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On this basis, the use of powerful GIS software for data acquisition and build library, display and editing, storage and management, spatial analysis, using the built-in VBA (Visual Basic for Application) platform for secondary development, customization and extension functions and development of landslide stability evaluation module, so as to realize disaster prediction, avoid people's life and property damage, the aftershocks, rainfall, and other areas more frequently has important practical significance.
Data and methods The research methods.Main research train of thought: the SRTM DEM data preprocessing, the spatial analysis function of ArcGIS to extract the elevation gradient, slope direction and derived data, such as basic attribute database is established.
Major stage of landslide stability analysis and comprehensive safety [D].Disaster prevention and reduction engineering and protective engineering, Chongqing traffic university, 2010, 1-80.

· The chip clock is up to 12MHz, speedy data processing.
Sometimes the image encoding compression technique is also used which can greatly reduce the amount of information, in order to achieve the reduction of the requirements of the computer storage capacity and the transmission channels.
Of course, the data and processes of the hardware of the processor are managed by the computer [5].
“Data image processing,”.
“The software achievement of data controlling functions of electricity machines ,”Shan Xi Machinery, pp. 14-15，Feb 2008 (references)

Yanjun Qi (2009) also constructed such an evaluation index system referring to the energy-saving and emission-reduction experience in the petrochemical enterprise.
However, due to different data sources, different indexes should have different methods to determine their reference values.
In this paper, the reference value is obtained by analysis and compare of the average evaluation reference data of the most advanced chemical enterprises in 2009 and the evaluation standard for clean production.
Table 1 listed the evaluation reference data.
The data used in this paper was referring to its annual report in 2009, which is shown in Table 3.

Experiments In the new experiments, a polycrystalline block of 99.995 % pure copper with a grain size of 35 µ was cold-rolled to a reduction in thickness of 92 %.
An example of the data and analysis from one of the grains is shown in Fig. 1.
The grain number Four data shown in Fig. 2 was anomalous in that there did not appear to be a rate change when the temperature was raised from 150° C to 160° C and all growth ceased before the temperature was lowered back to 150° C.
To analyze the data for all eight grains in more detail, the various growth regimes were fitted to an equation of the form r = A t−τ( ) 1−α( ) (3) where r is the spherical equivalent radius, t is the annealing time, τ is the nucleation time and A and α are fitting constants to be determined by least-squares analysis.
Unfortunately there were only very limited results that could be obtained from a data analysis at the second temperature change (from 160° C back to 150° C).

KT5A/P Hall current sensor collects three-phase current signals of spindle motor which was transferred to industrial control computer by NI6013 A/D data acquisition card.
Industrial control computers analysis and process the real-time data, connected with Single Chip Microcomputer (SCM) for serial communication to control SCM.
Table 1 presents part data of spindle current.
Table 1 Current of Spindle of One Bit (partial data) Drilling times First Phase (/mv) Second Phase (/mv) Second Phase (/mv) 1 8.665 6.432 7.456 5 8.775 7.135 8.461 10 9.251 6.892 7.957 15 9.125 7.718 8.324 20 9.245 8.231 8.039 25 9.175 8.471 8.172 30 10.151 9.450 9.372 Fig1 Experiment System of Micro-drilling on-line Monitoring Fuzzy Control Model of Drill Wear Based on Genetic Algorithm Fuzzy control model of drill wear was setted up with MATLAB, input parameters of fuzzy controller are three-phase currents of spindle motor, and output parameter is bit wear value.
Experimental results show that the fuzzy controller optimized by genetic algorithm could improve the drilling process performance and reduction of production costs by maximizing the use of drill life and preventing drills failures.

However, limited data are available on ASR behavior of geopolymer concrete made of fly ash produced from circulating fluidized bed combustion (CFBC) process.
First, the expansion versus age data for tested specimens were used to determine the rate constant and ultimate expansion (e.g.
Fig. 1-(a) plain-RA-1N data).
This is done by analyzing the expansion data at a constant temperature using Eq. (1): (1) Where, ε = relative humidity factor; ε0 = ultimate expansion; KT = rate constant at temperature T (1/day); t = actual reactive age at temperature T (day), and t0 = theoretical initial reaction time (day).
· Geopoymer mortar bars made with CFBC fly ash had greater reduction in ASR irrespective of aggregate reactivity and normality of test solution

In the research, they develop knowledge map creation and maintenance functions by utilizing information retrieval and data mining techniques.
The process of the knowledge map construction (1) The pre-processing of the documents Since documents are represented by the unstructured or semi-structured text, the unstructured text needs to be transformed to the structured data.
To transform unstructured document to structured data for eliciting key terms, the steps including these training documents will be processed by eliminating the stop words and stemming the word.
The eliminations of these stop words will also have an additional benefit which is the reduction of the size of dimensions.
Data & Knowledge Engineering, 68(11), 1271-1288

Signal-to-noise ratio is sometimes used informally to refer to the ratio of useful information to false or irrelevant data in a conversation or exchange.
DOE is a systematic, rigorous approach to engineering problem solving that applies principles and techniques at the data collection stage so as to ensure the generation of valid, defensible, and supportable engineering conclusions [10].
This will help in the reduction of environmental pollution and hence saves our planet.
(a) Factors Levels 0 1 2 Type of fiber Kenaf Coir Bamboo Natural fiber volume percentage (%) 60 55 50 Type of matrix Unsaturated Polyester Bisphenol A epoxy resin (Miracast Epoxy 1517) Novolac epoxy resin (BJC Epoxy) Empty Empty Empty Empty (b) Exp No Parameter Tensile Stress at Maximum Load (MPa) S/N ratio (dB) A B C D R1 R2 R3 R4 R5 Rmean 1 0 0 0 0 23.915 34.317 30.389 26.324 27.194 28.428 74.537 2 0 1 1 1 21.207 25.058 19.875 22.334 19.108 21.517 73.328 3 0 2 2 2 23.222 27.862 28.967 27.962 22.104 26.023 74.154 4 1 0 1 2 18.395 15.787 20.906 18.207 13.280 17.315 72.384 5 1 1 2 0 15.923 21.631 22.586 19.825 21.738 20.341 73.084 6 1 2 0 1 11.570 9.665 15.367 14.609 13.924 13.027 71.148 7 2 0 2 1 20.667 19.827 17.747 19.363 18.054 19.132 72.818 8 2 1 0 2 16.158 23.535 16.303 20.690 22.104 19.758 72.957 9 2 2 1 0 15.081 11.963 15.380 16.129 15.265 14.764 71.692 The factor effect can be obtained by finding the mean of sum of squares of measured data.
Spall, Factorial design for choosing input values in experimentation, Generating informative data for system identification, IEEE Control Systems Magazine. 30 (2010) 38–53.

Although some papers have discussed the issues, which proposed some production operational models for the resource-saving and environmentally-friendly manufacturing processes [7-9], the research on the tools aiming at the reduction of resource consumption and environmental impacts at the level of production operation is still very limited.
The system framework is built up with six components including system support layer, data layer, model layer, function layer, business layer and presentation layer, which are shown as Fig. 1.
The data layer provides various data to the model layer, function layer and business layer.
It is composed of system support, data, model, function, business and presentation layer.

Fig. 1 Flow chart for simulation calculation of heat transfer with gas injected in annulus Establish modified temperature prediction model The existing temperature predicting models[8,9] were built on the basis of transfer heat from tubing to the formation through annulus, and an empirical formula of Fc is proposed by fitting a large number of field test data.
If heat transfer process from annulus to tubing, annulus to formation, we just only modified the part of heat transfer process: ① Reduction temperature change caused by heat transfer from tubing liquid to external, ② Increase temperature change caused by heat transfer from annulus to tubing liquid
The basic parameter and production data of experiment well are shown in Table 1 and Table 2.
Table 1 Basic parameters for well XXX Depth of middle oil layer(inclined/vertical depth)[m] 2755/2691 specific gravity of injected gas[-] 0.65 Temperature of middle oil layer[℃] 125.92 Tubing size[in] 2-7/8″ relative density of crude oil[-] 0.8375 Casing size[in] 7″ relative density of produced water[-] 0.7103 temperature gradient[℃/100m] 3.76 relative density of formation water[-] 1.01 saturation pressure[MPa] 25.4 Table 2 Production data for well XXX Test time Oil pressure [MPa] Casing pressure [MPa] Gas injection volume [m3/d] Liquid production [m3/d] Gas production [m3/d] Gas-oil ratio [m3/m3] Water cut [%] gas tempera-ture [℃] 1 1.4 7.9 7940 12.2 12030 1231 19.9 40 2 1.2 7.7 7760 16.3 6090 289 22.6 65 To make prediction for the two different gas injection temperatures by using the modified model and non-modified model, and verified with observed data, they are described in Fig.2 to Fig.3.