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For data acquisition, this paper used load cell PCB Piezotronics with 203B ICP quartz force model that have a capacity of about 88.96 kN.
Data acquisition process is shown on Fig. 3 [5, 8].
Fig. 3 The data acquisition arrangement process.
The peak load force data shown in Table 3. 49.36 kN 19.19 kN Fig. 4 The best position of crush initiators on IEA specimen code A and C.
The energy absorption data shown in Table 3.

It is shown that increasing of alloying elements’ content, in comparison to austenitic steels, improves the strength characteristics while improving corrosion resistance and resulting in a dramatic reduction in machinability.
Regarding this issue there is a considerable amount of experimental data accumulated by various researchers.
Curves 2 and 5 are based on literature data, with power-law approximation (in contrast to the quadratic polynomials employed in [7]).

The data of 10 automatic monitoring stations of January to December in 2013 is used here.
The validity of data was audit by the provincial and municipal.
Apparatus and data acquisition.
The integrality and validity of the selected data was ensured within a day, with no tendency.
EXCEL2010 was used for correlation analysis of the data.

The presence of this uncertainty will inevitably lead to a reduction of the accuracy of monitoring and fault diagnosis, and even lead to missed and mistaken diagnosis.
The structure model of MIF is often divided by the level and integration position of the data processing.
According to the level of data processing, the structure model of MIF is often divided into three-layer fusion structures (the data layer, feature level and decision-making layer).
Industrial processes Information for decision-making Feature information Data information Recognition detect Decisio–making System (database / blackboard system) Multi-sensor networks Image information knowledge of the domain Data layer fusion Feature layer Fusion Decision– making layer fusion Signal level fusion Pixel level fusion Feature Extraction Symbol level fusion Multi-sen- sor manage- ment and resource allocation Fig.3 The structure of the industrial monitoring system Figure 3 shows that the information fusion of the system is divided into three types (data layer fusion, feature level fusion and decision-level fusion).
The diagnosis knowledge includes a variety of prior knowledge, such as rule-based knowledge and fault tree-based model knowledge and so on, and it also includes the new knowledge on the state of the object getting through data mining, such as rules, classification and so on.

Damping loss factor at each natural frequency could be determined from measured vibration data by some signal processing.
Experimental results showed close correlation with the calculated data.
Figure 5 shows stress history at the selected position compared with calculated data.
Under the static load condition, experimental results showed similar level of stress with the calculated data.
Close correlation between experimental results and calculated data showed that calculated data is good enough to predict the response of the system under static and vibration load.

Fig.4 System response at low speed neural network resistance observer 1 neural networks training data In this paper, neural network resistance observer have a data collection in different input current (i), different stator frequency(f) and consecutive time(t∈[0，140]min).
When Motor works in Figure.5 currents and frequencies, measuring one time every 10 minutes, the data is shown in Figure.6.
Fig.5 condition of experiment Fig.6 The actual waveform of △R prepare training data, namely input and output data.
create a network object train network input the new data, then Access network response.
Then the network trained by teacher has "learn" the knowledge and rules of training data, so it can be used for the job.

Impermeable layer so that rainwater can not penetrate, resulting in increased stormwater runoff, and cause urban floods and other issues﹝4,5﹞, serious cause heat island effect, countries are vigorously promoting the urban environment, a variety of energy-saving, carbon reduction reduce the temperature, environmental protection and other measures, including the green roof, façade, balcony, balconies and roads beams and columns, retaining walls face the three-dimensional space into the greening facilities for, or planting planting both landscape and edible crops, look at the patch of land inch fund urban environment, the green land resources lost ground to retrieve from the air, and immediately face the positive approach of the future, hopes to create a green ecological urban convenience modern living environment construction and urban agriculture trends.
Pers. (4) Monitoring instruments: TC(Thermo-couple) line and data acquisition and recording system, outdoor microclimate weather station; using temperature loggers,monitor of temperature of the indoor and outdoor thermal environment; using TC line roof exposed control area, extensive green areas, scaffolding area, facade vegetation area, different planting species and surface temperature monitoring; with data acquisition and recording system to store data for future analysis. a.
Record mode: every 10 minutes to monitor one data and using the data acquisition and recording system of data storage, weather monitoring. (5) different annual(2011,2012) electricity consumption in the same period of data collect.(6) Data analysis.
Time Control area Test below Planting mean 2 floor indoor 08:00 26.79 23.88 23.32 24.50 12:00 37.56 24.69 27.60 24.79 16:00 31.34 26.82 24.04 25.57 20:00 24.88 25.40 22.20 25.06 24:00 22.62 24.77 21.47 24.47 Mon.Ave. 28.64 25.11 23.72 24.88 Fig.4. 3-5Mon.average temperature change (2)The different annual power consumption in the same period Building from September to June is exposed roof total power consumption of 1767 K.Watts/hours, September to June applied for green roof total power consumption of 860 K.Watts/hours(Table3), saving power consumed 907 K.Watts/hours to reduce power consumption (48.67%); bythis data to know Taichung City residential area on the top floor facilities for green roof autumn, winter and spring indeed can reduce with power (48.67%), to achieve the energy saving effectiveness﹝1,2,3,6﹞.

One of its main selling arguments is the possibility of substantial reductions on the total cost of ownership of IT infrastructures.
Finally data tier is important factor to store and manage all of the cloud computing resources data.
It can be include memory capability in cloud computing server, distributed file system, and key-value data for cloud computing resources.
The architecture has 3 tiers; presentation tier, business tier, and data tier.
And data tier is most important factor and handle the management of data store and process such as cloud computing resources data, cloud application data, user data for cloud computing use.

Table1 Basic Framework and Function of Network Software Static data Dynamic data End user Basic database Testing database Data of dynamometer diagram Analysis software by dynamometer diagram method Dynamic monitoring Stop instructions Maintenance instructions State statistics Yield calculation from dynamometer diagram Yield calculation Performance analysis Production curve Fault alarm Well fault Communication fault Fault statistics System management Company management Personnel management Authority management 3.Development of wireless dynamometer diagram sensor for single well Model selection by on-site comparison.
According to the shape and data of the dynamometer diagram in on-site test, load sensor is to the first choice.
Mode of data transmission can be GPRS wireless network or ground optical fiber network.
The results of oil metering in traditional artificial method for three times in half an hour are considered as the fluid volume for 24 hours or even are treated as the data for three days.
(3) Establishment of management platform for remote measurement can collect and share relevant test data from geological dynamometer diagram of oilfield engineering.

In order to get the information of the pest in the vegetable before the damage was not serious, this research used a BP neural network to classify the leafminer-infected tomato leaves, and the fractal dimension of the leaves was the input data of the BP neural network.
In 1995, the leafminers caused a 30%～40% reduction of output in somewhere of China[7].
Thus, our objectives were to use leaf image data to determine the leaf damaged degree and the FD in diseased plants, and use the BP network to classify FD data of the tomato leaves.