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When the number of training samples are large,solvingwill take more time and space.However,increase remembrance algorithm can avoid this problem,its general description is: When there are n samples in training data,given,,,,equation(9),(10) can be written as: (11) (12) (13) When the number of samples in training data increases two,that is to include sample into original training data w,we have: (14) (15) (16) Here,,, is constituted by plus 1 column and 1 row,that is: (17) From equation(14) to equation(16)，we can know that,if we know,we can solve the value of ,,.In order not to calculatedirectly,but calculate it
, it doesn't insert all samples as support vector at a time, but increases two training samples in turn by the entropy biggest principle based on the initial training sample data.
Experiment and Result Analysis The origin and selection of sample.These research data of this article are from the CSMAR database,this article chooses 167 listed company of A stock market between 2002 and 2007 as research sample randomly from Shanghai and Shenzhen two stock markets, rejects finance class samples and samples whose data are not entire, and takes these left 152 samples as the final research sample.Thereinto,50 samples are test samples, other 102 samples are training sample.
Preprocessing of target data.This article uses traditional factor analysis method and neighbourhood rough sets reduction method to preprocessing pre-election indexes respectively.
It uses reduction algorithm based on neighborhood rough set to select targets on the basis of standardizing the 31 candidate target.In order to compare with the classical rough set method, we introduce CART classification learning algorithms in the experiment,the specific steps are:for the classical rough set attribute reduction,first use Equal frequency binning algorithm of Rosetta software to discretize data,then use Johnsonreducer algorithm to attribute reduce discretized data;for neighborhood rough set attribute reduction,use forward greedy numerical attribute reduction algorithm of MATLAB 7.0 to attribute reduce data.Because the attribute reduction number is influenced by the size of neighbourhood δ, this article use 0.1 as steps of δ, δ ranges from 0.1 to 1, and the results shows that, whenδ = 0.9,the classification precision is optimal(get the highest classification precision by the least number of attributes).Table 2 shows the attribute reduction circumstances of the two methods

Relevant Technical Data on Mining Areas Control System Control system in coal mine areas has lot of technical design, combustion parameters in these complex data must be focus on mostly.
Fig.1 PIC control diagram PIC control function is to transmit the combustion data in mining area control system to control instrument, make the terminal to control the whole system by adjusting the inverter and safety valve, and process the dashboard data quickly, which makes huge promotion to the traditional technology.
In the mean time, it also has a lot of convenience in operation, for the function keys on the operation panel can be customized and able to timely receiving data, and changing parameter.
They include gas transmission, system data automatic reset, automatic ignition, data acquisition and transmission to terminal, fault alarm, production complete system self-test and other related functions.
Applying the PIC system in the daily control of coal mining area combustion, it appears fast data operation speed, high precision, accurate and reliable, adaptability for coping with the complicated situation adeptly, and even convenience for dealing with control difficulty in the operating system.

UV-visible spectra showing gradual reduction of 4-NP over different catalyst dose; (a) 0.4 g/L and (d) 1.6 g/L.
The rate of reduction of p-nitrophenol was found to be directly propositional to the dose of the composite.
The peak at 400 nm gradually decreased in the reduction process and finally disappeared at its completion.
Das, Journal of Chemical & Engineering Data, 58 (2013) 3477-3488
Liu, Journal of Chemical & Engineering Data, 56 (2010) 138-141

The present paper summarizes the data obtained during the about 10-year period of such operations and contains the main conclusions based on these data.
Data about the microflora of the wetland are shown in Table 1.
Content of radioactive elements and heavy metals in different plant species from the constructed wetland Typha latifolia Phragmites australis Scirpus lacustris Elements I II I II I II U, mg/kg 28 - 95 ND 12 - 82 ND 12 - 59 ND Ra-226, Bq/kg 35 - 145 ND 35 - 125 ND 23 - 82 ND Cu, mg/kg 55 - 194 7 41 - 140 5 23 - 71 3 Zn, mg/kg 32 - 225 5 64 - 203 7 44 - 114 3 Cd, mg/kg 5 - 23 ND 5 - 18 ND 3 - 10 ND Ni, mg/kg 21 - 100 3 18 - 79 3 14 - 59 3 Co, mg/kg 15 - 71 2 15 - 105 3 10 - 51 2 Pb, mg/kg 12 - 64 5 8 - 54 5 9 - 32 7 Mn, mg/kg 91 - 325 10 44 - 262 8 41 - 154 8 As, mg/kg 6 - 68 3 10 - 59 3 6 - 28 2 Notes: I - Data about plant specimens grown in the wetland; II - Data about plant specimens grown in a wetland non-polluted by radioactive elements and heavy metals.
The microbial sulphate reduction was a function of the concentration of organic monomers dissolved in the waters.
Under such conditions the microbial sulphate reduction still proceeded, although at much lower rates.

However, analysis of the base current of the base-emitter diode shows that the degradation of the passivation layer could also influence the reduction of the current gain.
The BJTs with WE equal to 30 and 25 µm show a reduction of around 15% during the first 10 hours, while the BJTs with WE equal to 10 and 6 µm show a smaller reduction during the first three hours and one hour respectively.
Only the device with WE equal to 6 µm shows a reduction between 40 and 60 hours.
This behavior, i.e., the reduction of the collector current, has been detected in all the devices summarized in Fig. 1.
However, the value extrapolated from the measured data is around 1.5, suggesting that the degradation of the lifetime in the base region might be only one of the causes for the degradation of the current gain.

Higher rolling reduction led to a stronger cube texture.
This material was cold rolled to 90% and 95% reduction in thickness.
Pole figures as presented in Fig. 1 were calculated from the orientation data.
In the 90% reduction sample the volume fraction of the cube component was 5.6% after recrystallization, while in the 95% reduction sample it increased to 14.1%.
a) maximum density: 2.5 b) maximum density: 5.7 c) maximum density: 5.3 d) maximum density: 2.6 e) maximum density: 23.6 f) maximum density: 5.1 Fig.1 {111} Pole figures of different recrystallized samples: a) sample 1; 90% reduction, b) sample 1; 95% reduction, c) sample 2; 95% reduction, d) sample 3; 90% reduction, e) sample 4; 90% reduction, f) sample 5; 90% reduction.

China has made carbon reduction commitments ​​in the Copenhagen Climate Conference, which is that by 2020, China's carbon intensity is 40-45% lower than 2005.
Basic data.
Using the total energy consumption amount data of the third industries in China's in 2004-2011 as the basic data of the forecast, the data are from the 《China Energy Statistical Yearbook 2009-2012》.
Table 3 shows the CO2 emission and carbon intensity prediction in 2015 and 2020 and analysis reveals that China's current energy consumption structure cannot achieve the 2020 carbon reduction target.
Therefore, we should optimize China's energy structure from two aspects of energy saving and emission reduction.

This article presents a procedure for real data based assessment of industrial cleaning equipment.
The necessary data for determination of the OEE factors and energy consumption values ​​are recorded by a measuring system.
The continuously collected data for the cleaning process are shown in Figure 1.
Figure 3: Energy consumption and operating state data of an automotive component cleaning operation Linking Manufacturing Process Data and Energy Consumption Data for the Synergetic Increase of Cleaning Equipment The obtained OEE and energy consumption information based on generated data are then used to support continuous improvement processes (CIP) to define energy efficiency and productivity-enhancing measures.
Thus, by virtually the same data collection effort, the effect of operational optimization measures based on analysis has significantly increased.

In addition, a data acquisition system is used to collect experimental parameters, record data in real time, and perform data consolidation.
Through the use of a Data Acquisition (DAQ) card, load data can be transmitted to each sensing component.
Instead, the data for each axis and the power source for the hydraulic motor are entered separately based on the design of the external and internal axes.
In addition, the torque value detected by torque transducers between each axis can be sent back to the computer through the DAQ to aid in data recording.
When a load is applied, motor stall often occurs, and limited data are available.

The results show that in the upshifting process, active reduction of engine oil control can improve vehicle ride comfort, but the increase by reducing fuel consumption and reduce the reducing time will lead to increase shift smoothness and increase upshift power loss.
Maintain the fuel reduction of the engine in the process of upshift constant (reduced to 10% of the current amount of oil) when researching the influenceof reduction oil time, according to speed difference between of the both ends of the shifting clutch to decide whether supply the normal oil.Because the engine speed corresponding toin each gear shift points is different i.e. the energy released in inertia phase stage is different, it should be studied separately for each stall.
Fig. 3 Simulation curve of 3-4gear upshiftwhen t2 = 0s Simulation select timeof reduce oil t2 = 0s, 0.25s, 0.5s 0-4 gear upshift research.Through the analysis of simulation data shows the influence of reduction oil time to the performance of vehicle as follows:1.
Influence of the amount of oil reduction in upshift process.When study the influence of the amount of oil reduction, maintain the oil reduction time constant during upshift (T2 = 0.5s), according to speed difference between of the both ends of the shifting clutch to decide whether supply the normal oil.
Reduction oil is selected from the current amount of oil 0%, 20%comparison.