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The planning practice has a significant effect on runoff reduction.
The effect of runoff reduction for rainwater utilization measures is analysis.
Fig. 1 Overview of processes incorporated in the planning runoff estimation Mountain areas Overland flow (inflow hydrograph) Flow routing through drainage system Initial loss-constant continuing loss rate model Rainfall Overland flow GIS data Instantaneous unit hydrograph (IUH) SWMM Urban areas Initial loss-constant continuing loss rate model Rainfall GIS data Instantaneous unit hydrograph (IUH) Case Study Area [6].
According to this basis data, Futian River watershed stormwater control and utilization planning is carried out.
Effect of Runoff Reduction.

While fig.2b show the reduction efficiency for respective pH.
Typical graph of (a: absorbance at varying pH, b: reduction efficiency of silver ion).
Reduction efficiency at every pH can be seen at fig. 2b, as receive pH of mixture was pH 5, it will be used as reference to measure the efficiency of silver ion reduction for pH 6, 7, 8, 9 and 10.
The reduction efficiency starts to increase at pH 6 until pH 8 and almost constant at pH 9 to 10.
The XRD pattern of synthesized crystalline silver nanoparticle is shown in Fig. 3c the peaks are indexed as (111), (200), (220) and (311) plans of FCC silver by comparing with JCPDS data.

Because best estimators are needed in uncertainty evaluation[3], Kolmogorov and Grubbs criterion, at 0.95 confidence level, are used in the data analyze[4].
It’s obviously that tensile stress, both yield stress and broken stress, of specimens which under torsional pre-strain becomes greater, than those ones which are not in torsion or the twisting angle equals to zero, as while as percent elongation and percent reduction area become smaller.
(3) related standard uncertainty from data acquisition[7] Type B related uncertainty of a valid data acquisition computer is given.
Conclusion (1) Tensile stress, both yield stress and broken stress, of specimens which under torsional pre-strain becomes greater, than those ones which are not in torsion or the twisting angle equals to zero, as while as percent elongation and percent reduction area become smaller
JJF1103-2003, Evaluation for Computerized Data Acquisition Systems of Universal Testing Machines[S], Beijing: China Metrology publishing House, 2003,9.

Lack of new breakthrough in technical theory, researchers mainly focused on intelligent control [3-6], which relied on large production data and the application is limited to a certain degree.
This kind of methods, which applies self-learning function to control rolling process, relies on large production data and analytic models have not been established.
The value of Φ can be calculated by Eq. 4 according to the data of different products collected from production site.
Test data are listed in Table 3.
Table 3 Test data when target crown adjustment value is -0. 020mm (steel grade Q235B, inlet thickness 30. 230mm, target thickness 3.500mm) Stand No. 1 2 3 4 5 6 outlet thickness after adjusting rolling regulations h1[mm] 18.266 10.585 6.691 4.956 4.074 3.536 rolling force P[KN] 18842 17774 16378 12036 9420 7207 variation of rolling force [KN] -834 -723 -656 variation of crown [mm] -0.019 After adjusting the reduction of each stand by the combined gauge and shape control scheme, the variation of strip crown of last stand is basically eliminated while exit gauge remains constant.

Gyenge’s group[13] studied influence of surfactants on the electro-reduction of O2 to H2O2, in which they found that cationic surfactant increased the rate of O2 reduction to H2O2 and reduced the O2 diffusion coefficient towards the electrode surface.
Based on these datas, the energy consumption for the production of 1 ton water with 5mg/L H2O2 was calculated (Table.1).
As for the data of No.1-5 in Table.1, as flow rate increased, the produced H2O2 concentration decreased, while the current efficency increased more dramatically due to the further reduction of H2O2 was avoided by leaving the cell quickly.
For the data of No. 6-10, the concentration of H2O2 increased as the aeration rate, in other words, the concentration of O2 was increased.
For the data of No. 11-15, as the current density increased, both of the 2-electron-reduction and 4-electron-reduction of O2 were inhanced, resulted in the concentration of H2O2 increased while the current effiency decreased more markedly.

Results and Discussion Degassing and reduction of unalloyed steels.
Dilatometer, 10 K/min Reduction processes in prealloyed steels.
However, if taking extrapolated data from [26] for diffusion of Cr in ferrite, it can be calculated that the diffusion distance for Cr, assuming 2 min at 640°C, is about 50 nm, sufficient to convert a 6-7 nm thick iron oxide layer e.g. into chromite.
Reduction in mixed powder compacts.
The consequence is that reduction of the Fe surfaces is hardly visible externally but the reduction peaks are shifted to much higher temperatures, those required for reduction of the alloy elements.

The macromeritic tungsten powder was prepared by wet hydrogen reduction at medium temperature; the coarse powder of Ammonium paratungstate powder (APT) was used as raw material.
The effects of kinds, contents of alkali metal salts and the temperature in the reduction were studied.
The traditional method has high energy and cost. [2] Feng Zheng studied the effect of some metal elements on particle size of tungsten powder , it shows that the base metal elements of Li,Na and K can increase the partiele size of tungsten Powder. [3] In this paper, macromeritic tungsten powder was prepared by wet hydrogen reduction at medium temperature; the coarse powders of Ammonium paratungstate powder (APT) were used as raw material.
That the single particle grew and combined in proton reduction made the agglomerates shrink and the second particle firmly combine.
a b Fig. 2 Optical microscopy photograph and the SEM image of single particle tungsten powder a- optical microscopy photograph，b- the SEM image Table 1 lists the distribution data of the particle size accumulation.

Feature selection is to retain an effective reduction of information, the purpose is to remove redundant features, the knowledge of fault diagnosis based on attributes of attribute reduction, attribute reduction methods currently there are many, including rough sets, information entropy, Fuzzy identification and many scholars have studied and applied Rough Entropy[1] ,Attribute reduction rules[2] ,Chi-square statistics[3] and so on.
But also for machine learning, the need for a large variety of operating conditions of the data sample, but in reality, very few samples of mechanical failure, lack of adequate learning machine, in fault diagnosis and is not accurately identify the fault.
This paper based on information entropy method is applied to attribute reduction in decision table is established the method for each condition attribute values in accordance with the credibility to carry out after the information entropy, information entropy largest The redundant features; then the same for other faults, at last, the results of the outcome even if the attribute reduction, and finally the entropy as a feature vector; enter the SVM for fault identification.
Example research All data used in this article in the rotor test rig Bentley simulate a variety of turbine rotor failure of the data, the rotor speed is 3500n/min, the sampling frequency is 10000, the decision table in the value of the property as a training sample, Samples in the prediction as a prediction, classification operation.
[2] Jiayang Wang, Chao Liao, Research on the Attribute Reduction and Rule Acquisition of Time-Series Data Based on Rough Entropy [J] , Journal of Hunan University(Natural Sciences) 2005, 32(4),P.112-116(in Chinese) [3] Ming Yang, Ping Yang, Zhihui Sun, An Attribute Reduetion Algorithm Basedon Rules [J], Computer Science, 2003,30(6),P.122-125(in Chinese) [4] Lixin Wei, Chongzhao Han,A Novel Attribute Reduction Method Using Chi Square Statistics [J], Computer Simulation, 2007,24（5),P.72-74,106(in Chinese) [5] Runqiang Yin, Xiaokun Huan, Zhenliang Zhang, Decision Algorithm for Finding Reduct Based on Inter-information of Rough Set[J], Journal of Yunnan Nationalities University(Natural Sciences Edition, 2006,15(1),P.12-14(in Chinese) [6] Dongbo Zhang, Huixian Huang, Yaonan Wang, Feature selection neural network ensemble based on rough sets Reducts[J], Control and Decision, 2010,25(3),P.371-377(in Chinese) [7] Zhonghe Han, Xiaoxun Zhu, Selection of Training Sample Length in Support

Statistical data on doping and thickness of 25 µm to 40 µm layer growth show results similar to standard epilayer growth (5-15 µm).
Improvements in thickness and doping uniformity as well as the reduction of epitaxial defects has boosted the quality of 25 µm to 40 µm thick epilayers.
Results and Discussion Statistical product data of 25 to 40 µm thick layers show results similar to standard (5-15 µm) epilayer growth.
Absolute values of the maximum deviation from the target of each individual data point measured per wafer is shown for thickness and doping in Fig.2.
Progress in thick epi growth was demonstrated by improvements of thickness and doping uniformity as well as epitaxy defect reduction.

Reverse engineering mainly includes data collection, data processing, model reconstruction.
The preprocess of the point cloud mainly includes eliminating abnormal data, data sampling, data smoothing, filtering and noise reduction, multi-view cloud split, point cloud data filtering, redundant data merging, feature extraction, data compaction, point cloud blocking, and so on.
Such much data is a test for data operation.
The next data encapsulation can arrange the point cloud data uniformly.
The new data file can be opened in Ug.