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The former only need the past water usage data, and the operation process is simple, which can be suitable for the swing situation, but the results precision from this method is difficult to guarantee.
If the training data with large-scale and much field, these disadvantages become more obvious.
From the mathematical points, this belongs to the process of dimension reduction technology [3].
The data samples from 1995 to 2002 are selected for network training, the given samples from 2003 to 2005 are selected to inspect the network.
[3] Yu Jianying, He Xuhong, in: Data Statistical Analysis and the Application of SPSS, chapter, 9, 291-310, the People’s Postal & Telecommunications Press (2003)

Natural disasters(C1)= reduction/halt days of of mine production /(30*3)+ waiting days of ships /(30*3) economic growth rate(C2)=quater growth rate of GDP shipping market conditions (C3)=quater growth rate of CCBFI production equipment failure (C8)=days of production equipment failure/(30*3) Railway equipment failure rate(C10)=days of railway equipment failure/(30*3) Rate of press habor(C13)=number of press vessels because of vessel operation/total vessels in a quater Transportation accident rate(C14)=railway transportation accident rate+shipping transportation accident rate inventory shortage(C16)=days of the inventory below the red warning line /(30*3) Vulnerability of network structure(C17)=min(number of Organization involvement in the supply chain) Other qualitative indicators can be quantified processing with the expert scoring method.
Evaluation model based on SVM Data collection.
Table 2 Five samples of data collection sample C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 1 0.26 16.27% 1497.16 2.5 2.5 2.5 2.5 0.16 2.5 0.60 2 0.33 23.48% 1745.65 2.1 1.9 2.4 2.0 0.23 2.2 0.65 3 0.31 16.27% 1210.37 1.8 1.9 2.3 2.1 0.21 2.3 0.69 4 0.29 19.42% 1569.88 1.9 1.7 1.5 2.0 0.16 2.0 0.68 5 0.26 18.23% 1487.13 1.5 2.3 1.9 2.2 0.19 2.1 0.75 sample C12 C13 C14 C15 C16 C17 C18 C19 C20 C21 1 2.5 2.5 0.062 0.042 2.5 0.0556 5 2.5 2.5 2.5 2 1.6 1.9 0.065 0.045 2.4 0.0598 3 2.3 2.4 2.0 3 2.4 1.9 0.072 0.052 2.3 0.0591 4 2.3 2.0 1.9 4 1.9 2.3 0.069 0.055 2.0 0.0583 5 1.2 1.9 2.0 5 2.3 2.0 0.067 0.057 1.6 0.0641 3 2.0 2.1 1.8 Data Normalization.Two kinds of indice which are positive index and negative index in the thermal coal supply chain index system.
To normalize the data, scale conversion method is used.
(1) Here, X is the initial data; is the minimum of initial data; is the maximum of initial data; T is the conversion data.

In most cases very complex microstructures emerged from AFM data.
In this work, it is presented for the first time a comparative study of AFM and PDM data from the same bitumen either unmodified or after modification with a reactive polymer, as well as after thermal ageing.
AFM data strongly suggests that the true picture is much more complex.
After modification of the S bitumen, an increase of para-phase content and a decrease of the peri-phase content are observed, as well as a decrease of the number of branches in catana phase; AFM data strongly suggests that the reduction of the area around catana (periphase) was a consequence of adding a reactive polymer to the bitumen.
Conclusion We presented for the first time, AFM data (both topographic and phase detection data) of raw bitumen and bitumen modified by reactive polymers.

Spatial Information Management This module can realize the enlargement, the reduction, the remove, the edition, the revision, the refresh as well as the flash and centralization of element on the map, and the interactive operation between graphs and attribute data.
According to it, the function gains the related data from the database, and uses the MapGIS component for automatic mapping on corresponding line layer (objLineArea).
Application Example Take actual data of the lead-zinc-silver ore in Zhijiadi, Shanxi Province as the example.
The interface is shown in Figure 5, and its left half part for geological data processing, right half part for graph interactive display.
They provide the data and requirements of frontline workers.

Data Acquisition.
Survey was performed on a total of 163 corporations to produce meaningful data for IT utilization.
We have also performed correlation analysis over the data obtained from survey using SPSS to see the utilization on layer by processes.
While Table 4 simply notes the system name, in fact a detailed path map can be obtained after applying utilization data for each system.
Of course, such data needs to be applied differently by industry.

This value is deduced from data about solar radiation impinging on horizontal surface Ihor.
Solar irradiation data (Ihor) were available in 15 minutes steps, therefore they were aggregated by Eq. 4.
Ipan = Isun Ipan = 0 for c > 0 for c ≤ 0 (14) Input data Horizontal irradiation data were provided by weather station of Institute of Landscape Water Management (Brno University of Technology, Faculty of Civil Engineering).
Therefore more accurate data provided by weather station were used for simulation.
House electricity consumption data were known only in total day sum only.

They show very prominent effect in vibration attenuation and noise reduction.
In order to reduce errors, the vibration acceleration data caused by the same train when it respectively passes through the two cross sections at the same moment is taken for data analysis. 10 groups of such data are selected for analysis in order to ensure reliability of the data.
Fig. 3 Time domain waveform of vibration Fig. 4 Time domain waveform of vibration acceleration of steel spring floating slab track acceleration of general track 10 groups of data are respectively taken from samples with the waveforms like Fig. 3 and Fig .4 for time domain analysis.
Table 1 Statistical Result of Test Data Vibration Attenuation Structure Location of Test Point Effective Value of Acceleration（m/s2） Acceleration Level(dB) Steel spring floating slab track On vertical direction of steel rail 21.75 146.61 On vertical direction of tunnel walls 0.03 88.99 General ballast bed track On vertical direction of steel rail 31.30 149.28 On vertical direction of tunnel walls 0.37 111.35 It is shown in time domain waveforms in Fig.3 and Fig.4 that the vibration acceleration amplitude of the steel spring floating slab track is obviously smaller than the vibration acceleration amplitude of the general track in steel rails and tunnel walls.
Vibration attenuation effect is gradually increased with increase in frequency. 2.3 Result analysis of Z weighted vibration acceleration level In view of that vibration data of tunnel walls can directly reflect the vibration isolation effect of a track vibration isolator, while human bodies’ subjective feelings to each vibration frequency band are also involved in environmental vibration, so that the measured data in tunnel walls is analyzed according to GB 10071-88 Standard and ISO2631-1:1985 Vertical (Z direction) Weighing Manner (1-80 Hz) in order to obtain maximum Z weighted vibration acceleration level in the tunnel walls.

The main idea is to interpret the original data in the most variables with fewer variables.
The principal components can be used to explain the comprehensive index of original data.
The data of gradation plan and asphalt-aggregate ratio in the experiments is shown in table 1.
In this research, the deviation normalization method will be used to normalize the data, according to Eq.1.
Principal components analysis of protein structure ensembles calculated using NMR data.

Weight and time/temperature data were recorded, yielding the weight loss (TG) and differential weight loss (DTG) curves.
Weight and time/temperature data were recorded using TGA software, yielding the weight loss (TG) and differential weight loss (DTG) curves.
The data on the first 30 min of reaction were not processed, so the mass variations due to water loss were not considered.
Kissinger- Akahira-Sunose (K–A–S) and (7) Friedman, respectively, for the DTG data of the malt waste.
The data used in the regressions correspond to conversions of 15, 20, 30, 40, 50 and 65 %.

The risk tracking management of deep foundation pit engineering chiefly covers the field observations and monitoring of the identified risks and sudden risks, dynamic management of monitoring data and record and inquiry of risk occurrence status.
When abnormal phenomena and data occur and close to the alarm state, the frequency of monitoring should be improved and even continuous monitoring can be conducted.
The monitoring data of deep foundation pit engineering should be timely analyzed and processed and submitted relevant parties.
The analysis of monitoring data should be conducted in combination with the monitoring data of relevant items, natural environmental conditions, construction conditions and the past data and make prediction on its development trend.
In term of the risk analysis of deep foundation pit engineering, China has a late start, and the analysis methods that are purely established on the statistical data are not feasible currently.