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With the development of computer science and data mining technology, many scholars combine with different theories and industry backgrounds to discuss the importance measure method of attribute based on data.
Such as, Liu [3] according to the characteristics of failure data for nuclear power plant, using the powerful knowledge discovery capability of data mining method, proposed a new data standardization method, and using attribute reduction method using concept lattice, conducted a reduction process of attributes, then which can be drawn core attributes, so, we can accurately diagnose faults, Chen and Liang [4] showed the attribute reduction method based on rough set, and proved that it is a feasible reduction method in TCM syndrome data processing, the literature [5] processed one year flight test data of some flight test aircraft according to the data reduction method, and through practice, which shows that the method is useful and efficient to flight data reduction, and Qu [6] proposed the attribute reduction of the power big data pretreatment based on the cloud computing technology, and processed wind power measured data and grid fault diagnosis table on the Hadoop.
Therefore, how to combine with data mining technology and the accumulation data information, building an attribute importance measure which has the structural characteristics of fuzzy measure is an effective way to achieve information fusion.
This indicates that during the disease diagnosis process, the more aspects are considered, the more helpful for you to make the correct diagnosis; 2) there is an interaction among attributes, and the portion importance can not completely representative the sum importance (for example, when w1=0.8, w2=0.2, μ({a2})=μ({a3})=0.1<0.534=μ({a1}), but μ({a1, a2})<μ({a1,a3}); 3) μ({a1, a3})=μ(A)=1 indicates that headache or not has no effect on the diagnosis result when diagnosing whether a patient is suffering from pneumonia or flu (i.e. it only needs to consider the two attribute values that fever characterization and cough characterization); 4) μ(B) changes with w1 and w2 (for example, when w1=0.8 and w1=0.2, μ({a1, a2})=0.623, and when w1=0.2 and w1=0.8, μ({a1, a2})=0.691), this indicates that the established attribute importance measure mode not only has a good structural features, but also can take the decision consciousness into decision process simply; 5) though the amount of data in this

In the present study we attempted a 20% reduction of CEMI 42.5R (by total mass of solids) by adding 20% of limestone filler and subsequently added 1% of colloidal nanosilica, aiming (i) at leveraging strength loss due to the reduction of Portland cement, (ii) at providing early strength and (iii) at enhancing the microstructure.
In this research it was revealed that nanosilica particles improved early age strength by engaging in reactions during the initial setting, building additional calcium-silicate-aluminate, the backbone of cement [7], improved microstructure since nanosilica acted as nano-fillers, improving packing and counterbalanced the anticipated strength reduction due to the reduction in clinker, in certain cases providing even higher values compared to the control formulation.
• Corundum sand 0/4mm • Powdered corundum sand 0/2mm • Superplasticizer SP • Nanosilica (nS): Colloidal amorphous nS particles in an aqueous suspension With respect to the industrial nanosilica, according to the Technical Data Sheet it contains about 30% by mass of nS particles.
This was partly attributed to the large quantities of superplasticizers used and the 20% reduction of Portland cement.
c) The present results are very promising with respect to Portland cement reduction if early age strength gain is not a prerequisite.

Noise Reduction.
Lagrange function is used to determine the most optimal hyper-plane as shown as in Eq.8-9, where xi is i-th training data, and yi is the output of the SVM for i-th training data. xr is all support vector of positive data and xs is all support vector of negative sample data.
The best SVM model was used to classify testing data (the non-training data).
There are 60 testing data that contain 20 data for each weld defect type.
Table 3 is the result of testing using simulation data.

SJTs with a pre-stress hFE of 90 suffer only a 10% reduction of the hFE after 190 hours under a 200 A/cm2 DC current stress at a TJ of 125°C, while a similar stress on earlier generation SJTs resulted in over 25% hFE reduction in only 25 hours.
This paper will present hFE stability data obtained from stressing SJTs free of BPDs.
A current gain degradation of nearly 25% is observed in 6 hours of DC current stress at 125°C, whereas minimal hFE reduction results when the same stress is applied at 25°C.
A similar stress applied to Gen-II SJTs results in ≈ 10% hFE reduction after 190 hours of stressing.
A 10% reduction in hFE is within acceptable limits for this parameter, especially since the SJT switches are mostly operated in the saturation region, typically with a 50% base current overdrive.

The planning practice has a significant effect on runoff reduction.
The effect of runoff reduction for rainwater utilization measures is analysis.
Fig. 1 Overview of processes incorporated in the planning runoff estimation Mountain areas Overland flow (inflow hydrograph) Flow routing through drainage system Initial loss-constant continuing loss rate model Rainfall Overland flow GIS data Instantaneous unit hydrograph (IUH) SWMM Urban areas Initial loss-constant continuing loss rate model Rainfall GIS data Instantaneous unit hydrograph (IUH) Case Study Area [6].
According to this basis data, Futian River watershed stormwater control and utilization planning is carried out.
Effect of Runoff Reduction.

Instead, to need scalable and flexible end-to-end data stream management solutions, ranging from real-time low latency alerting and monitoring, ad-hoc analysis and early data reduction on raw streaming data, to long-term analysis of processed data.
Sensor Data Encoding Figure 1.
Sensor Data Hiding.
Sensor Data Decoding Figure 3.
Sensor Data Decoding Image Decryption Original Image Encryption Key Encrypted Image Embedding Key Extracting Sensor Data Sensor Data Image with Hidden Data Extracting Encrypted Image Inter-Packet Delay Generation Watermarked Data Network Secret Key In this section, a detailed description of sensor data decoding and data recovery is given.

However, many of these methods stop at the theoretical stage, because they lack the breadth and depth of data required in practice.
The equation parameters were obtained through data analysis.
Therefore, the results may have problems such as duplicated data or leakage.
According to the characteristics of water environmental degradation and data available for the Taihu Basin, the protection costs method and classification method were selected to estimate the water environmental degradation costs.
Because of data limitations, some evaluation methods failed to operate in practice.

First of all, from the viewpoint of the information management, the object of early-warning management is about the enterprise energy saving data.
Changle Yan, Jun Ma [1] pointed out that the process completion of energy-saving emission about industrial enterprises is not smooth, one of the important reasons is lack of the powerful data monitoring [2].
We can master the data about enterprise energy saving timely and accurately though strengthening the monitoring and management of energy saving data.
Firstly, Based on the perspective of energy-saving and emission reduction mechanism, designing the early warning indexes system from the two aspects of energy-saving and emission reduction respectively.
As for regulation enterprise energy-saving data, Hong Lin [2] put forward to construct intelligent energy management system which consists of five parts: the database of energy consumption, the identification and evaluation system of energy consumption, the online query system, the management system of energy-saving supervision.

In this paper, using the annals data of 1996-2011 establishes some kind of model of carbon emissions and economic growth regression analysis to determine the relationship of the carbon emissions generated by energy consumption and economic growth, and forecasts the carbon emissions trend, and will provide practical policy recommendations for low-carbon development in Hebei Province. 2.
Study of the relationship between carbon emissions and economic development in Hebei Province a) Data sources The historical energy consumption data in 1996-2011 (million tons of standard coal), the proportion of four major fossil fuels (coal, oil, gas, water and electricity) accounted for the total energy consumption, Calendar year's per capita GDP,　and the consumer price index which were used in the article were all from the Hebei Economy Yearbook.
Then foreign scholars using the time series and cross section data or panel data do a large of empirical analysis on the environmental kuznets curve.
By using SPSSv17, the paper carries out the data from 1996 to 2011 to do regression analysis, the graphics are shown in figure 3 below: The results derived from curve estimation, carbon emissions and the explaining variables equation is linear when R2 = 0.938, namely carbon emissions are highly correlated with economic growth.
In hebei economic annual energy consumption data, we can clearly see that in the consumption of energy, the coal consumption rate has reached 90% on average, while the consumption of natural gas and hydropower is very low, so we should change the coal-dominated energy consumption structure.

STEP-Tools company indicates that: After STEP-NC was used, there will be 35% reduction in time for job shop to set up machining job, 75% reduction in time for OEM to prepare data for job shop and 50% reduction in machining time[2].
The objects in this case are manufacturing features and their associated process data.
The STEP-NC data consists of instances of entities.
Fig.1 shows the STEP-NC data model[5].
A Framework for an Intelligent CNC and Data Model[J].