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The developed model is shown to predict texture evolutions in good agreement with experimental data. 1.
We focus on the recrystallization of cold rolled copper after two levels of deformation - 70% and 90% rolling reductions.
From the experimental point of view, some recent and accurate analysis of the experimental data clearly indicates that recrystallization in polycrystalline copper takes place by the following mechanisms [5,6]: - The so-called strain induced boundary migration (SIBM) in recovered grains having orientations close to the cube one, - Some uniform intergranular nucleation (IN) in all other rolling orientations, presumably at grain boundaries, where the gradient of orientation is usually quite high, - The subsequent growth of nuclei or recovered grains driven by the SE gradient.
In the case of 90% reduction, the deformation texture is very similar in terms of main components but is more intense (Figs. 1a and 2a).
In order to incorporate a proper boundary misorientation distribution, the EBSD data and a Monte-Carlo algorithm were used [7]

To balance economic development, energy saving as well as pollutants reduction, Shandong is faced with huge pressure.
REPI of the entire country or region is the weighted average one of saving index of various resources or pollution reduction index [13, 14].
With these data and the GDP data in Table 1, the saving indexes of energy, SO2, soot and industrial dust are available by Eq. 1.
The reduction percentage of industrial dust, soot and SO2 are 78%, 54% and 31% respectively.
Therefore, to balance economic development, energy saving as well as air pollutants reduction, Shandong is faced with huge pressure.

The reduction degree from concentrate to the metallic part is 80 - 91% for iron and 95 - 98% for copper.
The reduction temperature was maintained at about 1150 °C [12-14].
The degree of recovery from the concentrate to the metal part according to the experimental data of the conducted exploratory studies is up to 91% for iron, up to 93% for copper.
Sycheva, Solidphase reduction of ferrous oxides by carbon, Litie i metallurgiya, 2 (2012) 11-16
Vandar'ev, Features of ferrous reduction by coal and graphitized carbon materials, 3 (2002) 6-15

Head Detection by the Adaboost Algorithm for Head Pose Estimation Xiangyang Liu, Shaosong Zhu, Suqing Wu and Zhiwei Shen College of Science, Hohai University, Nanjing 210098, China Liuxy@hhu.edu.cn Keywords: Head detection, Pose estimation, Adaboost, Dimensionality reduction Abstract.
Secondly, we present a dimensionality reduction method to process the head patch.
Secondly, we present a dimensionality reduction method to process the head patch.
Head Pose Estimation For head pose estimation, we seek for a low-dimensional embedding of the original high-dimensional image data to provide intra-class compactness and inter-class separability [8].

These models allow fatigue data of the unimpacted and impacted composites under variable amplitude loading to be correlated with constant amplitude data of the unimpacted composites. 1.
The results are well in conformance with the experiments: therefore, Eq. (5) allows fatigue data of the impacted composites under variable loading to be correlated with constant amplitude data of the impacted composites.
(5), however, the information about the constant amplitude data of the impacted composites should be known.
It is also worthy to note that Eq. (7) enables us fatigue data under variable loading for the unimpacted composites as well as for the impacted composites to be correlated with constant amplitude data for the unimpacted composites.
These models enable us fatigue data of the unimpacted and impacted composites under variable amplitude loading to be correlated with constant amplitude data of the unimpacted composites.

In accordance with the data from medical reference literature, the energy dose flowing through a given body part is the reason behind the malfunction or injury of the body part affected due to the interaction with the machine [1].
The so far applied methods of the reduction of vibrations rendered no satisfactory results.
Significant reduction of power in subsequent points of reduction related to human’s subsystem is due to the introduced, innovative vibroisolation WoSSO.
In the point of reduction: handles-palms (H-P) mean value of power ranged from 0,0063 to 0,042 W, while in the other reduction point: forearm-elbow (F-E) from 0,0015 to 0,0036 W.
When we know mean power values in two points of reduction that are connected with the spatial vibroisolation WoSSO, we can determine the energy efficiency of the reduction in the energy flow in this system.

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.

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

The corresponding data are collected in Table 1.
On cathodic scanning, compound 1 exhibits one reversible reduction wave at -0.66 V versus Calomel and the second irreversible reduction wave at -0.89 V.
Compounds 5 also exhibit two reduction waves.
The optical band gap (Eog) levels, the lowest unoccupied molecular orbital (LUMO) and the highest occupied molecular orbital (HOMO) of these compounds were determined from their electrochemical data and their absorption data.
TABLE 1 OPTICAL AND ELECTROCHEMICAL PROPERTIES AND BAND GAP DATA OF COMPOUND 1 AND 2 Compound λonset(nm) E0g(eV) LUMO(eV) Ered(eV) HOMO(eV) 1 5 543.93 581.40 2.28 2.13 -3.74 -3.80 -0.66 -0.60 -5.93 -6.02 Eog= 1240/λabs onset

The measured data of standard in surface shape measurement were used for calibration, and the obtained value was confirmed to cause noise reduction and improvement of holographic reconstructed images in digital holography.
Accurate phase shift is also confirmed by reduction of noises in the surface shape image.
Line profile of each image, which was along line AB in Fig. 3(b), was measured and compared the data measured by the laser measuring microscope as shown in Fig. 4(a) and (b).
Reference lines Aa and bB were determined by the average of 25 and 20 data, respectively, chosen in the region where the variation of data was relatively small.
Yoshimura, Fast acquisition system for digital holograms and image processing for three-dimensional display with data manipulation, Appl.