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The test result shows that the algorithm can obtain an attribute reduction efficiently.
The theory has been widely used in machine learning and knowledge discovery, intellig-ence control algorithm, data mining and other fields.
Information entropy method explains the data table from the view of information theory, which can appropr- iately reflect the information deviation.
The experimental data set are summarized in Table I.
Table I Incomplete decision table Data set Samples Attributes Classes Data type Hepatitis 155 19 2 N Soybeanlarge 307 35 19 N Credit 690 15 2 N Vote 435 17 2 N TableII Experimental results comparison Data set Numbers of attribute T1(ms) T2(ms) Hepatitis 9 1238.6 648.4 Soybeanlarge 26 60087.5 50004.7 Credit 8 101609.3 100410.7 Vote 10 90632.3 85632.3 The experimental results are summarized in Table II.

At last, efficiency and applicability of this method is verified with a group of numerical simulation data.
The Neural Network established by training samples can implement nonlinear mapping between strength reduction factor and the monitor displacement data, as equation (3).
According to Finite Element model, horizontal displacement data of monitor point 1 to 4 is calculated under 14 strength reduction factors conditions.
Training samples are composed by displacement data under each strength reduction factor condition and its corresponding strength reduction factor, and train Neural Network.
At last a group of numerical simulation data is used to test the effective and practicality of this method.

The main earthquake prevention and disaster reduction law of American is “National Earthquake Hazards Reduction Program Reauthorization Act”.
Its main content is national earthquake disaster prevention and reduction planning.
The damaged statistical data is listed as Fig. 1A [2].
The damaged statistical data is shown as Fig. 1B [3].
A B Fig. 1 (A) Damaged data of all cultural relics’ protection units in Mianzhu city on Wenchuan earthquake; (B) Building damaged data in the ancient city area of Dujiangyan city on Wenchuan earthquake.

This model can provide theoretical support for enterprises emission reduction program.
Step 2: According to the emissions by the previous step, we find the difference between the emission reductions of emission reduction process and the emission reductions in the original (emission reduction potential) and then give the corresponding optimal treatment measures
The comprehensive evaluation index indicators is such a way that can make the raw data of the indicators "standardized", so that the indicators can be compared with each other in the same scale.
The method used to standard the raw data called the evaluation index, divided into individual evaluation index, the category evaluation index and comprehensive evaluation index[9-11].
The solution of the model and the calculation of index evaluation need further research due to model complexity and uncertainty of the data.

AlN can be also synthesized by the carbothermal reduction of alumina in flowing N2 gas [3-4].
At the same time, much effort has been spent on the carbothemal reduction of alumina to produce aluminum [5-6].
The present work is aimed at investigating the carbothermal reduction of alumina in a vacuum.
Therefore the potential reactions of alumina with carbon are Eq.(1), Eq.(2) and Eq.(3) as follows: Al2O3 + 2C = Al2O(g) + 2CO(g) (1) Al2O3 + 3C = 2Al(g) + 3CO(g) (2) Al2O3 + C = 2AlO(g) + CO(g) (3) The relationships of Gibbs free energy vs temperature for the reactions were shown in Fig.2, calculated using the data of the HSC and the way of the reference [11].
According to the calculations, the reduction of Al2O3 to Al2O and Al should occur above 1671K and 1744K at 100Pa, respectively, and the reduction of Al2O3 to AlO should not occur at a temperature ranged from 1200K to 1900K.

To solve this problem, we have been finding a way to minimize grain growth by using a low temperature reduction process such as electrochemical reduction at room temperature.
Schematic diagram of the electrochemical reduction system.
Such color change enlarged in the whole part of powder after 1 h reduction.
The comparison of the size data for particles and crystallites indicates that the Cu particles are composed of a few crystallites, forming agglomerate.
In order to observe the reduction behavior depending on voltage (electrochemical reduction power), the reduction of CuO powder was performed at 16, 20 and 27 V for 2 h.

Compared with the method of reduction roasting under resistance heating, the activation energy of microwave reduction roasting was small, and the reaction rate was fast.
AB Yu [4] studied the process of microwave carbothermic reduction of stannic oxide and discussed the influencing factors of the reduction rate.
Specific experimental processes are as follows: crude niobium concentrate powers were mixed with a certain percentage of carbon evenly in the mortar, pressed into pieces, then put into porcelain crucible with thermal insulation set, and then heated in microwave oven until reaching the enactment temperature and staying there for a while, at the same time, the electron balance was used to record the weight loss data and the temperature were recorded by person.
According to Eq.7, the TGA data of crude niobium concentrate were calculated, which were obtained from the reduction roasting process at the temperature range of 300°C~850°C, and the result was shown in fig.10.
Judging from the correlation coefficient R by the regression, it can be seen that the kinetic parameter values obtained by non-isothermal analysis were closer to the data obtained in the experiment.

The results present a validation of the CFD approach against experimental data, explore the drag reduction mechanism and examine the performance of the modification in the presence of a moving floor and underbody roughness.
Experimental data was only available in [7] at an onset flow velocity of 40 m/s; however, given the narrow Reynolds number range used in this work (2.15 × 106 to 2.87 × 106) it is sufficient to validate the highest velocity.
At lower flow velocities the delta CD and CL showed poorer correlation with the experimental data, with a maximum difference of 2.4 counts for drag and 14 counts for lift.
Note that the CFD simulation was performed blind, prior to the release of the experimental data.
The RANS approach was validated against experimental data, accurately predicting the drag reduction.

The sample data after dimension reduction is used for LSSVM training, and the LSSVM classification model is obtained, which forms the ability of detecting unknown intrusion.
Then, the sample data after dimension reduction is inputted into the LSSVM classification model for training, and the classification model is used for intrusion detection.
Step4: Using the reserved features (attributes) after attribute reduction and the original sample data, construct the simplified samples.
Therefore, the 41 features of the original data are simplified to only 17 features after attribute reduction, and most of the irrelevant features are eliminated.
The attribute reduction method of RS can remove the irrelevant or unimportant input features, and reduce the dimension of the input data.

The XRD data indicate the composite consists of elementary Fe and Cu deposited on the inert EG.
Chemical reduction processes have become a new focus in recent studies.
But nitrate reduction by Fe0, unlike halogenated hydrocarbon reduction, is relatively sensitive to solution pH.
The data acquisition was performed using Chromeleon 6.5 SP2 (Dionex, USA).
The XRD data (not shown) of the composite consists of the elementary Fe and Cu deposited on the EG.