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Therefore, effective energy management of these ACs is an essential task to energy saving and carbon reduction.
Computer GUI for supervisory control and data acquisition (SCADA) and energy management is also developed.
It can record the electrical load data so that the user can seize the electricity usage at any time.
The presented system can be divided into two modules: ”Demand controller” and ”RFID energy billing module”, wherein RFID energy billing module is responsible for reading the power meter data and the pre-paid card data on the card reader so as to use them as the basis for controlling relay and payment deduction.
(d) When the reading temperature button is pressed, the energy billing system will read back the temperature data.

Relative data is collected from National Bureau of Statistics of China.
Data analysis shows the proposed models, especially based on LS-SVMs, have more steady performance and higher accuracy.
We are given the training data set of d points, Here is the i-th m-dimensional feature vector obtained from the above data mining by PCA and inputted into the LS-SVM regression model for training and forecasting, d is the sample number of training dataset.
The output data zi∈R assumed as the construction worker scale.
,in: Credit Scoring using Least Squares Support Vector Machine based on data of Thai Financial Institutions.

Then, the data preparation, data preprocessing and the mathematical description of the algorithm for our method were presented.
Data Preparation.
Data Preprocessing.
Data preprocessing phases include: 1) data quality control.
According to related data quality standards, the raw data need to be reorganized and validated.

Based on the data sources collected by traditional data acquisition methods and modern information capture means, key techniques such as modeling of spillway, dynamic simulation of the flow and texture mapping and other corresponding methods are put forward.
The data sources are obtained both by traditional data acquisition methods such as engineering design drawings, charts documentation and field control measurement and by modern information capture means such as satellite remote sensing images, high-precision dynamic positioning technology and so on.
First of all, the disorder text data needs to be digitized and input; then depending on the important degree of the information, classification and summary are carried out, in order to reduce the redundancy of the data and guarantee the comprehensiveness of the information; next the information is checked in all respects to ensure the authenticity and accuracy of the original data; and then a unified data coordinate system is achieved; finally effective coupling and inventory analysis of the multi-source data is completed for further application of 3D solid modeling for the reservoir project.
A graphical flowchart illustrating the data acquisition and processing is shown in Fig.1.
According to the data source based on the traditional data acquisition methods and modern information capture means, the 3D visualization technology of the spillway flood discharge is in-depth explored.

The analysis of the recorded test data aimed on the distinction between cumulative material response (creep deformation, material hardening / softening) and spontaneous material response (material hardening / softening).
The subsequent analysis of the data used literature information to distinguish between cumulative material response and spontaneous material response [5,6].
Every 100 cycles peak/valley data pairs of force and displacement were recorded.
The analysis of the data was done automatically using a self-developed Matlab tool (MathWorks Inc., Natick, Massachusetts, USA).
However, to prove the latter further examination of the test data in the form of a detailed and quantitative dynamic mechanical analysis is required.

The data relative to the IBS superlattices are reported in fig.3.a for the Mo sub-layers and Fig.3.b for the Ni sublayers.
After ion irradiation, a significant stress reduction is observed for both directions but the splitting of the in-plane lattice parameters remains unaltered.
The whole set of data (as-grown + irradiated states) was fitted using a triaxial model which includes, in addition to σhyd and σfix contributions, the coherency stresses 001cohσ and 110cohσ .
For Mo, data have been measured for the φ=0 and 90° directions.
For Mo, data have been measured for the φ=0, 35.26° and 90° directions.

The related data of the zones should also be collected, mainly including the water quality, water intakes and sewage draining exits, the special requirements on water consumptions (such as the water consumed by fishing spaying areas, wintering grounds, aquatic sports grounds, and etc.), and the planning data (i.e., the planning for land fields and water areas, such as the development program in urban areas, the planning for wharves along the riverbanks and etc.).
During the pollution control zoning, a vast range of data should be collected, such as the distribution of the wastewater discharge outlets along the zoned rivers, the information whether the wastewater after treatment reaches the standards, as well as the wastewater discharge capacity.
In view of the insufficient data collection, few pollution control zones are involved in the present zoning.
We selected the monitoring data of river systems over several years and conducted the Kendall tests on the data selected from the Wangjianglou Station and Gaochang Station along the Min River, the Neijiang Station along the Tuo River and the Panzhihua Station along the Jinsha River[10].
Moreover, the results indicate that the strengthening of agricultural production management and the reduction of the agricultural wastewater discharge are the key points to alleviate the water quality deterioration.

When compared to the test data available from the literature, it showed that the porous zone model proposed by Liu and Weyers gives the best predictions.
Loss of the confining pressure eliminates entirely the frictional component of bond, accompanied by a significant stiffness reduction for the overall structural member.
Predicted results examined against test data endeavoured to draw conclusion on which rust deposition model is most suitable to apply in predicting concrete cover cracking due to steel bar corrosion.
Table 1– Critical corrosion penetration depth: model predictions in comparison with test data – assume a mean value for rust volume expansion coefficient n= 2.5.
Predicted results based these rust deposition hypothesis were compared with benchmark laboratory test data.

The result is a mix of Si +1, Si+2 and Si+3 oxidation states, seen in photoemission data [24].
The result is fully consistent with the experimental data that show H passivation only when molecular hydrogen is passed through a metal such as Pt, where it breaks up (Fig. 2).
This theoretical result is in agreement with the experimental data of Fig. 2.
If indeed a large density of Si-Si bonds exists prior to N passivation, the data rule out such a process because a large density of negatively-charged defects is not observed.
Si-Si interface states near the band edges, is in accord with the experimental data of Fig. 2.

A principle component model is then built up using the measurement data of sediments from the continental slope and shelf in southern South China Sea.
Theory of Principal Component Analysis Principle component analysis is the most important way in multivariate statistics, it is one way to make a large dimension data matrix dimensional reduction.
The research target is the data matrix formed in test procedure.
Cumulative contribution rate h of principal component can be obtained by the sum of covariance matrix X’s former k eigenvalue divide the sum of all eigenvalue, it is that: , clearly the cumulative contribution rate express the proportion of k principal component analytical data changes by all data changes.
According to the principle of principal component analysis, let , , , as the column vector data matrix to form a 124 dimensional data matrix, calculate the data correlation coefficient matrix R, then calculate the characteristic root of R.