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Fig. 1 S-N fatigue curve It should be noted that there are several short comings of S-N fatigue data.
Since there is a considerable scatter in the data, a reduction factor is often applied to the S-N curves to provide conservative values for the design of components.
Then actual load history data was extracted while referring to the crane usage record.
Fig. 3 shows the raw data about one week.
All the data was used as the basic input during the estimating residual life process.

The essence is observing data from different angles and looking for the optimum pursuit method which can reflect the data characteristic at utmost and dig data information sufficiently.
In essential, dimensional data is integrated into as the value in projected direction.
Through solving the maximum value of projection index function, we can obtain the optimal direction of projection, and fully reveal certain structure features of high dimensional data.
In literature[2] the index data of three countries’ cyberwar’s synthetical ability evaluation has been shown in table 1.
The Index Data of the Three Countries’ Cyberwar’s Synthetical Ability country A 70000 12500 100 1400 252 168 616 B 10000 7200 50 380 208 72 324 C 20000 4500 20 120 187 127.5 336 B.

Fig. 1 CO sensor and portable CO alarm 68 sensors are installed in excavation working face and they will issue warning signal when wind speed is lower or higher than the specified value; 11 wind speed sensor and 11 wind pressure sensor are respectively installed in fan outlet of mine main ventilation room, the sensors upload the data to surface monitor center, so that the movement detection and monitor for total air quantity and air pressure of whole mine will be realized accordingly.
The high accuracy total station is utilized to observe and record data that will be input into monitor host to proceed with analysis, meanwhile the ground subsidence can be forecasted and alarmed.
Table 1 Ore exposed area Item Roof exposed area (m2) Hanging area (m2) slice filled stope <990 slicing method 100~150 open stope <1000 <2060 shrinkage stope <252 1200 The underground pressure detection and monitoring system is consist of GS, one-way vibration absorber, three-way vibration, communication unit, data control center, GPS and industrial control machine, in addition, this system is also assembled by the corresponding software package, including timing software, data process software, visual of slight shock, explanation software and real-time display software of slight shock event etc. 6 one-way vibration absorbers and 2 three-way vibration absorbers are set in -180m halfway and -300m halfway of roadway respectively, these vibration absorbers are distributed as every 150m at the areas that ground pressure of is obvious in roadway.
Fig. 4 Structure chart of ground pressure slight shock monitoring system Fig. 5 Configurations of vibration absorber and GS Mine Safety Monitoring System The parameters and information that collected by underground monitor instrument are delivered to earth surface monitor center by computer technology, the data experience analyzed, processed, filed and alarmed by monitor software and achieve mine safe and economic operation.
Display function: the multi-screen display can provide dynamic graph, real-time data, historical data, parametric curve, statement show and simulation column display; User rights management function: the system can provide a unified standard login screen and application picture template, different permissions are opened to different users to improve people do their own work, which will promote the standardization and safety of monitor management.

Brief description of model and data The Jinta oasis is an inverse triangle, situated between 98º39´E and 99º08´E and 39º56´N and 40º17´N in the northern side of the middle of the Heihe river basin in northwestern China.
The WRF model is a next generation mesoscale forecast model and data assimilation system was developed by NCAR, and NCEP [8].
At the same time, we applied the soil moisture and temperature data from the aforementioned Gobi station to all the model grid cells with the desert land use type for the initial fields in the WRF where soil temperature and soil moisture replaced by observations in four layers Table 1 Model configuration of nesting structure of 3 grids Grid ID Center Lat/Lon Dimension Grid Spacing(km) Time Step(s) Data Resolution 1 40.1°N, 98.8°E 37×37 9 54 10m 2 40.1°N, 98.8°E 40×40 3 18 2m 3 40.1°N, 98.8°E 61×61 1 6 1km Model results 1.
Land use/Vegetation Fig. 1 show the land use/vegetation maps of defaults data for the fine domain.
With the growth of population, more and more grassland is used to plant crops, which results in the reduction of the shrub-land and grassland.

The data shown are the result of only 100millisecs (20millisecs being the actual "event") of data collected during hard but not abusive door slams.
Without such vibrations a 30 second integration time gives data of acceptable quality.
The stress intensity factor was successfully determined from the thermoelastic data for each notch using the method developed by Tomlinson et al [7] and it is intended to fatigue test the blade to destruction in order to compare the reduction in fatigue strength with these thermoelastic results.
If a SPATE system were to record data of similar resolution then it would take several hours and it would be likely that he crack would grow or the blade fail during data collection.
Fig. 9 Thermoelastic data around a notch on the leading edge of the compressor blade.

(4) General identification scheme Altenbach-Gorash-Naumenko model parameters are determined with the following method: Given sets of creep and stress relaxation experimental data, then apply the DE method to adjust the parameters until the experimental data is in accord with the relation of Eq. 1 and Eq. 4.
Since both experimental data and calculated results are discrete ones, the objective function of the present problem can be written in a summation form as: , (5) where ψi,j is the experimental result, φi,j is the calculated result, NExp is the experiment number, and NDataj is the number of data for the ith experiment; ωi, ωj are weighting factors, which can be set as: ωi = 1/NDataj, ωj = 1.
Results and Discussion One set of minimum creep strain rate versus stress data and four sets of stress relaxation test data each with a different initial stress at 550°C for 12Cr-1Mo-1W-1/4V stainless steel bolting material are given in the NRIM data sheets[8].
The comparisons of experiment data and calculated results with parameters identified using the step-by-step, GA and DE algorithm methods are shown in Fig. 2 (a) and (b).
Price: Journal for Global Optimization Vol. 11(1997), p.341 [8] NRIM Creep Data Sheet No. 44, National Research Institute for Metals, Tokyo (1997).

From the perspective of the ICA, the noise data and image data are generally independent to each other.
But sometimes there is an aliasing phenomenon between the noise and useful data in the IMFs.
Sparse Code Shrinkage based on ICA Sparse Code Shrinkage (SCS) is same as ICA to extract features from the image data.
Fig.2 shows an initial PET image and a data set of IMFs after the EMD decomposition.
An ICA Based Noise Reduction for PET Reconstructed Images.

This clearly indicates the effect of data scattering on mean values of mechanical properties and ductility. 1.
Observation of data (from figures 6 to 9), confirm the same optimal value for the percentage of fiber content regarding the compressive and the tensile strengths in concrete mix designs, but the data do not approve the same argument for aggregate size.
The argument can be further discussed that the data in Fig. 8b for determining the optimal aggregate size, were collected based on the mean values of tensile strength whereas the data collection in Fig. 9, were based on the comparison of each mix design with respect to the samples with no reinforcement.
Similarly, the data from Table 7 are used to draw the error bar in Fig. 15.
Paired t-Test is illustrated for statistical analysis since the data are in the form of paired observations.

Gray online prediction Grey prediction is based on sample data with uncertain poor condition; the accumulation make gray information turned white and build systems change trend model that predict future state.
The single input/output system, assuming that measure the input/output time sequence in e.q (1): (1) Because there are some random disturbances, the input/output time data can be regarded as gray data.
Generated accumulate sequence could greatly weaken the influence of random disturbance, and get a generation the first time accumulate data sequence
The control model uses the five continuous sampling data before the present moment k and obtained predict values of k+M moment through grey predict algorithm.
System adjustment time is not better than fuzzy PID except the reduction of maximum deviation.

With the low-carbon technology becoming more sophisticated, the trend of emission reduction increasingly clear, various types of low-carbon power (mainly renewable and distributed power) will continually enter the market, and the competitive with conventional power will also be a rising trend.
But there are some problems of China's demand side management system, such as less involvement of user, poor client real time data, and flexibility shortage of decision-making.
So there are needs of increasing modules of the client real-time data acquisition, visual query and price response of users, building demand-side intelligent management system that have strong real-time data, high participation of user.
In order to monitor real-time load data and electricity consumption data of electricity consumers, target to adjust the load in real time and electricity price in various time according to the demand and spread to the terminal user and detect abnormal electrical power users, make user know his own load and power consumption and make a adjustment.