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With these, the product design and manufacturing cycle has shown time reduction [1].
The data combined system is available for scanned view registration, data reduction, and data merging etc.
Data Registration In a combined method, data collection using a touch probe and an optical scanning probe must be transformed into one common coordinate system.
Data Merging When the registration has been performed, the data from a touch probe overlaps with the triangular mesh model.
The high accuracy data of locating feature holes is preserved, and the overlap region of triangular mesh is removal by data merging, as shown in Fig.3(e).

The main problem is the multiplicity of parameter estimation solutions arising from using spatially sparse and noised-polluted data.
Almost all feature extraction procedures perform some form of data reduction.
The measured data is compressed into small dimension feature vectors.
The main problem is the multiplicity of parameter estimation solutions arising from using spatially sparse and noised-polluted data.
The main problem is the multiplicity of parameter estimation solutions arising from using spatially sparse and noised-polluted data.

The advantage of the IDA is that it can readily be applied to any available data at any level of aggregation[9].
(Data resources: ASREC[4]) The article is organized as follows.
Section 2 describes the LMDI I method and the data used.
Methodology and data Principle and basic model.
Taking into account the availability and consistency of data and the detailed classification of industrial sub-sectors, the data, spanning from 2001 to 2009 used in this study, were taken from various issues of ASREC[4] and CSY[22].

A Method of Load Prediction in District-heating System based on Data Mining Lanbin Liu*, a, Aijuan Zou and Yufei Ma 1 School of Mechanical Engineering, University of Science and Technology Beijing, PR China aliulanbin@ustb.edu.cn Keywords: Load prediction, District-heating system, Data mining.
This paper provides a load prediction method based on data mining and considered the physical mechanism of heating load formation.
In order to further identify the characteristics of systemic energy the hourly energy consumption of the building is used for further analysis and data mining.
Therefore during the data mining process, the moving weighted average method is used to give different weight value to indoor and outdoor temperature differences of each time to identify the system.
In order to eliminate the effects of solar radiation and ventilation, the simulation data at night time (20:00 - 4:00) is used only.

Heavy vehicle cooling fan is seen as one of the main means of vehicular fuel efficiency reduction.
Therefore, in this research, the performance data of motor is to make use of measured data of Dynamo.
Motor efficiency in equation 1 is obtained from the measurement of motor performance data.
When we apply algorithm for taking account of CFD analysis and test data of motor, starting from random rotating speed, torque of motor testing data and torque of CFD analysis will converge to the same value following enough time of calculation.
Figure 8 demonstrate graphs for linking CFD of axial flow fan with testing data of motors.

Reduction of high speed railway speed has an influence on not only high speed railway passenger flow but also passenger flow of the public transport system within the scope of relevant transportation corridor.
This paper will use the Logit choice model, analysis of changing of passenger travel mode choice facing reduction of high-speed railway.
Efficient operation results will guide further diversification of high speed passenger products and high speed railway passenger transport organization based on data and analysis.
The Actual influence of Wuhan Guangzhou High-speed Railwayspeed reduction Combined with the actual case, the market changes brought to the train diagram adjustment of high-speed railway have the intuitive description of the image.
High-speed Rail quantity statistics before and after July 1, 2011 areshown as follows: Table 5 The Schedule of High speed rail before and after the change of capacity G D sum Before 128 0 128 After 122 20 142 From June 20, 2011 to June 26th and July 4, 2011 to July 10.Analysis of ticket data collected in these two stages,mainly put 20 on the D prefix train.

Before we get stuck inlisting the benefits of this solution for retail chains and the possibility to yield information and knowledge from the obtained data, let us define two terms.
For example, the most important strength is the ability to automate the data collection which will significantly reduce the costs of marketing surveys.
The negative publicity and initial distrust of customers due to the loss of personal data or sensitive data were determined as the threat with the highest degree of importance here.
Table 11 ST strategy   Strengths   Threats 1 Automatic data collection 1 Negative publicity 2 Localization of cart position 2 Initial mistrust of customers (loss of personal data, sensitive data) 3 Minimisation of costs of marketing research 3 Destruction of tags 4 Bulk reading of carts 4 Decreased interest of employees in work (control, loss of privacy) 5 Reuse and high resistance of tags 5 Theft of tags 6 High reading distance 6 Software threats - viruses 7 Easy to install RFID infrastructure 8 Optimisation of storage of cleaning equipment utilisation 9 Minimisation of theft and loss For the confrontation strategy, it is very important to use all strengths and eliminate as many threats as possible.
The work will be led up to the possibilities of data mining and visualization of outputs using sophisticated software tools.

Introduction The theory of rough sets [3], proposed by Poland mathematician Pawlak in 1982 is a mathematical method to deal with insufficient and incomplete data.
It is a set-theory-based technique to handle data, that is, through the known information to approximately describe the uncertainty concept [2, 4].
And now, rough set theory has been successfully used for reduction [5], describing dependency among attributes, and dealing with inconsistent data in knowledge and data analysis [8].
Information Sciences 11, 341-356 (1982) [4] Pawlak Z Rough sets: Theoretical Aspects of Reasoning about Data.
Information Sciences 181, 3878-3897 (2011) [9] Xu WH, Zhang XY, Zhong JM, Zhang WX Attribute reduction in ordered information systems based on evidence theory, Knowledge Information System 25, 169-184 (2010) [10] Yao YQ, Mi JS, Li ZJ Attribute reduction based on generalized fuzzy evidence theory in fuzzy decision system.

Summary of Background Data Kyphoplasty (KP) involves placement of inflatable bone tamp via unilateral and bilateral approaches.
Results Both unilateral and bilateral kyphoplasty resulted in significant pain reduction.
After reduction of the fracture bone, cement is deposited into the cavity created by the IBT to repair the fracture.
The two groups of KP resulted in marked and sustained pain reduction.
Chung et al[8] compared the outcomes of unilateral and bilateral KP and found out that bilateral group had a greater advantage in the reduction of kyphosis and the loss of reduction was less than the unilateral group.

For example, based on the panel data posed by 22 OECD countries, Inmaculade Martinezarzoso[10] found that there is N-shaped relationship between GDP and carbon dioxide emissions.
Empirical Analysis Data Sources This study selected industrial GDP as an indicator to reflect the industrial economy, with per capita industrial wastewater, waste gas and solid waste emissions as the three indicators of reflecting the degree of environmental degradation.
In this study, the timing dimension of data analysis is 1990-2010, and data sources are obtained by Organizing and calculating data from China Statistical Yearbook and Sichuan Statistical Yearbook each year in Table 1.
If the sequence has a non-stationary line, it must be obtained stationary data after difference in order to further conduct cointegration test and impulse response analysis.
Economic Growth and Environmental Pollution: An Empirical Analysis Based on China's Time Series Data (1985～2003) [J].