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Multiple clamping is characterized by a series of economical advantages, due to the reduction of total machining time.
The measurement equipment consists of: force transducer type S9 made by Höttinger Baldwin Messtechnik; inductive displacement transducer type Wl/ 10mm made by Höttinger Baldwin Messtechnik; multi channel electronic PC measurement unit made by Höttinger Baldwin Messtechnik; and computer using Catman Easy/AP, versions 2.2 data acquisition software (Fig. 1).

After the signals de-noising, combining the envelope demodulation analysis with the spectrum zoom technology, which can effective extract the weak fault characteristic frequency The result of simulated signals and experimental signals shows that the proposed method has better noise reduction with a higher signal noise ratio (SNR) and lower mean square error (MSE), and it can successfully extract the fault characteristic frequency of rotating machinery early AE signals.
Samples of the input data are divided into odd sample sequenceand even samples sequence (1) (2) Predict.

With the reduction of temperature, liquid phase separation phenomenon is more obvious.
The potentials were fit to the following set of experimental data: elastic constants, vacancy formation energy and cohesive energy for Cu, Co as well as the mixing enthalpy and defect properties for alloy compound CuCo.

Stop experiment when the failure data reaches sample size.
There is a conflict of test accuracy and cycle time reduction.
Through the probability distribution hypothesis test of failure data, that failure data obeys Weibull distribution is obtained.
The two step analysis method, construct data method and maximum likelihood estimation are common methods utilized in constant stress accelerated test data analysis.
Data collection and analysis of reliability[M].

According to the pyramid data structure [1], the image could be defined as a sequence , and “” is the depth which is the key point observed by a human.
This approximation becomes exact in the limit of infinite data.
The evaluation is performed on the data set by us, because there is no data set which has several types of object having the same semantic and the different vision features.
The data set has 200 pictures(all the resolution of the images in the experiment are ), and each chair has the same number of pictures.
Jordan, Modeling Annotated Data, Proc. 26th Ann.

On the basis of data availability, we establish the competitiveness evaluation index system of urban Logistics, which included 8 levels and 17 aspects, as follow: 1） Logistics service capability: Logistics in essence is service industry. 2）The level of modern information technology: Information technology is the technical foundation and support for the modernization of urban logistics.3）Logistics standard: Standardization is the foundation of the urban logistics modernization. 4）Logistics cost efficiency: This is another important factor of the competitiveness of urban logistics.5）Infrastructure: Evaluation indicators include the circulating capacity of the logistics network design, infrastructure grade, and the total investment of logistics infrastructure.6）Regional economy: The development of urban logistics is restricted and influenced by the regional economic level.7）Supporting factors of logistics: Policy and the talents are both the important factors.8）Environmental factors: From
the viewpoint of sustainable development, we need to improve the production together with reduction of energy consumption and pollution.
Step one: Standardization the original data of 4 enterprises in Table 2 Table 2 Standardization data C1 0.8 0.8 0.7 0.7 0.7 0.9 0.9 1 1 0.7 0.7 0.8 0.8 0.7 0.6 0.7 0.5 C2 0.9 0.7 0.6 0.7 0.9 1 0.9 0.8 0.6 0.7 0.7 0.6 0.5 0.7 0.7 0.8 0.9 C3 0.7 0.8 0.9 0.9 0.7 0.7 0.5 0.7 0.7 0.8 0.7 0.7 0.6 0.5 0.8 0.7 0.7 C4 1 0.9 0.9 0.7 0.9 0.7 1 0.9 0.7 0.9 0.7 0.9 0.8 0.8 0.8 0.6 0.8 Step two: From the Eq. (1) to (6), obtain the matrix data of the correlation coefficient was in Table 3. 0.56 0.56 0.45 0.45 0.45 0.71 0.71 1.00 1.00 0.45 0.45 0.56 0.56 0.45 0.38 0.45 0.33 0.71 0.45 0.38 0.45 0.71 1.00 1.00 0.56 0.38 0.45 0.45 0.38 0.33 0.45 0.45 0.56 0.71 0.45 0.56 0.71 0.71 0.45 0.45 0.45 0.45 0.45 0.56 0.45 0.45 0.38 0.33 0.56 0.45 0.45 1.00 0.71 0.71 0.71 0.71 0.45 0.45 0.71 0.45 0.71 0.45 0.71 0.56 0.56 0.56 0.38 0.56 Table 3 The correlation coefficient matrix data Step three: Using Delphi method to obtain index weight and Eq. (10) to find the rank of
Conclusions The evaluation of urban logistics competitiveness involves a large number of different kinds of quantitative data, as well as a large number of qualitative indicators related to logistics service and logistics links.
Data processing of small samples based on grey distance information approach[J].

To solve above problems, the manufacturers of the capacity find that lead-acid battery capacity of discharging termination voltage can be indirect reaction after testing the size of the battery capacity through a large number of testing data and expert experience.
The model is designed to make the battery production cycle become shorter and more applicability, to achieve energy conservation and consumption reduction, to improve the product factory qualified rate, intelligent, finally to realize the battery cost reduced, to adapt to the modern industrial development.
Taking a power system of Chongqing valve control type sealed lead acid battery production technology for example[4], this paper builds up the model of battery capacity and streamline of the auxiliary variables in the soft sensing from the enterprise long-term accumulation of the formula, craft and the rich real-time data of battery performance, using data mining methods to realize we only measure some of battery performance parameters to obtain indirectly the purpose of the lead-acid battery termination voltage instead of all, which provides theoretical feasibility of omitting the battery discharge capacity in the process of production inspection process, achieving corporate goals to reduce energy consumption and cost savings. 2 RReliefF Algorithm Using feature selection of regression algorithm RReliefF algorithm[5], calculate respectively weight of the original auxiliary variable values, according to the following steps: 1) Select the sample from sample set, and choose k samples nearest
the dominant variable value of the sample i, and is the maximum and the minimum of m samples. 3) Calculation weight set of the sample under the condition of original auxiliary variable A, press type, the equation is ; stands for the original auxiliary variable A value of the sample , （）is the original auxiliary variable value of the sample i, and is the maximum and the minimum of m samples. 4) Calculate weight set of the sample under the condition of the dominant variable and the original auxiliary variable A, the equation is ; 5) Repeat the four steps (m-1) times, each time you select different samples, obtaining m m m ; 6) Calculate,, in turn, where is the sum of , is the sum of , and is the sum of ; 7) Use the following type to calculate weight value of the original auxiliary variable A, the equation is ; All weights are calculated according to the original auxiliary variables. 3 Lead-acid battery production data
Table 1 The importance of eight different auxiliary variables Battery voltage of charging 32hours Battery voltage of charging 8hours Battery voltage of charging 31hours Battery voltage of discharging 0 hour Battery voltage of charging 26 hours Battery voltage of discharging 6 hours Battery voltage of charging 6 hours Battery conductance Sorting 0.1798 0.1953 0.1978 0.2100 0.2278 0.2426 0.2487 0.2562 83754261 According to this data set, in the learning process of BP neural network, each group randomly selects 130 samples as training data, 42 group of data as the test sample, all trained 10 times, and get the best results, training forecast output, training the relative prediction error, inspection predicted output and the relative prediction error.

Test channel showing locations of thermocouples and test tubes with inserts Table 1 Sizes of micro-fin tube with inserts Data Reduction The quality entering the test section (), can be solved in the following equitation (1).
As expected, the data showed the heat transfer coefficient (h) increases with increasing Reynolds number (Re) for all the test tubes.
Fig. 4 shows the comparison of the experimental heat transfer coefficient data with the calculated values from the correlation of this study.
All the data were within 30% of the predicted values.
Pate, Using solubility data for HFC-134a and ester lubricant mixtures to model an in-tube evaporator or condenser, ASHRAE Trans. 99 (1993) 383-391

Attribute reduction technology in the standard rough set model depends on the lower approximation, so it is sensitive to noise data and results in many valuable rules unextracted.
In VPRS, an error precision is introduced in the basic rough set model, and it allows a certain degree of fault tolerance in computing the approximate dependence value, so it provides a good way to deal with data inconsistency caused by the noise [6,7].
Variable Precision Rough Set In variable precision rough set theory, an error precision is introduced in the basic rough set model, and it allows a certain degree of fault tolerance in computing the approximate dependence value, so it provides a good way to deal with data inconsistency caused by the noise.
[2] Wu Z, Huang N E: Ensemble empirical mode decomposition: a noise assisted data analysis method, Advances in Adaptive Data Analysis, vol. 1(2009): p. 1-41
[9] Xu Huijian, Guo Feipeng: Combining Rough Set and Principal Component Analysis for Preprocessing on Commercial Data Stream, JCIT, Vol. 7 (2012): p. 132 ~ 140.

Based on the water quality investigation data in December of 2010, the water environment quality of Jilongshan sea area in Zhanjiang in winter was assessed using single water quality parameter model, integrated water quality index model, organic pollution index model and eutrophication assessment model.
Besides the enormous economic losses, eutrophication also causes a reduction in biodiversity and has potential threat to human health. [2].
Based on the water quality investigation data in December of 2010, the water environment quality of Jilongshan sea area in Zhanjiang was assessed.
Sample collection, pre-treatment, storage, transportation, analytical method and data treatment were determined based on Specifications of Ocean Survey [3] and Specification for Marine Monitoring[4] .
Conclusions The water environment quality of Jilongshan sea area in Zhanjiang in winter was assessed based on the water quality investigation data in December of 2010.