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In this paper the process of direct reduction of vanadium slag was adopted.
The test results showed that the regression equation curve fitting of the experiment data was very significant, the main factors affecting the vanadium slag reduction (according to the primary and secondary order) was the content of anhydrous sodium carbonate, roasting temperature, roasting time and reduction of carbon content.
The reduction process of iron oxides is a crystallization of chemical reaction.
In the table the baking reduction temperature was 1150℃, baking reduction time was 4h, the mass percentage of anhydrous sodium carbonate and slag was 5.56%.
Therefore the reduction temperature under the optimum condition was 1100℃.

But compare with these "massive" data, the ability of people to analyze the data and acquire knowledge exist a considerable gap, formed a "data glut" and "information poor" in a passive state.
Wisdom: Effective use of knowledge Meta-knowledge: Knowledge of the rules Knowledge: The rules of the use of information Information: Potentially useful of knowledge Data: Potentially useful information Noise: No obvious information Meta-knowledge Wisdom Knowledge Information Data Noise Fig.1 Hierarchy structure of knowledge In the hierarchy of knowledge, the lowest level (layer 6) is a noise which is almost made up of meaningless by the obscure nature of the data issues and the composition.
Level 5 is the data, some potentially significant issues.
Layer 4 is the information, is the result of a significant data processed.
The minimal attribute reduction sets is the serial code for {2,4,6,7,9,10,11,13,14}, TCF names are {Data communication, The importance of the system, Online data Processing, Multiple screens and multiple operations, Complex input and output, Complex internal processing, Code reusability, The perfect functionality and performance, Easy to maintain and modify}.

From the reduction results, it could be observed that with increasing reduction temperature reduction rate has increased.
From the results it can be observed that reduction temperature is the major factor on degree of reduction.
The kinetic models were examined by using experimental data.
It was found that the experimental data would fit perfectly with the Mckewan interface chemical reaction (1−(1 −R)1/3 = kt).
Low temperature reduction of magnetite, Thermochim.

Tectonic data marts.
Data preprocessing.
Set of attributes is called after the simple attribute reduction set reduction set is usually not only find an information table reduction set, even seeking the smallest reduction set (the reduction set) with the least number of attributes is a fairly difficult NP-hard problem.
After data preprocessing, the data obtained are shown in Table 2.
We are most concerned about the properties in the sea-battlefield situation submarine threat assessment by the sea-battlefield situation data attribute reduction.

Introduction The theory of rough sets [1], proposed by Poland mathematician Pawlak in 1982, is a mathematical method to deal with insufficient and incomplete data.
So it is a set-theory-based technique to handle data, that is, through the known information to approximately describe the uncertainty concept [2, 3].
Reduction of a -consistent covering decision system.
Reduction of an inconsistent covering decision system.
Pawlak, Rough sets: Theoretical Aspects of Reasoning about Data, Kluwer Academic Publishers, boston,1991

Next, based on the method of knowledge reduction using rough set, the redundant properties and samples of data in one month were removed.
Due to the advantage of the rough set in dealing with redundant data, large amount of data and uncertain data, more and more scholars pay more attention to the combination of rough set and the neural network [5-6].
Data is shown in Table 1.
In these data, the temperature unit is Celsius ().
The rough Reduction of Data An information system can be expressed as S=(U,A).

It gave us several advantages by allowing us flexible data reduction depending on users’ purposes, especially for time resolved experiments.
A data acquisition (DAQ) system and a data reduction software are also important for in situ measurements because they heavily affect experiment method and quality of data.
This paper demonstrates in situ neutron diffraction experiments during tensile load using these advanced DAQ system and data reduction software to utilize sets of event data including experimental conditions.
Data Reduction Software Raw event data measured by most of neutron scattering instruments at MLF are reduced to histogram data using software programs based on a Manyo-lib which is a data analysis library for neutron scattering experiments [7, 8, 9].
Major upgrade of the library was developed and data reduction software programs for TAKUMI were modified in order to enable data reduction using not only neutron and time information but also information of experimental conditions recorded by the TrigNET module.

Although the sample of multivariate can provide a lot of information for the scientific research, to some extent, the workload of data collection will increase.
Factor analysis is a method to use the less independent factor variables instead of the original data.
In order to compare the effect of energy-saving and emission reduction, the basic data of power plant 3, 4, 5 should be calculated.
Tab.2 Calculation data of power plants Plant Coal consumption t/h Flue gas/(m3/h) SO2 kg/h NOx kg/h Water consumption t/h Order Power plant 3 186 1116000 68 245.5 300 2 Power plant 4 192 1152000 57.6 138.2 80 1 Power plant 5 181.8 1090800 81.8 272.7 350 3 As is shown in table 2, although the coal consumption of power plant 5 is low, compared to power plant 4, the SO2 and NOx emission per hour is higher.
According to the comparison of the computational data, the proposed evaluation method of this paper can fully reflect the situation of energy-saving and emission reduction.

These problems are caused by large-scale training sample set and outlier data immixed in the other class.
Moreover, for training the sample data mingled with outlier data in the relatively class of sample, it often can not improve the classification capability.
The ideal of SVM is to search for an optimal hyperplane to separate the data with maximal margin.
Of the 8st ACM SIGKDD international conference of knowledge discovery and data mining, Edmonton, Canada, 2002
Of SIAM International Conference on Data Mining，Lake Buena Vista, FL, USA,2004

These systems inherit data filtering network (DFN), automatic data interpretation, advanced CCTV camera, intelligent early warning system.
All data confirmed in the local DCEIMS will be provide to victims’ families and the media.
Backup system is used to store data.
Data from various data sources filtered through filter network share to all DCEIMS systems, education institutions and research centers through internet.
Research on sharing of international disaster data and information, Disaster, vol.3, pp.109-113, 2008