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On the base of measuring data, the noise resource is analyzed and the character of the noise resource is discussed.
Perfect effects of noise reduction can be obtained in the working project.
Table 1 is the measurement of sound data.
From the test data could be drawn, cooling tower near point1 to point10 average noise reached 72.8dB ( A ), and with the increase of distance, the energy of noise attenuation is slow.
Therefore decided to install muffler at the fan exit so as to achieve the purpose of the noise reduction.

EMD decomposition process is based on the following assumptions: (1) The signal data has at least one maximum and one minimum. (2) Maximum time interval is the characteristic time scale. (3) if the signal data just has turning point without extreme point, the signal data is be differentiated one or several times to obtain the extreme point, and then get the decomposition result by integral.
Upper and lower envelop contain all the data point.
It is the highest frequency component of the original data.
Separate from the original data, and get the rest signal : (6) still contain the frequency information of the original data, so set as a new signal and repeat the process above.
Original signal can be construct as follow: (9) Now, the original data signal is decomposed to IMF and one rest component .

They reported that the porosity of iron changes from 20 to 62% upon reduction.
However, it is possible to make a reasonable approximation to explain the experimental data.
Thermal diffusivity changes with the progress of NiWO4 reduction.
Summary of data on time dependence of diffusivity changes Temperature, K Initial diffusivity, α0 x 106 cm2s-1 Final diffusivity, αt x 106 cm2s-1 Time to completion, min Incubation time, min 973 0.0058 0.0141 120 22 1048 0.0053 0.0161 110 14 1123 0.0051 0.0161 98 6 1198 0.0038 0.0159 49 2 1273 0.0036 0.0159 41 2 The activation energy of the reduction of NiWO4 was calculated by an Arrhenius plot using the isothermal reduction rates, which can be represented by the change in thermal diffusivity, at different temperatures.
Arrhenius plot for the reduction of NiWO4.

Online mining of closed frequent itemsets over streaming data is one of the most important issues in mining data streams.
Different from data in traditional static databases, data streams have the following characteristics[1].
All data streams are represented by average transaction length (T), average pattern length (I) and the size of stream data (D).
MOMENT: maintaining closed frequent itemsets over a data stream sliding window,in:Proceedings of the 2004 IEEE International Conference on Data Mining.
CFI-stream：Mining closed frequent itemsets in data streams.

It can provide a method and data for search energy saving and CO2 reduction potentials in iron and steel industry by LEAP model.
With its flexible data structures, LEAP allows for analysis as rich in technological specification and end-use detail as the user chooses.
The data is actual data of devices used in iron and steel industry in 2010.
The data in 2040 is a forecast data which is the ratio of device used in iron and steel industry.
Specific data is shown in Tab.1.

The synthesized catalyst was utilized for reduction of nitrobenzene.
Introduction Reduction of nitrobenzene is an important in the industrial process.
Table 1 Data from XRD and BET method.
Table 2 Effect of Na-clay, Al-PILC and Pd/Al-PILC on the reactivity for the reduction of nitrobenzene.
Choudary, Reduction of nitroaromatics with a new heterogenised MCM–silylamine palladium (II) catalyst, J.

The greatest feature of current stage is the presentation of high dimensional data (data above three dimensions), which describes the complex data through visual stereoscopic model that can help users to check and handle the data more clearly.
Data analysis and operation module: After receiving data from questionnaire, questionnaire users can analyze, process and display the data visually here.
This module uses three-dimension data cube as main graph, thus we can display the high-dimension data through dimension reduction.
If it is 3D data, then show as 3D data cube, therefore, users can operate data cube directly with mouse operation.
Multi-Dimensional Data Visualization.

In this way, vibration fault data table meets the requirement of rough sets data analysis.
Thus, fault information which hidden in huge signal data is extracted.
As rough sets can only deal with data of discrete attribute, those original fault data of continuous attribute must be discretized.
Table1 is extracted experimental data which is after envelopment analysis.
Then we have discretizated data table 3.

Reduction ability of carbon and silicon as well as reduction characteristics of Nb2O5 and Fe(NbO3) were compared.
As related work was concentrated on technological conditions, while reduction characteristics of Nb compounds in steel were the fundamental research to be studied, therefore, the research on this paper was carried out, so as to provide data for the technology of direct alloying Nb.
I/a.u 2θ/o Fig.2 XRD patterns of synthetic product Results of reduction characteristics of Nb compounds In CaO-SiO2-Al2O3 slag system, reduction compositions of Nb compounds in Fe-C and Fe-Si melt are shown in Table1 and Table2 respectively, equilibrium ω[Nb] has no obvious difference, while the equilibrium time of reduction of Fe(NbO3)2 is longer than that of Nb2O5; Meanwhile, the reduction capability of silicon (reduction rate 92%) is more powerful than that of carbon (reduction rate 30%).
Therefore, reduction compositions of Fe(NbO3)2 and Nb2O5 have no obvious difference, but the equilibrium time of reduction of Fe(NbO3)2 is longer than that of Nb2O5.
Although reduction equilibrium composition of Nb2O5 is the same as that of Fe(NbO3)2, reduction time of Fe(NbO3)2 is longer than that of Nb2O5.

Different experimental conditions adopted could be the other major factor that affects yield data, which shows variations in total yield and well as the products patterns.
Possibly the differences in the mode of activation of CO2 on these metals, modulated with the nature of the supports, could explain the rate data for various products.
Data with UVC radiation, presented in Table.14 shows that Cu & Fe, when loaded separately on TiO2, display lower conversion rate to ethylene.
CO2 photo reduction data presented in Table.17 show that in-situ CoPc/TiO2 catalyst is more active than a simple mechanical mixture of CoPc and TiO2 indicating a co-operative effect between dispersed CoPc and titania surface for the effective transfer of photo-generated electrons.
Lo et al. [162] could successfully validate their model with the experimental data with a pseudo first order reaction rate equation.