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Introduction With the reduction of mineral resources, people attach the growing importance on the utilization of ocean wave energy.
Data Acquisition Card.
While data are acquired by the data acquisition card, they are also being displayed in the form of curve.
Save and Open Of Data.
Input and Output of Wave Data Files.

This study is aimed at the statistical survey of lightweight steel structures for industrial and residential use (current status): using data from project investigations to present the site overview and hazard risk factors.
Risk assessment for buildings with accommodation inside the factory (validation): through the statistical survey data (34 of the 188 surveyed have accommodations inside the factory, a ratio of 18.1%), the hazard risk is assessed, and the potential risks are analyzed.
The presentation of the data shows the proportion of high fire risk in violation of the iron factory.
Use the factory diagnostic data to analyze feedback, understand the fire risk distribution, to grasp the key areas, and follow-up fire prevention and propaganda and disaster rescue war chess deduction, and to give suggestions to the site by the building structure and decoration materials.
Smokeview, A Tool for Visualizing Fire Dynamics Simulation Data Volume I: User's Guide, NIST Sp.

The work-hardening behaviour has been analysed for different temperatures and strain rates by fitting a generalized Voce rule to the tensile data.
The data processing of the test results from the SHTB was done in accordance with the procedure explained by Chen et al. [2], where more detailed information about the applied SHTB may be found.
Only processed data will be presented here.
Again, the generalized Voce hardening rule is here used for convenience, to facilitate the comparison of the data at the different strain rates.
A generalized Voce rule has been fitted to the experimental tensile data.

Installed capacity of China’s coal-fired generation technologies Data source: IEA 2011b[9] 2.2 Scenarios for China developed in past studies 2.2.1 Low carbon scenarios for the Chinese electricity sector As lowering carbon emissions has become an increasing concern worldwide, more and more research is being conducted to forecast Chinese emission trends.
CO2 emissions in China: historic data and projections Data source: IEA (2011a)[12] In the IEA projection in Figure 4, we can see that the electricity sector is expected to reduce CO2 emissions from 5339 Mt in the current policies scenario to 4278 Mt CO2 in the low carbon scenario, which means that annual CO2 emissions should be reduced by 1061 Mt by 2030. 2.2.2 Previous work on clean coal scenarios in China We reviewed four articles that forecast future scenarios based on existing coal-fired technology.
Figure 5 Technology structure in different clean coal scenarios in the literature Data source: Yu, F., J.
The data are based on information from 2000.
[9] IEA "OECD – Coal balances", IEA Coal Information Statistics (database).doi: 10.1787/data-00552-en (Accessed on 5 June 2012)

Therefore, considerable impetus aimed to develop novel and efficient technologies for excess sludge yield reduction.
The study of TCS showed, when the concentration of TCS is 0.8-1.0mg/L, the sludge reduction rate was above 40% [1].
However, there have been few investigations on excess sludge reduction by combined metabolic uncouplers.
In order to obtain the daily sludge growth data, MLSS, COD, NH3-N, and sludge volume index (SVI) were measured according to (APHA, 2005).
This founding revealed a feasibility of applying a combined uncoupler to achieve the reduction of excess sludge in SBR.

Based on the textile industry, the author utilized the regression analysis theory to conduct the regression analysis of wastewater reduction intensity to capital asserts investment, wastewater reduction intensity to the global market share of textiles and the Revealed Comparative Advantages Index (RCA )respectively, and finally drew a conclusion that the correlation between cleaner production investment and wastewater reduction remains positive, so does the correlation between wastewater reduction intensity and the global market share of textiles.
It is the accurate reflection of the internal correlation between wastewater reduction and global market share. 3.
The scatter graph of index of wastewater reduction intensity and RCA of Chinese export textiles is expressed as Fig. 3.
The wastewater reduction intensity is also directly proportional to the global market share of textiles.
The improvement of Chinese textiles competitive power in the globe is stimulated by cleaner production References [1] Chib,s.and Greenberg,E.(1998):Analysis of Multivariate probit Models,Biometika,85,347-361 [2] Charles J.Stone: A Course in Probability and Statistics,China Machine Press,2003 [3] John A.Rice: Mathematical Statistics and Data Analysis, China Machine Press,2003 [4] Michael H.Kutner:Applied Linear Regression Models,Higher Education Press,2005 [5] HongBin Xie ,ZhaoDe Liu, Wen Chen: Resources and Environment in the Yangtze Basin, 2004(7)

But, there is the redundant in the fault data, which influence the clustering effect.
Rough Set attribution reduction algorithm can process the redundant data, and need not any prior knowledge [2].
In the paper, one condition entropy attribution reduction algorithm is chosen to reduce the fault data, and fuzzy equal relationship clustering is applied to get the fault diagnosis results, finally the examples is test the validity.
Application example and analysis On the base of the original fault data, Rough Set attribution reduction algorithm is applied to get the reduced decision table and reduced condition attribution value, and then original fault data of the reduced decision table is substituted to the fuzzy equal relationship clustering, and the fault mode is divided.
Table 1 The original fault data No.

Brief introduction of ROSETTA ROSETTA is toolkit software for analyzing tabular data within the framework of rough set theory, which is developed by Norwegian University of Science & Technology (Department of Computer and Information Science) and Warsaw University of Poland (Institute of Mathematics) to cooperate, namely, A Rough Set Toolkit for Analysis of Data.
ROSETTA is designed to support the overall data mining and knowledge discovery process: From initial browsing and preprocessing of the data, via computation of minimal attribute sets and generation of if-then rules or descriptive patterns, to validation and analysis of the induced rules or patterns.
It provides a variety of data preprocessing functions, such as decision table completion, decision table discretization and so on, but also common reduction and rule extraction algorithm within rough set [4], so its support the whole process of rules from the data pretreatment to the prediction and analysis.
The system provides the main function of a data acquisition and output, data preprocessing (decision table completion, discrete), computing (reduction), and other functions.
ROSETTA Discretization Method At present, the data obtained from the experiment mostly are continuous values, but in rough set theory, data reduction is based on discrete data table basis, therefore, for the continuous attributes discretization of real-valued attributes of decision table is a pivotal step in the pretreatment of data.

Before performing the finite element analysis, the actual shoe insole was scanned by a 3D laser scanner to capture the surface contour and translate into point-clouds data with shape.
The data scanned is imported into CATIA V5 to restructure and develop a 3D solid model using surface and part modeling technique.
Dynamic loading pressure data collected and Table 1 shows the outcome measurement for four subjects using two different types of insole materials.
The data gave valuable support to the subjects’ statements, where the peak contact pressure decreased when insole made from PU and poron worn as compared to when using EVA insole.
Based on the statistical analysis of sEMG data (mean values), the authors found that the muscle effort level in all muscle were reduced when wearing shoe insole made from combination of PU and poron.

In a multi-sensor network, large amount of data are transmitted from various sensor nodes to the base stations and intermittent loss of data in signal packages received at the base stations is to be expected.
Next, by using only data from ns-1 sensors, another set of PDE can be identified.
Data Loss Reconstruction for Wireless Sensor The reconstruction makes use of Fourier transformation.
Damage is simulated by imposing a reduction of 20% in the Young's modulus of column element 7 and 20% reduction in axial stiffness (EA) of truss member 14.
This can be confirmed by omitting data from the sensor at node 9 instead.