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The order in which have been conducted the tests has been established through the use of the Minitab software, in order to ensure the data normality and the absence of auto-correlation between the tests.
An automatic procedure allows the drawing of the data in files containing numerical input and experimental data.
It also analyzes the obtained data comparing graphs in terms of fluid pressure, punch stroke and force for each actuator.
As it can be appreciated, thanks to the reported data, there is a good agreement between numerical and experimental values with a discordance of about 3 %.
As it can be appreciated, thanks to the reported data, there is a good agreement between numerical and experimental values with a discordance of about 4 %.

Software Architecture The architecture of human reliability evaluation system is consist of four layers: data source layer, data service layer, business logic layer and client layer.
Data sources include EMS, WAMS, OMS, PMS, IDP and so on.
The second layer is the data service layer.
This layer acquires data from the data source layer as well as some real time input data, and stores them in a SQL database.
The data mainly includes two parts: real time data and historical data.

Receive of monitoring instruction, data acquisition, image processing, prepare of basic data, information extraction and evaluation, results analysis, results report and results distribution.
They may data provider, information sharing organization, etc.
Coordinate with Data Provider and Relevant Organizations.
Data are usually obtained from the following methods(Table 1).
Table 1 Channels of image acquiring NO method Detailed information 1 Business Buy remote sensing data from business channel 2 Data sharing platform Via data sharing platform to MODIS, EOS Data, FY, etc. 3 Obtain data by agreement Sign agreement with data provider of China and obtain data freely. 4 Cooperate internationally Access data as a member of International Charter on Space and Major Disasters（CHARTER）while disaster happens.

Results of the application of a model to manage complexity in some automotive companies in the West Midlands (UK) suggest that complexity reduction leads to quality improvements.
This can be done by using existing data or by directly asking the person or group of people (on the customer side) who decide whether to purchase the company supplies the following question: "Is (supplier name) the top supplier for (product supplied)?"
Selection of convenient paths This straightforward way to the company customers' requirements contributes to the system's complexity reduction.

The PCI-2 data acquisition (DAQ) card and R15 sensors are used to improve the precision of UAEs detection.
Use the tomato plant data with the empirical deduction under the modern greenhouse conditions, the relationships among UAEs, transpiration and UAEs signal strength is described by curve.
The plant water stress leads to reduction of root absorption, so that the limited water of soil can be used longer.
The UAEs are detected with the μDiSP™ Digital AE system via PCI-2 data acquisition card and R15 transducer which made in Physical Acoustics Corporation and connected with computer through PCI port.
Data acquisition was performed with a detection threshold of 30 dB, a frequency range between 100 kHz and 1 MHz (Sandford and Grace 1985), and a rate of 5M samples/second.

AANN reshapes the distribution of the data and improves the similarity of the feature data in the same distribution type of noise.
Gaussian mixture model (GMM) [5] methods in describing statistical properties of the data has got a great success, as GMM has a good data distribution capabilities, as long as there are enough items, enough training data, GMM can approximate any distribution model.
GMM and AANN in Data Fitting GMM can be seen as a HMM of one state.
It has made use of GMM’s ability to express data distribution and the ability of AANN to learn data structure.
“Maximum likelihood from incomplete data via EM algorithm,” J.

Make diagnosis data clearly show on the panel, and at the same time show the fault type and other necessary data.
Then the bus data tracking, were analyzed.
Arbitrary data of the fifth kinds of simulation data.
This design uses three nodes are: data input nodes, data output nodes, fault display node in the CANoe simulation configuration window.
Bus data statistics F.

The data were logged at the frequency of 1000 Hz which means 60 data one second.
The force data at point A and B were recorded in the same experiments.
The data formed the red and black lines are chosen from one single experiment.
They are original data not the average data.
It is inferred from the present force data that larger amplitude can make penetration force lower.

The data normalization Experimental data from paper-making factory on-line detection process measured data.
Taken 200 groups of sample data for training, 6 groups of testing data as shown in table 1 Table 1 Input sample NO.
Using BP network toolbox training data, and using table 1 in the 6 groups of data testing, threshold for 0.7, results of simulation error as shown in Fig. 2 and 3.
Testing the trained network, we selected 3 groups of new data as testing data in table 2.
Table 2 Test data NO.

The instruction pipelining and the independent hardware multiplier structure make the processor to be very suitable for digital signal processing and real-time data monitoring.
It is very suitable for application in industrial control and data acquisition.The harmonic analysis of low voltage power system use TMS320F2812 as the core to realize the function of data acquisition, FFT algorithm, spectrum analysis and man-machine interface.
The TMS320F2812’s on-chip AD module can periodically sample the signals on the 6 channels, it can get 128 data points per cycle on each channel.
The simulation data has the 3 times, 5 times, 9 times harmonics in the power frquency signal, the sampling frequency reaches 2000 Hz. 160 data points are used as the original data, the original 128 data points in time domain is shown in Fig. 2.
Original 128 data points in time domain Figure 3.