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Let, where S represent the set of clustering data， represent the individual，the will be expressed in the form of binary, determining the length of the required binary according to the size of , for example,，the length is fixed.
The results show that, for a given mixing sample set, the reduction of within-class scatter will lead to the increase of between-class scatter.
Experimental Analysis The authors have simulated two sets of data, data1 and data2, to validate the performance of the proposed algorithm, which data1 have 400 data, data2 have 500 data.
We can also see that the superior of the new clustering algorithm are obviously to the traditional clustering algorithm from the experimental data, either in error sum of squares or accuracy.

Introduction Synthetic aperture radar is an active microwave imaging radar, and the unique characteristic of it is that can obtain data under all-time and all-weather.
EMD is a new and complete data-driven processing method and it need not choose the basis function and perform the self-adaptation adjustment according with the signal itself characteristics.
The description of SAR target detection algorithm EMD uses the self feature of signal to decompose the signal, therefore, not only it is a full data-driven method for signal processing, but also it is an adaptive, multi-scale and multi-resolution analysis method.
The experimental data comes from the SAR image databank of the moving and stationary target acquisition and recognition (MSTAR), and they are shown in Fig.2.
A novel method for speckle noise reduction and ship target detection in SAR images.

Next, upgrade the CAD to CAE from 3D data model in the SolidWorks file to higher level dynamic simulation software ADAMS.
Before conducting the simulation test, the more complicated and precise motion limitation and simulation data shall be re-set up again.
The tradition machine tool design is based on specific design data to pre-produce numbers of prototype of physical machine tool.
The exact number is based on the needs of test objectives mainly for accumulating huge amount set of comparative data, at relatively same test condition, which is adequate enough for analysis in order to feed back for design change and modification.
After transforming solid data model to dynamic analysis software ADAMS, the movement limitation module built inside the software shall be applied to allow the moving parts being properly limited to some degree.

It provides storage and services to the mass of text and video data.
Data center subsystem.
It is constituted bythree separate functional modules, the data access distribution system, distributed database system and distributed mass storage system.
It is composed by message data bus.
It is middle layer tool for highly fault-tolerant and high-powered data transmission, which is applied for managing each server, storage node and various control messages between server and client, is the foundation for cloud computing. 3.

Table 1 shows the concrete data and Fig. 1 shows the curve of relationship between safety factor Fs and slope height H.
Table 2 shows the concrete data of relationship between safety factor Fs and slope angle, Fig. 2 shows the curve of relationship between safety factor Fs and slope angle.
Table 3 shows the concrete data of relationship between safety factor Fs and unit weight of soil, Fig 3 shows the curve of relationship between safety factor Fs and unit weight of soil.
Table 4 shows the concrete data and Fig .4 shows the curve of relationship between safety factor Fs and cohesion.
Table 5 shows the concrete data and Fig.5 shows the curve of relationship between safety factor Fs and internal friction angle.

Fig.1 OPC of Color Production Simulation model Runs Initial Model and Data Analysis.
Table 2 Data validation Items Actual data Simulated data Achievement Ratio Number of input 410225 403086 98.2% Number of output 205506 191150 93% Average of total production time 9002.39 8532.37 94.8% Improvement and Discussion After simulating the production line of this company, the obtained result indicated the lack of proper balance and production control in production line.
Afterwards the results were evaluated with real data and its validity was tested.
Average price of the products in this scenario declined from initial 18005$ to 16020$ that is mostly because of the reduction in production time of total orders.

The present lack of experimental data on ionization coefficients is in stark contrast with the situation for silicon (see. e.g. [3] for a review) and also for GaN [4] and SiC [5,6].
First data on the multiplication factor measured on a SMIP diode are shown in figure 2.
Assuming that the holes dominate the impact ionization, it can be seen from figure 2 that Chynoweth's [12] equation (αp=apexp(−bp/E)), with ap= 4.0·10 6 cm -1 and bp=1.1·107 V/cm is consistent with the data.
However, due to the low signal to noise ratio, a wide range of ionization coefficients are in fact consistent with the present data, e.g. ap= 6.0·10 5 cm -1 and bp=0.8·10 7 V/cm (see figure 2).
To be able to reliably calculate ionization coefficients from carrier multiplication data, the signal to noise ratio must be increased by at least an order of magnitude.

This work enrichs the experiment content of boiling heat transfer outside enhanced tube of R134a, and also provides basic data to the refrigeration industry for the design and manufacture of high efficiency evaporators.
Table 1 Parameters of test tubes Tube outer diameter (mm) Wall thickness(mm) Length measured(mm) fin (fpi) fin height (mm) fin pitch (mm) Esmooth 18.0 1.40 1100 E27 18.0 1.40 1100 42 0.63 0.20 E28 18.0 1.40 1100 46 0.63 0.20 E29 18.0 1.40 1100 56 0.63 0.20 Data Reduction and Uncertainty Analysis The overall heat transfer coefficient is determined by the following equation[4]: (1) where is the outside area of the test tube.
The comparison was performed for the data of R134a boiling outside the smooth tube shown in Fig. 2.
The results from Cooper Equation agree within 20 percent of the experiment data.
The predicted boiling heat transfer coefficients from Cooper equation with R134a agree within ±20 percent of the experiment data. 2.

The experiment was performed as follows: (1)take quantitative Omethoate accurately, and determine the optimal experimental conditions according to spectrophotometer; (2)add 1% NaOH, 0.3ml of 3.75mol / L H2SO4 and 0.1ml of 0.1% KMnO4 by sequence, and measure the relative data after mixed and placed for 10min. (3)Under optimal conditions, the removal effects of pesticide residues by natural cleaning agents[3,4], salt water and distilled water were compared in terms of the time, temperature and other factors.
Mechanism of color development Under the alkaline conditions, hydrolysis of organophosphate led to the forming of both alcohol and phenolic substances, which can make the oxidation reduction reaction with KMnO4, producing green MnO42-, and the reaction was performed as follows: organic phosphorus→ phosphate + alcohol, etc.
As for the mechanism of color development, we could tell that under alkaline conditions, hydrolysis products of organophosphate made the oxidation reduction reaction with potassium permanganate, which led to a green solution

Brightness reduction will not only reduce resource consumption, pulp costs and the pollution caused by dioxins and AOX.
So brightness reduction is an effective way to maximize harmonious development between humans and nature.
It utilizes analytic hierarchy process (AHP), artificial neural network (ANN), fuzzy mathematics to establish paper material evaluate model, analyze parameters like reducibility, printing density and printing contrast by means of three model output calculations of sample data substitution.