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Fig. 5 shows a comparison between the experimental data and ASME design curves.
Some literatures' data [3, 4] were also included.
Most of experimental data fell on the regions above the design curves, implying sufficient fatigue safety margins.
However, a few data obtained under the conditions of low strain rate, high DO and high sulfur contents fell on the regions below the design curves, suggesting a potential strain-rate dependent reduction in the safety margins of low-alloy pressure vessel steels in high temperature water in connection with the material and environ- mental factors such as the sulfur contents in steels, DO in water and temperature.
There exists a potential reduction in fatigue safety margins for low-alloy RPV steels in high temperature water depending on the strain rate, DO in water, temperature and steel sulfur contents.

The research achievements of current water quality soft-sensing model mainly include simplified mechanism model, soft-sensing method based on the data and the soft-sensing method based on multiple model, as follows: The simplified mechanism model.
As the paper[2] adopts scale separation method for model reduction (application).In paper [3] , the linear model is simplified by linearizing switch function of components reaction rate based on nonlinear in activated sludge, paper[4] proposes the simplified model after reduction treatment based on mechanism analysis of biochemical reaction.
The soft-sensing method based on the data.
Paper[7] adopts the fuzzy c-means clustering algorithm to divide the run data space ,adopts fuzzy model as a partial sub-model, establishes the soft-sensing model of goal water quality COD.
Multi model decomposition use the method of condition identification division, use the fuzzy clustering method to identify the operating region number [11], local sub-model structure can use data driven model, also can use the simplified nonlinear or linear mechanism model of corresponding conditions[12], local sub-model parameters uses prediction error method for estimation, multi-model synthesis mechanism includes hard switching and weighted sum, synthesis the global dynamic model from the local sub-model.

Experiments have been carried out with different compression ratios of 14, 16, 18 and 20 at loads of 0, 25, 50, 75 and 100% in B23 fuel mode and the values are compared with the base line data.
It is concluded that the ignition delay at CR18 is significantly lower than the base line data.
This EGT reduction reduces the dissociation along with CO emission [6].
Variation of smoke emission with load and CR With reference to the base line data, smoke reduction for CR18 is found as follows: 0, 20.19; 25, 10; 50, 11.03; 75, 29.35; 100%, 21.3%.
Ignition delay and maximum pressure crank angle for the CR18 is most significant with the base line data. 5.

In the practical calculation, in order to reduce the error, guarantee the accuracy, the need for the vibration signal noise reduction gearbox fault processing.
According to the characteristics of the gear box operation, we normalized the selected sample data, the results are listed in the table 1.
Table1 Ant colony neural network training data samples No.
Based on the conduct of the weights and thresholds on ant colony optimization algorithm, we select five sets of data, which was brought into the neural network test samples, the corresponding diagnostic results in the table 2.
In order to verify ant colony neural network fault diagnosis ability, this paper uses the same data, using MATLAB software to the neural network training samples.

An accurate simulation can save cost by a multitude of ways like material savings, process optimization, cycle time reduction, improved quality of parts and so on.
Scan data Fig. 5(b).
CAD data Fig. 5(c).
This data is imported into Geomagic verifier a reverse engineering and inspection software in .stl format and the nominal CAD data (shown in Fig. 5(b)) which is also imported to Geomagic software in .step format is superimposed and the deviation is compared, The result of this comparison process is delivered in the form of a color map deviation (shown in Fig. 5(c) and 5(d)) which pictorially describe the differences between the scan and CAD data.
By considering the best gate location and feed system the part is produced through the injection molding process and the physical part is scanned and the scan data of the physical part is superimposed with CAD data to check the warpage in Geomagic verify and the deviation report is generated.

This information includes the operating pressure, operating temperature, component composition and etc. where all relevant data is obtained from Lv et al. [11].
The simulation using small inventory of H2 data as it refer to the experimental set up as said by Ganci F, et al [12].
Prior to that, basic gasification process was performed in HYSIS to get the required output gasifier data due to the lacking of published data in the literature.
Other reaction may have occurred resulting in the reduction of the concentration of CH4 and CO.
This is due to the competition to gain oxygen increased and other reactions that may occur resulting in the reduction of the CH4 and CO concentration.

Sensitivity to noise Noise is one of the parameters which can affect the signals significantly, in particular if the data is acquired in harsh conditions such as in flight.
Most of the available research adds the same noise levels to both baseline and current data which does not consider the uncertainty in the data.
The main reason is given by the reduction of the amplitude of the wave.
When determining the scattered signal at each sensor, the effects of the amplitude reduction due to bonding are mixed with the reflections due to damage, so that the precision of the methodology decreases.Therefore, to increase the reliability of the detection algorithm it is highly recommended to check the integrity of the bonding for each transducer prior to interrogation via Electro-mechanical impedance technique [10].
Numerical data were generated simulating lamb wave actuation and sensing.

The objective of feature extraction is to project high-dimensional data into low-dimensional subspace so that the face image samples are made more compact and useful for classification in the transformed subspace.
Therefore the PCA method is optimal for data reconstruction, but not for discriminant analysis.
In Section 3, the dimensionality reduction method of 2DOUDP is presented.
Defining affinity matrix is as follows (1) Where represents the neighborhood relation between data sampleand .
From Table 1, we find that 2DOUDP is the most efficient dimensionality reduction method, and is much more efficient than with UDP and OLPP.

(3)Core displacement measurement.①Single core displacement measurement:Break-through pressure grade, water plugging rate and water flushing resistance ability measured with different permeability cores.②Parallel cores displacement measurement: injected water plugging agent to high permeability core,then flushed the parallel cores, recorded the outlet velocity data of the two cores, evaluated the adaptability of reservoir with different permeability ratio for different water plugging agent.
Experimental results 4.1 Optimum formula.50~60% resin+0.3~0.5%sodium dodecyl benzene sulfonate/0.2~0.5% Octadearyl dimethyl ammonium chloride+0.1%CMC+0.1~0.15%mineral salt+1~5% paraffin + water 4.2 Performance data.Density 1.05~1.10 g/cm3,oil solubility>95%,acid solubility<2%,particle size range1~5μm,viscosity 2mPa.s,permitted temperature range40 ~130℃. 4.3 Core displacement measurement.①Single core displacement measurement.Flooded the single core with 200 pore-volume water after injected 2 pore-volume plugging agent.
Table 2 Data of single core flooding Core number Plugging agent type  Core permeability [mD] Water plugging rate after flooding with100 pore-volume water[%] Core length [cm] Break-through pressure grade [MPa.m-1]  Before plugging  After plugging 23 emulsified heavy oil 1534 89 82.1 7.99 10 27 oil-in-water emulsion 1538 21 97.0 7.99 26 Permeability,mD Volume of water flooding,pv No.23 No.27 No.
The cumulative water reduction of Ren-467 is 23788.0m3 after water plugging construction, the cumulative oil increment is 771.58t.
The cumulative water reduction of Ren-454 is 8742.93m3 after water plugging,the cumulative oil increment is 90t.

The results of the Tukey Test, before and after the degradation, showed statistical differences, ensuring that the data analysis was effective.
Using exactly 5% (0.05) for the MLS test, it was considered that the cumulative frequency function was distributed in classes and the data behaved as a normal distribution.
The standard deviation and the lot average are calculated by Equations (c) and (d), respectively: (c) (d) After calculating the minimum initial lot number, the minimum lot size (MLS) is calculated using Equation (e): (e) Where N is the number of batch data.
Based on this, the number of pieces utilized of five (5) was considered satisfactory.Then, to analyze the data outside of the central tendency, the Chauvenet Criterion was used.
Longnecker: An introduction to statistical methods and data analysis. (6th Edition. 2008)