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The data obtained when the experimental samples breached, in which the intense influence of the aggressive medium was carried out during Δt=10 days didn’t show any significant changes.
The obtained data at break of the experimental samples, in which the intense influence of the aggressive medium was carried out during Δt=30 days, determined the decrease in the strength of the welds, prone to forced corrosion, in relation to the usual ones by 5-8%.
The obtained data at break of the experimental samples, in which the intense influence of the aggressive medium was carried out during Δt=100 days, determined the decrease in the strength of the welds, prone to forced corrosion, in relation to the usual ones by 10-15%.
Experiment data for welds
Experimental data for reinforcing rods

The peak cancellation method is a powerful PAPR reduction technique.
In this paper, the PAPR reduction techniques are the main topic.
Figure 1 PSD of input and output data Figure 2 Frequency domain of the APC Advanced Peak Cancellation Method Implementation First, The APC is implemented using Quartus version 10.0., and the simulation results are presented using modelsim and matlab.
Input and output data are quantized to16 bits.
Breiling, ‘‘SLM peak-power reduction without explicit side information,’’ IEEE Comm.

So the Gabor feature need dimension reduction.
After sampling, the dimension of data is reduced from 655360 to 163840.But it is still a very high dimensional, so is needed to reduce dimension further.
Locally Linear Embedding attempt to discover nonlinear structure in high dimensional data by exploiting the local symmetries of linear reconstructions [7,8].Supervised LLE algorithm is the complement to LLE by Dick and Robert [9],because the original LLE does not make use of class label information.
For different data structure, use different methods.
The linear dimension reduction method FastPCA is firstly used to deal with the linear structure of data, then use SLLE to deal with the nonlinear structure of data, can get more effective results.

The weight loss rate was calculated based on the data of Fig. 1, and DTG curves were shown in Fig. 2.
Fig. 4 Relationship between [1-(1-x)1/3] and reaction time The value of 1-(1-x)1/3 for different reaction time t was calculated by using the data in Fig. 3.
Reaction Rate and Activation Energy Deforming (5): k= (6) where k can be calculated by using the data in Fig. 5.
Taking the natural logarithm of both sides of Equation (7): lnk=lnA-Ea/RT (8) Taking the data of the peak part of DTG curves, which had a fastest weight-loss rate, lnk were plotted versus 1/T in Fig. 5.
Table 2 The activation energy and frequency factors for each reaction Atmosphere Activation Energy Ea, kJ/mol Frequency Factor A Correlation Coefficient r Ar 337.590 1.13×1010 0.9918 N2 439.611 7×1013 0.9926 According to the data given in Table 2, the empirical formula about the reduction reaction rate and the temperature were obtained.

Data collection.
Simulation Analysis Data collection.
Relevant data were collected from them and then, based on the data collected, their green degrees in supply chain were evaluated.
The data in Table 1 includes the following attributes: Data preprocessing.
Attribute reduction and choose branch node set to classify.

With respect to the utilization of digitized tooling data within the finite element analysis, further investigations were performed on the impact of data reduction strategies.
The data can be used for i.e. the surface reconstruction using common CAD systems, the comparison of measured real geometries with CAD data or with other measured data, the inspection of dimensions and last but not least the generation of NC machine data in CAM systems [3].
If now STL data of formed parts is imported from PAM-STAMP 2G into the ATOS software in order to be compared with measured data, the data needs to get an offset.
Here, the three above mentioned reduction levels of the measured data were investigated in order to identify the influence of the mesh density on the calculation results.
When using digitized data of real tools for the purpose of finite element analyses, a reasonable data reduction of the generated meshes has to occur.

It is a kind of novelly promoted nonlinear methods for dimension reduction, and can effectively find out the intrinsic low dimensional structure from high dimensional data.
Manifold learning algorithms are kinds of novelly promoted nonlinear methods for dimension reduction, which can effectively find out the intrinsic low dimensional structure from high dimensional data.
Fig.1 The framework of KIsomap-LSSVM 3.2 Data Selection The data diesel oil solidifying point was obtained at Soutwest Research Institute (SWRI) on a project sponsored by the U.S.
In this article we select freezegatest data as research objective.
These 201 samples of pretreated were randomly divided into training data and test data.

To optimize the reduction technology, kinetic model of SSI reduction was established.
The main objectives of this study are: (1) to establish the kinetic model of SSI reduced by hydrogen on the basement of unreacted core model, (2) to analyze the restrictive key through calculating effective diffusion coefficient De and chemical reaction rate constant k according to the reducing data, and (3) to provide basement for optimization of reduction technology.
Reduction of SSI Spherical Sponge Iron (SSI) with 1~5 mm diameter was prepared through the process of palletizing, roasting and direct reduction by H2.
Thus the oxidative pellets reduction, with higher intension and ferric oxide content was ferric oxides reduction.
To optimize the reduction technology, kinetic model of SSI reduction was established on the basement of unreacted core model in this paper.

This study was aimed at developing a composite coal-based fuel for maximum emission reduction in addition to improving the degree of heating of coal fuels.
Procedure With the completion of manifold-analysers-data logger assembly, combustion for emission analyses began starting with the pure coal sample followed by the formulated composite coal-clay samples.
During the combustion, emitted gases were measured continuously by the gas analysers and the concentration stored via a data logger every 60 seconds because the instruments response time was 60 seconds per sampling and analysis.
It is evident that there was a significant contribution of the clay solution to the coal in SO2 reduction at 400 o C whereas at 800 o C, the reverse was the case.
The advantages include high emission reduction, while maintaining good heat retention potentials.

Through drilling powder of concrete surface at various depths,chloride ions’concentration at different depths of corroded samples were determinated, and the diffusion coefficients of chloride ion after 90,180,240 days of corrosion were calculated on the basis of Fick’s second law.It turns out that obvious convection zone exists in corroded concrete, and the thickness of conveciton zone increases with the erosing time and the reduction of concrete strength grade.In addition, fitted datas of chloride ion diffusion coefficient increases with the reduction of concrete strength grade.
The former is carried out in the natural environment, the main advantage is that experimental data is true and reliable,but long test period is needed.Therefore,experimental study mostly uses simulation test.
According to equation(2),the regression was carried out on the experimental data and by curve fitting of Fick’s second law of diffusion,the values of Dcl and Cs were determined.Besides,fitting needs to meet the condition that correlation coefficients R approach 1 as far as possible.The values of chloride diffusion coefficient Dcl and Cs are given in Tab.4.
The results of chloride diffusion shown in Tab.4 indicate that the chloride diffusion coefficient of the same concrete decreases with the time increasing.Also,chloride ion diffusion coefficient increased with the reduction of concrete strength grade.In addition,the surface concentration continues to increase.
Tab.4 Fitted datas of chloride diffusion Grade Erosion time(days) T/s Dcl/m2/s Cs/% R2 C30 90 7776000 2E-8 0.784 0.980 180 15552000 6.005E-9 1.086 0.958 240 20736000 3.305E-9 2.354 0.960 C50 90 7776000 2.742E-9 0.663 0.992 180 15552000 1.988E-9 0.683 0.991 240 20736000 1.661E-9 0.874 0.997 Conclusions This study was conducted to investigate the effect of three exposure time period and concrete in both strength levels on the diffusion of chloride ions into concrete.