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Section 3 presents an algorithm for the attribute reduction.
Attribution reduction in decision-theoretic rough set models.
Attribute reduction based on minimum decision cost.
Minimum cost attribute reduction in decision-theoretic rough set models.
Reduction of Rough Set Attribute Based on Immune Clone Selection.

(2) The analog to digital begins to sample noise signal and executes the data conversion for which the micro control unit keeps waiting
Enclosed space noise source sampling data diagram The curve describes the noise data in confined space when the system is not started, the coordinates of the horizontal axis represents the sample time length (300 seconds) and the vertical axis represents the sound pressure space (unit: dB).
When the system runs for some time, we sample sound data during 3 minutes in the confined space.
Enclopsed space noise reduction sampling data diagram It’s obvious that the effect of noise reduction is significant by comparison of the data before and after the noise reduction, the average amplitude decreases to 6 db (ref Fig 7).
Enclosed space noise reduction noise reduction diagram The black solid line represents the sample data from noise source in the confined space, the dashed line represents sound sampling data through noise reduction processing.

Moreover, the following input data were taken for simulation: tool temperature, 60°C; ambient temperature, 20°C; friction coefficient, 0.3; friction factor, 0.7; the coefficient of heat exchange between the material and the tool, αnarz = 3000 [W/Km2]; and the coefficient of heat exchange between the material and the air, αpow = 100 [W/Km2].
Effect of the relative rolling reduction, ε, on the magnitude of the strip curvature, ρ, for different values of the asymmetry factor, av, and a constant strip shape factor value of h0/D = 0.035 It can be stated from the data in Figure 2 that for the 35 mm-thick strip (h0/D = 0.035), in the examined range of rolling reductions ε, a straight strip will be obtained for the following rolling reductions: ε≈0.18÷0.19 (at av=1.01÷1.03), ε≈0.21÷0.22 (at av=1.05÷1.08), ε≈0.25 (at av=1.15) and ε≈0.28 (at av=1.10).
The data shown in Fig. 6 indicate that straight strips, for h0/D = 0.016, on exit from the deformation zone can be obtained for a relatively wide range of rolling reductions and peripheral speed asymmetry factors: for ε≈0.25, at av=1.01÷1.03 and at av=1.08÷1.10, and nearly straight strips (with a very small curvature) for rolling reductions of ε≈0.30÷0.50, except for the cases, when ε≈0.30; at av=1.08÷1.10 and when ε≈0.50; at av=1.15.
It follows from the data in Figures 1 to 7 that the rolling process parameters examined significantly influence the magnitude of strip curvature and the direction of strip bending upon exit from the deformation zone, and the relationships between the process parameters examined and the strip curvature have a periodic character, as confirmed by the author’s previous results obtained from the investigation of the asymmetric sheet hot rolling process in the continuous Rolling Mill [7, 8].
It can be seen from the data shown in these figures that as the thickness of rolled strip decreases from 50 mm to 14 mm (h0/D = 0.05÷0.014), the magnitude of rolling reductions, for which a straight strip is obtained, changes.

This paper implements a particular type of multi-criteria method, Data Envelopment Analysis (DEA), to compare 21 conventional and renewable energy plants.
In this paper, the multiple criteria optimization problem is approached through Data Envelopment Analysis (DEA).
Both models include 2 inputs and 1 output, that are based on physical and economical data.
Data show that plants differ so much as to their productivity index.
Petersen, A Procedure for Ranking Efficient Units in Data Envelopment Analysis, Management Science, Vol. 39 (1993), pp. 1261–1264

It showed that drag reduction technology with DRA would be the inexorable trend of drag reduction of the nature gas pipeline transportation.
The testing, collection and analysis of the field data were accomplished.
The drag reduction effect is obvious.
Drag Reduction in Gas Pipeline Coating Technology[M].
(In Chinese) [6] Reduction in Crude Oillines.

This paper discusses the impact factors of the energy-saving and emission reduction benefits of electric vehicles.
It calculates how much energy cost electric vehicles save and how much carbon emissions reduction value they get each year.
The number of electric vehicles according to the twelfth five-year plan in Guangdong Year 2011 Year 2012 Year 2013 Year 2014 Year 2015 large-scale commercial vehicles 2500 5700 7010 8520 10260 light commercial vehicles 0 0 1320 3030 5250 taxies 1600 3800 6050 8960 12760 the other passenger vehicles 11000 19900 41370 71440 113530 Total 15100 29400 55750 91950 141800 (1)Energy saving benefits Table 3 shows the estimated relevant parameter of the energy-conservation benefits, according to the combined fuel consumption of all vehicle kinds in the Ministry of Industry and standard power consumption on the current market, among these data, the standard coal consumption is converted by the corresponding fuel consumption or power consumption, It assume that the price of unit standard coal consumption in Guangdong province is 800 Yuan/t.
Conclusion The case show that, the method we provide can measure the directly benefits of energy conservation and emissions reduction of the electric vehicle per year effectively.
Fuel reduction and electricity consumption impact of different charging scenarios for plug-in hybrid electric vehicles[J].

Table 2 Energy coefficients of different industrial sectors in 2010 Industrial Sectors GDP (billion RMB) Energy Consumptions (thousand tce) Energy Coefficients (tce/million RMB) CO2 emissions (million ton) CO2 Coefficients (ton/million RMB) Primary Industry 4053.36 64770 15.98 771.7 190.4 Secondary Industry 18758.14 2373280 127.75 6169.6 328.9 Tertiary Industry 17308.7 465760 26.91 1299.7 75.1 The latest official data showed that China’s CO2 emissions reached 8240.958million ton in 2010 [10].
With the data of energy consumption of the three industrial sectors and the total amount of CO2 emissions, the CO2 coefficients of the different industries are calculated (see Table 2 and Fig. 2).
Industrial restructuring contributes to 0.87% and 0.9% of CO2 emissions reduction in 2015 and 2020.
This indicates that energy efficiency improvement has more effects on the CO2 emissions reduction.
This target on CO2 emissions reduction is likely to be achieved in the optimal condition of scenario 4.

Introduction In rough set theory, knowledge reduction is one of important research contents, through the knowledge reduction simplify the processing of large amounts of data information, in order to rapid get useful knowledge.
Value reduction is a very important aspect of knowledge reduction.
Currently, people have done much work, proposed a lot of reduction algorithm.
Based on this, attribute union method is used in this paper to obtain attribute value reduction.
Attribute Value Reduction Algorithm Design of Attribute Union.

The wavelet transform provides an effective method for signal noise reduction.
Therefore, wavelet noise reduction [2] has become the leader in signal noise reduction.
Local wave decomposition [3] is a completely data-driven algorithm.
So the local wave decomposition is feasible that is used as a method of signal adaptive noise reduction.
Ensemble empirical mode decomposition: a nosie assisted data analysis method.

The critical path is not only affected by the program data dependence but also by the influence of the number of available functional units, and memory access latency caused by the cache misses.
For the sake of this example, we only consider the impact of the data dependence for the critical path.
Assembly code fragment and its data dependence graph (DDG) are shown in Figure 2.
This compiler analysis is based on the method to find a program’s critical path taking data dependences and resources into consideration.
This reduction increases the opportunities for power reduction.