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Also, with analyzing the stability of tunnel surrounding rock, we defined the safety used ADINA software to establish a simulation model, from which we can get the relationship between the strength reduction factor and data such as the maximum principal stress and the maximum coefficient as 2.5 at last.
The principle of the strength reduction method.
In the formulas, means soil virtual cohesion after reduction and  means soil virtual interal friction angel after reduction.
Therefore, reduction factors are shown in Table 2.
It means when the soil is subjected to environment factors such as heavy snow, rain and storm, the strength of the soil will be lower to be 40% of its original data.

In order to research the way of dimensionality reduction for data, we also processed the sample of stress fatigue concentration factor to compare with Principal Component Analysis(PCA).
In order to research the way of dimensionality reduction for data, we also processed the sample of fatigue stress concentration factor to compare with PCA.
Collect data and train by linear kernel function The data about fatigue stress concentration factor is come from Metal material design, selection, prediction.
A part data of sample is listed in the Table.1.
Ning: Design, Researches on Feature Selection and Stability Analysis for High Dimensionality Small Sample Size Data (MS., Xiamen University, China 2014)

Therefore, the reduction rate could be expressed as Eqn.4
Figure 1 shows a comparison between the experimental data and the calculated data.
So it can be used to forecast the reduction process and analyze the reduction mechanism.
Fig. 3 Influence of pellet porosity on reduction rate Fig. 4 Influence of pellet radius on reduction rate Figure 4 shows that the radius has significant effect on the reduction rate, under the furnace temperature at 1373K.
When the reduction time is 23 minutes and the radius varies from 7mm to 13mm, the reduction rate changes from 99% to 78%.

(7) Table1 Thermodynamic data of reactants and resultants substance 3MgO·2SiO2·2H2O(s) -4 364 079 222.170 317.231 132.241 -73.555 MgO(s) -601 241 26.945 48.953 3.138 -11.422 SiO2(s) -910 857 41.463 43.890 38.786 -9.665 H2O(g) -241 814 188.724 29.999 10.711 0.335 MgO·SiO2(s) -1 548 917 67.781 92.257 32.886 -17.866 2MgO·SiO2(s) -2 176 935 95.186 153.929 23.640 -38.493 In order to determine the reaction formula, the Gibbs free energy change in Eqs. 5-7 can be calculated by the thermodynamic data of 3MgO·2SiO2·2H2O(s), MgO(s), SiO2(s), H2O(g), MgO·SiO2(s) and 2MgO·SiO2(s)(see Table1)[11-12], whereandare the standard molar formation enthalpy and the standard molar entropy at the temperature of 298K.
Determination of Reduction Process.
Therefore, the reduction condition is improved effectively.
[11] Dalun Ye, Jianhua Hu, Practical Thermochemical Data of Inorganic, Metallurgy Press, Beijing, 2002
Barin, Thermochemical Data of Pure Substance, Science Press, Beijing, 2003

This paper, basis on the rough set theory in data mining and preferential information ,we improve the rough set attribute reduction algorithm, and applied to civil aviation accident analysis to indentify the potential law of accident.
Data Mining, also known as Knowledge Discovery from database, it is a complex process that extracts unknown and valuable mode or knowledge from large data.
Rough sets and data mining is closely related, it provides a new tool for data mining.
First, the implementation objects of data mining are more for relational databases, relational tables can be seen as a decision table in rough set theory, knowledge can be substituted by data, Knowledge processing can be achieved by the data manipulation, it brought great convenience to the application of rough set methods.
In order to extract useful information from these complex data better, to find out the internal rules and patterns in the main affect safety of flight and the accident involved of factors in the wealth information and data, the article improve the algorithm which based on Rough Set Attribute Reduction, it can provide a reference for aviation safety.

First pre-treatment basic data is proceeding.
On the clinical data, in order to analyze changes of various indicators and their relationship in the process of differential treatment with traditional Chinese medicine, to screen clinical evaluation of TCM on the orderly’s bacterial pneumonia, you need to use data mining processing (data preprocessing, method selection, dimension reduction ,modeling, etc.).
Data mining is a method for search the knowledge and implicit rules in large amounts of data..
The above information as a basic sample set of data mining is completed data management by MICROSOFT SQL SERVER database. 2.1 Data Preprocessing 1) To Deal with the null values data in the database.
The tables are incomplete in some indicators, data collection items phenomenon (target item data to a null value), for the difference of the data to carry out (null value data are not subtracted), null value data items in the database will be assignment for the arithmetic mean of the indicator data. 2) To change Numeric variables into multi-value variable.

The model was testified to be reliable by compared with the experimental data.
For temperature exceeding 200℃ the kinetic data are described well with a model based on E-R mechanism.
The detailed kinetic data required for solution based on [5] (see Tab.
The calculated results were compared with the experimental data from [6] as shown in Fig. 2.
Fig.2 Comparison with the experiment data and simulation data of the reactant outlet concentration The concentration and temperature distribution.The concentration and temperature distribution along the channel direction was studied, which contributed to determine the inlet concentration and the optimal temperature of the SCR reactor.

The current attribute reduction methods include data analysis, discernibility matrix, information entropy [4-5], etc.
So the attribute d is the core, that is REDUCTION = REDUCTION∪{d}=φ∪{d}.
So we can add the attribute a into the Reduction, expressed as REDUCTION←REDUCTION∪{a}.
[2] Pawlak Z, in: Rough sets and intelligent data analysis, Information Sciences (2002), p. 147:1-12
[4] Yuqing Peng, GuoXi Xiao, and Xin Yang: “Data Structure Algorithm Animation Demo implementation of CAI software,” Journal of Continue Education of Hebei University of Technology, Vol. 15 (2000), p.1-4

Mechanical Diagnosis based on Similarity Extraction of Time Series Chunjie Lv Yongyu Yao Department of Mechanical Engineering, Luoyang Institute of Science and Technology Keywords: similarity, dimension reduction, fault diagnosis, historical data Abstract: The intelligent diagnosis emphasizes the processing method of knowledge of the historical data.
This paper discusses the possibility of the application of similarity extraction and pattern discovery of time series in fault diagnosis by using these historical data, presents the method of time series feature extraction and pattern matching, and advances the possibility of data clustering and pattern discovery based on dimension reduction.
So it is necessary to transform time series data.
DFT is a very transformation, which is separated from the common data.
Retention of 64 bit DWT coefficient Fig. 3 The graph after compression with DWT The results of Wavelet transform could also reflect the overall data trend through data reduction.

And CO2 reductions from inland waterborne transportation modes are therefore urgently required to reduce the impacts of climate change.
CO2 emissions from inland shipping of Jiangxi Province are calculated by utilizing activities based energy consumption data, and potential CO2 reduciton are estimated using ASIF equation.
As a result, there is a growing awareness about CO2 emission reduction from shipping.
The former methodology is used in this study by using fuel based emission factor’s and fuel consumption data to establish the aggregated emission figures as presented in Eq. (1)
Relative reduction potential of CO2 emission from water transport (r) can be estimated using Eq.(2)