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The point cloud data should be preprocessed with noise rejection, multi-view stitching and data reduction by Geomagic Studio software.
After that, the model surface data should be converted into Solidworks parts.
Data measurement A three-dimensional 3D human model point cloud data can be gotten by using point cloud data acquisition software for measurement work and parsing the collected two-dimensional images.
Interpolation refers to a given set of data points which are known, and then construct a curve connecting the data points in order.
Data.

Assessing the energy saving of natural ventilation is difficult, especially for large scale naturally ventilated buildings, due to the lack of deterministic data and method.
However, there is not still deterministic data and method about the energy saving effect of natural ventilation in residential building.
The wind speed and direction data in ESTPNV in TMY [5] were obtained for the ventilation environment simulation of rooms with the PHOENICS software.
Rate of Cooling Loads Reduction.
The meteorological data for thermal environment analysis in China.

ZrB2 crystals are obtained completely by the carbothermal reduction process with the release of carbon dioxide at 1500°C.
All the FTIR data were consistent with the standard spectrum of methylamine.
In addition, MS was used to investigate the pyrolysis process of ZrB2 precursor and the MS data were shown as follows: m/z=30 and 31(methylamine), m/z = 16 (methane).
According to the FTIR and MS data as shown in Fig. 2 and Fig. 3, the maximum value of weight loss at 110°C was mainly caused by the release of a large amount of methylamine and some methane.
FTIR and MS data were shown as follows: 670 cm-1, 912 cm-1 (C-H); 1650 cm-1 (C=C); 1710 cm-1 (-C(O)-); 1373 cm-1 (-CH3); 930 cm-1, 964cm-1 (characteristic absorption peak of methylamine); 803 cm-1, 1620 cm-1(NH2); 3014(characteristic absorption peak of methane).; m/z=16(methane), m/z=30 and 31(methylamine), m/z=39, 41 and 42 (propene); m/z=43(acetone).

Analysis of Data Center Power System Hui Song1, a, Zhitan Wang2, b 1Jilin Technology College of Electronic Information, Jilin 132021, China 2Jilin Data Center of Petrochina, Jilin 132000, China asonghay@sohu.com, b36910008@qq.com Keywords: The power system Data Center diesel generator UPS SVG Abstract: Data center presented in this paper, has a high requirement on the power supply system, that needs emergency power supply, SVG compensation device, large diesel generator set, two lines of mains large capacity UPS and other electric equipments.
Data center interrupt is not allowed in most of its load, especially load cabinet once interrupted, is likely to cause "terminal system", "important production data" and other key cabinet data loss, the loss is immeasurable.
And huge data center generator capacity, the basic unit can take up all load data center, and the great amount of oil required, noise is bigger, need to increase the outdoor storage tanks and noise reduction device.
In order to ensure the data center of power supply reliability, reduce interference, set up the data center dedicated to match/substation and power supply equipment, data center substation in the post room module layout, shortening the distance between the power supply as far as possible, reduce the low voltage line loss.
References: [1] Data center design and planning of energy-saving power supply system, Jiying Wang, Beijing publishing house of electronics industry, November 2011 [2] Data center UPS power supply system design and fault handling, Zhimin Zhou,AiHua Ji Beijing Publishing house of electronics industry, January 2012

Raw data of samples On the case of historic data in one hospital, part samples are extracted from lack data and normal data changed into the original data sheet involving 20 samples (as Table2).
In view that rough set supports merely discrete data, those data in Table need to be discretized.
Picking up original data According to minimum reduction {b,e,f,h} in Table 4, original data in sample is taken out and listed as table5.
Original data after attributes reduction 3.2.
Because SVM has higher ability of accurate forecasting based on normalized data, so data in Table4-5 will be normalized in advance.

With regard to this the integration of adequate energy data into factory planning and design requires efficient data acquisition strategies depending on the prevailing purpose.
Therefore the general impact of energy data on factory planning is shown.
They usually refer to insecurities in metered data, the corresponding and required accuracy of measuring and the data transmission [7].
The use of measured data, on the other hand, scales with the required level of detail, i.e. the data requirements depend on the granularity of the curves to be used in the prognosis.
Means of energy data acquisition.

The furnace (reduction furnace) is used to simulate the heating process in iron ore reduction measurement system.
Principle of Reduction Measuration The iron ore reduction performance measuartion in high temperature is shown in Figure 1.
The sample is restored into the reduction tube.
N2 is poured into the reduction tube, standard-state flow 5L/min.
At the beginning of 15 min, the sample quality should be recorded at least once every 3 min, then every 10 min record the data, until the end of test, 180min.

Given n-dimensional data set containing N data objects {xi, i =1,...., N}.
Input a parameter 0<ε≤0.5 and the n-dimensional target data set containing N data objects, {xi, i =1,...., N}
Record the maximums and minimums of the target data in each dimension, and then normalize the target data
To a data set containing n objects, we scan every data only one time to divide the target space, and the time complexity is O(n).
The algorithm simplifies the training data set by dividing the target data set space with STING technology.

Based on the reduction, obtain the optimal decision support system.
Introduction VPRS[1] (Variable Precision Rough Sets) is characterized by data analysis methods do not require any prior knowledge of the data itself, only using the information provided can be achieved on the data attribute reduction and have the access to the minimum expression of knowledge.
But it fault tolerance and generalization ability is weak, can only deal with quantized data.
Bearing fault diagnosis model based on variable precision rough set theory and neural network technology 2.1 The building of a fault diagnosis model Bearing fault diagnosis model based on Variable Precision Rough Set Theory has the following main steps: ① The collected bearing failure data as the domain U, determine the condition attribute set C and decision attribute set D;② Discrete attribute values ​​for continuous processing to form a decision table ;③ Calculation condition beta attribute reduction of decision table, to obtain the relative minimal set of attributes ;④ Build sub neural networks for each selected reduction, using the simplified decision table to train sub-networks ; ⑤ Merge output of each subnet, and get effectively converged network architecture;⑥ Predict new samples and achieve the final result . 2.2 Specific steps ( 1 ) determine the condition attribute set and decision attribute set Perform statistical analysis on the collected data of bearing failure ,
table 3 , in this paper, in order to identify the strong attributes and patterns in the data, β is set to become a higher value , orderβ=0.95,this step to achieve 6-reduction as shown in table 3.

But there is no decision attribute in data Table 1 thus making it cannot do supervised learning and then the classification algorithm does not directly apply.
In data Table 1, most experts in this field would take the attribute of urgency as first and for most, also give it maximum value when determining its weight.
So you can get such a loading priority evaluation data sheet as Table 1.
Conclusion This paper firstly analyzed the shortcomings of the loading priority evaluation using the AHP and proposed a kind of algorithm of property weight evaluation based on PRSIM classification by using data mining classification and attribute reduction technique, combing expertise and characteristics of the objective data itself together, with a load instance set as algorithm example set and the priority evaluation carried out.
Data Mining: Practical Machine Learning Tools and Techniques (2nd edition) [M].