used for training, validation and test.
The input vector defines data regarding magnetic field intensities on specific points of interest - that could be considered as sensors.
In order to have comparable results, the following neural network architecture created for each number of samples, 40 or 60, used the "divideInd" function, which divides the data index, based on the first data division [20].

The study found that there had been as 8% reduction in the bus travel time [3].
Reader decodes the data encoded in the tag's integrated circuit and the data is passed to server.
Application software on server processes the data, and may perform various filtering operations to reduce the numerous often redundant reads of the same tag to a smaller and more useful data set.
The response of a passive RFID tag may include a unique ID number and data.
Experimental Results The performance of the proposed system has been tested based on hypothetical data as well as real-life traffic data from local transportation authority.

However, a wide discrepancy existed on the prediction of the SPHB test data with strain rate of 1000/s [9,10].
Since only uniaxial tensile test data is available, parameter γ is not calculated in this paper.
The proposed model is then fitted to the experimental data of the PC/ABS blend as described in Figs. 3a and 3b.
In Fig. 3a the proposed model fits well to experimental data up to large strain.
The DSGZ model fitted to experimental data (a) (b) Fig. 3.

The experiment data is shown in table 2.
The experiment data is shown in Fig.5.
The experiment data is shown in table 3.
The experiment data is shown in Fig.6.
The experiment data is shown in table 4.