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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.

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.

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.

Substantial increase in the number of data dimensions has brought unprecedented difficulties to the cluster; therefore, before using FCM algorithm, the original sample data reduction has very important significance.
However, dimensionality reduction by manifold can find low dimensional embedding hidden in high dimensional data.
This paper uses manifold dimensionality reduction algorithm for high dimensional data, existing methods use dimensionality reduction for feature vector firstly, further FCM training, this type methods of dimensionality reduction do not use the data correlation .
FCM algorithm base on L-Isomap dimensionality reduction Isometric mapping algorithm built on the basis of MDS[2],Use local neighborhood distance calculate approximate manifold geodesic distance of data points, Complete data reduction through the establishment reciprocity between geodesic distance of the original data and spatial distance of dimensionality reduction data.
Assumedis data set,N is the number of samples, C the number of data set is divided into .

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.

Model Validations Test Computations of the averaged drag coefficients and Strouhal numbers for a single cylinder at Re=200 and Re=5.5X104 for three dimension are carried out and the results compared to the published data to validate the computational method.
The result shows that the tripping rod has a better control effect for force reduction at higher Re.
All this phenomenon relate to the drag force reduction.by C.H.Kuo.et.al[9] Fig. 3.
This optimum angular position was found to be 40° for Re=200 where the drag coefficient is reduced by 21%,while for Re=5.5´104 the optimum position is 30° and the drag coefficient is reduced by 36%.The effect of tripping rod for drag reduction is more effective at high Reynolds number is in good agreement with previous experiment data.
“Numerical study of Fluid Force Reduction on a Circular Cylinder Using Tripping Rods”.

There can be many reductions of.
So, means all the reductions of.
Intersection set of any reduction is a sole kernel.
Thus the minimum attribute reduction is obtained.
The implementation tool is the data mining software WEKA.

In AUSCS, ultrasonic flaw signals acquired in the form of digitized data are first preprocessed; then the informative features are extracted using various digital signal processing and pattern recognition techniques; finally, the set of selected features are taken as the basis of flaw identification by training the proper classifier.
Thus, dimensionality reduction techniques need to be applied to transform the original features into a lower dimensional space which is useful for feature reduction to avoid the redundancy [3].
Given training data X with n samples and c classes, let ni be the number of samples in the ith class ().
Therefore, some schemes need be used for dimensionality reduction.
Finally, the features after reduction were fed to BP networks for flaw classification.

Based on a user-defined range of parameters (module, helix angle, etc.) the software presents a large number of possible solutions covering the full parameter space and presents a list with many data (geometry, safeties, characteristics as sliding, losses, price, transmission error) including a qualification of every solution based on required characteristics.
KISSsys windows of the Gearbox-Variant-Generator User Interface: Provides for input of required torque and speed Sizing: Includes the various sizing functions and functions for user data input Table of Variants: List of the different reducers, of which any solution can be selected Solutions: 3D display of main results for the 90 reducer variants calculated Optimization of a Compact Micro-Planetary-Gearbox The tool was used in a project for a Chinese company, producing small compact gear reducers with a high speed reduction.
The design data for one of the required gearbox was: Output speed 31 rpm and 16 Nm torque.
Therefore, the required total reduction is i = 160.
The design data for the gearbox was: Input speed 15 rpm and output power 3.2 MW.