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In recent years, studies had been carried out in the field like different site conditions[1-2], different coverage[3-5], hydraulic parameters and erosion process[3-4], and many achievements had been received in soil erosion processes and the vegetation’s reduction of runoff and sediment.
The simulation system generalized model According to the rainfall-runoff data for many years and the relevant research literature on Shenmu County [8], high frequency and high probability of rainfall intensity was 1.5~2 5mm/min, determined the scouring flows according to plot size and fills the runoff, scouring flows was identified as 9l/min and lasted for 40min by analyzed and calculated.
Analysis and statistics based on the experimental observation data, list a few parameters such as runoff time, sediment yield, runoff and infiltration rate
Runoff and sediment relations data points were shown in Fig.3, artificial grass slope and ecological-restoration slope which data points were located in the bottom side of the chart, but slope data point of the bare slope distributed the entire chart.
Both compared with the bare slope, reduction of sediment yield more than 98% and flow reduction about 50%, infiltration rate increased more than 114% on ecological-restoration slope; For artificial grass slope, sediment yield and runoff respectively reduction of 95% and 20%, increased of infiltration rate about 50%

For this reason the method has a poor noise reduction ability, in addition the imaging system itself is disturbed by various of noise inevitably, so it is necessary to choose suitable methods to do de-blurring and de-noising [5].
The imaging results of experiment data show that the modified algorithm proposed in this paper improves the performance of RL algorithm, and it can reduces the influence of noise well.
The enhancement of the sparsity is achieved by grouping similar 2D fragments of the image into 3D data arrays, the arrays are called ‘‘groups’’.
The original image, the original image spectrum, the reduction clarity image with noise, and the reduction clarity image with spectrum noise are shown in Fig. 1(a) to Fig. 1(h).
The second experiment is an image with complex scene, and the original image, the original image spectrum, the reduction clarity image with noise, and the reduction clarity image with spectrum noise are shown in Fig. 2(a) to Fig. 2(h).

Again, the curve of elasticity modulus is fitted in the light of the experimental data, which can reveal how the elasticity modulus changes with plastic deformation.
It was concluded that the non-uniformity of yield stress can greatly impact the reduction of residual stresses.
The test data can be accurately expressed by
The parameters, including the yield strength, elastic strain of post-quenching, equivalent plastic strain of quenching, elastic modulus and shear modulus after quenching, can be achieved by substituting the data in Table 1 and Fig. 1 into Eqs. (1-7).
The comparison of the improved simulations with experimental data [7] is shown in Table 2, as well as of the unimproved simulations with experimental data.

The formation of some reducing agents in the heat-treatment process should be responsible for the reduction from Eu 3+ to Eu 2+ ions.
Introduction In recent year, persistent spectral hole burning (PSHB) has been paid to considerable attention because of its potential application in high-density optical data storage using the spectra holes [1-6].
These holes allow frequency-domain data storage, so that storage density increased considerably by an optical multiplicity which roughly equals to the ratio of the inhomogeneous line width to the homogeneous line width [4].
However, PSHB materials pose some problems for the practical application in optical data storage.
These formed reducing agents leaded to the reduction from Eu 3+ to Eu 2+ ions.

In detail network configuration, power consumption, data acquisition and data aggregation, signal analysis and data reduction as well as reliability and robustness are discussed.
wireless RF data transmission MEMS Central unit Sensor data analysis Sensor mote Sensor cluster Sensor cluster Cluster data analysis Structural data analysis Fig. 2.
Data Aggregation and Signal Analysis.
Such data aggregation and data reduction methods are also in general needed to minimize the data traffic that is of interest if the monitoring system consists of a high amount of motes.
The next steps are to look for efficient data reduction methods as well as for sufficient algorithms for signal analysis, so that the great amount of measured data is reduced to only a few relevant data.

Introduction In essence the lean philosophy has two principles, namely the reduction of the wastes and respect for human [1].
This method’s relevant aim is the reduction of the wastes with improvement of the logistics processes.
Naturally before making of the current state map we have to know the examined processes and collect the more important data.
Creation of the dynamic value stream mapping: The value stream managers have to know their product lines’ material- and information flow and have to collect the data for the current state map.
The main objective of the process improvement is the reduction of the production lead time in the interest of making products for the customers’ orders.

For analyzing the transient behavior due to thermal effects, a series of projections were taken from two consecutive data scanning using the same number of projections.
Simulated CT data: (a) reconstructed sphere with total displacement of 10 mm and (b) with total displacement of 0,1 mm; (c), (d) representations of the circumferential line extracted from (b) Correction of thermal drift effects on CT measurements Projection based correction.
It can be observed a significant amplitude reduction of the 3 UPR harmonic for the corrected profile.
With the correction, a reduction of approximately 60% on the average RONt value can be observed.
Comparison between RONt values obtained from original and corrected profiles Conclusions This paper described the effect and an approach to minimize the influence of thermal drifts during the data acquisition with CT systems.

In the next step, detailed material characterization of the multilayered sheet metal in combination with the short-term heat treatment is required as input data for the finite element analysis of forming processes.
Tensile tests and layer compression tests were carried out for the evaluation of material data.
A reduction of the true stress of about 75 MPa can be achieved.
In the next step, the evaluated material data is used as input data for the finite element analysis of a deep drawing process and a subsequent validation of simulation and experiments.
In this context, true stress – strain curves and yield loci determined by means of tensile tests and layer compression tests were established as input data for the finite element analysis of forming operations.

The measured data were the amount of transmitted shortwave radiation, compared with a measurement point under unobstructed sunlight.
Default datasets are available for most of the data, separately for the different climate zones, tree sizes, etc.
Transmissivity was calculated from 10 minutes averages of irradiance data.
As described transmissivity data were obtained from field measurements, and were used for further modelling in order to give more general approach of the shading effect of alley trees.
Metrological data were taken from the meteorological station of Szeged (run by the Hungarian Meteorological Service) and imported to ECOTECT.

Ni-powder is obtained by the reduction of nickel nitrate in hydrogen.
After reduction, 2% paraffin dissolved in alcohol is introduced, followed by a homogenization for 90 minutes.
Before utilisation Cu-powder was deoxygenated through reduction into hydrogen.
STATISTICA is a comprehensive, integrated data analysis, graphics, database management, and custom application development system featuring a wide selection of basic and advanced analytic procedures for business, data mining, science, and engineering applications.
STATISTICA program realizes linear and non-linear, logistic, exponential statistical data processing.