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Based on the statistical data of Changsha-Zhuzhou-Xiangtan (CZT) urban agglomeration from the year 2005 to 2010, the environmental learning curves of sulfur dioxide emission per 10 thousand Yuan (RMB) production value and per capita gross domestic product (GDP) were established, and sulfur dioxide emission reduction potential of these cities was analyzed.
Data Source.
All data used in this paper came from Statistical Yearbook of Hunan Province, Changsha Statistical Yearbook, Zhuzhou Statistical Yearbook, Xiangtan Statistical Yearbook and the web site of Hunan Provincial Statistics Bureau.
From the data, it also can be seen that the decrease of sulfur dioxide emission per 10 thousand Yuan production value in Changsha city is the largest, and then in Zhuzhou city.
Sulfur dioxide emission reduction potential.

The example of value reduction based on the improved algorithm illustrated that it is an effective value reduction algorithm and an important supplement of basic value reduction algorithm.
Introduction The task of knowledge discovery in database is to find the internal links among large amount of data and finally access to interesting decision-making rules.
[2] Chan P and Stolfo S: On the Accuracy of Meta-learning for Scalable Data Mining, Journal of Intelligent Systems, vol. 8, 1997 pp.5-28
[3] Anannd S: EDM: A General Framework for Data Mining Based on Evidence Theory, Data & Knowledge Engineering, vol. 18, 1996, pp.189-223
[4] Chen M.: Data Mining: An Overview from a Database Perspective, IEEE Transactions on Knowledge and Data Engineering, vol. 8, 1996, pp.866-883

In practical engineering, equipment condition data are usually produced incrementally, so how to minimize repeated reduction on the new data set has an important significance to improve the efficiency of the diagnosis algorithm.
The resolution matrix for data expansion.
By calculating the matrix and doing reduction, we can get the reduction result after data expansion: Table 1 : Reduction results after data expansion Reduction results after data expansion Data type Changes of U1 and U2 Changes of M1 and M2 Reduction results 1) R(U)={C4,C7,C10} 2) R(U)={C3,C8,C9} 3) R(U)={C4,C7,C10} 4) R(U)={C3,C7,C10} In the examples after attributes added, here, we mainly extracted the following 4 frequency domain characteristics, which are expressed as P1, P2, P3 and P4 [7]: ,,, (4) In the formulas , is spectrum , K represent for the number of spectral lines , and K=1，2，…K .
What’s more, with the reduction results and bearing data, we can get the bearing running states and can better monitor the bearing workings.
Dang, Attribute reduction for dynamic data sets, Applied Soft Computing. 13 (2013) 676–689

In order to explore the internal characteristics of large amounts of data, VDM obtain the overall structural features by dimensionality reduction on the original data, which can then use other visualization techniques to analyze the data.
The network contains a hidden dimension reduction ability with topology preservation ability.
When the data dimension is greater than 3, fixate one dimension value to a constant value, then achieve the data dimension reduction.
LLE is a method for nonlinear reduction which can keep local geometric features.
Visual expression of reduction result Barely research into the inlet start or unstart.

Make Huangbai criterion and sample data as weibull distribution to calculate similar.
Information reduction method description Information reduction method is based on rough set[3, 4].
Rough set theory can be regarded as a new mathematical tool for imperfect data analysis.
Rough set based data analysis starts from a data table called a decision table, columns of which are labeled by attributes, rows---by objects of interest and entries of the table are attribute values.
After calculated, the fuzzy centre data of recommended samples are(0.0694±0.0731, 0.1158±0.3044, 0.0131±0.0144); the fuzzy centre data of no-recommended samples are(0.1684±0.0983, 0.0318±0.0177, 0.0077±0.0083).

Basic Attribute Reduction Algorithm Based on Discriminability Matrix Algorithm Description The general steps of attribute reduction based on discriminability matrix are: firstly obtain the core of attribute reduction set with discriminability matrix and then to calculate attribute reduction set with reduction algorithm.
Improved Attribute Reduction Algorithm The minimum reduction is same as others reduction of an information system, which are both NP complex problems.
References [1] Chan P and Stolfo S. : On the Accuracy of Meta-learning for Scalable Data Mining, Journal of Intelligent Systems, vol. 8, 1997 pp.5-28
[2] Anannd S. : EDM: A General Framework for Data Mining Based on Evidence Theory, Data & Knowledge Engineering, vol. 18, 1996, pp.189-223
[5] Chen M: Data Mining: An Overview from a Database Perspective, IEEE Transactions on Knowledge and Data Engineering, vol. 8, 1996, pp.866-883.

Considering seismic data are always nonlinear and non-stationary, many people applied EEMD to noise reduction.
(2) Make EEMD to seismic data
(a) Original noise-free synthetic seismic data, (b) original noisy synthetic seismic data, (c) noise reduction by wavelet method, and (d) noise reduction by our method To further test the performance, our method is applied to a real post-stack seismic data as shown in Fig.2 (a), consisting of 100 traces with a sample interval of 2ms.
Local singular value decomposition for signal enhancement of seismic data.
Advances in Adaptive Data Analysis, 1(1), pp. 1-41, 2009.

The Analysis of Dynamics Incenting incentive on Enterprise to Energy Saving and Emission Reduction The so-called dynamics incenting enterprise to energy saving and emission reduction are the driving force which can promote enterprises voluntarily and positively behave on energy to conservation and emissions reduction.
The concrete analysis results are as follows: Table.1 lists related data from 2005 to 2010 of industrial enterprise energy consumption and pollutant emission in Jilin province.
Taking data in Table.2 on behalf of the situation about energy consumption and pollutant emission of enterprise in Jilin Province, we take the above indicators as driving forces to do the corresponding scatter diagram as shown in Figure.1 to Figure.5.
That is to say, each index is higher; the energy consumption and pollutant emission is lower, and the better energy conservation and emission Table.1 Related data from 2005 to 2010 of industrial enterprise energy consumption and pollutant emission in Jilin province Item Year Energy consumption per unit industrial added value (tons of standard coal/ten thousand Yuan) Carbon dioxide emissions per unit value added (tons/ten thousand Yuan) Sulfur dioxide emissions per unit value added (tons/ten thousand Yuan) Effluent volume per unit value added (tons/ten thousand Yuan) Exhaust gas emissions per unit value added (million cubic meters/ten thousand Yuan) 2005 3.25 7.34 0.02 30.20 3.62 2006 2.80 6.77 0.02 23.70 3.23 2007 2.37 5.74 0.02 18.27 2.64 2008 1.98 5.10 0.01 14.27 2.29 2009 1.62 4.27 0.01 12.30 3.15 2010 1.62 3.48 0.01 9.84 2.10 Data source: statistical yearbook of Jilin province or calculated on the basis of statistical yearbook Table.2 Energy Consumption and Pollution
The marketization of energy conversation and emission reduction will give way to enterprises that actively promote energy conservation and emission reduction, which are much more able to benefit from it.

In this work, the RSM, simulated in FORTRAN, is validated with hot rolling experimental data for higher reductions.
The modified model was simulated and validated with hot rolling experimental data for hot rolling schedules at low strain rates and low reductions (<10%).
The modified model was then simulated in FORTRAN and validated with hot rolling experimental data for different hot rolling schedules at high reductions (up to 22.7%).
The required input data were rolling speed, furnace temperature, initial and final height of the specimen, and specimen width.
Results and Discussion The output of the FORTRAN codes shows temperature data for AISI316 specimen with different geometrical forms.

The data from the field blast test was compared to transducers with vibration reduction and transducers without vibration reduction.
Test equipment was several Kistler211B series sensors, Kistler5148 M06 signal conditioning module and PXI data acquisition instrument.
The Analysis of Vibration Test Data.
Applied the installation structure in explosion shock wave pressure test, can effectively restrain the additional impact caused by vibration, thereby improving the accuracy of the measured data.
The research of shock wave pressure testing in blast field and the data processing method.