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Processing methods of the line points cloud data 2.1 Preprocessing technology Generally, the data processing work can be divided into these methods, such as filtering of point cloud data, data multiple visual alignment, feature extraction, and data segmentation.
And the key pre-processing technologies of line point cloud data mainly include multi-view split, noise removal, data reduction and gridding.
Noise reduction processing can make data smooth, reduce the deviation of the model.
The main operation processes is as following: data package, interception of feature section, sheet data import to CAD, extract data as demand.
Summary Against the features of line point cloud data, establish the appropriate track model, by filtering of point cloud data, data multiple visual alignment, feature extraction, and data segmentation.

Combining rough set attribute reduction with SVMR theory, using rough sets as a front-end processor and attribute reduction to eliminate redundant attributes, this paper will train and test obtained data by SVR method.
The major steps are: (1) Data preprocessing Screening, filling historical data and eliminating one of the singular data (2) Selecting a set of similar days According to the type of forecasting day, under the premise of properties of season, using similar day theory to select similar days which relational degree as sample set of rough set attribute reduction.
Therefore, this set of data selected as attribute values of the train and test sets of SVMR.
According to historical data of PV plant, this method is able to forecast output directly, and it can avoid specific modeling of inverter model for PV systems, collecting and converting process of illumination data.
On the premise of completeness of information, it can provide streamlined modeling data for subsequent data fitting that using rough set theory to do discretization and attribute reduction analysis for decision table which consists of a variety of influential factors and output.

It begins with acquiring experiments data.
Usually, the data cannot be directly treated by RS, and corresponding preprocessing should be adopted to improve the quality of data, where discretization is to convert the continuous data to discrete value.
Application in Rotating Arc NGW Obtain raw data To obtain the experiment data, the work piece is machined as showed in Fig. 1.2(b), which imitate the weld of multi-layer and single pass welding.
This will help the welding experts acquire knowledge from the experiment data.
Model reasoning Approximate reasoning is implemented when the RS model is use to predict unseen data.

Reverse Engineering and 3D Printing Technology's Application in Sculpture and the Restoration Shigang Wang1, a, Fansong Meng1, b, Bo Qu1, c 1School of Mechatronics Engineering, Qiqihar University, Qiqihar 161006, China ahljwangsg@163.com, b1161764472@qq.com, c446986768@qq.com Keywords: Reverse engineering, 3D printing, Point data acquisition, Data reduction, Surface repair Abstract.
The key link is getting the 3D model through digital and data [4].
The Stage of Point Cloud Data Processing.
Data reduction.
Smoothing data.

Development of a baseline methodology The methodology includes two parts which are the power supply part calculation of the CO2 emission reduction and the heating part calculation of the CO2 emission reduction. 1.
According to the data of the project, CO2 emission reduction can be calculated as Table 2.
Table 1 Parameters of IGCC power plant Parameter Unit Value Installed capacity MW 250 Power generation efficiency % 48 Running time h 5000 Power generation efficiency of the baseline % 35 Power used in site rate % 3 Desulfurization efficiency % 99 Dedust efficiency % 100 Bituminous coal calorific value GJ/Kg 0.02717 Thermoelectrical ratio [4] % 35 Table 2 Annual emission reductions (unit :) CO2 emissions Baseline emissions Project emissions Leakage Emission reductions Power part 1,083,429 860,269 0 223,160 Heating part 240,875 0 0 240,875 Total 1,324,304 860,269 0 464,035 Environmental benefit and economic benefit According to the 250 MW IGCC power plant data, it can be calculated that IGCC power plant can save 227,763 tons of coal per year [5], and at the same time, the following pollutants (Table 3) can be reduced annually.
Table 3 Pollutants reduction annually (unit: t) Pollutant Baseline emissions Project emissions Leak Emission reductions SO2 634.1 25.5 0 608.6 NOx 4143.7 2496.1 0 1647.6 Smoke 190.3 0 0 190.3 According to the project, it can be calculated for project CO2 emission reductions (CERs) income under the condition of internal rate of return (IRR), detailed data shows in Table 4.
By using the methodology developed, the IGCC emission reductions are calculated.

Then, a GCSC evaluation model is proposed based on Data Envelopment Analysis (DEA).
Data Envelopment Analysis (DEA) is an effective non-parametric statistic evaluation method.
Pollution reduction.
Experiment Data.
The experiment data are collected using Delphi method, which is show in the below Table 2.

Then it took advantage of Poyang lake for empirical analysis, and got the function of pollutant reduction cost and function of environmental damage cost by means of multiple regression analysis with the five main data of Poyang lake from 2001 to 2010.
But according to statistics yearbook and other data, Shangrao information is too little to be in operation.So the paper mainly discuss the pollutant reduction between Nanchang and Jiujiang.
Due to some data is not easy to get, we do the following process, The reduction cost of pollutant i is as follows:.
Through the above process,we can get the models of each pollutant with the data of 2010.
And due to limited data samples are few.

Potential of Size Reduction of Flat-plate Solar Collectors When Applying Al2O3 Nanofluid M.
Some studies were made on the potential of size reduction of various engineering applications by using nanofluids.
Work had been done by Saidur and Lai [17] in vehicle’s weight reduction, Kulkarni, Das and Vajjha [18] in building heat exchanger’s heat transfer area, Leong, Saidur, Kazi and Mamun [19] on the reduction of air frontal area of a car radiator and Leong, Saidur, Mahlia and Yau [16] on the size reduction of shell and tube recovery exchanger.
None of the studies focus on the size reduction of flat-plate solar thermal collector.
Thermal efficiency of a flat-plate solar collector can be calculated from: η=QuITAc (1) After the thermal efficiency of solar collector been determined, the potential of reduction of the size of collector’s area can be estimated by: Ac=mCpTout-TinITη (2) Size reduction calculation is carried out based from experimental data of Yousefi, Veysi, Shojaeizadeh and Zinadini [4] under the best operating conditions.

The tracking servo control has played an important role in the data storage servo systems.
However, higher data transfer rate and higher data density make it difficult to maintain the desired tracking precision during normal disk operation.
The next generation optical data storage systems should have a storage density of more than 5 TB/in2 and a storage capacity of more than 10TB with a high data transfer rate more than 1 Gbps [1].
To follow a data track, the track servo or track controller should be turned on at the moment a data track is crossed.
Conclusion The reduction of tracking error is critical to increasing the data storage density in next generation optical data storage system.

To the shortcomings of neural network in fault diagnosis, such as multiple input dimensions and the huge amount of data, some reductions from data based on rough sets theory are derived and unessential attributes were eliminated, an optimized rough set-neural network intelligent system was established.
Based on the above analysis, input data of BP neural network was processed firstly by using rough set, a diagnostic network was built according to reduction results, more satisfactory was achieved.
First reduction rules were mined from data set through rough set, BP neural network was designed by reduction rules and trained by reductive data set.
However, a considerable amount of data was continuous in practical applications, and therefore the data must be discretization.
The discretized data was reduced by using rough set theory, each sub-neural network input after the reduction was achieved.