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The test results achieve the predicted effects with acquisition, processing and analysis numerous data.
Real-time cross-correlation algorithm The real-time correlation detection is achieved, the details are as follows: (1) In the system for machine abnormity detection during mass production of CNC machine tools, firstly normal machine power signalwill be sampled, at a sampling rate of , number of sampling points is , forming the length of sampling sequence , take the sampling with the same sampling interval , real-time power , we only need to record the first real-time power, expressed by , where .(2) To determine whether the number of elements in sequence is or not, if the elements are not enough, returning to Step 1 to continue collecting data, when the number, is enough, get out the first data of and multiply with the data of ,the corresponding .Results are: ; ;;......the results of will be stored in main register.(3) Add the new multiplication result obtained from Step 2 to the last accumulated value of the corresponding term in the register.
It is shown from the amplitude, frequency, phase data on the diagram of frequency spectrum that wavelet decomposition and reconstruction has good linear phase characteristics.
Study on Selection of Wavelet Adaptive-Threshold and Method on noise reduction.

Here the RTK system need to use the reference station and rover two receiving terminal complete real-time positioning, the former for the latter to provide differential correction data, the measured value of the rover reduction to the original survey control net, in order to consistent with the national unified coordinate system, as shown in Fig. 1.
The new work, input the surveyed area known high grade control point (more than three) results material, as measured area coordinate system calibration data.
To build a homework, in the surveyed area control point WG - S84 coordinate system respectively under static GPS measurement, store the data.
Using GPS RTK technology acquisition of each point is independent observation point, there is no error accumulation, and data safety and reliability.
For reference station and between starting on data chain link, as long as the radio power enough, radio emission range is the range of practical work.

Finally, by using the non-delayed effects and stability of the earthquake magnitude prediction data, the state-transition matrix is obtained through the Markov chain, and the state interval and corresponding probability of the GEP model prediction are obtained.
It will have important significance to earthquake prediction, earthquake prevention and disaster reduction to collect partial geological parameters from a certain location with modern instruments and seek the relation between these parameters and the earthquake magnitude of this area through scientific analysis.
Earthquake magnitude prediction based on the improved GEP algorithm The experimental data came from Reference[4] , which represent 6 representative indices of earthquake on the aspects of time, space and intensity: frequency, creep, energy, B value, lack-earthquake and n value, as well as the earthquake magnitude of the next year.This paper has selected samples of No. 1-20 as the training samples and samples of No. 21-27 as the test samples.
(iii) Comparison between the GEP model and the Neural Network model Reference[4] used the Elman Neural Network to conduct prediction of the data in this paper, and Table 1 shows the comparison between the GEP model and the Neural Network model on the prediction of test samples.
Therefore, in accordance with the Markov theory[5], this paper has converted the earthquake magnitude data obtained by the GEP model to the change intervals and obtain corresponding probabilities, so that the prediction result of the GEP model will have more values.

The most general redistribution method of manufacturing and assembly line is to adjust by work planner who plans production schedule according to experience, but the benefit is often less than the expenses coming from the reduction of the circulation time.
All type of packaging data are put into the database in unison, in this way we are very easy to predict speed of the manufacturing and assembly in each process location.
All type of packaging data are put into the database in unison, in this way we are very easy to predict speed of the manufacturing and assembly in each process location.
Its process planning and production planning are all revised in real time; (3) Serving module: Outputting the statistics data, connecting with the other software, and realizing calculation and modifying about the work piece.
Its process planning and production planning are all revised in real time; (3) Serving module: Outputting the statistics data, connecting with the other software, and realizing calculation and modifying about the work piece.

Though for the solvent distillation column and residual solvent stripper the interation parameters were provided by the data base of Aspen Plus [7], and those of the extraction column were correlated from the liquid-liquid equilibrium data of the MIBK-water-phenol- hydroquinone quaternary system [8].
The steam flow of the coal-gasification wastewater in the simulation was 100 t/hr, other involved input data including composition for streams was shown in table 1.
Table 1 Input Data for streams Process Parameter Wastewater Solvent Mass flow rate [t/hr] 100 Temperature [°C] 40 40 Pressure [atm] 1.05 1.05 Compositon for Input Streams (mass fraction) Water 0.994590 0 Phenol 0.00275 0 Hydroquinone 0.00266 0 MIBK 0 1.0 Results and discuss Extraction Process.
Fig.4 Relation of theoretical stages and reflux ratio Fig.5 Effect of feed stage on reflux ration The increase of the number of theoretical stages means the increase of the equipment cost, while the decrease of the reflux ratio means the reduction of energy consumption.
It meant that when the number of stages was more than 14, the reduction of reflux ratio by the increase of theoretical stages had been very small.

The setup for the experiment consisted of a mold and two supporting devices: i-) a data acquisition system and ii-) a furnace.
The data acquisition system consisted of a labtop computer, Omega Personal DaqView Plus data acquisition/control units and data acquisition software package.
Perhaps the most significant improvements were more uniform contact along the mold-shell interface, and a reduction in crack nucleation in the ingot due to slower, but more uniform heat extraction.
Perhaps the most significant improvements are more uniform contact along the mold-shell interface, and a reduction in crack nucleation in the ingot due to slower, but more uniform heat extraction.

The available measurement data are explored in order to some clues may emerge in the pick-up signal that potentially represents structural modifications of the bars.
Both the measurement data and the geometric localization will be considered as fuzzy numbers, i.e., the reading will be associated to a "confidence" interval that measures our trust about that number.
Results were then improved both using an algorithm of automatic extraction of FIS from numerical data [7] (MATLAB® GENFIS System) and introducing learning (MATLAB® ANFIS).
Performing a careful analysis of the experimental database it has been possible to reduce the number of the inputs and the cardinality of the fuzzy data bank .
This feature turns out to be decisive in practical real time applications, where the reduction of the inspection time is a major demand, and in the design of novel large systems where an effort must be made to reduce diagnostic concerns.

Thermal expansion/shrinkage of compacted sample was measured by dilatometry and the data obtained were plotted as ΔL/L0 (thermal deformation) according to temperature, T (ºC), in a dilatometer NETZSCH DIL 402 C, with temperature range from 30 ºC to 1500 ºC with heating rate of 10 ºC/min in air.
The method measures the thermal expansion of the sample length and the data plotted as ΔL/Lo according to temperature.
Experimental data of bending breaking stress were analyzed by Weibull statistics and distribution parameters, characteristic resistance () and Weibull modulus (m), were determined by linear regression method.
The result of Weibull method applied to the 4-point flexural strength data in 17 developed samples is shown in Fig. 4a.
The high furnace temperature makes critical the reduction of α-Al2O3 properties, which determine aging, mainly through grain growth.

The proceed of model analysis 1）calculate the steady and unsteady flow field in Fluent 2) when the flow field is dynamic stable, use FW-H model, get surface pressure fluctuating data(.index and .data files) 3) read data from Fluent into Sysnoise as surface distribute sources, find the main noise source 4) put forward some improved schemes, calculate the steady-state flow of these schemes using BNS model, compute acoustic power of each scheme in the form of surface and volume contribution 5) calculate aerodynamic noise of each scheme, compare the overall sound pressure level on field points, compare the conclusion with the results with BNS model Comparison of simulation results G.
Data from Fluent is read into Sysnoise to compute the propagation of noise.
Fig.10 Spectrum of sound pressure level Fig.11 Sound pressure level around drivers’ears Fig.12 Average sound pressure level of plane field point To examine the overall noise reduction effect of the improved schemes, compare the average sound pressure level of the 5 group plane field points.
(2) As the sound propagation is directional, the reduction of sound source power doesn’t mean all field points will drop.

In order to calculate the damage parameters, a system of equations, in which the number of unknowns is less than the number of equations, has been derived using a combination of experimental data, finite element results and the damage law.
Introduction In many industries such as aerospace and automotive, weight reduction is of a major concern.
Its main advantage is not only the weight reduction but also the manufacturing costs saving.
These data are summarized in Table 2.
As we can observe, the curve fits the measured data very well.