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Then, a GCSC evaluation model is proposed based on Data Envelopment Analysis (DEA).
Data Envelopment Analysis (DEA) is an effective non-parametric statistic evaluation method.
Pollution reduction.
Experiment Data.
The experiment data are collected using Delphi method, which is show in the below Table 2.

Moreover, the reduction temperature of the NSR catalyst decrease first and then increase with the increase of calcination heating rate, and the reduction temperature of as-received NSR catalyst is the lowest as the reduction temperature is 6 °C/min, that is its reduction performance is the optimal.
The specific surface area and pore size distribution of the catalyst were performed by nitrogen adsorption data (NOVA2000, Kangta, USA).
Moreover, the catalyst with the calcination heating rates of 6 °C/min obtains the minimum reduction temperature (459.2 °C), indicating that the catalyst has the best low temperature reduction performance.
Towards optimal operation of sequential NOx storage and reduction and selective catalytic reduction, Appl.
NOx storage and reduction pathways on zirconia and titania functionalized binary and ternary oxides as NOx, storage and reduction (NSR) systems, Catal.

Arithmetic average, wavelet noise reduction and low-pass filter combination of data processing is used to overcome the very low SNR.
The computer obtains many sets of data of all points, and then it processes the data to obtain the temperature value.
After averaging, the data are processed using wavelet modulus maxima reconstruction noise reduction filter.
a) data of untiStakes intensity b) data after arithmetic average c) data after wavelet de-noising Fig 3 Data processing results After the arithmetic average and wavelet noise reduction, we obtain the effective sampling data of each point.
System testing data is basically consistent with the reference data.

With slenderness ratio changing, the variations of experimental data such as axial strain, lateral deflection at mid-height, ultimate bearing capacity, and peak strain were comparatively analyzed.
The ultimate load reduction factor is obtained by regression analysis based on the experimental results of Larch Glulam short columns.
Comparing curves in Fig. 3 with data in Table 2, the conclusions can be made as follows: (1) The trend of the lateral and vertical strain of each specimen was almost the same, so the failure process is divided into three phases [11,13]: elastic phase, plastic phase and failure phase.
Combining the curves in Fig. 4 and the data in Table 2, the conclusions can be made as follows: (1) As can be seen from a) and b) in Fig. 4: 1) Small lateral deflection appears at the preliminary stage of loading, which is caused by physical eccentricity and defects of columns; 2) At the initial loading, specimens suffer mainly compression stress, and lateral deflection at mid-height is almost zero.
The formula of the ultimate load reduction factor for long Larch Glulam columns is obtained by nonlinear fitting, which is: .

Carbon Nanotubes-Doped Glassy Carbon Ceramic Composite Electrode and Its Electrocatalytic Reduction of Nitrite Danzi Sun1, a 1Shandong Provincial Key Laboratory of Fluorine Chemistry and Chemical Materials, School of Chemistry and Chemical Engineering, University of Jinan, China adanzisun@yahoo.com.cn Keywords: carbon nanotubes, glassy carbon ceramic composite electrode, electrocatalytic reduction, nitrite.
The electrode thus prepared exhibits electrocatalytic behavior to the reduction of nitrite and facilitates the detection of nitrite at an applied potential of 0.0 V.
A CHI 660 electrochemical workstation connected to a personal computer was used to control the electrochemical measurements and collect the data.
Electrocatalytic reduction of nitrite at the MWNTs-doped GCCE.
The resulted modified electrode shows catalytic property toward the reduction of nitrite and could be used as amperometric nitrite sensor.

Due to the possibility to collect a huge amount of production-relevant data [8], it is possible to use methods of data mining to obtain more insight into assembly processes and subsequently to identify top consumers and focus stations for example for energy saving projects.
This new data flow enables real-time tracking of energy consumption as well as long-term analysis and advanced data mining for e.g. quality improvements.
The data for the average and standard deviation of this program is shown in Table 2.
By using power measuring instruments, it is very important to generate a large number of data to confirm data accuracy, since power measurement has significant uncertainties.
This data mining approach aims to improve product and process quality.

There can be many reductions of.
So, means all the reductions of.
Intersection set of any reduction is a sole kernel.
Thus the minimum attribute reduction is obtained.
The implementation tool is the data mining software WEKA.

Based on a user-defined range of parameters (module, helix angle, etc.) the software presents a large number of possible solutions covering the full parameter space and presents a list with many data (geometry, safeties, characteristics as sliding, losses, price, transmission error) including a qualification of every solution based on required characteristics.
KISSsys windows of the Gearbox-Variant-Generator User Interface: Provides for input of required torque and speed Sizing: Includes the various sizing functions and functions for user data input Table of Variants: List of the different reducers, of which any solution can be selected Solutions: 3D display of main results for the 90 reducer variants calculated Optimization of a Compact Micro-Planetary-Gearbox The tool was used in a project for a Chinese company, producing small compact gear reducers with a high speed reduction.
The design data for one of the required gearbox was: Output speed 31 rpm and 16 Nm torque.
Therefore, the required total reduction is i = 160.
The design data for the gearbox was: Input speed 15 rpm and output power 3.2 MW.

Rough set theory has powerful data reduction capability, this paper rough set theory to model the stock time series data, reduction, rule extraction, study the ups and downs of the relationship between the stock price, the use of advanced data mining techniques to dig out price linkage between stock association rules, has a very important significance.
In these data, a large part of the data type of the sequence of the presentation time (time series).
Clearly, data mining is such a tool.
Rough Set Theory has a good effect in processing the large amount of data and eliminate redundant information, and it is widely used in data mining, data preprocessing (data reduction), rule generation.
In addition, time series data mining, too much energy focused on the prediction of future data trends.

The power structure optimization, energy conservation and emission reduction will become the two major focus.
Then, is hydropower of Guizhou will not be able to make new contributions to emissions reductions?
Therefore, centralized data exchanges have been adopted to resolve the problems of varied software and hardware compatibility, resource sharing and data security.
With the electric power data network as basic network and the satellite communication as standby network, the redundancy requirement of different networks has been satisfied.
Acting as a communication bridge, the data exchange platform has been developed to connect automation systems in centralized control center.