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It shows the significant increases in carrying-capacity of the new A-380, while achieving reductions in aircraft noise.
In the world of megabuck test programs it is possible to have use of an on-board readout from a ground based tracking system that will provide real-time data on position, including lateral deviation.
A radio altimeter signal is also recorded to provide the third coordinate data.
When operating properly, with judicious location of the ground units, selection of an appropriate location on the airplane for the DMU antenna, and a good computer program to analyze the data, this method produces very good results.
Section A36.2.3.2 calls for recording of position data during the entire time period in which the recorded (noise) signal is within 10 dB of PNLTM.

FTIR spectroscopic data were recorded using the Thermo Fisher Scientific–Nicolet iS5 spectrometer in the range of wave numbers 4000 to 400 cm-1.
To study the thermal stability, data were recorded using a differential scanning calorimeter (DSC) and measuring the heat from 30 ℃ to 300 ℃ at 5 ℃ per minute.
Thermal Analysis DSC data analysis.
The reduction may also be due to the spin canting effect [52, 53].
From the experimentally observed data, even at T = 10 K, the value of HC is 299 Oe, which is much higher than the value of the theoretically calculated H0 at T = 0 K.

The main models found in the literature for predicting UTS of pearlitic lamellar graphite iron are based on either regression analysis on experimental data or on modified Griffith or Hall-Petch equation.
The present work data correlation between eutectic cell size and UTS presented in Fig. 5 shows that different cooling rates provide different linear dependencies between the eutectic cell size and UTS.
As seen in Fig. 3 the experimental data covers a broad range of eutectic cell sizes.
(1) (2) Table 3: Microstructure data and UTS.
Fig. 11 shows the predicted UTS from the Eq. 7 and 8 in comparison with the experimental data.

This combined factors make appropriate research for estimating the changes in ecosystem services value due to land use and tourism development. 2.2 Land use/cover date collection The land use datasets of Yangshuo selected the TM remote sensing image in 1999, 2005, 2010, and reference Yangshuo(1:50000) land use planning , take the forecast data of land type and area in 2020 as a part of this study.
In the data forming process, we asked some experts for analysis, each type of land data is reliable.
In this study, on the basis of national standard land use types and combined with the actual situation, the data sets were reclassified into six categories, including woodland, cropland, water body, wetland and unused land. 2.3 Calculation of ecosystem services value The change of ecosystem service function depends on the change of ecosystem service value, and different land use types have direct effect on ecosystem service value.
Contrast the change situation of cropland’s area in three towns, we can found that, from 1999 to 2005, the area of cropland have suffered different degrees of reduction.
The unused land area reduction is the most obvious, from 2005 to 2010, the value reduced 15.94 million.

According to the original measurement about joint centers and the corresponding matrixes, at time t (after movement) joint centers: Ps , Pe are given by Eq.8: ； (8) The positions of radius and lunar side markers on wrist and center of wrist are given by Eq.9: ;; (9) Based on the foregoing data, joint coordinate systems are to be built: (1) Shoulder joint coordinate system: original point at the center of shoulder joint, assuming that this coordinate system or frame has no relative movement to trunk
Range of joint angle of basic action performed Joint DOF Range of joint angle of basic action performed（°） shoulder elbow wrist flexion(+)/extension (-) -71.2~125.8 1.2~147.1 -35.2~73.9 internal(+)/external(-) rotation -2.8~58.3 2.4~138.8 abduction(+)/adduction (-) -68.8~52.3 1.3~29.8 In our view, the combination of several actions(such as handle from neutral position to head vertex, handle from neutral position to occipital bone etc.) can perform a whole motion (such as combining hair), The tested angle of combining hair action were compared with other reference data in table 2: At year 1990, Safaee-Rad et al. used two plane camera recorder to measure and analysis the motion range (joint angle) during the combining hair act of 10 persons, the original position adopted the standard anatomical position.
The shoulder internal (+)/external (-) rotation angles of our data are relatively bigger and the wrist angles relatively smaller than the Safaee-Rad’s, but totally speaking they had no contractions; At year 1994 Romilly et al. made the similar research as Safaee-Rad’s, they adopted three dimensional video capturing system to record 22 acts of ADL for 19 persons, including drop water, open door, dial phone, combing hair etc.
Because that the shoulder motions in Romilly’s paper were relative to shoulder blade, and the definition of shoulder joint motion is different to this paper, so the corresponding data can’t be compared, but the other parts have no significant difference.
The data shows the freedom of upper limb bionic mechanism can be appropriately reduction when the motion simulation accuracy demand is relatively too high, or the main motion can play motion compensation role to some extent.

Although the direct evaluation method is more intuitive comprehensive, but assessing project classification is more complex, need to consider all round; Required data not only diverse but also as complete and accurate as possible, to some extent limits its usefulness.
In this method, a computer model through the specific location of data as a small amount of several input parameters.
Quantitative steps to determine the oil spill caused by the natural resources damage caused by the reduction of natural resource service.
Shahriari and Frost proposed a mathematical regression model to assess the clean-up costs, based on data from ITOPF of 1967 cases (1967-2002) in 2008[10].It contains the main influence factors for it: volume, oil density, distance from the shore and transmittance, under the specific results as shown in the table 2.
Therefore, it is recommended that we should extracte from the historic claim cases related fuzzy quantitative factors and conduct data processing.

Some scholars have been made some progress in the study of the relationship of materials flow stress from measured data of the experimental material to describe the characteristics of deformation[4-7].
It is verified that the prediction model of the material is correct in the conditions of warm forming through comparing the model and the obtained experimental data.
Fig. 1 Hot compression samples Specimens are tested on the compression conditions of strain rate at 0.5, 1, 5, 10, deformation temperature taking 280 ° C, 350 ° C, 390 ° C, 430 ° C and reduction rate was 60%.
Flow Stress Constitutive Equation Data Fitting and Creation of Constitutive Equation Fig. 3 The Influence of strain rate on flow stress curves The study shows that There is a certain relationship among the flow stress σ, strain rateand temperature in warm deformation.
From the discuss above we can know the overall trend of the flow stress equation is the same as the experimental data.

Then the time response function of model(1)is (2) And the .We can get the fitted value of accumulate generating sequence of by calculating the Eq. 2, as the data is the predictive value in the context of .The fitted value can be attained by direct reduction of original time series by inverse accumulated generating operation (3) 1.2 forecast model of Markov chain 1.2.1 state division Any state can be expressed as, for stochastic sequence according with n-th-order Markov non-equilibrium, and,, ,,,,,,is the mean value of the original data.
In the ordinary course of events, we can divide the residual series into three, four or five states on the basis of the data distribution and fitted residuals of the coal bed methane dynamic productivity of coal mine area.
Examples of calculation The research data of coal bed methane dynamic productivity of coal mine area were from the NO 7011 drilling field at 3# coal seam in some mine Jincheng city Shanxi province.
Fig. 1 The layout of drill holes in NO 7011 drilling field Dim the data of coal bed methane dynamic productivity of coal mine area at different period as the original gray series,as the fig. 2 shows.

Solving process The solving steps of the problem of reactive power optimization based on improved particle swarm algorithm are as follows: Step 1: Read data.
Read the structural data and control parameters of system from the specified text; Step 2: Initialize particle swarm.
Decode each of the particles in the swarm separately, begin power flow calculation according to the decoded data, get the value of power loss, bus voltage and other system operating parameters under each group of control variables; Step 4: Calculate the objective function value.
Table1 Power loss before and after optimization BEFORE AFTER System power loss (p. u.) 0.070540 0.046051 Rate of decline 34.72% Table 2 Bus voltage before and after optimization Bus number 1 2 3 4 5 6 7 8 9 Without fan 1.00 1.00 1.00 1.00 0.99 1.00 0.96 0.98 0.99 Before optimization with fan 1.00 0.94 1.00 0.95 0.95 0.99 0.93 0.96 0.97 After optimization without fan 1.05 1.02 1.05 1.03 1.03 1.05 1.00 1.03 1.04 What we can see from the data in Table 1 is system power losses is significantly reduced after reactive power optimization, this is one of the purposes of reactive power optimization; What we can see from the data in Table 2 is that except for voltage constant node, other nodes voltages drop after wind farm is accessed, especially the wind farm access point, there are 2 unqualified node of which voltage is lower than 0.95, this is because of the local reactive power shortage caused by asynchronous generator.
[4] HAMEDANI GOLSHAN M E, AREFIFAR S A: Distributed generation, reactive sources and network-configuration planning for power and energy-loss reduction, IEEE Proceedings: Generation, Transmission and Distribution, Vol. 143 (2006) No. 2, p. 127-136

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