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EMD decomposition process is based on the following assumptions: (1) The signal data has at least one maximum and one minimum. (2) Maximum time interval is the characteristic time scale. (3) if the signal data just has turning point without extreme point, the signal data is be differentiated one or several times to obtain the extreme point, and then get the decomposition result by integral.
Upper and lower envelop contain all the data point.
It is the highest frequency component of the original data.
Separate from the original data, and get the rest signal : (6) still contain the frequency information of the original data, so set as a new signal and repeat the process above.
Original signal can be construct as follow: (9) Now, the original data signal is decomposed to IMF and one rest component .

The concrete application research of drop and pull transport in energy saving and emission reduction —with SF's case Liang Xue School of Automobile and Traffic Engineering, Transportation Department, Nanjing Forestry University, Nanjing 210037, china.
Ph: 13913302896, email:shiling97322@163.com Keywords: Drop and pull transport; energy saving and emission reduction; application Abstract：Drop and pull Transport is an economic, environmental protection, a high efficient mode of transportation.
Thereby obtain energy saving and emission reduction. drop and pull Transport has raise transportation economic efficiency and social efficiency advantage.
Its main features are: (1) Low transport costs. (2) Transport efficiency. (3) Logistics costs low. (4) Energy conservation and emission reduction.
Through to the high-speed toll , oil,Fuel tax,Buy the car cost, maintenance costs,Staff salaries, Depreciation's analysis etc, the sum based on Shenzhen (Quanzhou) freight program's total cost, that is shown in Table 3: Table 3 The total cost of operating the program Variable costs (Million) Fixed costs (Million) Total cost (million) Drop and pull transport 5212.686 858.45 6071.136 General cargo 5939.146 1058.05 6997.196 Shenquan trunk drop and pull transport of total operating cost of 60,711,360 yuan Total operating cost of ordinary goods is 69,971,960 yuan Drop and pull transport Total operating cost savings = 9.2606 million yuan for the 6997.196-6071.136 Drop and pull transport savings ratio of total operating costs 926.06/6997.196 = 13.2% According to the calculation of the actual circumstances of specific data, the operation of the program variable costs, fixed costs and total costs, that the program economically viable.

Online mining of closed frequent itemsets over streaming data is one of the most important issues in mining data streams.
Different from data in traditional static databases, data streams have the following characteristics[1].
All data streams are represented by average transaction length (T), average pattern length (I) and the size of stream data (D).
MOMENT: maintaining closed frequent itemsets over a data stream sliding window,in:Proceedings of the 2004 IEEE International Conference on Data Mining.
CFI-stream：Mining closed frequent itemsets in data streams.

In this paper, the principal component analysis method is applied in the underwater image data for detecting the image objects.
To these problems, the most promising solution is to perform dimensionality reduction on the image data.
By this assumption, PCA is limited to express the data as a linear combination of its basis vectors.
Let be the original underwater image data set, in which each column is corresponding to a single sample.
Experimental results 4.1 Feature extraction by dimensionality reduction Figure.3 Dimensionality reduction In order to visually represent and compare the inter-class difference and the inner-class difference of the feature extracted by the linear dimensionality reduction methods, we embed the image data in the high-dimensional space onto the two-dimensional space.

The microphone data were collected using a two-channel B&K analyzer 2144.
Measurement of these parameters were performed using pressure transducers, temperature and humidity sensors and recorded and processed by the data acquisition station - SAD-2, equipped with the ADAM modules 4000+, an integrated PC with the application GeniDAQ, equipped with a Visual Basic language [43].
In each measured points the data were recording by 10s with resolution 0,1s.
All figures present the data from 100 Hz to 10 kHz (Fig. 5).
It could offer the data basis and bionic model for the application of owl silent flight in engineering.

A Study on Bore-sighting for the Error Reduction of the XK11 Woon-Joo Yeo1, a , Je-Wook Chae 1, b, Chan Lee 1, c , Eui-Jung Choi1, d and Jun-Ho Lee 1, e 1 Agency for Defense Development, Yuseong P.O.
Image processing method with Charged Coupled Device camera is chosen for the Error reduction of bore-sighting.
� Noise Reduction Erosion Erosion is used to get rid of the remained noises after Binary-Coded work
So dilation is used to restore the laser points reduced by the erosion for noise reduction
{ }ˆ( )z A B z B A ⊕ = ∩ ≠ ∅ (3) Labeling The work dividing the data cognized two laser points to only one color called Labeling.

The greatest feature of current stage is the presentation of high dimensional data (data above three dimensions), which describes the complex data through visual stereoscopic model that can help users to check and handle the data more clearly.
Data analysis and operation module: After receiving data from questionnaire, questionnaire users can analyze, process and display the data visually here.
This module uses three-dimension data cube as main graph, thus we can display the high-dimension data through dimension reduction.
If it is 3D data, then show as 3D data cube, therefore, users can operate data cube directly with mouse operation.
Multi-Dimensional Data Visualization.

How to manage energy consumption appropriately and energy saving and emission reduction are becoming crucial issue of the problem.
Research on energy consumption, conservation and emission reduction system of Beijing 1.3 Factor analysis of the system First, comparative analysis of raw coal consumption intensity has been made and shown in Figure 3.
A: Structure share B: Efficiency share On the basis of the analysis theory it could be found out that the raw coal consumption intensity analysis result is like the data shown in Table 1.
Table 1 Energy intensity of Beijing Year Structure share Primary Industry Secondary Industry Tertiary Industry 2006 -0.9336 0.0434 -0.6256 -0.3514 2007 -0.2675 -0.034 -0.4108 0.1773 2008 0.1738 0.0139 0.2541 -0.0942 2009 0.1582 0.014 0.2277 -0.0835 2010 -0.1491 -0.0198 -0.2097 0.0804 Year Efficiency share Primary Industry Secondary Industry Tertiary Industry 2006 1.9336 -0.086 0.0222 1.9974 2007 1.2675 -0.005 0.3278 0.9447 2008 0.8262 0.0075 0.4608 0.3579 2009 0.8418 0.0123 0.4653 0.3642 2010 1.1491 -0.0212 -0.6413 1.8116 Seeing from the data from Table 1, structure share reflects unreasonable industrial structure.
The data is normalized by using Min-max method.

However, for these ideals as the above, the testing data on surface structure of composite materials need to manage and analyze in some elementary, in other words, that are the testing data of mathematical or statistical models on the surface structures.
More concretely, due to the data being excessively complex, the data must be in a great extent contracted and purified to satisfy many applying objects.
Furthermore，it was indeed pointed that the effects of these data reduction modes are vary valid and significant especially in the updating age that composite material systems link closely with information network and large data, however, it is pity in some extent that there are few references on these topics at present.
At last, it should be pointed in data analysis for this paper that the element model, the picking indicator and the simulation on data are very important.
Journal of Data Acquisition & Processing, 2005,20(1): 34~39

Different experimental conditions adopted could be the other major factor that affects yield data, which shows variations in total yield and well as the products patterns.
Possibly the differences in the mode of activation of CO2 on these metals, modulated with the nature of the supports, could explain the rate data for various products.
Data with UVC radiation, presented in Table.14 shows that Cu & Fe, when loaded separately on TiO2, display lower conversion rate to ethylene.
CO2 photo reduction data presented in Table.17 show that in-situ CoPc/TiO2 catalyst is more active than a simple mechanical mixture of CoPc and TiO2 indicating a co-operative effect between dispersed CoPc and titania surface for the effective transfer of photo-generated electrons.
Lo et al. [162] could successfully validate their model with the experimental data with a pseudo first order reaction rate equation.