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Based on the deep analysis of the working principle and using equipment, the air separation unit product data management (PDM) system is developed by Visual Basic (VB) in this paper develops.
Product Data Management (PDM) refers to integration and management the information and application of products and product data which distributed between various systems and media in every enterprise [4].
It uses software technology as the foundation, product as the core, and realizes integrated management of the database system among data, process, and resources which relates with product.
PDM can enhance the efficiency of product design, manufacture and delivery; product data management system saves and provides the important information data of product design and manufacture, and provides products maintain support, namely the products whole life management.
Some “hot spots” are indicated in the area, and detailed information such as particular data set, access tool or technology could be found by clicking on these areas.

The data size of the object can be reduced by it.
The data processing speed is increased by it based on keeping the useful information.
In this paper, the FastICA algorithm is used to process the spectroscopic data.
The spectroscopic data is converted to the two-dimension form from the EEM matrix form.
The SVM is used to process the data after dimensionality reduction to realize the fast detection and identification.

By analyzing the operating character of the bus through the data from real road we find out the suitable components in the HEV driveline.
By analyzing the operating character of the bus through the data from real road we find out the suitable components in the HEV driveline.
In order to size the engine and motor, the data from real road is used.
Statistics of the Real Road Data We make statistics of the real-time velocity .
The road data is characterized from the real operational test.

Both fault analysis results and fault diagnosis results are completely correct, and fault pattern which is not in fault history data can be recognized.
Maximum - minimum standardized data conversion is used to calculate the normalized value of thermal parameter in sample : , (1) where and are maximum and minimum value of thermal parameter.
Data preprocessing includes signal preprocessing, maximum - minimum standardized data conversion, and dimensionality reduction process [6].
After data preprocessing, we get a matrix by , whose row is samplenumber and whose columns respectively correspond to 10 fault symptom parameters: maximum combustion pressure , scavenging air pressure , exhaust gas pressure , outlet pressure of air compressor, air cooler pressure difference , exhaust gas temperature , outlet temperature of air compressor , scavenging air temperature , air cooler temperature difference and the RMP of turbocharger .
Tab. 3: Diagnosis results Given fault Parameter vector to be recognized Diagnosis result 1 0.481 0.526 0.288 0.667 0.287 0.544 0.589 0.526 0.831 0.991 0.383 Fault 1 2 0.591 0.618 0.270 0.647 0.235 0.526 0.650 0.631 0.855 0.953 0.343 Fault 2 3 0.306 0.301 0.255 0.434 0.218 0.373 0.316 0.301 0.727 0.768 0.787 Fault 3 4 0.364 0.474 0.632 0.539 0.219 0.441 0.511 0.489 0.291 0.995 0.298 Fault 4 5 0.223 0.236 0.008 0.359 0.289 0.336 0.228 0.238 0.901 0.569 0.277 Fault 5 6 0.129 0.066 0.258 0.447 0.682 0.255 0.392 0.067 0.721 0.564 0.068 Unknown fault pattern The results show that the fault diagnosis method is accurate, and is able to recognize the fault pattern which is not in the fault history data.

This paper discusses the offshore oil spill data received between 2010 and 2011.
This paper was based on the data compiled from a report of the Oil and Gas Coordination from the Brazilian Federal Environmental Agency – IBAMA [2] Incident Notification As enforced by the law 9966/2000, the incident notifications shall inform the installation that originated the incident, date, hour, geographical position, volume and type of the released substance, presumable cause beside other data.
In order to improve the quality of the notifications data, the Brazilian Environmental Agency should ask for more detailed information regarding the incident causes.
Most of the countries with relevant offshore E&P activities, like Australia, Canada, UK and USA, publicly reports oil spill data through websites.
But the inclusion of the data in a publicly available website should be a must as it is the government’s responsibility to inform the public on the extent of environmental impact from a regulated industry.

Experimental data.
(a) A fringe pattern (upper image) and its boundary judge map (lower image), (b) one line data in (a) at the shadow boundary (a pink line in (a)), and (c) reconstructed 3D result before (upper image) and after (lower image) shadow boundary removal.
With detecting the beginning and ridge points in one line data shown Figure 5(b), the boundary area of shadow can be determined.
Saldner, Error-reduction methods for shape measurement by temporal phase unwrapping.
[14] Zhang, S., Phase unwrapping error reduction framework for a multiple-wavelength phase-shifting algorithm.

For example, Shen Jing proposed the rough set attribute reduction model.
With the equipment at hand, we need to have true and accurate monitoring data to complete the supervision of enterprises, especially the seriously-polluted ones.
The collected data should be timely feedback to the local monitoring stations for processing.
Of course, to do real-time data collection and processing is not an easy thing.
However, we should try to understand that only by doing so can we get convincing data and take effective restrictions on the enterprises.

Introduction Telecommunications network fault diagnosis system when received the massive network the original events, the need for its reduction, and fault diagnosis will filter out unrelated events, for the purpose of reducing the number of alarms which will be processed in the process of fault diagnosis.
Even after the original events reduction, but still very large amount of alarm, nor are the root causes of failure, the need for further diagnosis of these alarms to determine the root cause of the alarm - fault.
Meanwhile, the paper also tests the reasoning part of the process and gets some intermediate data, and the number of actual failures have been confirmed by network maintenance staff.
All the data as shown in Table 2: Table 2 Efficiency of Results verification method Number of alarms Faults before verification Faults after verification Confirmed faults False faults 10000 39 31 27 4 According to the data in Table 2, we can see through the results verification step, a decrease of 8 false faults and reduce the false faults rate of 8/27 = 29.63%, faults accuracy was improved (27/31-27/39) = 17.87%.
Then according to the characteristics of telecommunications networks, to improve the performance of Bayesian reasoning networks, two improved methods are proposed on the Bayesian networks reasoning, that is narrow range method and pretreatment method, and tested in the current network obtained the experimental data verified the two improved methods to improve the performance of Bayesian reasoning is valid; the same time, The third improvement method, that is results verification method is also verified to improve the accuracy of the Bayesian network reasoning.

However, high optical accuracy and disturbance reduction are required to project high-coherence light.
Figure 5 shows data for a precision steel sphere with 10 mm diameter.
Figure 5(c) shows the data for the cross-sectional shape of the sphere.
The circles denote the experimental data.
However, the processing time will increase owing to the increase in the data for processing.

The quantitative analysis methods mainly include rigid body limit equilibrium method [1], strength reduction method based on FEM theory and strength reduction method based on finite difference theory and so on.
The specific steps are dividing the total variation square sum of the data into the sum for variation square sum of factors and random error square sum, then F-test is carried out, and the significance of factor effects can be obtained. 2 Calculation models of slope and verification for calculation method 2.1 Calculation models In this paper, the assessment question 1 (a) used in Australia slope stability analysis program in 1987 for ACADS is adopted as calculated slope model.
Table 4 Range analysis data of factors Factors level A（/（kg.m-3）） B（C/kP） C（/（°）） 1.026 0.990 0.917 1.007 1.008 1.007 0.990 1.025 1.098 （Range） 0.036 0.035 0.181 In the upper table, is the average value of test results for level 1. 3.3 Variance analysis In order to better study the degree of influence of various factors on the slope stability factor, variance analysis method is performed for the further analysis of slope stability calculation results under the combination of factors.
Table 5 Analysis data of variance Factors level tests number A （/（kg.m-3）） B （C/kP） C （/（°）） Safety factor 1 1 1 1 0.917 2 1 2 2 1.025 3 1 3 3 1.135 4 2 1 2 0.989 5 2 2 3 1.097 6 2 3 1 0.934 7 3 1 3 1.063 8 3 2 1 0.901 9 3 3 2 1.006 3.077 2.969 2.752 3.020 3.023 3.020 2.970 3.075 3.295 Deviations sum 0.00191 0.00187 0.04914 Contribution rate 3.61% 3.53% 92.86% It can be seen from Table 5 that, the deviation sum of the first and the third column are much small, so the results can be incorporated to error estimates, that is The sum of errors deviations The degree of freedom for errors the ratio of the factor C is showed as followed： Given significance level，，it can be obtained by checking the distribution critical values table of F ， For So the results of significance level are： Factor A（bulk density）………………………………………（not significant） Factor B（cohesion force）………………………………………（not significant） Factor C（internal friction angle）………………………（highly significant
Therefore, In the course of engineering survey, special attention must be put on the value of the geotechnical internal friction angle, ensuring that the data provided reflect the actual situation, thus ensuring that the numerical results can be more realistic evaluation of slope stability condition. 2) Variance analysis shows that, when the parameters have the same relative rate of change, the changes of internal friction angle have highly significant impact on slope stability factor, while the changes of bulk density and cohesion force have much lower significant impact on slope stability factor.