Search results

Methods of data collection and data process will be introduced.
The problem needed to be solved mainly involved data acquisition and data processing.
When doing data acquisition, one needs to ensure a high quality and complete point cloud data while minimizing data acquisition time and improve efficiency.
Through point cloud stitching, matching coordinates, point cloud data extraction, noise reduction, the point cloud regular gridding and other steps, the whole space concrete diaphragm wall model can be got and finally the overall deformation can be got by comparing the model of different periods.
Such as the machinery and human disturbance during field data collection, data processing error and so on.

The AE technique requires a permanent data acquisition system with a sufficiently high-sampling rate.
Fig. 3 Accelerated corrosion equipment Fig. 4 Acoustic signals for a possible wire break on 27/11/2000 at 1:33am Test results Corrosion data.
Acoustic monitoring data.
Vibrating wire (VW) strain gauge data.
Initially the VW strain data fluctuated significantly over time.

The data are captured after the image is converted into a bit pattern by using optical scanner.
Data acquisition was performed by collecting 6 genuine, 1 simple forgery and 1 skilled forgery signatures from each of the 90 signers.
This experiment was implemented by using a set of signature data consisting of 220 signatures for genuine and 110 signature for forged.
(repeated sentence,perhaps) Potential application areas are including postal automation, bank cheque processing, automatic data entry etc.
The preprocessing were performed by noise reduction, binarization and normalization.

Reference data and the results of chemical dosing.
In Table 3 are presented the structural data of the 4161 batch investigated: the network parameters of the batch matrices and their standard deviation.
The data obtained has been set as the network parameter (CFC) and of the solid solution of α-Al is a = 4.058 Å.
Corroborating the diffraction data with the elemental distributions shown, the natures of the compounds as set forth in figure 6a) were determined by colors, namely: green - compounds with Fe, red - compounds with Si, yellow - phase S, magenta - phase S`; blue - other compounds with Fe.
Analyzing the data in Table 3, we can say with certainty that the Al network parameters are higher than the standard because this increase is higher than the associated standard deviation.

X-ray diffractometer provided and analyzed data for cement mortar samples on the Bruker D2 PHASER.
It is based on the measured data of the reference sample, as per the calculations Eq. 2 and Eq. 3.
Based on the presented data and analysis these conclusions could be drawn: · Composites with glass and eggshells performed closest to the reference compressive strength
Data. 10(4) (2018) 2213-2239
Data. 10(1) (2018) 405-448

Hydrological analysis carried out using the last ten years rainfall data of Cemara rain station and Husein Sastranegara airbase rain station.
The consistency test on the rainfall data was carried out using the double mass curve method, while the distribution characteristics were tested by annual method maximum series.
The correctness of the rainfall distribution data is tested by the compatibility test using the Chi-Square method and the Kolmogorov-Smirnov method [20].
Data gathered from the infiltration test was used in numerical analysis and simulation using Geostudio SEEP/W software.
Pervious Concrete Physical Characteristics and Effectiveness in Stormwater Pollution Reduction.

As shown in Figure 4, the calculated orientations of the filling simulation are by an average of about 10–20 % higher compared to the Micro-CT data.
Furthermore, the filling simulation shows only a slight change of the core zone, whereas the Micro-CT data shows an increase in the core zone depending on the plate position from inside to outside.
The shifted core zone, which can be seen particularly at the position L3 of the Micro-CT data, is caused by the eccentric film gate in the tool.
In contrast to the aging in water, where a further reduction of tensile strength takes place [26], no further reduction in tensile strength after the first 96 hours of aging in climate conditions of 85 °C and 85 % RH was recognizable.
Jin: Comparison of recent fiber orientation models in autodesk moldflow insight simulations with measured fiber orientation data.

For the particular case of reaction (2), its equilibrium constant is obtained by using the sulphur capacity concept, i.e: (9) The data used for the estimation of the constants are given in Table 1.
Thermodynamic data used in the calculation of equilibrium constants.
(17) The combination of equations (14), (16) & (17), yields the solution for the convective mass transfer for each species within the system, namely: (18) To evaluate and discuss our model predictions, data was taken from two different steel shops.
The largest variation between our transfer coefficients and those estimated from Fruehan's data is in the order of 50%.
ModelConclusions From the development of our model and its comparison to real data, we conclude the following: 1.

In every notch radius, the reduction in two-step test occurred.
The stress intensity factor range ∆K was calculated for the second step stress range (2σa2) at fatigue limit and was compared with the constant amplitude data.
(∆Keff)th was used as the constant amplitude data in the comparison.
The two-step test data for different notch depths are shown by ● and ■ in Fig.13.
The fatigue limit data shown by ● and ■ agreed well with the (∆Keff)th data.

The BP model observes the algorithm ideas: first of all, inputting any initial weight values, then the model data is calculated by the two formulas above.
Quantification of factors Collecting the different mines measured data for 10 years, we choose 10 mines of them to be the sample of the prediction model and put the data in Table 1.
According to the result of the quantization factor, in 10 sample data, choosing NO.1-4 and NO.7-10 as the training samples, then NO.5, 6 as the test samples.
The network training error reduction curve is shown in figure 1.
Figure.1 The network training error reduction curve Table3.