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For N-sampled data such as, the formula of the moving mean filter is as follows: (1) Here, m is the length of filter window, is the object element processing.
For overcoming these disadvantages of the traditional median filter and the traditional mean filter, we will change the size of filter window according to the degree of pollution in the different data areas.
Realization of the filter algorithm is as follows: (1) The data which is being processed is copied into tow the same sets.
Result Fig.3(a) is the original data wave, which signals have been mixed the white noise and the high frequency noise.
When m = 4, a good results can be got, by contraries, when m is bigger, more data will be lose.

Figure 2 - Distribution of cumulative frequency - Thermal data acquired from 5sensors - July and August 2011 Both duringthemonitoringinthemonth ofJuly than inAugustyou can see howthermal valuesrecordedare locatedwell beyond therangeof valuesconsidered optimalby the reference standardforthe different typesof artifactspresent.Particularly interesting isthecumulativefrequency distributionfor the hygrometric data:you can noticehow oftentherelative humidity valuesrecordedby the sensorsremain withinthe rangeidentifiedas optimalforpainted wood sculpture(Fig. 3).
Titanium dioxide paint Steikos art: experimental results of pollutant reduction The photocatalytic coatings and paints “Fosenergy” produced by Steikos - division promoted by AZ Tech, leader in the field of photocatalytic products and nanostructured materials- are made using titanium oxide nanoparticle product not for dust’s dispersion, rather for synthesis in a solvent [13].This treatmentmakesthepaints more active and effective in the action of environmental pollutants photodemolition.The experimental data following are provided in relation to light radiation exposure period and to the mass of pollutant converted.
Possibilities and limitations of an experimentation still under development Although the experimental data acquired are unquestionably very beneficial in many ways, at the same time it is necessary to consider the potential limitations of the application of nanotechnology in areas other than purely experimental.
Cannistraro, R.Restivo, Thermohygrometric monitoring using wireless sensors: study of seventeenth-century church’smicroclimatic conditions andenclosed reliquary,Science Series Data Report Journal, pag.101-123 - Vol 4 No. 4;Issn: 1307-119X ,(2012) [6] S.
Restivo,The conservation of sacred art: a case study purpose to search for an index of correlation between particle concentration and mass of fine dust, Science Series Data Report Journal, pag.63-84 - Vol 4, No. 4;Issn: 1307-119X ,(2012)

The tasks are summarized as, in planning stage: make prediction for future power system and power energy demand; collect technical and economic data of equipment; draft the reliability criteria and design standards, evaluate the system performance according to the criteria, and identify the weak links of system; select optimal scheme.
(5) the optimized model of load curtailment When the problems arose in system caused by the outage, it is need to apply the special optimization power flow (OPF) model to reschedule the generation, thus can eliminate the illegal limitation and constraints of system; meanwhile, should avoid the load reduction as far as possible, or make the load reduction be minimum when cannot avoid it happening.
Focus on the transmission system, using the state enumeration method for actual example analysis, the system wiring diagram is shown in Fig.5. the generation and load data of each bus node is shown in Tab.1; the data of transmission lines is shown in Tab.2; the events statistics that lead to system faults is shown in Tab.3; the data statistics of interruption, state probability, and state frequency of the actual system is shown in Tab.4.
Fig.5 The wiring diagram of actual system Tab.1 Data of generation and load bus Number of bus Generation Load Rated capacity（MW） Regulation output（MW） % MW 1 100 100 10.5 80 2 0 0 31.6 240 3 200 200 5.2 40 4 0 0 21.1 160 5 0 0 31.6 240 6 750 460 0 0 SUM 1050 760 100 760 Table 2 Data statistics of transmission lines Number of lines Number from start to end of the buses Length (Km) Impendence Max. transmission capacity (MW) R X 1 1—2 40 0.1 0.4 100 2 1—4 60 0.15 0.6 80 3 1—5 20 0.05 0.2 100 4 2—3 20 0.05 0.2 100 5 2—4 40 0.1 0.4 100 6 2—6 30 0.08 0.3 100 7 2—6 30 0.08 0.3 100 8 2—6 30 0.08 0.3 100 9 3—5 20 0.05 0.2 100 10 3—5 20 0.05 0.2 100 11 4—6 30 0.08 0.3 100 12 4—6 30 0.08 0.3 100 13 5—6 61 0.15 0.61 100 Tab.3 The events statistics that lead to system faults Number of lines in outage condition Node number of the bus LSC Insufficient power supply capacity of the system（MW） Normal —— 875 0 1 1—2 867 0 2 1—4 865 0 3 1—5 879 0 4 2—3 879 0 5 2—4 874 0 6 2—6 736 24 7 2—6 736 24 8
2—6 736 24 9 3—5 803 0 10 3—5 803 0 11 4—6 649 111 12 4—6 649 111 13 5—6 791 0 Tab.4 Data statistics of interruption, state probability, and state frequency of the actual system Number of lines in outage Original data MTTR(h) MTBF(h) Outage rate Normal —— —— 0 0.645002 —— 1 8 200 0.04 0.026875 0.0034 2 8 133.3 0.06 0.041170 0.0051 3 8 400 0.02 0.013163 0.0016 4 8 400 0.02 0.013163 0.0016 5 8 200 0.04 0.026875 0.0034 6 8 266.7 0.03 0.019949 0.0025 7 8 266.7 0.03 0.019949 0.0025 8 8 266.7 0.03 0.019949 0.0025 9 8 400 0.02 0.013163 0.0016 10 8 400 0.02 0.013163 0.0016 11 8 266.7 0.03 0.019949 0.0025 12 8 266.7 0.03 0.019949 0.0025 13 8 133.3 0.06 0.041170 0.0051 According to the reliability indexes calculation formulas introduced before, the deterministic reliability indexes can be calculated as follows: ① utilization coefficient of generator FGU: ② coefficient of transmission equipment FTR: ③ minimum load supplying ability min LSC: ④ maximum non-sufficient capacity

These data sets are summarized in Table 1.
The inserted or removed object is selected randomly from the data sets, and the experimental results are shown in Fig. 1 and Fig. 2.
Table 1: Data sets Data sets Number of rows No. of attributes No. of classes Data sets No. of rows No. of attributes No. of classes Hayes 132 6 3 Breast 569 32 2 Iris 150 5 3 Balance 625 5 3 Wine 178 14 3 Pima 768 9 2 Haberman 306 4 2 Connectionist 991 14 11 Liver 345 7 2 Yeast 1484 9 10 Climate 540 20 2 Fig. 1.
A distance measure approach to exploring the rough set boundary region for attribute reduction.
IEEE Transactions on Knowledge and Data Engineering, 22(2010): 306-317

For example, the data in the interval of flurry can also be divided into breeze, soft breeze, strength wind and moderate gale four intervals.
The basic idea of segmented meteorological factors: Firstly, segment the data of mean wind velocity according to the wind velocity lever.
In order to more accurately predict, again segment the rainfall data according to the rainfall level.
Insert the wind velocity and rainfall data in the interval 1 into Eq.4 to obtain the predicted value of daily maximum load.
Application of two-phase reduction method in load forecasting for regions with abundant small hydropower[J].

A verified nonlinear finite element model is developed to generate NNM training data.
To select the training data for the NNM, computer generated D-optimal designs are utilized.
However, the neural network used in these studies are primarily trained using experimental data such that these studies considered only special cases, limited materials, and geometrical bending tool dimensions.
In this study, the neural network metamodel (NNM) was trained using data generated via the FEM to map a large range of materials and geometrical bending tool dimensions.
The required training data for the metamodel should be selected so that it can provide a wide range of information between the inputs and outputs.

This paper introduces the basics of wireless sensor networks, focusing on the concept of sensor network data collection, data collection mechanisms, data collection in-network data aggregation, data collection and data collection applications Research.
In this process, involving many important mechanisms to sensor networks, such as: data collection mechanisms, data collection node scheduling mechanism and data collection in network data aggregation mechanism.
The data collection mechanism is an important part of its performance will directly affect the network energy savings, data collection mechanism involves data collection tree construction, data transmission routing, data aggregation mechanism within the network and node scheduling delay and other important content.
This paper introduces the basics of wireless sensor networks, focusing on the concept of sensor network data collection, data collection mechanisms, data collection in-network data aggregation, data collection and data collection applications Research.
Time network described using energy consumption data traffic conditions, the ability to aggregate data set,then copies the data transmission network to transmit an energy reduced the energy consumption data, and network data aggregation with the energy consumed far less than the application of network data aggregation to save energy by.

The data were analyzed by using standard fitting Igor Pro 9.0 software.
Moreover, the superconducting dome peak (Tc dome) can be confirmed by these two data pressures point.
Thus, the data point was adopted from the temperature region at which an antiferromagnetic spin fluctuation occurred, evidenced by 13C-NMR measurement [8].
The open grey diamond is the data point as magnetic spin fluctuation since there is no anomaly in the resistivity.
A.A.F. analyse all the data and write the first draft.

After performing a linear fit for t/ε vs. t data, as seen in the inset of Fig. 3, εb and td/ε were obtained from the reciprocal of the slope and the y axis-intercept, yielding εb=15 and td/εd=3.1.
A direct analysis of our data is made by plotting the coercive voltage as a function of film thickness.
The measured data can be described by a linear relation between Vc and t, which agrees with the blocking dead layer model[12].
The solid lines are the linear fitting of the experimental data.
Data taken from both heating (dashed curves) and cooling (solid curves) cycles are shown.

The experimental data [29] data indicate that the yield strength is particularly strongly dependent upon the hydrogen concentration.
The simple dependence (3) allows us to make a good approximation of the experimental data.
Despite the seeming simplicity of this approach and the large number of published data, not all of them can be used for the approximation.
Almost all experimental data were obtained as a result of the saturation of specimens either in the electrolyte solution or in gaseous hydrogen.
As a result, the data obtained are not suitable for approximation since the hydrogen is localized on the surface.