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There will be a lot of benefits if these DGs are properly connected, such as investment deferring, loss reduction, peak shaving, and environmental pollution reduction[1].
According to Eq.(1) to Eq.(8), the distribution network expansion planning model considering DGs is firstly formed. 2) System data and parameters initializing.
Input data of distribution system which usually include transformer data, line data, switch data, shunt reactive power compensator data, and DG data.

That is to say, pseudo-static method is adopted to make vibration inertia force equivalent to a static force on slope bars and then strength reduction method or limit equilibrium method is used to calculate the safety factor, which can directly evaluate the stability of slope.
That is, pseudo-static method is adopted to make vibration inertia force equivalent to a static force on slope bars and then strength reduction method or limit equilibrium method is used to calculate the safety factor, which can directly evaluate the stability of slope.
After replaying the on-site records and reading the data, the peak vibration velocities in all sub-directions of each test point can be obtained through calculation, see Tab.1.
That is, pseudo-static method is adopted to make vibration inertia force equivalent to a static force on slope bars and then strength reduction method or limit equilibrium method is used to calculate the safety factor, which can directly evaluate the stability of slope.

China 3 Institute of Multidisciplinary Research for Advanced Materials (IMRAM), Tohoku University, Sendai, 980-8577, Japan adaiwenbin5210@163.com, bleeuu00@sina.com Keywords: glass-ceramics; Al2O3; BOF slag; chemically pure reagent Abstract: Directly recycling residual iron element in modified BOF slag after reduction and manufacturing glass-ceramics from these remaining inorganic slag materials is an effective way to reuse the waste heat and slag materials of hot BOF slag.
With the help of waste heat and some inorganic reagents within BOF slag, the hot slag undergoes modification and reduction processes, which not only recovers the residual iron element and waste heat in slag, but also manufactures the glass-ceramics (GCs) and increases enterprise economic benefits [1-4].
These processes were determined by the DTA data in Fig.2, with a heating rate of 7°C·min-1 for sintering process and 5°C·min-1 for crystallization process, respectively.
Yang, et al., Study on Preparation of Glass-ceramics from Reduced Slag after Iron Melt-reduction.

In fact, there is growing evidence that complexion transitions are accompanied by a reduction in interface energy.
It has also recently been shown that the existence of a nanometer thick intergranular film reduces the energy of the Au-alumina interface. [7] Grain boundary energy measurements in Ca-doped yttria have also shown that grain boundaries in a sample with a bilayer of segregated Ca had a higher energy than grain boundaries in the same material that, after a high temperature anneal, transformed to a complexion consisting of an amorphous intergranular film. [11] While the case for an energy reduction associated with a complexion transition is strong, there are some inconsistencies in the data reported in the earlier papers.
The dihedral angles were then determined from the widths and depths of the thermal grooves, using previously described procedures. [15] Topographic data along lines perpendicular to grain boundaries were measured on both the NGG and AGG samples.
Results Figure 3 shows examples of topographic AFM images from which thermal groove data was extracted.
Topographic data was obtained for lines perpendicular to boundaries with well-defined grooves in both samples.

The linewidth is given on wafer scale for a 4×reduction system.
The linewidth is given on wafer scale for a 4×reduction system.
This scatter plot on the right of Figure 8 shows a comparison of the n and k data of the optimized absorber stack (dark diamonds) in comparison with typical n and k data at 193nm wavelength (grey circles).
The n and k data were extracted from all available materials from the RIT-Web page [7].
For a linewidth of 150nm (and a numerical aperture of 0.36) the mask features of a 4× reduction system are large compared to the wavelength of light.

As a result of the study, the algorithm of making decision on the choice of optimal technology for the development of man-made deposits based on graphical data was developed, taking into account the type of minerals to be extracted, depending on the structure of the deposit and the specific features of its formation.
Therefore we formulate the basic requirements for the development of man-made deposits: · increase in the efficiency of minerals extraction; · reduction of the negative environmental impact on the region; · land reclamation after industrial activity.
The empirical data, obtained in the research, need to be grouped by certain features, and the connections between them need to be identified, that is, it is necessary to find a way to systematize them.
It allows us to combine multiple data into a certain number of groups and show connections between them.
The primary requirements to the development of man-made deposits have been formed: increasing the efficiency of minerals extraction; reduction of negative ecological influence on the region and land reclamation after industrial activity. 3.

The kinetic results showed that LHHW provided the best representation of the experimental data with the BaCl2-C catalyst.
The model's capability was evaluated by validating the experimental data, showing a good relationship.
The equations were used to find values that minimize the sum of squared differences between the measured rates and the calculated rates for the given data, as shown in Equation (24).
Polynomial equations were created using GC-MS data to calculate the concentrations of different species participating in the reaction, thereby enabling the determination of each reaction rate.
This prediction was simulated using MATLAB 2014 and graphically presented in Figure 15, which demonstrates a strong agreement between the experimental and predicted data.

The frame data contain types of hazards, disaster causing factors, induced events, vulnerability factors and model parameters etc., data types including spatial data and attribute data.
Besides the model data is stored separately, access attribute data and spatial data in computation, then output the results into risk dataset.
Data Management and Update.
Data is the basic of hazard research, fresher data is more reliable.
These parameters is extracted by historical data fitting, but data for some node is not sufficient, can only do research through Monte Carlo or experts experience.

Based on statistical data, the main sorts are as follows: water-inrush, slag, ground subsidence, landslide collapse, and ground crack et.al.
Based on the sequence forecast theory of grey system GM (1, 1), a forecast model is established for grey data sequence.
GM (1, 1) shows a differential equation involving a function of one variable, which basic thinking is that builds dynamic or static white module based given data sequence according to a kind rule, then depends on some change or solution to calculate grey module in the future; in the grey module, according to a kind rule, find gradually dynamic variation and forecast its future development trend. [1] Building model step as follows: Given original data sequence X(0)={x(0)(1), x(0)(2),…x(0)(t),…x(0)(n)} (1) Meanwhile, X(0)(t) represents the data of time,n≥4 Based on sequence X(0),in order to overcome the defect of original fluidity and randomcity,then generate a new sequence X(1),by accumulation generation operator, build a middle information model.
Table 1 Forecast Accuracy Grade [1] P C Forecasting Accuracy Grade > 0.95 < 0.35 Excellence > 0.80 < 0.50 Eligibility > 0.70 < 0.65 Reluctant eligibility 0.70 ≥ 0.65 Disqualification Application of Grey Model By analysis of these hazards including water-inrush, slag, ground subsidence, landslide collapse, ground crack in XXX mining of Pinglu District, Shanxi Province during last eight years, the output data have been chosen as the original data sequence since 2003, and GM (1, 1) adopted as the grey prediction model[3-6].
Improve mining geohazard prevention and reduction laws, and promote mining geohazard management system.

MacGregor and Kourti [1] established a PCA model from the training data and detected the abnormal behavior of online processes.
Monitoring based PCA-ICA algorithm PCA can handle high dimensional, noisy, and correlated data by projecting the data onto a lower dimensional subspace which contains most of the variance of the original data [4].
After applying PCA on the observed data, the PCs matrix can be expressed as linear combination of unknown independent components, that is,
The training and test data set can be downloaded from http://brahms.scs.uiuc.edu.
E.: Pattern matching in historical data.