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Methods Rough Set Rough Set (Rough Set, also known as the Rough Set) theory is proposed by Z.Pawlak Professor who is a scientist from Poland in 1982, which is a reasoning method to quantitative analysis imprecise, inconsistent and incomplete information and knowledge data.
This method is widely applied in machine learning, knowledge discovery, data processing, decision support and analysis, expert systems and pattern recognition.
In order to ensure the validity and accuracy of the survey data, the sample of objects was reasonable arrangements for cooperation and investigation units, the Research Group distributed 770 questionnaires by using hand delivered questionnaires etc., and got 710 responses.
Through judgment consistency and logic of the data in the questionnaires, we screened out of 667 valid questionnaires, as a final research data samples; the response rate was 92.2%, an effective rate of 93.9%.
Reduction and analysis of influencing factors of LC implementation based on rough set theory In this paper, using rough set Rosetta software, compiled decision table based on questionnaire data.

The good vibration reduction efficiency of active solutions, however, comes at a cost of high energy demands.
The work [9] explores the potential applications of a platform mechanism to vibration reduction in several DOFs.
Directly measured data yield the error signal to be used in the control process.
The vital stage of the design process involves the computer simulation of the ASC, using the pertinent data.
Another constraint acting upon the hydraulic drive is the maximal delivery of the pump supporting the drive 4, which allows for finding the maximal velocity to be implemented by the drive once the piston area has been established. 10o Simulation data yield the velocity pattern in the cylinder 4 for the maximal inputs from the road.

According to the result of particle size analysis before and 24 hours after milling, there was a reduction of the average diameter of agglomerate 95.25% which is suitable to assist sintering of the composite, because this reduction increases the reaction rate the raw material during firing.
The intensities of Al2O3 peaks there was an increase of γ-Al2O3 with small reduction in α-Al2O3, confirmed in the plane (202) at the point of approximately 46o and plans (018) at ~64o and (214)~66 respectively.
According to the result of particle size analysis before and after milling for 24 hours, there was a reduction in the average particle diameter of 95,25%, which proved to be best adequate for sintering the composite, as this increases decrease the reaction rate of raw material during sintering.
[15] JCPDS - Joint Commite on Powder Diffraction Starndard, International Center of Diffraction Data
[20] JCPDS - Joint Commite on Powder Diffraction Starndard, International Center of Diffraction Data

We should find a an optimal classification hyperplanem in the high dimensional space, which makes the classification effect is best, assumed that there is a sample data set, for the sample data , with the non-linear transformation , thus: .
In high dimensional data transform, the kernel function is used to solve the nonlinear data conversion problem.
Here, the advantage of kernel function is that we don’t need to find a data mapping function from low dimension to high dimension, only need to know the output after transformation.
Rough set theory On the basis of rough set theory, the attribute set R is processed, and the data in the attribute set should be processed for getting the discrete data, and each distance data should be discrete, next, we will take a reduction algorithm based on conditional information entropy.
For each classifier , all the class data of the samples are used as the positive training samples, and all the remaining samples are used as the negative training samples for the training.

One such possibility is the reduction of atmospheric carbon dioxide emissions, a major greenhouse gas widely thought to be responsible for global warming.
Analysis of the results obtained is in good agreement with literature experimental data.
Experimental values were used as input data in the membrane simulation.
Synthesis of reverse‐osmosis networks for waste reduction.

Some suggestions on CO2 emissions reduction in Guangdong are given based on the analysis.
Section 2 describes the data and LMDI model.
Data and Methodology Data.
All data used in this study are shown in Fig.1, obtained from the Guangdong Statistics Yearbook (1981, 1986 to 2011)[18].
Therefore, energy intensity powerfully contributes to the reduction of CO2 emissions growth.

The decreasing in strength is defined as the ‘‘Group Effect’’ reduction.
Applied axial load and overall specimen deformation were recorded every second during the test using data logger.
(c) Effect of screw spacing on Multiple Screw (b) Effect of Screw Spacing (a) Effect of Number of Screws Figure 2: Comparison of Data Conclusion The experiment carried out in this study demonstrated that connection strength increase proportionally to the number of screws in connection with no significant group effect reduction although multiple screws were used in connection when the screw spacing more than 3d.
However the connection strength decreased and show some group effect reduction when the screw spacing is less than 3d.
Bhd. for providing the test specimens and Ms Tang Su Yii for test data collection and laboratory work.

High-purity methane is used as the reductant in the reduction stage of CLC while air as the oxidant in oxidation stage.
Data in reduction–oxidation TGA tests is directly obtained as a sample weight evolution as a function of time.
These weight data can be transformed into conversion data by using the following equations [6]: For reduction: (1) For oxidation: (2) Where is the actual mass of sample, is the mass of the sample fully oxidized and the mass of the sample in the reduced form.
Similar steps can be found in the reduction stage of these three curves both in Fig. 3 (a) and 3 (b).
Meanwhile, as the flow rate increases, the end time of both reduction and oxidation stage was brought forward.

The EIS data were analyzed with an appropriate equivalent circuit using Z-view simulation software.
Figure 1a shows the scanning electron microscopy (SEM) images of Cu nanostructures deposited directly on ITO substrate by the potentiostatic reduction at -0.8 V during 30 s.
According to Figure 1b, larger Cu nanoclusters were deposited on the electrode due to a rapid reduction rate.
It is well known that coating a thin-layer of Pt on the TCO surface of the counter electrode increases the electrolyte reduction rate and makes the reduction to be diffusion-controlled for the tri-iodide anions to diffuse to the electrode.
The CV diagrams of Pt modified electrode prepared by electrodeposition (gray line) and thermal reduction (black line) at a scan rate of 100 mV s−1.

Within this research framework the present paper will concentrate on aluminium and aluminium alloys deformed by rolling from medium to a maximum strain of εvM = 6.2 (99.5% reduction in thickness).
In the analysis of the EBSD data a lower threshold of 2º has been used in the misorientation angle and for consistency this threshold has been used for both deformed and annealed specimens.
The change of texture during annealing as determined from the EBSD data is reported in Table 2 as the volume percent of rolling texture components (S, copper and brass components), random components and the cube component.
The texture data show that for both CR4 and ARB6 the fraction of rolling components only decreases from about 80% to 60 - 70% after high temperature annealing.
Samples cold rolled to higher strains up to εvM = 4.5 (98% reduction in thickness) have also been examined in recent experiments [12], which show conventional recrystallization in AA1200 with one exception.