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When the thickness reduction was 0 % and 20 %, the microstructure consists of GB, LB and a little AF, whereas as the thickness reduction increase to 40 %, large grain size of LB and GB disappear, and only AF and M/A remained.
The raw EBSD data were post-processed with software Channel 5.
After the raw crystallographic data were being post-processed with software Channel 5, the difference of the microstructure under different DT is obvious, as shown in Fig. 5.
M/A AF LB (a1) (a2) GB LB (b1) (b2) M/A AF (c1) (c2) Fig. 7 Effect of reduction on microstructure (DT=770 ℃) (a1), (b1), (c1): microstructure by OM; (a2), (b2), (c2): Euler maps of the microstructure, (a1) and (a2): reduction=0 %; (b1) and (b2): reduction=20 %; (c1) and (c2): reduction=40 % Fig. 8 The effect of reduction on hardness of the microstructure (a) (b) Fig. 9 Effect of reduction on (a) grain misorientation, and (b) effective grain size Fig. 10(a) and ((b) shows the effect of thickness reduction on transformation kinetics and B, respectively.
(3) DT is not the lower the better, reduction is not the greater the better, there are the best value ranges for the two parameter.

Very few of them mentioned catalytic reduction.
Finally, the CTL signal was transferred into visible data shown on the computer.
A simple chemical reduction method was utilized in the synthesis of Al2O3/Pt particles.
Two experiments were carried out to investigate the effect of catalyst on catalytic reduction of benzene.
Li, Effect of reduction method on the surface states of γ-Al2O3/Pt, J.

But it is lower than the theoretical value by 31%.The data Energy consumption monitoring platform is higher than the electricity supply bureau by 12.68% .The data of Sub-item measured close to the calculated value.
The data provided by administration of power supply.
(2) Electricity supply bureau collect data at high voltage side and monitoring and control system collect data at low voltage side
But it is lower than the theoretical value by 31%.The data Energy consumption monitoring platform is higher than the electricity supply bureau by 12.68% .The data of Sub-item measured close to the calculated value.
It should ensure that we provide accurate data to Operations Management Department

Data Fusion Model of BP Neural Network The BP neural network sensor data fusion and can greatly reduce the feature dimension data transmission, data fusion to improve the efficiency of a wireless network.
BP neural network algorithm uses a single hidden layer of the data collected by wireless sensor networks for data fusion.
After the data fusion algorithm BP neural network applications for each cluster, the neural network models for each cluster, the nodes in the cluster to collect data using the input neurons function to preprocess the data collected and handled well data to the cluster head node.
Eigen value data fusion is much less than the amount of data collected, thereby reducing the amount of data transmitted, reducing power consumption and prolong the life of the wireless sensor network.
Improved model incorporates the processed data is much less than the amount of data collected by the sensor nodes before treatment, thereby reducing the energy consumed during data transmission achieving the purpose extend network lifetime.

Total Quality Cost Reduction in Head Stack Assembling Wari Dansakuncharoenkit, Damrong Thawesaengskulthai Department of Industrial Engineering, Faculty of Engineering, Chulalongkorn University Bangkok, Thailand dwari@hotmail.com, damrong.t@chula.ac.th Keywords- Quality Cost, Head Stack Assembly, Cost Reduction Abstract— The PAF model, which is one of techniques about the cost of quality (CoQ), has been introduced to the head stack assembly process in the hard disk drive industry.
Therefore, cost reduction can be arranged and set as measurable goals in terms of the effectiveness of the quality system [1].
Cost of quality before improvement Data before improvement Percentage of cost of quality per cost of finished goods PC AC FC COQ Product A 0.74% 0.32% 0.39% 1.45% Product B 0.48% 0.26% 0.37% 1.11% Identificaition for further improvements.
According to data analysis of the cost of quality, failure costs have been considered to find the opportunities in cost reduction.
If the improvement plans could be more effective, the rate of cost reduction will be more significant.

Sonmez[2 ]use a new empirical equations to estimate the strength of rock masses of varying hardness with the data of five groups of slope failures and four sets of uniaxial compressive strength.
Strength reduction method is adopted to reduce the strength parameter for stability safety factor.
Key control point displacement Variation curve with the strength reduction factor k Reducing the strength parameter and with the reduction factor k gradually.
key control point displacement of slope surface Control point elevation Strength reduction factor K [mm] 1.0 1.2 1.4 1.6 1.8 655.5m displacement 5.31 5.29 5.27 5.30 5.68 678.6m displacement 4.22 4.30 4.61 5.76 9.65 693.6m displacement 3.49 3.68 4.29 5.92 10.08 708.6m displacement 2.48 2.72 3.42 5.14 9.14 723.6m displacement 1.84 2.10 2.81 4.43 8.10 738.6m displacement 1.18 1.34 1.80 3.00 6.05 Table 5 is the data of key control point displacement of slope surface with the different location elevation and the figure6 is the relative Variation curve with the strength reduction factor k. according to the fig6, the change of displacement in the different elevation is basically gentle in the range of strength reduction factor k 1 to 1.8.
Strength reduction method is adopted to reduce the strength parameter for stability safety factor.

As shown in the survey data issued at the C40 Large Cities Climate Summit on May 19, 2009, 80% of global greenhouse gas emissions come from urban areas occupying only 2% of the earth's surface[1].
Data Source and Methodology As it is impossible directly obtain the original data of the survey report on the environmental awareness of all the cities in China, the relevant papers included in CNKI between 2007 and 2009 have been taken as the data source, and the urban residents have been selected as the research objects to carry out analysis based on the percentage data publicized in the survey report.
For example, the average scores in 2 relevant survey reports have been selected for data of Yunnan, Lanzhou and Dalian, and that of data in 3 relevant survey report have been selected as data for Shanghai, while that of data in 3 urban survey data in Hohhot, Baotou and Ordos has been adopted as the data source for Inner Mongolia Autonomous Region.
Such data can represent the current situations of the level of environmental awareness of urban residents in the eastern, central and western areas of China in recent 3 years to a certain extent [3-6].
All the data came from "China City Statistics Yearbook 2008", and data processing was finished by SPSS, while the principal component analysis was adopted for analysis [9].

According to the IPCC WGII Fourth Assessment Report, more than 89% of observational data series and studies are consistent with the greenhouse gas change, which is produced from human activities, as a response to global warming.
According IPCC WGII Fourth Assessment Report, more than 89% of observational data series and studies are consistent with the change of greenhouse gas, which is produced from human activities, as a response to global warming [1].
These data is useful for calculating the willingness to pay for GHG emission.
The data from Taiwan Industrial Development Bureau was applied for calculating the CO2 equivalent of all GHG.
The calculating process demonstrated below: to identify the emission sources, to collect the activity data, to calculate the GHG emission amount, and to calculate the GHG emission levels of each person.

Spatiotemporal system can be effectively analyzed through separating data into spatial and temporal or using data as a whole entity.
In either way, we can conclude that both the spatial data and the temporal data will affect the final result (the system).
In Section 3 and 4, spatial data and temporal data representation are discussed.
The nature of time series data includes: large in data size, high dimensionality and necessary to update continuously.
In this method, a rate of m/n is used, where m is the length of a time series P and n is the dimension after dimensionality reduction.

As the data collected in the wild are often subjected to the interference of noise, is not conducive to the analysis and interpretation of seismic data, therefore removing noise is the premise of data interpretation.
Various forms of the existence of noise can make useful signal completely submerged in the noise. resulting in signal to noise ratio seriously reduced, which caused much inconvenience to the latter part of the data processing and analysis.
The Fourier transform is noise reduction processing method, in handling the non-stationary signal and transient signal, Fourier transform will ignore transient information.
The results show that the Fourier analysis noise reduction are not good original, especially in the waveform mutations place, the denoising waveform has some distortion.
But wavelet soft threshold denoising waveform is smooth, and it can better reconstruction original waveform, so it is a useful tool of the seismic data processing.