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In the real fault prediction, the test data is redundant and incomplete, and Rough Set Theory can reduce the redundant information and extract the useful information.
The classical attribution reduction algorithm given by [4].
In which, the attribution reduction algorithm are as bellows: Input: decision table S=(U,A,V,f); Output: all the reduction REDC(D) of decision table.
The grey prediction model steps are as bellows: Step 1: x(0)(t) is accumulated generating to get ; Step 2: Calculate data matrix B and the data row Y; Step 3: Calculate a and b by ; Step 4: Establish model ; Step 5: Calculate prediction accumulated value ; Step 6: Calculate the prediction value ； Step 7: Calculate ,。
Firstly, the data is discretized because Rough Set Theory only can process the discretized data.

Accuracy of data reduction stage was lowly, the reason was mainly software and human aspects.
It had resulted error or distortion in reduction of data.
As a result, high quality data and high precision data reduction need a set of feasible solutions.
(3) Data reduction was completed by grid processing command and processing command.
The method can effectively solve key problems such as complicated product structure design, data loss, data reduction inaccurately.

Reformulating Input Data for Linear Regression of Demographic Data Xiaoheng Pan, Yangping Li∗, Huaqiang Yuan Dongguan University of Technology, China ∗Contact author.
Independent and identical distribution is a fundamental assumption often made in data sampling, but it is no longer valid for demographic data.
Neighborhood augmentation achieves the most significant MSE reduction on both datasets.
(f) plots MSE vs ρ on crime data.
Statistics for Spatial Data.

And then using the selected data gear is to be designed based on compressive and the bending strength using general procedure for the design of gears.
, two stage reduction spur gears are used as the reduction cannot be achieved by single reduction using spurs.
Therefore the module chosen is correct. 3 Results and discussions The gear is modelled using the software with data specified in the previous chapter.
Fig. 3.3 Gear with elliptical and circular holes 3.3 Mass Reduction of Gear The other objective of the project is mass reduction.
[9] Faculty of mechanical engineering, PSG College of Technology, Coimbatore, “Design data – Data book for engineers”, M/s.

And the reduction factor is defined as the slope stability safety factor.
Calculation model and mechanical parameters According to the design and measured data, the calculation models are shown in Fig. 2～Fig. 6.
Table 4 Stability calculations of high fill embankment with limit equilibrium method Section(m) YK123+910 +920 +940 +960 +970 +980 YK124+020 YK124+040 Minimum stability factor 1.373 1.321 1.316 1.316 1.318 1.378 1.325 1.392 3D FE analysis of stability of high embankment on rock slope Calculation model and mechanical parameters According to the design and measured data, the calculation models are shown in Fig. 15～Fig. 16.
Table 8 Stability calculations of high fill embankment with limit equilibrium method Section(m) BK0+043 BK0+076 BK0+203 BK0+233 BK0+273 BK0+305 BK0+325 Minimum stability factor 1.467 1.464 1.291 1.535 1.502 1.487 1.521 D FE analysis of stability of High Rockfill Abrupt Slope Embankment Calculation model and mechanical parameters According to the design and measured data, the calculation models are shown in Fig.19～Fig.20.
Slope stability analysis by strength reduction[J].

A comparison on grounding resistance value for copper rod, steel rod and galvanized iron rod was examined and the selection of the best grounding rod was determined from the experimental data.
Based on experiment data, the performance for each grounding rod are compared and analyzed.
The formula for percentage of reduction is given as in Eq.1
The percentage of reduction for the parallel installation system between three types of rod clearly shows that the peak of reduction is at day 2, which the reduction for copper is 29%, galvanized iron 14% and steel 18%.
Discussion Based on the data that collected for both parallel and single grounding system, it shows that copper rod have the highest resistance value compared to steel and galvanized iron.

author Keywords: Non-thermal plasma, Dielectric barrier, NO reduction, Emission controls, Catalyst.
Selective catalytic reduction (SCR) [4] and selective non-catalytic reduction (SNCR) [5], use ammonia (NH3) or urea as a reagent to reduce NOx via Eg. 1 - 3.
Experimental Setup and Methodology DBD-NTP setup The experimental setup is depicted in Fig. 1 which consists of simulated gas system, gas heater chamber, NTP reactor, catalyst reactor, gas analyser, and data acquisition system.
In this case, a selective non-catalytic reduction (SNCR) reaction is induced using DBD-NTP system.
YU et al., “Cold Plasma-Assisted Selective Catalytic Reduction of NO over B2O3/γ-Al2O3,” Chinese J.

The reduction product of PNP was identified through HPLC and UV-vis.
The effect of cosubstrate concentration and PNP loading rate on PNP reduction and reduction product formation was also included.
Results and discussion Effects of different external cosubstrates on PNP reduction and PAP formation External cosubstrates play an important role in PNP reduction process.
Furthermore, the peaks with retention time of 6 min in HPLC of effluent were identified as PAP (data not shown), further demonstrating that PAP was the dominant product of PNP reduction in UASB.
However, no further degradation of PAP was observed in UASB, even when HRT was extended to 24 h or longer (data not shown), indicating that PAP could not be degraded under anaerobic condition.

Namely, the related data of the materials characters, cylinder compression bulging, and how they were associated with friction coefficient was obtained by the finite element method.
A number of testing methods have already been employed in an attempt to obtain quantitative data on the friction coefficient of workpiece/ die interface in metal processing.
Namely, the related data of the materials characters, cylinder compression bulging, and their relations with the friction coefficient was obtained by the finite element method.
PSE= FSE + pK , where FSE is the average squared error of the network for fitting the training data and pK is the complex penalty of the network, shown as the equation: N Q CPMK p p 2 2σ = , where CPM is the complex penalty multiplier, Q is a coefficients in the network, N is the number of training data to be used, and 2 pσ is a prior estimate of the model error variance.
Employing these analyzed data, the predictive model of billet properties and bulging deformation to the friction coefficient was constructed by using the abductive network. 2、Construction of the friction coefficient predicted model of cylindrical compression In this study, one hundred data sets were used as training data for abductive network to construct the predictive model of friction coefficient, as shown in Fig 4.

This paper takes automobile panel as the study object, and adopts non-contact 3-D scanner to obtain the point cloud data of the automobile panel for point cloud data sampling and noise reduction processing.
With the help of Geomagic Studio, the perfect polygon model and network can be built easily based on the scanned point cloud data, and be conversed to NURBS surface automatically [1-3]. 1 NURBS surface reconstruction theory 1.1 NURBS curve.
Fig.2 Point cloud data 2.2 Point cloud data processing.
Figure 3 is the surface comparison before and after noise reduction.
Before noise reduction After noise reduction Fig.3 Surface quality comparison before and after noise reduction processing 3 NURBS surface creation Sealing the processed point cloud data, and then selecting the surface boundary that needs to be edited; inputting control point amount for smoothening the boundary to make the boundary a spline curve finally. 3.1 Surface patch construction.