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PLSRM was achieved through actual measurement data and calculation results of geo-stress, then fitting data was ready.
(2) calculated covariance matrix V of standardized data matrix X, V is the correlation coefficient matrix of X
The data was extracted as dependent variable Y for subsequent calculation.
The independent variable data matrix would be written as E, the dependent variable data matrix as F.
Usually due to cost constraints, the measured sample points were relatively few and valid data obtained from tests was very limited.

Managerial decision-making process being environmentally friendly is based on experimental data.
This conclusion is supported by analytical data, which indicate that car wash wastewater is a significant source of environmental pollution due to the diversity and high concentration of pollutants contained in it.
Complex indicators are calculated by integrating heterogeneous data that comprehensively characterize the analyzed process.
Table 1 shows the initial data for calculating the prevented damage from the discharge of detergent solutions after car washing into surface water bodies.
This leads to a targeted reduction of environmental costs by enterprises.

Meanwhile, the median filter can easily adapt themselves, which can be further improved its properties, therefore, it can replace some of the linear filter is not capable of digital image processing applications. 4.1 Using median filter optimization wavelet ECG denoising experiment Median filter optimized wavelet threshold denoising step is shown in figure 1, first read into the ECG data, select the appropriate wavelet bases Of ECG wavelet decomposition, and then extract the wavelet coefficients at each scale, obtained layers denoising threshold, then median filtering of ECG signal, according to the threshold of the median filtered ECG data denoising and wavelet reconstruction, the final denoising effect evaluation and output denoised ECG.
Fig. 1: Median filter optimized wavelet threshold denoising The procedure according to figure 1 and using the data in the MIT-BIH standard ECG database 100.dat programmed in MATLAB simulation results as follows.
Noise reduction by wavelet Threshold-Ing [M].

Practical design method of the snow load for the low rise roof structures Fan Meng1,a,Fanghui Li1,b, Ming Gu 2,c , and Shizhao Shen3,d 1School of Architectural Engineering, Heilongjiang University, China 2State Key Laboratory of Disaster Reduction in Civil Engineering, China 3 School of Civil Engineering, Harbin Institute of Technology, China a790693001@qq.com , bfhli_2000@163.com, cminggu@tongji.edu.cn, dszshen@hit.edu.cn Keywords: design method; snow load; load code; low rise roof structures Abstract.
The data gathered here indicates that buildings may be at risk of failure due to large or uneven snow loads, and that his susceptibility is particularly apparent in certain types of building construction, as well as those structures that are poorly maintained or designed.
——basic snow depth (m) on the ground, is defined as the annual maximum value for the whole season with a return period of 100 years, and is estimated from meteorological data of the ground snow depth observed for a certain period. —— equivalent unit weight for ground snow, .
The environmental coefficient, basic snow depth on the ground and equivalent unit weight for ground snow is influence on the value of the snow load on the ground. 2.5 The Chinese Load code (GB50009-2001) The distribution factor of snow and ground basic snow load is given directly by meteorological statistic data in Chinese Load Code (GB50009-2001).

Data is clocked into the 32–bit shift register on each rising edge of CLK.
The data is clocked in MSB first.
Data is transferred from the shift register to one of six latches on the rising edge of LE.
A Study on Phase-Noise Reduction Method in Phase–Locked Loop Systems.

The methods for recovery metals from the wastewater include chemical precipitation, electrolytic reduction, membrane technology, ion exchange and adsorption [2].
Fig. 5 Effect of initial concentration on the removal efficiency The equilibrium adsorption isotherm is one of the most valuable data to research the mechanism of adsorption.
Table1 Isothermal adsorption model Ion Langmuir Freundlich Qmax (mg L-1) R2 KF (mg g-1) 1/n R2 Ag+ 4.2599 0.9931 1.04 1.4257 0.9993 From table 1, we can know Freundlich isotherm model proved an excellent fit to the isotherm data, according to R2 values, which indicates that a multilayer adsorption process plays an important role in the whole process.
The Freundlich model gives an excellent fit of adsorption isotherm equilibrium data, which suggests that the most Ag+ adsorbed are multilayer arranged on the surface of vinasse.

Besides the product should meet the requirements about functional implementation, cost reduction, assemble and disassemble conveniences etc. there is a more important property a product should be monitored and controlled is its environmental impact.
Many companies and manufactory process researches also achieved many valuable data and experiences in the energy related practice process researches
And the different technical phrases have to be connected with the uniform data relationships expect for that of simple collaboration.
Fig.5 EFM application2: energy saving technological parameters optimization design Conclusions Take good use collaborative design network information and data science technology in energy footprint mapping frame methodology.

The speech signal is decomposed using EMD into the data adaptive bases up to the level of fundamental oscillations [3].
We can separate from the rest of the data by: (5) Note that the residue still contains some useful information.
Under certain statistical assumptions, soft thresholding can result in slightly greater noise reduction [4].
The noised experiment data is the pure speech signal in Figure 1 corrupted by white noise and SNR=7.5dB.

Secondly, in different areas out of net the change regularity of extrapolation error is further discussed with types of data in different length baselines.
Therefore, in this paper based on the traditional interpolation model, from the characteristic of mathematical model and example data, the extrapolation accuracy in different areas outside the net will be analyzed in detail.
We select simultaneous observation data of nearly 20 minutes (sampling rate is 1 sec) from these reference stations, and then use GPS processing software with high precision to calculate the double-differential integer ambiguity of every baseline.
During the observation period the extrapolation accuracy is decreasing as the reduction of the satellite’s altitude angle.
According to the analysis from error propagation law and example data it shows that for the CIM, the extrapolation accuracy is decreasing with the increase of the distance away from the net, however, it can be reached within 5 cm in the areas that not more than 40km to the net external.

Finally the data are analysed to find appropriate damage metrics.
Data acquisition.
Fig. 3: Measuring system for data acquisition Signal Processing The most challenging task during the development of a health monitoring system is to extract the desired information out of the huge amount of acquired data.
In the approach of this project the first step to reduce the amount of data is the calculation of the frequency spectra from the captured time series data.
Damage Diagnosis After the calculation of amplitude and phase of the transfer function, a further data reduction and interpretation is essential.