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The data match perfectly well with each other.
Comparison of the data obtained by Single Pellet (TG) and Multiple Pellet (Muffle Furnace) experiments for CR/89/10C/1 at 1500oC (f-t Plot) Fig 3.
The data and figures of the powder carried out at 1350oC and 1400oC are shown in table and as f – t plots.
From these data extent of O2 removal was calculated and FR ‘f’ was obtained.
Both these data can be utilized for kinetics studies. 2.

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 configuration mode of the second place is h=7mm, l=8mm, and the drag reduction rate of model with which is 32.16%. 3.Experimental analysis 3.1 Experimental equipment Drag reduction test platform of bionic jet surface include experimental section, power section, jet supply section and data collection section, whose schematic diagram is shown in Fig. 6. 1-piezometer; 2-flowmeter; 3-swivel joint; 4-torque signal coupler; 5-transmitter; 6-acquisition card; 7-computer; 8-frequency transformer; 9-variable frequency motor; 10-coupling; 11-steering box; 12-coupling; 13-water channel; 14-water pump; 15-valve Fig.6 Schematic diagram of experimental platform The way measure torque applied by experiment was converting the fluid resistance stressed on experimental model into torque, so that the data measured could reflect the actual change of resistance.
Then store the signal acquired by torque signal coupler in computer, the data acquired from different models differed from each other, so the torque could be computed by different torque signals, and then the drag reduction rates could be got.
The analog signal output by the torque signal coupler was converted by data collection system [15] into the digital signals identified by computers, based on the torque change of different experimental samples, the data measured could be acquired, dispose and store by computer, and achieve the dynamic display of data. 3.2 Experimental sample Select U-PVC pipe as the experimental carrier, whose height was 100mm, external diameter was 140mm, wall thickness was 10.3mm.
Based on the optimum configuration mode with h=8mm, l=11mm and that of the second place with h=7mm, l=8mm gained from numerical simulation, treat respectively both models with l=8, 11mm and h=7, 8mm as fixed value, select the corresponding models with h=7~11mm and l=7~11mm, as well as a sample with smooth surface to process, the total number of the experimental samples was 21. 3.3 Experimental test method The data should be collected every 0.05s, the times of the total collection are 500 under the same jet velocity.
As is shown in Fig. 9, when h=7mm, the drag reduction rates of models with l=8, 9mm are similar, and when v=1.6m/s, the proximity is best; when v=2.0m/s, the drag reduction rate of model with l=9mm can reach the peak of 22.10%, while the one with l=11mm get minimum of 21.83%, the maximum drag reduction rate and minimum drag reduction rate is within 0.27%, suggests that changes in this case l has little effect on the drag reduction rate.

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.

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.

The study objective is to simplify test data, reduction and analysis which lead to significant time saving.
Typical PPMT curve to obtain engineering parameters Pencel Pressuremeter Testing and Data Reduction Following equipment saturation, the calibrations are performed.
Raw and reduced data with calibrations applied Data Acquisition Hardware Improvement The study aim is to simplify data collection, reduction and analysis of the test data used for calibrations, testing and determining the subsequent engineering parameters.
Accuracy of the Collected Data.
Using the digital implementation and APMT software, additional time is saved, including time taken for data collection, data reduction and determination of engineering parameters.

Energy conservation and emission reduction have become important issues people concern.
Table1 Current situation of energy consumption for key industries in Jilin Province Year Industry 2006 2007 2008 2009 2010 Farm and sideline food processing industry 2.62 2.25 1.99 1.70 1.14 Raw chemical materials and chemicals manufa- -cturing industry 4.48 1.47 1．62 1.20 1.04 Electricity and heating power production and supply industry 32.96 28.20 29.00 18.31 16.23 Data source: calculated from Statistical Yearbook of Jilin Province from 2006-2010 and Statistical Database for China’s Economic Development Table2 Current situation of SO2 (Ton) emission for key industries in Jilin Province Industry Year 2005 2006 2007 2008 2009 Thermal power industry 171361.77 220157.8 236382.08 186382.4 192122.6 Electricity and heating power production and supply industry 188518.09 243246.78 277980.68 210295.04 217607.09 Data source: first draft of “the 12th five-year” plans for environmental protection in Jilin Province The harm of high-energy consumption
Table3 Current situation of nitric oxide (Ton) emission for key industries in Jilin Province Industry Year 2006 2007 2008 2009 Thermal power industry 208542.6 226585.3 261908.3 288958.4 Electricity and heating power production and supply industry 288937.6 263760.67 283173.06 312779.12 Data source: first draft of “the 12th five-year” plans for environmental protection in Jilin Province Table4 Current situation of COD (Ton) emission for key industries in Jilin Province Year Industry 2005 2006 2007 2008 2009 Farm and sideline food processing industry 5480 9314 10628 11617 11278 Beverage manufacturing industry 9841 12069 9206 5354 6358 Data source: first draft of “the 12th five-year” plans for environmental protection in Jilin Province Reasons of high energy consumption and pollution of enterprises are as follows: 1) Laws and regulations on energy conservation and emission reduction of enterprises are imperfect, and legislation punishment is not enough
At present, as marketization degree of enterprise energy conservation and emission reduction improves, the contradiction between energy conservation and emission reduction and profit pursuit becomes increasingly prominent, especially when energy conservation and emission reduction can not make up the cost which has been paid, the motivation of enterprises to participate in energy conservation and emission reduction will be at a discount.
Meanings, Approaches and Strategies of Energy Conservation and Emissions Reduction.

Simplification of decision tables has been investigated by many authors, the current attribute reduction methods include data analysis, discernibility matrix, information entropy [4,5], etc.
References [1] Pawlak Z: Rough sets and intelligent data analysis, Information Sciences (2002), p. 147:1-12
[3] Pawlak Z: Rough sets: Theoretical Aspects of Reasoning about Data, Warsaw (1991), p. x
[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(Mar. 2000), p.1-4
[9] Pawlak Z: Rough sets: Theoretical Aspects of Reasoning about Data, Warsaw (1991), p. 60

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].

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].