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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).

The article reviews, summarizes and concludes the existing literature researches at home and abroad, and mainly from the meaning and the present situation of energy conservation and emissions reduction, the relationship with economy, society and environment, index system of energy conservation and emissions reduction, and efficiency evaluation methods of energy conservation and emissions reduction.
To sum up, the researches mainly focused on the significance and the status of energy saving and emission reduction, and the relationship with economic, social, environmental, the index system design, policy advice and evaluation method of energy saving and emission reduction. 1.The Significance and Present Situation of Energy Conservation and Emissions Reduction The formulation and implementation of energy saving and emission reduction is of great strategic significance for social sustainable development.
Suqin Chen(2012) designed the chemical enterprise index system from the energy saving and the effect of emission reduction, economic benefit and social benefit of the pollution treatment. 4.Research Method on Energy Conservation and Emissions Reduction Efficiency Evaluation Data Envelopment (DEA) evaluation method is one of the most widely used evaluation method.
Zhonghua Wang, Huiting Liang (2012) using data envelopment analysis (DEA) method, regarded the various industries in Hei Longjiang province as the basic unit of energy saving and emission reduction, established the industrial efficiency evaluation model of energy saving and emission reduction in Hei Longjiang province.
Jie Lu (2010) established DEA energy conservation and emissions reduction model of Qingdao city, and made a comparative study on the partition efficiency and potential of energy saving and emission reduction in Qingdao city, then summarized the optimal energy saving and emission reduction index distribution of Qingdao city.

Quantitative Analysis of Steels using PLS with Three Data Reduction Methods based on LIBS Haiyang Kong1,2,3,a , Lanxiang Sun1,3,b, Jingtao Hu1,3, Yong Xin1,3, Zhibo Cong1,3 1Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China; 2University of Chinese Academy of Sciences, Beijing 100049, China; 3CAS Key Laboratory of Networked Control System, Shenyang 110016, China.
aE-mail: konghaiyang@sia.cn, bEmail: sunlangxiang@sia.cn (corresponding author) Keywords: Laser-Induced Breakdown Spectroscopy, Partial Least Squares, Spectral reduction, Quantitative analysis.
The PLS models were built based on the data after dimension reduction to quantify the Mn concentration of samples.
Spectra in Calibration and Validation set Category Sample number Calibration set Validation set 1 1-7 1, 2, 5-7 3, 4 2 8-16 9-11, 13, 15, 16 8, 12, 14 3 17-21 17、19、21 18, 20 4 22-27 22, 24, 26, 27 23, 25 Data Sets: Two data set were constructed to build and validate the PLS model respectively.
So selecting intensive spectral partitions is an outstanding way of dimension reduction for the original spectra with the complexity reduced and the generalization ability enhanced.

The paper fully aware of the advantages of the attribute reduction, putting forward an attribute reduction algorithm based on mutual information, by introducing the concept of information theory, and proving it’s reliability.
Flow chart The above examples can explanation the operational principle of the mutual information in attribute reduction,and it’s operation process provides reference for the attribute reduction algorithm, now the basic flow chart of the attribute reduction algorithm which based on mutual information as follows: Figure 1.
We got the potential factors by the questionnaire and expert’s advice and literature, determined the risk level based on the data of teh previous.
Then,in order to verify the efficiency of the algorithm, now take part of the data as the decision table of the information system, as shown in chart 2.
Rough Sets—Theoretical Aspects of Reasoning About Data [M].

The material balance calculation of producing stainless steel crude melts by chromium ore smelting reduction in a 150 t converter is carried out by use of the empirical data and the calculation method of refining plain carbon steel in a converter, according to the blowing conditions of 185 t smelting reduction converter of No.4 steelmaking shop in Chiba Works of JFE Steel.
In this paper, the material balance calculation of producing stainless steel crude melts by smelting reduction in a 150 t converter is carried out by use of the empirical data and calculation method of refining plain carbon steel in a converter, according to the blowing conditions of 185 t smelting reduction converter of No.4 steelmaking shop in Chiba Works of JFE Steel.
Material Balance Calculation The Required Raw Data of Calculation.
The absolute error of material balance is only -2.859 kg, the relative error of material balance is -0.169 %, such a small error can be considered to be caused by the handling of the calculated data.
The absolute error of material balance is -2.859 kg and the relative error of material balance is -0.169%, such a small error is considered to be caused by the handling of the calculated data.

XML data is used to describe the irregular data.
This method of data integration provides a unified data source and integrated data model for data access of the client.
Fig. 2 Part of the reduction gear's XML representation Fig. 3 View of reduction gear's structure in Teamcenter Case Study Take the product life-cycle software Teamcenter as the data integration platform, a data integration management platform which is based on PLM XML is constructed.
Take the structure data of reduction gear as the experimental data.
With the mature PLM XML Schema provided by Teamcenter, the integration of reduction gear's structure data has been verified, the integrated management operations of heterogeneous data's transfer and conversion has been realized.

Increase of reduction time and temperature led to total iron reduction in the whole ore lump.
Reduction of lump ore by graphite at 1200 ºC for 240 min reduction time.
According to the data obtained using the X-ray phase analysis, after reduction experiments the ore lumps contained metallic iron (with manganese impurity according to the data from electron-probe test), silicate with (Mg1.145Fe0.855)SiO4 composition and small amount of FeO.
Reduction of iron from sideroplesite at 1200 ºC for 60 min reduction time: (a) – by graphite; (b) mixed with coal.
According to the data obtained by using the synchronous thermal analysis device STA449C Jupiter, the process of dissociation developed at 350…700 °C in 2 stages [10].

For theoretical data, the singular values should go to zero when the rank of the matrix is exceeded.
For measured data, however, due to random errors and small inconsistencies in the data, the singular values will not become zero but will become very small.
Use the truncated singular value decomposition (TSVD) technique with an appropriate value of rank r estimated from the data to obtain a low rank approximation to the Hankel data matrix.
Our experimental data were measured from two accelerometers respectively, of a cantilever beam.
[9] Tufts D. and Shah A., in: Estimation of a signal waveform from noisy data using low-rank approximation to a data matrix[J].

Traditional data mining is based on the relational database and data warehouse, how to dig out in the form of XML data becomes a hot research issue.
Due to the XML document is a kind of semi-structured data, using the traditional data mining methods for mining of XML data is not applicable.
Data mining model Knowledge discovery (data mining) process can be roughly interpreted as three processes: data preparation, data mining and interpretation of devaluation. [3] The figure is as below: Data preparation stage Data preparation stage Data preparation stage (1) Data preparation stage Data preparation can be divided into three steps: data selection, data preprocessing, and data transformation).
Business Understanding Data Understanding Data Preparation Modeling Evaluation Deployment XML data mining model based on rough set theory Knowledge acquisition based on rough set theory is mainly through converting XML data into a decision table, and then to carry out reduction of decision table.
Data reduction of decision table is divided into two parts, one is for attributes reduction, and the other is for attribute value reduction.

The microstructures of the produced alloy together with the kinetics data obtained from reduction process were used to elucidate the reduction mechanism under isothermal conditions.
The microstructures of the produced alloy together with the kinetics data obtained from reduction process were used to elucidate the reduction mechanism under isothermal conditions.
At the early stages of reactions, the compact show very low reduction extent where the low reduction temperature is still not enough to accelerate the rate of reduction.
The extents of reduction were calculated as a function of time and the reduction curves and reduction rates were plotted as given in Fig. 3 and Fig. 4, respectively.
The obtained reduction curves are reflecting the effect of temperature on the reduction rate through the whole reduction stages.