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Finally, we introduce efficient TCB reduction.
If outside data or programs want access to the vault, they must pass muster with the TC system and obtain decryption keys.
Instead, they identify users, their computing systems (based on a unique identifying digital signature), and the applications or data they want to run[7].
In NGSCB and La Grande, it is incorporated into the main CPU, thereby avoiding the problem of unencrypted data going over the data bus to the dedicated processor[8].
The function of the analysis stage is to identify components that handle security-sensitive data or perform security- sensitive functions.

And the third is the index layer: energy efficiency indexes related to statistical data.
The assessment of the single index refers to the process of observing the change curve or data change of the single index value and finding the abnormal data.
Such indexes can be established with the data in the energy information warehouse.
l To classify and screen the huge historical data collected and construct various kinds of data bases step by step to facilitate directly the modeling of the urban development research model system, including the basic data base of the basic building information, the business data base of the real-time building energy consumption data base, the service data base of the building energy consumption analysis results, and the model data base of the statistical and operation models.
Energy Data Modeling and Analysis for Improved Energy Management Planning and Performance.

Currently on the stability of hinge joint concrete block revetment research data is still rare, mainly in Europe and America and other countries abroad[3-5], only the Nanjing Hydraulic Research Institute of China study wave action on the stability of the slope experiment[6].
Fig.1 Map of Bird Island Fig.2 Hollow and solid block size dimensions Fig.3 Foot and the top of the hill form paving Strengthen reduction method of the basic principles and FLAC calculation principle Strengthen reduction method of the basic principles.Strength reduction in slope stability safety factor is defined as the slope just to reach the critical damage state of the soil shear strength reduction of the extent that the safety factor defined as the soil shear strength and critical damage to the actual time after the reduction ratio of shear strength.
as a strength reduction factor, but from the style that F is the strength reserve coefficient, or the actual degree of strength to play a factor.
Strength reduction finite element points is to use the formula (3) and (4) to adjust the soil strength index c and ( c is the cohesion after the reduction, is the reduction after friction angle, F is the discount factor), then the numerical analysis of slope stability, and constantly increasing reduction factor, repeated the calculation until it reaches the critical damage, then get the discount factor is the safety factor Fs.
Table2 Safety factor of slope protection in different angel Slope angel/(°) 20 30 35 40 45 48 49 50 Stability safety factor 2.42 1.69 1.69 1.47 1.10 1.03 1.01 0.98 From Table 2 data are available, with the slope gradient increases, the stability of the lower slope gradient of 40° or less, the difference between stability safety factor of greater magnitude of changes; at 45° or more, the difference between stability safety factor magnitude of changes small.

Fig. 1 shows the data collection field.
Fig. 1 The field of data collection Fig. 2 3-D data obtained by the laser tracker Cloud Data Processing It is unavoidable of the existence of noise points and background disturbance points in the cloud data collected with 3-D measurement [5].
Noise reduction and smoothing of cloud.Some noise points are involved in the data collected with T-Scan for the influence of field condition, light source and reflection characteristic of blade surfaces.
Noise reduction and smoothing comprises the following two aspects: (1) Noise reduction and smoothing for disordered cloud: For the disordered part, feasible projection angles can be selected according to the characteristics of blade surface, 3-D data points will be reflected on the vision plane, and data cloud will be filtered to remove noise based on experience threshold values. (2) Noise reduction and smoothing for ordered cloud: For ordered part, since the cloud is a kind of scanning line data, smoothly standard Gaussian filter method.
There are 19,053 original 3-D data points, only 8,958 of whom are kept after noise reduction and downsize (as shown in Fig. 3).

Introduction With the development of computer, network and communication of information technology and data storage technology, especially the appearance of data warehouse, an increasing number of data is stored.
For these massive data, not only do the traditional data analysis methods and data retrieval mechanism consume a lot of time, but also they completely depend on the advance of the data on the relationship between assumptions and estimates of data, which has been unable to satisfy people's eager on implicit knowledge in these data.
Data Mining Theory and Power System Load Forecasting Closely related areas to Data Mining include inductive learning, machine learning and Statistics analysis.
Data Collection and Pretreatment The first collection is historical date related with all load forecasting, including weather data, historical load data, holiday type data, transforming the data format, removing the noise in the data, and cleaning incomplete and inconsistent data, after the full exploration of load forecasting related with history data, selecting strong prediction capacity variable establishing load forecasting mining model.
This paper describes Data Mining technology and its application in power system, and applies the similarity of Data Mining to analyze load forecasting.

The application of the model is presented via a real world company using the last four years of data.
Estimating Model Parameters The data in the case study were obtained from a C2F6 manufacturer located in north Taiwan.
Table 1 displays the data of the past three years.
where the values 0.0255, 0.248, 0.0388, and 0.714 are obtained by sorting the data in Table 1.
The following illustrates the proposed method for a data of (N, t), optimal sample size n*, and critical numbers (c1, c2).

Weather data files used in the simulation are created based on Chinese Typical Year Weather (CTYW) data.
The source data used for CTYW came from the International Surface Weather Observations [4].
SAGSHP design data.
This model depends on user-supplied data files containing catalog data for the capacity and power draw.
China building standard meteorological data sets [M].

Then the equivalent forces are calculated according the data in the first step, using the formulation of the equivalent force.
But it causes a huge model, and it is also often difficult to obtain the earthquake data nearby the corresponding fault.
The effect of external domain to internal domain is expressed with equivalent forces which are calculated from the data of the ground motion in the first step.
Domain reduction method for three-dimensional earthquake modeling in localized regions, partⅠ: theory.
Domain reduction method for three-dimensional earthquake modeling in localized regions, partⅡ: verification and applications.

The data was then collected using a Dewetron DAQ module and analyzed using Dewesoft (version 7) software in a powerful Dell workstation.
A Kistler 50g accelerometer was used for vibration data acquisition.
The vibration data was then recorded and analyzed using Dewesoft (version 7) software in the frequency domain using fast fourier transforms (FFT).
It can be seen that there is significant reduction in vibration amplitude for all 15 experimental runs with a maximum reduction in run 1 of 86%.
Table 1: Table showing the reduction in vibration amplitude obtained by using magnet Tool flank wear measurements were also compared for the experimental runs and the percentage reduction calculated from the SEM images.

First, three scenes of carbon emission reduction have been set, including standard scene, moderate control scene, strict control scene, which corresponds to different demand of carbon emission reduction.
(2) Scenario B - target constraint Under Scenario B, based on the further emission reduction targets, various plants determine their own emission reduction progress.
Case study In this paper, the carbon emission quota and generation capacity in Jibei under different scenarios will be analyzed based on the generation and electricity data of Jibei in 2013.
The annual growth rate of electricity consumption is 3%. 4.1 Carbon quota allocation results Based on the historical data in 2013, the carbon quota and the generation power situation of thermal power units in Jibei region in 2014-2017 are calculated, shown in Table 2.
But it may lead to urgent emission reduction in some companies.