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Energy Flow Analysis and Energy Conservation and Emission Reduction in Sichuan Province, China LIU Wei*, FENG Xiyu, HUANG Guan, HUAN Hanqing Chengdu University of Information Technology, Chengdu 610225, China E-mail: weling9@163.com Keywords: energy flow analysis; energy conservation and emission reduction; scenario analysis; Sichuan Province Abstract: Based on the relative data and materials, the energy flowchart of Sichuan province in 2008 was drew at first, then the energy input, output, consumption, corresponding pollutants emission of Sichuan province from 2000 to 2008 were analysed simultaneously.
Finally, suggestions of energy conservation and emission reduction in Sichuan province are proposed.
How to optimize the regional energy flow and how to implement energy conservation and emission reduction has become the urgent work.
(3) Optimizing energy consumption structure, accelerating technology research and develop of energy conservation and emission reduction
(4) Strengthening the energy conservation and emission reduction of transportation sector and building sector.

The matrix of correlation data is setup, the elements of which are transformed to dimensionless parameters.
Calculation principle of shear strength reduction Shear strength reduction theory.
The grey relational analysis is a part of grey system theory, make up the deficiencies of using regression analysis, variance analysis and principal component analysis etc in mathematical statistic method which requires a great deal of original data, sample to obey, according to the similarity degree of sequence curves’ geometric shape to judge the factors of change (compare factors) and reference factors’ relevance (in relational express), the bigger the correlation is, the stronger the relevance between compared factor and the influencing factor is.
The specific steps first, define the factors influencing slope stability as comparative column Xi : Xi ={X1, X2, ∧ Xm}t, corresponding slope stability coefficient as a reference column Yi : Xi ={Y1, Y2, ∧ Ym}t, Then dimensionless on each sequence Data make sequence comparable, finally obtained grey relational degree with grey relational matrix [4].
Dimensionless of matrix Due to the large difference in the compare column X and the corresponding reference column Y , we can’t compare directly, the original Data is required to be processed to make comparable, that is make the matrix Dimensionless.

When analyzing complex data, it is rather difficult to reach expected effect by using merely WNN.
In initial sample data, some data is important, some is incomplete.
The selection of UCI data set and experimental design information are as follows Tab 1.
According to the above steps, each data set precedes 10 time experiment; the results take its average.
Table 1 The choice of UCI data set and experimental design information Result comparison is given in Tab 2.

The vibration amplitude data for the two conditions were compared to identify the influence of magnet on chatter reduction.
In the present paper a novel chatter control method has been tried for reduction of chatter in turning.
The software FFT module was used to obtain the frequency data from the time domain input.
For the remaining runs there were different degrees of reduction due to magnet application, however, the average of all the reduction is 49.44% or approximately 50%.
Experiment with magnet provided a maximum and average reduction of 87% and 50% respectively. 2.

The most prominent feature is selected by the method of nuclear, to transform the original sample data to the nonlinear characteristics of space, to achieve the final dimension reduction and data visualization.
Nonlinear manifold learning is an unsupervised and nonlinear data dimension reduction method.
A large nonlinear number data set a data image, whose attribute value in form of multidimensional data can be observed from different dimensions.
Fig. 3a is punctured sphere data set, which is a data set with a hollow ball shape.
Conclusions It is concluded that the nonlinear manifold feature extraction method is researched for data reduction which realizes the sampling data of original data dimension reduction.

It indicates that analyzing slope stability with strength reduction method is feasible.
Strength reduction method Calculation Principle.
is the reduction factor.
According to the geological exploration data, after ground leveling, the slope security level is secondary.
In Fig. 4, three curves mutate near the reduction factor of 2.0.

Experiments were carried out in order to obtain slabs without thickness reduction and with thickness reduction of about 20% and 40%.
One of then is the liquid core reduction [5].
The variations of temperature at different positions in the slabs and in the mould were measured by thermocouples coupled to a data acquisition system.
(a) slab without thickness reduction and (b) slab with 20% reduction. structure.
Variation of secondary arm spacing with the distance from metal/mould interface: (a) slab without reduction, (b) with a reduction of 20% and (c) with a reduction of 40%.

Porosity Defect Reduction Reduction of Gas Porosity.
Cycle Time Reduction.
Fig. 6 shows the property data of 226 alloy as a representative.
Strength and elongation data of the 226 alloy produced by SDC and GISS processes.
Other benefits such as the cycle time reduction of 16%, rejection reduction to about 1%, energy reduction of 18%, and die lube reduction of 73% have also been reported.

Band selection algorithm is most important in data dimension reduction of hyperspectral image.
The surface image data of the hundreds of spectral bands can be got by hyperspectral sensor to form a data cube.
Band selection methods are used first to do dimension reduction and filtration, and then data fusion is done to reduce the amount of data and improve the classification quality and precision.
Data fusion quality evaluation is the important step in data fusion.
The experimental data is cut out from the big image.

Analyzing the test data mean, standard deviation SD and coefficient of variation COV of casing Q125, some conclusions are obtained as follows: coefficient of variation of tensile strength is less than 1.00%, and the maximum coefficient of variation of yield strength is 1.78%, indicating experimental data is discrete to a lesser extent, with good agreement.
High Grade Steel Casing Material Strength Model at High Temperature Defining a strength function which is temperature related (MPa), where T is the temperature variable (℃), and 20℃≤T≤350℃, fixed value represents the test data at room temperature; represents the test data at high temperature.
Based on the above results, yield strength reduction factor and tensile strength reduction factor are estimated at each temperature, respectively, fitting all the data and material property model under the effect of temperature is established (Table 2), and the fitting curve has been shown in Figure 3 and Figure 4.
To test the accuracy of fitting formula, some statistical analysis have been done between the fitting formula and the experimental data, showing that the correlation coefficients of the expressions are all above 0.9 (Table 2), which indicate that the estimated value of the fitting formula and the corresponding actual data have been fitting well.
But during the casing design under high temperature, we need to select the casing under this temperature, at the situation of the absence of experimental data, a strength reduction factor bottom line at high temperature should be require.