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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.

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.

The data basis is deduced by an analysis and synthesis of the existing press system data.
As a result the data basis of existing press systems enlarge.
The prediction model uses data of existing press systems as well as the dependencies between the data to predict the press system configuration for the required development task.
After collecting the data, every parameter is opposed to every other parameter.
The cross is designated as machine data point.

According to statistical data, appearance of cracks in the areas of barrel support pads of tank wagons often causes damages to the barrels.
For reduction of computational resources, FE models of tank wagon are replaced with high-quality metamodels which are based on locally weighted polynomials.
Distribution of damages in our case is similar with statistical data of tank wagons for transporting of liquefied hydrocarbon gases given in the work [8] (Fig. 2,b).
a) b) Fig. 2. a) Areas of damages of barrel of tank wagon: 1 - welding seams of manhole hatch, 2 - welding seams of shaped pads, 3 - welding seams of discharging device, 4 - welding seams of the barrel shell near supports, 5 - welding seams of dome; b) Statistical data [8] of damaging of tank wagons 903Р (light color columns) and 15-1407 (dark columns) accordingly: 1 - cracks of welding seams of shaped pads, 5 - damage of support, 10 - defects of the barrel welding seams, 13 - unfastening of shaped pads, 14 - unfastening of retaining bands.

Based on the geological and the experimental data of the 13#coal seam of Sun-Cun Coal Mine, the relationship curve between the depth of coal seam and the amount of CH4 produced has been fit out.
Table 1 The coal quality indicators in the 13#coal seam Coal seam Moisture content (%) Ash content (%) Volatile content (%) Heat （kJ/g） Total sulfur (%) Phosphorus (%) Glial layer thickness （mm） Coal type 13# 1.07～2.76 1.61 29.30～42.98 34.98 39.01～47.64 44.68 32.11～34.42 33.27 1.76～5.00 3.01 0.016～0.019 0.017 39.86 gas-fat coal Table 2 The seam features in 13#coal seam Coal seam Thickness （m） The number of dirt band The thickness of dirt band Coal seam spacing（m） Structure Stability 13# Complicated Stable Table 3 The industry analysis in 13#coal seam Coal seam proximate analysis（%） ignition temperature（℃） △T spontaneous combustion tendency Moisture content Ash content Volatile content T(oxidation) T(original) T(reduction) 13# 0.56 21.31 46.30 355 359 360 5 Class III Not easy to spontaneous combustion Experimental Data Analysis There are a lot of heat sources to cause the temperatures increasing in Sun-Cun Coal Mine.The mine is heat conduction type, so the main
Table 4 Ground drilling temperature Depth（m） Temperature（℃） The 1# hole The 2# hole The 3# hole The 4# hole 20 20.0 20.9 7.70 100 21.0 21.3 17.5 200 23.3 24.2 19.1 300 25.6 27.7 21.7 400 27.9 23.7 500 31.1 29.8 26.7 600 34.0 32.2 29.6 700 36.2 37.8 29.0 31.6 800 38.5 36.9 35.5 900 40.8 42.0 1000 43.5 Based on the data in the table, the linear relationship between the temperature (t) and the depth (h) has been gotten.
In order to study the relationship between them, based on experimental data and the analysis of the mathematical optimization model[2,3] as following: (2) the experimental data of the time during on spontaneous combustion of coal as shown in Table 5.
Table 5 Experimental data on the coal spontaneous combustion t(i) (K) VO2 (10-6mol/min) VCO (10-6mol/min) VCO2 (10-6mol/min) Q (J/Kg·min) (m3/Kg) WP (%) (d) 289 0.3376 0 0.1510 5.8641 12.4371 0 322 0.3636 0 0.1374 5.6269 7.3945 16.0876 343 0.4835 0 0.1307 6.0376 4.2240 9.3627 368 0.5302 0 0.1222 6.0355 2.5621 0.06 7.3679 385 0.7095 0 0.1177 6.7955 1.8237 1.27 7.8373 402 1.0921 0.0037 0.1162 8.6515 1.2981 3.3431 418 1.6571 0.0090 0.1278 11.7805 0.9426 2.1505 432 4.0425 0.0148 0.1370 23.7608 0.7124 1.0319 447 8.1192 0.1268 0.2941 48.7261 0.5278 0.4881 Based on the data above, the curve about coal seam depth and the amount of CH4 produced is shown in Figure 1: Fig.1 The curve of depth of coal seam and the amount of CH4 produced As is shown in the figure 1, the curve fitted exponential function expression.

A comparison between finite elements calculations and experimental data has been carried out.
The good agreement between the experimental data and the numerical ones has demonstrated the possibility to obtain an optimized design of bonded patches with the numerical tools.
The measured experimental data has allowed also a comparison among different patch, adhesive and surface preparation properties.
This configuration could avoid the removal of the damaged component and in general could lead to a significant reduction of maintenance costs.
In particular the SIF was measured at the crack tip using strain-gauge data.

According to the fractal dimension evaluation method, can be determined for any given an asphalt mixture gradation fractal dimension range, according to the standard selected representative gradation of asphalt mixture, the determination of its technical indicators, through the analysis of the data can be obtained for each index trend.
On the basis of previous test methods and experience, although a reduction in the amount of orthogonal design test, but also need to do a lot of testing on each gradation are the indexes determination, by comparing the adjusted repeatedly to get the optimal mixture ratio design.
With the fractal dimension increases, aggregate of fine aggregate and mineral content increased gradually, the gradation becomes fine, coarse aggregate reduction is not conducive to the skeleton structure formation, affect the mixture of Marshall stability increases, the Marshall stability trend curve and peak state of development, when more than a fractal dimension 2.5354 stability decreases gradually.
Using fractal dimension to reflect the Marshall results in the use of more intuitive, convenient data analysis.
The fractal dimension as a gradation of the quantization value, establish the fractal dimension and the mixture characteristic indexes between data contrast schema, according to the narrowing of the fractal dimension range, guidance and inspection of asphalt mixture ratio design, determine the goal of grading and reduce the volume of test purpose.

The inspection data were obtained by PIG ultrasonic method.
The processing of data was done in Matlab, with programs carried out by the authors.
In the matrix of data (56824,7), the columns retrieved from the inspection report (the rows correspond to the individual defects) has the following signification: the distance from the beginning of the pipeline to the welds (1), the distance from the weld to defect (2), the thickness of the wall of the pipeline(3), the length of the defect (4), the width of the defect (5), the type of defect (as the cause) (6), the depth of the defect (7).
This selection is useful for the reduction of time for calculus.
The characterization of these defects was made by extracting information from the matrix of data.

Structural Wing Model Wing Data.
The geometric data of the supersonic wing as has been derived in [7] are summarized in Table 1.
Table 1 Supersonic Wing Planform Data Wing Swept Angle Taper Ratio Aspect Ratio Average Chord Length [m] Wing Root [m] Wing Tip [m] Half Span [m] 30º 0.5 5 1.5 2 1 3.75 Fig. 1 Front view of the external store locations Composite Structure.
In order to construct the composite structure in MSC Nastran data, the transversely isotropic material had been calculated based on [8].
Fig. 5 V-g diagram of Case 1 Fig. 6 V-g diagram of Case 2 Table 4 Weight Reduction Percentage Case Flutter Velocity [m/s] Composite Skin Total Weight [kg] Skin Weight Reduction -7943 ft 0 ft 10000 ft 20000 ft 30000 ft Base line (Aluminum) 515 585 675 795 955 212.4753 0 1 595 665 775 915 1095 104.6855 50.73 % 2 525 585 685 815 965 64.4149 69.68 % Fig. 7 V-f diagram of Case 1 Fig. 8 V-f diagram of Case 2 Fig. 9 Case 1 – Mode 6 Fig. 10 Case 2 – Mode 6 Conclusion An optimization procedure to perform aeroelastic tailoring of a supersonic wing with external stores is presented in this paper.