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The strength reduction factor of concrete at high temperature Fig.1. shows the static strength reduction factor of concrete at high temperature decreases while the temperature increases.
According to the high temperature strength data, the strength reduction factor of concrete is fitted: (5) Because the test data is obtained at a certain temperature range,so equation (5) extended to 20 ℃ ~ 650 ℃.
According to the test data, the DIF expression is obtained: (7) Dynamic compressive failure criterion of concrete at high temperature.
However, because of the discreteness of the concrete and other factors, the error of individual data points reached to 15%, and this aspect should have a further study.
While because the discrete of concrete and the test data was obtained within a certain range of temperatures and impact velocities, this aspect should have a further study.

Then noise reduction is performed on the operating noise signal by spectral analysis.
Final drive out of production line End Y N Repair Reverse loading operation Fig.1 Detection processing of abnormal sound for final drive (a) before noise reduction processing (b) after noise reduction processin Fig.2 Waveform of qualified final drive (a) before noise reduction processing (b) after noise reduction processin Fig.3 Waveform of tooth surface roughness final drive (a) before noise reduction processing (b) after noise reduction processin Fig.4 Waveform of tooth scraping sound final drive (1).
The waveform of these final drive noise signals after noise reduction processing is shown in Fig.3 (b).
The waveform of these final drive noise signals after noise reduction processing is shown in Fig. 5 (b).
Discrete-time Signal Processing (Xi'an Jiaotong University Press, China 2001) [6] Qingjie Liu, Guiming Chen, et al: Data Acquisition and Processing 2009,(24):58-60

Physical and thermal properties of epoxy resin materials (Adapted from manufacturer data sheet).
Physical and thermal properties of reinforcing fiber (Adapted from manufacturer data sheet).
As specified in the data sheet, the individual cast of polymer composite was allowed to cure under a load of 40kg at room temperature for 24 hours before it is removed from the mould.
Statistical analysis shows that G10E30 offers 2.2% weight reduction when compared with G10E despite the fact that they contain same fibre contents.
Conclusion This experimental study has been able to offer weight reduction as evident in the panel weight analysis.

One way ANOVA for hardness and impact toughness data results having a confidence level of 95% with hot rolling reduction ratio are shown in Fig.1 (a, b) for heat treated low alloy steels.
It is found that hardness increases slightly with hot forging reduction ratio.
Hardness increases by 20% as reduction ratio increases from 11% to 29%.
Note that an increase in reduction ratio will also be accompanied by a reduction in impact (Charpy) toughness, Figure 1.
Reduction Ratio in all alloy samples in Fig. 2(a, b) around ~6%.

In accordance with the statistical data from Baoshan Iron & Steel Group Company, the content of steel slag is the amount of each additional 10kg/t in the raw sintering material, the phosphorus content of sinter will increase by about 0.0038%, the phosphorus content of molten iron will increase accordingly 0.0076%[2].
Therefore, we can add a reducing agent to the sintering process, after the reduction of phosphorus is removed in the form of gas.
Current Situation of Reducing Dephosphorization in Steel Slag Microwave dephosphorization Lv Yan et al[5] adopted the microwave method to do the experimental study of microwave carbothermal reduction phosphorus of slag.
Li Guangqiang, Zhang Feng et al[7] study dephosphorization of the converter slag to adopt the high temperature carbothermal reduction method and analyse the dephosphorization effect of converter slag in the induction furnace at 1650℃ and 1800℃.
273.15=1287℃ After adding SiO2, the starting temperature of reduction can be decreased, and the temperature can be reached at the combustion layer, so the reaction can occur in the sintering process.

Due to its advantage, which including the elimination of the need for additional information about data and the ability to extract rules directly from data itself, this theory has been developed and used in more and more domains [6-8].
The data characteristic information is extracted by using the reduction theory of rough set to delete the redundant dimensions and irrelevant variables.
The unit data group amounted to 50.
Using “training-test” method, divides given data set into training data set and test data set (respectively random distribution according to 80% and 20%). 40 groups sample are selected to fault feature selection using rough set and train the BP diagnosis model, and left 10 groups samples will be used to validate the fusion fault model.
Fisher, A fault diagnosis method for industrial gas turbines using Bayesian data analysis, J Eng Gas Turb Power 132 (2010) 82-89

The design of RS controller RS theory of the basic framework can be summarized as follows: taking advantage of up and low approximation sets approach described object, obtain the simplest knowledge through rules reduction.
There are 3 steps to learn the RS rules, shown in Fig.1.(1)Build the RS decision table based on collecting data, the original controller rules as a condition attribute and the output as the decision attribute.(2)These data are processed with discrete way to classify roughly, and eliminate duplicate one.(3)Attribute reduction aims at eliminating unimportant or small property importance attribute.
Attribute reduction can eliminate redundant condition attributes, ultimately, a number of rules stored in the rule base[3].
Table 1 Rules acquisition and reduction Rules acquisition Rules reduction U R1 R2 R3 R4 D U R1 R2 R4 D 1 D C B A Intermittence(low) 1 D C A Intermittence(low) 2 D B B A Intermittence(high) 2 D B A Intermittence(high) 3 D C B A Intermittence(low) 3 D C B Intermittence(high) 4 D C B A Intermittence(low) 4 C A A Intermittence(high) … … … … … … … … … … … 72 B A B A Intermittence(low) 17 C D A Intermittence(low) The example will explain the design of the RS controller.
Then it collects covered all condition characteristics data as far as possible to discrete these and classify four levels(ABCD) , in the end the pretreatment condition attributes corresponds to the operator's decision attribute from engineers’ direct experiences to form heat-control decision table.

According to the empirical data, carbon emission's factors has been listed in table 1.
Datas of chemical fertilizer, pesticide, agricultural film and diesel oil were all quoted from Statistical Year Book of Anhui(2004-2009), data of plough came from the crop's real sown area of Suzhou in the same year, data of agricultural irrigation came from the crop's real irrigate area in the same year, the crop's sown area and crop's irrigate area were quoted from Statistical Year Book of Anhui(2004-2009).
Compared to the base period in 2004-2009, efficiency factors of agricultural production, agricultural structures and workforce scale factors only cumulative contributions is 24.8764×104t, 17.1003×104t, 4.1680×104t of carbon reduction respectively.Overall, the effect size of the order for agricultural carbon reduction is: labour scale factor > agricultural structural factors > efficiency factors of agricultural production.
Increments of various factors stabilized since 2006, with the improvement of the efficiency of agricultural production and the optimization of the structure of agriculture ,it can get agricultural carbon reduction.
With the transfer of non-farm agricultural labor, it’s conducive to agriculture to realize the scale of operation, thus conducive to agricultural carbon reduction.

When ，，, is the reduction of . 1.3 Calculation of reduction is a set of attribute which satisfies attribute of ,so, if ，then . is a expression of Boolean, if,, then ，else, is the extract of the attribute which is contained in .
The expression is as follows: （3） （4） 1.4 Decision rules Any decision-making table can be seen as a form of generalized decision-making rules:, in the formula, ，is called conditional parts of the rule, is called decision-making parts of the rule. 3 Knowledge discovery from oil analytical ferrography of hydraulic system The decision-making table was shown in Table 1 by the use of analytical ferrography data from oil sample which was sampled from military launch vehicle hydraulic system.
The problem to find the optimal decision rule was limited to the problem of attribute reduction.
The attributes of {a3, a4} were the reduction of decision-making table after calculation.
The simple and intuitive decision-making rules were obtained from the decision-making table after reduction

Laurenson4,d Deyang Pei5,e and Hua Jiang6,f 1,2,5,6Suzhou University of Science and Technology, Suzhou, China 3College of Electronics & Information Engineering, Tongji University, Shanghai, China 4 Institute for Digital Communications, University of Edinburgh, Edinburgh, UK aluoheng1981@163.com, balanjpchen@yahoo.com, ctjgah@mail.tongji.edu.cn ddave.laurenson@ed.ac.uk, epeideyang@163.com, fjiang9093@sina.com *Corresponding author 86-13912772146 Keywords: PM pollutant measurement; Sensor network; Energy reduction; Abstract.
(a) Classroom #1 (18m*9m) (b) Classroom #2 (6m*9m) Fig.1 Two sampling locations 2.2 Sampling equipment The direct reading monitoring device, Dylos Air Quality Monitors (Model DC1700, with external dimensions of 17.78mm*11.43mm*7.62mm) were deployed in 5 different locations. 2.3 Data collection The instruments were operated for 47 days from 2 September 2013 to 18 October 2013.
The concentration of particles greater than 2.5 microns in Fig.2(b) doubled that in Fig.2(a) because of low temperature at that day, leading to the reduction of indoor-outdoor air exchange.
Fig.4 Samples with different intervals in 8:55 ~9:40 on 17 October, 2013 (18℃ ~ 22℃ , Cloudy) 3.4 Estimated Energy reduction (1) where Preduced_all is the total power reduction, Preduced_sample and Preduced_TX denote power reduction by sampling interval increase as well as less transmission times respectively.