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It studies the key technology in the process of data acquisition, data pre-processing and model reconstruction, and put forward a model reconstruction method to improve the blades design efficiency.
Data acquisition and pre-processing Data acquisition In reverse engineering, data acquisition method for object surface subdivided into contact and non-contact[3].
Pre-processing of point cloud includes noise point remove, data compaction and data extract.
Data compaction can reduce total data amount and improve the data pre-processing speed, but too much data reduction will effect the precision of model construction, so the point cloud is reduced in the condition of guaranteed precision The separation parts should be kept after point cloud reduction, it is useful for making surface shape when surface is designed, and also can provide precision data for subsequent surface modeling, Importing point cloud in Geomagic software for pre-processing, in order to indentify the characteristics of the model, the point cloud display style is changed to grid establishment style, as shown in Fig. 2.
British library Cataloguing in Publication Data.2008:05-120

Developed in Google Colab with Python, it ensures flexibility and is linked to Google Sheets for real-time data input without additional software.
The model was tested on production data from the Southern section of the Mezhyrichchia quarry using a CAT 980H wheel loader and a KrAZ-65055 dump truck.
Experimental data indicate that on road sections with a gradient exceeding 8 %, fuel consumption may increase by 1.5 to 2 times.
To carry out simulations based on real production data and compare the efficiency of using a wheel loader versus a dump truck, justifying the feasibility of each option depending on operational scenarios.
Dimensions, volume, and mass of blocks (empirical data collected by the author at the Mezhyrichchia deposit) (developed by the author based on the research by [18] and modeling results) Fig. 3.

The studies of intersection safety evaluation mainly rely on accidents and conflict data.
Data Description These intersections cover four cities from East China to West China, major cities and some towns, therefore, these safety data can represent widely.
Besides that, these accident data also contain the crash type of each accident.
The third type of intersection (called T-3 intersection for short) has some accident data, but these data is not full in recently three year.
IDP is weights by IADP and ICDP with different weights, which determined by the relationship and quantity between accident data and conflict data.

After that, selected samples were cold rolled to 30 and 75 % reduction.
Figure 2a shows the microhardness data of PA samples measured after aging at 100 °C versus aging time up to 6 days.
Figure 3 shows the DSC curve obtained at the heating rate of 10 °C/min for the cold rolled (to 30 and 75 % reductions) materials.
Figure 4 shows the microhardness data of both the deformed materials measured after ageing at 100 °C versus aging time up to several days.
Figure 4 The microhardness data of both the deformed materials measured after ageing at 100 °C versus aging time.

Furthermore, for this welding travel speed (200 mm/min), taken overall (Fig. 2(d)), the data suggests that there is a small increase in particle size when averaged over the whole PZ with tool RPM.
This data is presented in Fig. 2(c).
This data is summarized in Fig. 3(a), which gives the standard deviation of the cell area distributions across the process zone.
From this data it is clear that, as well as the average particle size increasing, the degree of clustering, or inhomogeneity in spatial distribution also rises across the PZ from the advancing to the retreating side.
However, this behaviour is less pronounced when a higher rotation speed is used and the particle's spacing becomes more homogeneous across the processed zone, resulting in a reduced gradient in the data in Fig. 3(a).

According to the r-factor, SENSE causes the data collecting time to reduce and speed up.[11][12].
The reduction in SNR is caused by two factors, the first is the reduction in sampling time.
SNR Value Difference Test for MRI Phantom T2WI TSE SENSE with R factor 2 Between Before and After Denoising Non Local Means (NLM) Technique SNR p value r2 pre * r2 post < 0,001 where r2 pre indicates r factors 2 before denoising NLM r2 post shows r factor 2 after denoising NLM and * indicates the comparison SNR Data Fig 5.
This study used data from two R factor trials.
Rosseel, “On the Definition of Signal-To-Noise Ratio and Contrast-To-Noise Ratio for fMRI Data,” vol. 8, no. 11, 2013, doi: 10.1371/journal.pone.0077089

In order to assess the impact of railway traffic on the environment it is necessary to have data on noise levels in the vicinity of railway tracks.
It also provides sufficiently detailed data for analysis of a train passage by the measurement point.
The data needed for calculating noise indicators by prediction model "Schall 03" were collected during noise measurements.
The main lack of "Schall 03" model refers to inability of changing data that describe technical conditions of rolling stock and railway infrastructure.
These data are presented as constant input parameters and relate to the conditions of the German railways.

Fourier transform infrared spectrometer (FTIR) and electron dispersive spectrometer (EDS) data suggested that the hybrid material were composed of Au, AgCl and PPy.
The catalytic reduction and oxidation of H2O2 by Au nanoparticle-AgCl@PPy was investigated.
It can be seen in Fig.5 that the reduction peak currents of Au nanoparticle-AgCl@PPy increased 1.5 µA on addition of 20 µM H2O2 (curve a and b), demonstrating that Au nanoparticle-AgCl@PPy can catalyze the reduction of H2O2 more efficiently than AgCl@PPy.
After adsorbing large amounts of electrons, the Au nanoparticles turn from electron acceptors to electron donors, indicating that Au nanoparticles-AgCl@PPy can catalyze both the oxidation and reduction of H2O2 efficiently. 4.
After the incorporation of Au nanoparticles, AgCl@PPy showed a greatly improved catalytic activity on the reduction and oxidation of H2O2.

Data was collected using monochromatic Cu Kα1 radiation (λ = 0.154 nm) over a 2θ range of 30-100°.
The modified Williamson-Hall method [2] was then used to determine dislocation densities from the fitted data.
X-ray line broadening measurements were used to estimate the increase in dislocation density after stretching and artificial ageing. 2θ scans of the {111}, {200}, {220} and {311} diffraction peaks were performed to obtain high resolution diffraction data and a Pseudo-Voigt function was subsequently fitted to each diffraction peak, in order to calculate its FWHM.
Overall, this data indicates a reduction in plate diameter with pre-strain, a narrowing of the size distribution, and a corresponding increase in precipitate number density, that continued to the high pre-strain levels investigated in this study.
(c) (a) (b) Fig.4 (a) The increase in strain hardening predicted by Eq. (2), using dislocation densities measured by XRD, compared to the T4 tensile stress strain curve, (b) the strength contribution from the T1 phase ( Eq. 3), using TEM data, compared to the expected yield strength if strain hardening is deducted.

So it is required that the initial data are as close as possible to the solution.
The reduction factor is usually chosen between 0.9 and 0.99.
It can be observed that, even if the parameter sets are very different from each other, there are good agreements between the numerical results and the experimental data.
Two identifications were performed on the basis of experimental data found in the scientific literature and were compared with other material parameter sets, obtained with a least square optimization technique.
Other experimental data, like the yield stress in equibiaxial tension and in plane strain, or anisotropy coefficients along different orientations, are necessary to definitely do such a choice.