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Filter Infant COPE Database Image Principal Componen Analysis Principal Components Analysis (PCA) [11] constructs a low-dimensional representation of the data as possible to describe as much of the data variance.
In mathematical equation, PCA attempts to find a linear transformation, which maximizes, where the covariance matrix of the zero mean data X.
It can be shown that this linear mapping is formed by the d principal eigenvectors (i.e., principal components) of the covariance matrix of the zero-mean data.
The low-dimensional data representations yi of the datapoints xi are computed by mapping them onto the linear basis, i.e., [12].
The learning rate and momentum factor are chosen as 0.1 and 0.9 respectively Experimental Results Conventional validation scheme is used for testing the effectiveness of the results of the classifier. 80% of data are used for training and 20% of data are used for testing.

The author participated in the on-site audit period investigation, data collection, data check and audit report writing process, Through to the buildingenergy audit of Taiyuan Hall organ office buildings and large public buildings, we make an objective assessment to energy consumption level of the building,and make a quantitative analysis to the energy use status of energy building which found the building energy-saving potential, and complete completion of existing public buildings energy-saving seven million square meters during the " 12th Five-Year Plan" for Taiyuan, the unit area energy consumption fell more than 20 percent compared with the former building’s, and during the" five-second "period, public buildings achieved 10% reduction in energy consumption per unit area , the large public building energy consumption reduced by 15% to provide a basis.
Data processing.For the 252 energy consumption data from energy consumption statistical analysis to collect, according to unit energy consumption from low to high order, We find the minimum, median, four percentile, four percentile and the maximum value，from the above building energy consumption per unit area on the four percentile of 25% buildings, in accordance with relevant state regulations aside a certain amount of building construction as a key energy audit; From the construction unit area of energy below the four percentile of 25% buildings, take a certain number of buildings as the benchmarking building to audit, the 38 were selected as the energy audit object.
Data analysis and confirmation.Summary and analysis the data obtained from research and measure, and comparison of these data with the standard reference values ​​and implementation of standards.By analyzing the thermal insulation of buildings to identify existing problems, explore the potential for energy savings, corresponding energy-saving measures proposed.
On the part of the energy saving buildings for the overall energy consumption reduction has play a decisive role role.
Through meta-analysis of the data obtained building the proportion of energy consumption, As shown in Fig.2(Breakdown of large public buildings energy scale drawing).

Hagen et al. [3] and Hagen and Larsen [4] conducted a numerical study to generate data for developing a design model for the shear force capacity of steel beams with web holes, both with and without transverse steel stiffeners and opening strengthening.
The program was calibrated and verified using experimental data from the literature.
Fig. 9(b) shows a bar chart with data on how ultimate load percentage and effective stiffness losses are impacted due to the size of the opening.
These data demonstrate that all unstrengthened steel I-columns suffered local buckling of column sections, although at various places along the column length depending on the size of the web holes.
Fig. 11 shows the sizes of the web holes against the final axial compressive strength ratio by using data from Table 2.

Based on these data it is known that the viscosity of slurry increases with increasing addition of fused silica.
The STA data that has been interpreted into DSC and TGA data at once in one graph can be seen in figure 5.
DSC data is depicted by black lines and TGA graphs are depicted by blue lines.
The STA data that has been interpreted into DSC and TGA data at once in one graph can be seen in figure 5.
DSC data is depicted by black lines and TGA graphs are depicted by blue lines.

Therefore, this method is characterized by massive measurement data, low inspection speed and high labor intensity.
It should be point out that the data acquired by using the ASTM E1268 standard cannot be simply compared with the data obtained by other methods.
The AI value in American standard is a quantitative rating data of the steel's banded degree or directionality degree.
According to the author's experience, for the same picture, the data obtained in American standard will be closer to the data acquired by other methods with the increasing degree of banded structure.
Massive measurement data, low inspection speed and high labor intensity.

Data description.
The data is also restricted by statistics of energy in 2007, which was compiled by the NBS.
The only problem is the statistic of sectors in IO table and the data base do not match.
So we calculated the growth rate of exports and imports of each sector based on OECD data.
We can find another way to estimate the data.

In addition, high-throughput has been confirmed by comparing the current data with the recent results reported.
Furthermore, throughput has been evaluated by comparing the previous data reported so far.
Then, the throughput of the present epitaxy will be benchmarked with the state-of-the-art data.
However on C-face, high-throughput has been confirmed by comparing our data with the recent results reported.
By benchmarking the throughput among the reported data, the high-throughput has been confirmed on the C-face epitaxial growth.

Therefore, the data from first and second step need to make dimensionality reduction by factor analysis in this step and multiple standards will be condensed, extract and transform into smaller number of standards. 4) Make weighted distribution of impact factors: Retain several impact factors of evaluated design program.
Moreover, factor analysis belongs to areas of multivariate statistical analysis techniques and its main purpose is to condense data.
It basically achieves the purpose of data simplification, and determines the number of factors and communalities for each variable.
Moreover, compare each program tender process and feedback with the result of data analysis, which shows a high-degree match of each item.
Furthermore, modify the short-board factors of the feedback in tender process and data analysis, and balance them in next round of program tender.

The support system, which gets data by methods of remote sensing(RS),geographical information system(GIS),global positioning system(GPS), survey data, statistics data and experiment data, being mainly composed of database management system, knowledge management system and models management system, can make regulating and controlling decision for mining area’s ecological security.
Data sources Experiment data Statistics data GPS data RS and GIS data Survey data Spatial database Statistics database Attribute database Ecological models Eco-environment knowledge Models management system Databases management system Knowledge management system Economic models Social-economic knowledge Tables output Information querying Graphics output Decision Fig. 1 The framework of decision support system of mining area’s ecological security Major function of subsystem The system is composed of five subsystems including subsystem of data input and process, data management system, model management system, knowledge management system and output subsystem[4-6].
Subsystem of data input and process: The quantity and quality of data are important to the accuracy of assessment and decision-making.
Some approaches of data pre-processing are as follows:The graphics digitizer or scanner is used to deal with the maps or remote sensing images, and transform the data form to a vector form, then store them in database.All kinds of attribute data can be input and stored in the attribute database by keyboard.The data of statistics reports, testing and analysis and investigations can be input into the attribute database using the same methods as the attribute data input.
The support system, which gets data by methods of remote sensing(RS),geographical information system(GIS),global positioning system(GPS), survey data, statistics data and experiment data, being mainly composed of database management system, knowledge management system and models management system, can make regulating and controlling decision for mining area’s ecological security.

The main functionality that can be extended from this class is: extending the usability of the device, capture the flow of data from the glove to computer, view and so on.
In this way, RoboSIM can be used for both immediate testing and validation of virtual reality scenarios and for saving scenario data for later use.
This will shorten the time of the transfer in real type the calculated data from virtual environment, and also will ease the interaction with the real and the virtual gripper by saving the validated data.
The data gathered can optionally be sent by RoboSIM application to the real gripper (Fig. 10).
RoboSIM can communicate too with the RoboVISION recognition module, thus substantially easing the data collection process for functional simulation achievement.