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High resolution infrared cameras and specialized data processing can be used to assess these small temperature variations during mechanical loading.
For the purpose of data processing and noise reduction, periodic loading is especially suitable which explains the special eligibility of the methodology for fatigue testing.
Few further developments of the available techniques have been achieved in the last years despite the tremendous advances in camera and computation technology as well as in related fields as digital data and image processing.
The underlying data processing technique is outlined somewhere else [5, 6] and shall not be repeated here.
Some specialized filtering and correction techniques have been used to improve the data quality prior analysis.

Freeform optics has some advantages such as the reduction of optical aberrations, number of components and favorable positioning of optical elements.
For tool path generator, its main function is to get cutter contact data (CC).
Then CL data is input to post-processor (PP) of a specified 5-axis machine which transform them to axis commands.
In summary, CL data generation, interference, and tools are all needed to be studied in order to get the NC program for the fabrication of freeform optics.
Tool path generator can get NC program directly from the optics design data.

CS depends on the linear dimension reduction [4], the main idea of the CS can be described as: use a measurement matrix to gain the measurements of original signal firstly, and then use recovery algorithm to reconstruct signal [1].
The difficulty of CS is that compressed data is not a subset of data before compression.
If we want to collect a small amount of data and decompress a large number of data from them, it is necessary to ensure: the collected data contains global information of original signal and there is an algorithm can recover the original information from the collected data.
Experimental Results and Analyses This section shows many experimental results in subjective vision and objective data respectively that illustrate the superiority of our method.

For PCA analysis, a data set was taken from the literature [2] and from our experimental results.
The data set and the parameter values are presented in Table 1.
Data reduction and analyses were carried out using SAXSIT program [6].
Each PC explains a definite amount of the total information contained in the initial data, and the first PC (PC-1) includes the greatest source of information in the data set.
PCA was performed using the data set listed in Table 1.

The accuracy of the data acquisition system NI9203 is ±0.18% for percent of reading (gain error).
All the experimental data were obtained in horizontal channels, with the gravitational pressure drop considered negligible.
Therefore, it is neglected for data reduction in the two-phase experimental pressure drop.
Fig. 3 shows the single-phase friction factor obtained from experimental data as a function of the Reynolds number.
Moreover, the single-phase friction factor was deduced from the experimental data.

Results and Discussion The scattering curves obtained by SAXS data were analyzed by EasySAXS software [9].
It is worth mentioning that from SAXS data it is not evident whether the main phase is nanocrystalline or amorphous, but this can be evaluated from the complementary XRD data, together with the phase identification and quantification.
Right: XRD data of Sample #1 at variable temperature with thermal evolution of phase fractions by sequential Rietveld refinement (HighScore Plus).
Data were collected up to 950°C at 10°C steps, through the phase transition anatase to rutile (see Fig. 1), and sequentially refined by Rietveld method in HighScore Plus.
The data from Total Scattering experiments were processed by RAD software [16].

For the data reduction and examination, a routine was written in the program environment IDL ©.
Another possibility is the use of a smoothing or filtering routine during the data evaluation.
This figure shows the data quality of the images used for the evaluation.
The stress at this position is 40 MPa ± 10 MPa (data from image analyses).
Rothkirch for their assistance during the measurements and during the data analysis.

According to statistics, the measured or forecast data, evaluation indexes are assigned values and standardize.
Table3 The results of performance indexes of example structures[6] Structure ID internal damage index number of inelastic cycles maximum inter-storey drift energy-concentrated quotient Equivalent vibration cycle/s 1 0.783 10.564 0.019 4.201 0.411 2 0.762 3.338 0.025 5.334 1.968 3 0.861 2.278 0.043 4.616 1.191 4 0.726 6.073 0.017 4.238 0.752 The data of the internal damage index, number of inelastic cycles, maximum inter-storey drift and energy-concentrated quotient in the table3 are standardized according to equation (3) and equation (4), and the results are shown in the table4.
Table4 The normalized values of performance indexes of example structures Structure ID internal damage index X1 number of inelastic cycles X2 maximum inter-storey drift X3 energy-concentrated quotient X4 1 0.783 0.789 0.380 0.457 2 0.762 0.933 0.500 0.619 3 0.861 0.954 0.860 0.517 4 0.726 0.879 0.340 0.463 Selecting the normalized formula of the butterfly catastrophe model, using complementary criteria, mutation membership function values of reinforced concrete structures for the three level earthquake resistant design and earthquake damage of four kinds of structures are calculated using the indexes data in table2 and table4.The results is shown in the table5 and table6.
It is convenient to deal with data by the method, that decreases the subjectivity impact.
But due to the incompleteness of selection of indexes and data, and the uncertainty determining the index weight order, extension of the index system, the accuracy of obtaining data, and the index weights determined need further study and improving.

All the data were averaged from 9 replicas.
All data were calculated by Excel 2003 and subjected to statistical analysis of variance (ANOVA) and correlation analysis in the SPSS 11.5 statistical package.
Data shown are means and standard deviation of nine replicates (μg g-1dw h -1).
Data shown are the means of nine replicates (%).
Data shown are the means of nine replicates (%).

The current approaches, to evaluate the failure probability or frequencies of equipment, including the Measurement Statistical Method, the Probability Assessment Method and the Fuzzy Assessment Method, need abundant sample data for the modeling and parameter estimation.
The traditional assessment method derives the probability density function or membership function of equipment voltage tolerance capability in uncertain area from sample data.
With personal computers (PCs) as sensitive equipment, the IPDF of voltage tolerance capability of PCs is derived from the sample data obtained in the laboratory tests.
It is assumed that three-phase faults occur on a selected transmission line, and the distribution of fault positions obeys uniform distribution [6].The sample data of voltage sags magnitude at the supply bus 29 are obtained and the probability density function fsu(u) could be derived.
In the laboratory tests of PCs, 60 sample data of voltage sag magnitude and duration tolerance are obtained, and the IPDF are calculated by the method mentioned in section 2, shown in Fig.3 and Fig.4.