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The Appropriate selection of the element type will reduce the amount of input data and computation time.
It has played the vital role on enhancing the improvement design of pump's structure dynamic performance,noise reduction and so on [9].
Fig. 1 Entity unit modeling Fig. 2 Entity unit modeling Fig. 3 Entity unit modeling of first-order vibration of second-order vibration of third-order vibration Fig. 4 Beam unit modeling Fig. 5 Beam unit modeling Fig. 6 Beam unit modeling of first-order vibration of second-order vibration of third -order vibration Data Analysis.
The reasonable choice of unit type may reduce the quantity of input data and the time of computing

After the establishment of the actual system and measurement, the data were compared and analyzed for conclusions and discussions.
The three-hour actual measurement data of the personalized air-conditioning of single duct design are as shown in Table 1.
Table 2 shows the actual measurement data of the personalized air-conditioning of the double duct design .
Fig. 4 Comparison of the actual data and the simulated data on measurement points A, B, C Conclusions In this study, regarding the proposed single duct and double duct personalized air-conditioning systems, when the ambient temperature was 29℃, the air outlet temperature was 22℃, 24℃ respectively, and power consumption was 133W, 134W respectively.
Coupled with the CFD software for the calculation of the fluid mechanics software, actual and simulation data were compared, confirming that the trends of the actual measurement data and the simulated data are the same.

The gray prediction model is mainly gray system theory, data from the columns to create dynamic models, which, GM (1, 1) is the most common practical application of a gray model, is the core model of gray system theory.
GM (1, 1) model theory Grey identification system through the development trend of the different factors the degree of processing the raw data generated to find the variation of the system to generate a strong regular data sequence, and then create the appropriate differential equation model to predict things future trends.
GM (1, 1) model and model checking Data Processing In this paper, the original sequence data (million kWh conversion units) is from the China Statistical Yearbook 2001-2010, electricity consumption in Shandong Province, which used for gray prediction.
Data processing is as follows: The original data series: Accumulation of once to generate the data column is: Smoothness and exponential test The gray GM (1,1) model for the exponential equation in real terms, requiring the sample data should be used to predict exponential law, therefore, to conduct regular inspection sequence.
Which Solving was Therefore, GM (1, 1) model of the corresponding sequence of time Will get a and b are the values into the formula, Shandong Province, electricity consumption by the prediction model: According to the type, the data obtained can be reduced through the cumulative reduction the predicted value of the original data: The prediction results in Table 1.

Medium and Long Term Power Load Forecasting Model Is the essence of power load forecasting using the existing historical load data and related information, in a certain way to identify changes in the law of power load in order to predict future changes in electrical loaded.
Grey theory is based on the amount of random quantity as gray variation within a certain range, and the law is not obvious raw data generated by the accumulation to become law with a certain number of columns generated model equations constructed and bleaching process.
Vocabulary A ^ = [a ^, u ^] T, and a ^, u ^ are a and u predicted value, the parameter A ^ is determined by the least square method: (5) Where, B and Yn is a known quantity, A ^ parameters to be determined, and B and Yn obtained by the following two equations: (6) (7) To the obtained a ^, u ^, Back substituting equation (4), with (8) Available again after discrete GM (1,1) model to generate a gray sequence (9) (2) New Information replacement projections Improved optimization for GM (1,1) model, although as long-term forecasts, but the real practical significance and high accuracy predictive value is only the most recent one or two data, and other data only further reaction trend said the plan values ​​or value (long-term
planning of the forecast), so the model will predict new information obtained in each column of data into the same time, they remove one of the most stale data, namely (10) This metabolic data processing dimension and new information technologies is the approach the prediction accuracy is significantly improved.
This paper proposes a new portfolio optimization forecasting model, its basic idea is to improve the original sequence given gray prediction residuals, corrected for residual prediction model sequences were obtained before treatment and other reformers to improve the residual interest gray forecasting model which combines the advantages of improved prediction residuals gray prediction model and so on Innovation Gray predicted with a higher accuracy than a single prediction model their application as follows : 1 ) Accumulating the raw data processing ; 2 ) For processing data accumulated column GM (1,1) model to predict ; 3 ) Reduction of the prediction process ; 4 ) Experience poor after verification, such as the accuracy requirements are not satisfied, then take the local residual build residual data sequence ; 5 ) Residual data columns then create GM (1,1) model ; 6 ) Amended the original gray model with residual gray model ; 7 ) Re predictive analysis.

The result obtained show that the proposed technique has an acceptable performance to simulate the data and voltage dependent load models have a significant effect on total losses of a distribution system consequently will affect the cost of the system.
Problem Formulation The overall efficiency of power delivery in the distribution system could be improved with the minimum loss reduction.
The data were rank and sort based on the objective function set in equation (4).
Based on the ranking data, the best ‘n’ population was selected as an initial location of ‘n’ numbers of fireflies.

(1) Assuming UID label length k bit, due to the dynamic binary search algorithm like binary search algorithm for each tag and reader are sent complete UID length k bit, dynamic binary search algorithm tag and reader communication to send commands length parameter of the two : 1) the first is the start time of the paging cycle of each round, all commands sent 2k bit data to be transmitted; data length 2) for further communication with the paging command to the tag reader needs to be transmitted to k bit.
Which is based on reducing tag reader communicates with the amount of each transfer of data to improve the binary search algorithm is not based on reducing the number of reader and tag communication excellent binary search algorithm improvements in the transmission time.
Secondly, dynamic binary search algorithm, for example, analyze the reader every time a binary search algorithm and data tags based communication transmission reduction by comparing the binary search algorithm simulation and dynamic binary search algorithm paging time, finally to return to the binary search algorithm, for example, based on a binary search algorithm to reduce the number of reader and tag communication is analyzed, and Matlab simulation by comparing the three algorithms paging time.
Provide theoretical support for the improved binary search algorithm, it can be concluded that the improved algorithm to reduce the number of reader and tag communication than the start of each tag communication with the amount of data transmission to reduce the reader to proceed from the better.
Hatamlou: In search of optimal centroids on data clustering using a binary search algorithm, Pattern Recognition Letters, Vol.33 (2012), p.1756-1760 [3] A.

Nevertheless, according to the data obtained by differential thermal analysis (DTA) the total amount of stored energy in the nanopowder was 348 J/g.
While heating of nanopowder on air desorption of water and gases from the surface and the volume of the nanoparticles occurred with 1% mass reduction.
According to DTA data the thermal effect of Al NP oxidation while heating up to melting temperature is 3549 J/g, the temperature of oxidation start is ~440 °C and oxidation level while heating up to 1000 °C is 53.7 wt.%.
Hence, based on the data obtained by XRD and DTA one can conclude that the forms of energy stored for nanoparticle are both the surface and the stress-strain state of the metallic component, occurred under extreme conditions of nanoparticle obtaining during the conductor electric explosion.

Test results show that (1) BFRP sheet perform better than CFRP or GFRP sheets under high freeze-thaw cycles; (2) exposed hybrid FRP sheets not only show very little loss in mechanical properties, but also contribute to the stability of test data; (3) mechanical properties of rein epoxy decrease significantly with increasing freeze-thaw cycles.
Each group contains 7 specimens to insure that a minimum of five valid data is available for the statistic analysis.
Test data of GFRP sheet exposed to the same freeze-thaw cycling condition from reference [8] was added to compare the durability behavior of three different FRP types, as shown in fig. 2.
About 10% reduction was found in ultimate strain of 50 and 100 cycles exposed 1C2B specimens, but there were nearly no decreases in 150 and 200 cycles exposed specimens. 1C1B performed better than 1C2B FRP sheets under freeze-thaw cycling condition.Fig.4 summarizes COV values of different FRP sheets.
Hybrid FRP sheets not only show very little loss in mechanical properties compared with homogeneous FRP sheets, but also contribute to the stability of test data after exposure to freeze-thaw cycling. 3.

The crystal structure of both samples has been solved by powder X-ray diffraction, data in the tetragonal space group I4/m (a= b= 5.55182 Å, c =7.86955 A0) for SrLaFeNi0.5W0.5O6sample and (a=b= 5.49129Å, c= 7.82233Å) for CaLaFeNi0.5W0.5O6 sample, and shows an almost perfect ordering between Ni2+ and W5+ cations at the B-site of the perovskite structure.
Results and Discussion Crystallographic data and structural aspects The prepared SrLaFeNi0.5W0.5O6 and CaLaFeNi0.5W0.5O6 perovskites show identical powder diagrams indicating the formation of a pair of isostructural materials.
The refined unit cell parameters, together with other relevant crystallographic data, are shown in Table 1 and the complete indexed powder diagram is presented in Table 2.
On the other hand, symmetry reduction observed in numerous of the investigated materials allows predicting additional spectral complexities [19].
Conclusions The Rietveld refinement of the XRD data show that both samples SrLaFeNi0.5W0.5O6 and CaLaFeNi0.5W0.5O6 are single phase without any detected secondary phase and their crystal structure is tetragonal unit cell with space group I4/m, but in SrLaFeNi0.5W0.5O6 shows that there is small impurity of SrWO4 and We observed that there is small difference in the lattice parameters due to ionic radius of the Sr and Ca in A site [20].

The magnetic properties and their dependence upon temperature data are interpreted in terms of the Herzer random anisotropy model for nanocrystalline materials.
Saturation magnetization values were obtained by extrapolation of the magnetization data to high fields.
Table 1 shows the summary of MS and HC data obtained in the present work for nanocrystalline Ni-15%Fe samples in comparison with Ni samples.
It is seen that increasing temperature from 2 K to 298 K leads to a very small reduction in magnetization, what agrees well with other studies [12].
The magnetic property data are interpreted using Herzer’s random anisotropy model for nanocrystalline materials.