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It is easy for data collection.
Data Collection.
According to PAS2050, the data of the carbon calculation have two types: the activity level and the emission factor.
The activity level data refer to energy caused by activity of the product life cycle; the emission factor refers to the number of unit energy GHG gas emissions (such as CO2e/kwh, CO2e/ tCe, Ce/ tCe, etc.).
To buildings product, the data of fossil energy that is coal, oil and natural gas can be solved through the actual measure, and the data of land utilization can be solved through calculating carbon emission, then back calculating into standard coal consumption (this paper called virtual energy, assuming that the all CO2 from standard coal burn).

It is the data that the ESPI system captures and their post processing that produce the full field stress-strain map for the entire scanned region.
In order to minimise such noise, two types of averaging techniques were carried out on the ydisplacement u(x,y) data, but for generating the stress-strain curves displacements were only recorded in the longitudinal (y) direction.
The calculated stresses which correspond to 5% plastic strain were obtained from the same ESPI raw data as the 0.2% PS values above.
A similar drop in the 5% PS values is observed across the weld as shown in Fig. 6 Basic metallography agrees well with micro hardness measurements and ESPI data in the definition of HAZ width.
All data are from the same FE model running the same flight cycle and values taken at the same time point in the cycle.

Their review shows tremendous variability in the available data.
Mathematical data treatment was carried on using routine procedures; the data obtained are reported in [20].
The quantitative data on mechanical characteristics of alloys are listed in the Table.
The data agree with the results of previous studies [21].
Analysis of data was made by the statistic method for physical experimental data treatment using a double t-criterion [20].

At each hazard intensity level, a group of displacement data is obtained as the output of corresponding structural simulations
Each vertical line of scattered data corresponds to an intensity level.
A statistical distribution is fitted to the data for each intensity level on each vertical line.
The mean and standard deviation values of the response data are also given in the Fig.6.
The curves become flatter as the nature of the statistical distribution of the response data.

With comparing the datasets of VQ/DTW, VQ/DHMM, CHMM state-1 recognition algorithm and CHMM state-2 recognition algorithm, the results of our experiment show that: (1) CHMM state-2 branch method performs primely in reduction of the recognition time; and (2) the recognition accuracy is improved eventually.
Meanwhile, another feature is to extract a characteristic sequence from each phonetic frame data, including Linear prediction coefficients of signals, Linear Predictive Cepstrum Coefficients(LPCC) and Mel Frequency Cepstrum Coefficient(MFCC).
Conclusions and Future Work We propose some improvement strategy on account to the problems mentioned above: (1) Restrict the data sample accuracy; (2) Marking manually could reduce the accuracy of classification as well as the capability of self-learning, while program is implemented simply as well as in favor of training

In general, this parameter has been found by varying it to minimize the sum of the squared deviations between the actual and predicted data.
According to the data reported in Table 1 we can see that water absorption for the composites is higher than for the matrix.
Table 1 - Moisture content and geometric data for the water sorption in vegetable fiber reinforced composites (30% w/w fiber contents).
Numerical results of average moisture content of the composites under investigation were compared with the experimental data.
Data comparison indicates that the model employed here is appropriate because the good agreement obtained in all studied cases.

In-situ data reveal that deformation of interchamber pillars in time is realized mainly in creep with their gradual failure [1].
Average value of long-term strength received on the base of these results is, which integrally corresponds to factual data.
Fig. 2 presents typical graph of subsidence growth constructed according to factual data of surveyor observations.
Estimation data presented as well in “” are illustrated as curve 2 in Fig. 5.
Implemented verification showed acceptable correlation of mathematical modeling results with in-situ data.

Therefore, the main focus of this study is to observe difference between lifetime predictions based on CT and CRB data and possible application of CT data for CRB specimens.
Finally, obtained results were compared with experimental data.
In this manner, the K-calibration was carried out for different crack lengths in range of experimental data.
It is even more evident in the case of fatigue crack propagation data, taken from CT specimen.
Therefore, application of experimental data from one specimen to another is not perfectly accurate.

Both of this factors relies fully on laboratory data.
However, the UKM viscosity model (ηUKM) relies fully on the experimental data.
Based on this hypothesis, a mathematical model of viscosity which has been developed whereby the technical temperature factor value (kst) and technical time factor (kmt) are obtained through engine oil’s technical data.
VI is the viscosity index that can be obtained in the technical data from the engine oil’s manufacturer.
At the same time, for the viscosity of viscosity index numerical order (ηVIno), the referenced viscosity value is determined at the temperature of 15°C, and the values of kst and kmt are obtained by the use of the data noted in Table 1.

As to the above issue, an improved steam flooding experimental method is launched to obtain accurate base data, and then simplified JBN method is applied for data processing.
Both reservoir engineering and numerical simulation need relative permeability data for project design and performance prediction.
Third, the migration of unconsolidated silica sand may cause the water and oil production fluctuant, so data processing is difficult.
Data processing method.
Relative permeability data processing is done by applying the classic JBN method.