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The average value of lattice contraction over the whole system is larger than that of the experimental data, and the average value of lattice contraction in the inner core has a better agreement with the experiment results.
It is well established that the lattice parameters of nanoparticles decrease with the reduction of size as a result of large numbers of surface dangling bonds and a high surface-to-volume ratio [4-9].
The experimental values [8, 9] lie between the average lattice contraction over the whole system and that over the inner core, and the average over inner core has a better agreement with the experiment data.
The thermodynamic model predictions for spherical nanoparticles [4] are lower than the experimental data and much close to present results from averaging over the whole particle system.

It include multi-base such as part information, green material, performance data of resource and environment, equipment resource, green process, cutting parameter and other data etc.
Data Source.
Data Evaluation and Certification.
Therefore, the data collection is difficult and complex.
The system adopts these new technology can reduce the resource and energy consumption or short the development cycle and cost reduction.

It evolves with a high speed traditional transformer in addition to power electronic base construction will eventuate to oil elimination, dimensional size and weight reduction.
It will provide a platform for delivering the traditional power with new services evolving the exchange of data and information in a dynamic style.
A multi level Intelligent Universal Transformer (IUT) emerging a next-generation of distribution transformers base on power electronic construction for incoming distribution automation [20]. automatic sag voltage compensation, real-time voltage regulation, outage compensation, Flicker mitigation, capacitor switching protection, harmonic compensation in nonlinear loads conditions, DC output service, reliable diverse power as variable frequency like 400Hz option, storing power, dynamic system monitoring, output voltage compensation in case of the built-in energy storage, capability on delivering three-phase power from a single phase, oil elimination, size and weight reduction are the most advantages of IUT rather than current traditional transformers [16], [22].

In addition, they can be produced extensively in substitution to all materials used in the coating of almost all heating equipment as well as contributing to the reduction of energy consumption.
According to data furnished by some suppliers, ceramic wool and glass wool have their theoretical compositions as described in Tables 1 and 2, respectively.
Fig 5 – XRD of glass wool residues Fig 6 – XRD of ceramics wool residues Conclusions The glass and ceramic wools presented good performance in grinding, with easy size reduction.

Moreover, the proposed framework allows resource allocation and a reduction of the total power consumption of cloud data centers (DCs).
This task corresponds to the transformation of raw data into more suitable data for modeling.
In general, it involves operations such as data cleaning, feature selection, data transformation, feature engineering, and dimensionality reduction.
In particular, one week's data was extracted from the Europe/Brussels time zone data. 5.
Energy-aware scheduling schemes for cloud data centers on google trace data.

Principal component analysis (Principal Component Analysis, PCA for short)[6] is an important feature extraction method, with it high-dimensional data is projected onto low-dimensional space via linear transformations, so as to achieve the purpose of noise reduction and redundancy.
If the cumulative contribution ratio is greater than 85%, then the first m principal components can represent most of the information of the original data, not only reduces the number of dimensions, but also minimize the loss of original data information.
Composition of the experimental data sets Data sets name Number of the samples Number of the categories Original dimension Dimension after PCA Iris 150 3 4 2 Weld_defect 200 4 10 4 In this paper, using Principal Component Analysis technology to reduce the dimension of data, so as to achieve the effect of data compression to reduce the amount of computation, set the parameter to explain the degree in threshshold=90.
In order to verify the effectiveness and feasibility of the KNN algorithm, respectively do the classification study of the standard Iris data set and welding defect ultrasonic signal data set, as shown in “TAB.
Wherein, Iris data set is the feature vectors constituted by the three kinds of different types of irises.

Knowledge discovery in data bases (KDD) concerns several research areas including data preprocessing, data modeling and feature extraction, pattern recognition as well as information visualization etc [1].
Data preprocessing As discussed, data preprocessing are aimed at improving the accuracy and efficiency of further analysis.
Hence, in the data preprocessing step, response data should be selected based on this criterion.
A PCA based data management and retrieval method are proposed by authors to select data set corresponding to system changes [4].
This method can be also seen as a data preprocessing method to select data for further analysis based on efficiency consideration.

This layer is made up of data acquisition and control module of various types of composition, main function is to complete the IOT application information, data acquisition and control facilities, is an important foundation for the Internet of things.
The data acquisition layer, data through the Internet of things Internet technology information exchange layer and upper layer of the transfer and exchange, for hair, transmission, distribution side application to electricity to provide data to support side application, make more strong and smart grid.
Real time data acquisition, measurement, analysis of user of electricity in support of the system, and through the communication layer allows data upload and send.
Then the temperature data through CC2530, sent to the sink node by the wireless transmission, the sink node will pack all temperature data information which monitor region, then the packet data send to the remote asset monitoring and control system by Ethernet, the system sends the data packet information is split, and then evaluation and analysis of temperature data, when found abnormal or equipment failure, maintenance personnel can use mobile operation equipment carry on receiving the abnormal equipment data through the GPRS network, the rapid response, the repair efficiency is improved, the power cost is reduced.
Monitoring data information of each tower through a wireless network and multi hop transmission mode the system collected data is transmitted to the monitoring center, the monitoring center for data processing and analysis.

Moreover the fault is predicted respectively according to amount of data sample.
Through the generating method of gray process, a random data column could be transformed into a column of data with strong regularity.
When data quantity is small sample data, running time of machine is relatively short.
Therefore the grey theory is introduced to process the large sample data in order to avoid the error caused by the difference in large sample data.
Finally predictive values could be obtained through the regressive reduction.

It is a reduction of the layer compression test to one single sheet layer.
According to Eq.1 [2], the rb-value is calculated with the measured geometry data.
A change in the friction caused by oil can lead to both an increase and a reduction of the biaxial anisotropy depending on the material.
Thus, tests with a sheet thickness of less than 1.0 mm must first be examined with further verification tests, since a reduction of the sheet thickness according to Eq. 2 would increase the frictional influence.