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A cutting force measurement system composed of Kistler9257B dynamometer, 5070A12110 charge amplifier and data acquisition card has been used to record the cutting force signals in three directions, as shown in Fig.1.
Fig.5 Original signals and the signals after noise reduction (T1,v=40m/min) In the same cutting conditions, cutting force involving tool 2 and tool 3 is smaller than tool 1, especially for axial force Fp.
For a set of cutting force data, the signal characteristic can be observed by analyzing the time domain and frequency domain characteristics.
Firstly, the noise reduction has been done for the original cutting force signal, as shown in Fig. 5.

The data set was analyzed with the CTAn software package and the ImageJ Java image processing software in order to create a complete 3D representation of the microstructure of the superalloy and to calculate the porosity [6,7].
The relative reduction in the porosity, caused by such data processing, was checked to be around 1% and always below 3% of the true value.
An interesting result is, that the heat treatment brings about an increase of the porosity and reduction in their size.

In this paper, PAC2002 is selected which fits experiment data by trigonometric function.
According to the experiment data of Hoosier 18×6-10 tire, the property file could be conveniently built by magic formula.
Reduction of accident risk for high sided speed vehicle in crosswind.
Computational Fluid Dynamics (CFD) Simulation of Drag Reduction by Riblets on Automobile.

In order to maintain its unique high measurement accuracy, taking quantitative approach to the measurement data has become an important issue of optical measurement technology. 3.
Therefore, the discontinuous phase must be connected to acquire the real continuous phase value and this process is called phase reduction [6], shown Figure 9.
Phase Reduction Fig. 10.
[4] Yi-Ling Hsu, Data fusion of 3D profiles measured by projected fringe profilometry, Sun Yat-sen University Institute of Materials Science, (2005)

The applications of these technologies can contribute to highway-accident reduction, i.e., ensuring that the vehicle is in good condition for use.
The main benefits are the increase of reliability and environmental friendliness and the reduction of maintenance efforts.
A monolithically packaged SAW radio transponder and pressure sensor are developed for the application to a TPMS [12], showed in Fig. 4 The device contains the wireless transponder, which converts analog signal into digital one without any auxiliary electronic circuits and transmits the converted data wirelessly.
The accurately measured temperature and pressure values were obtained by using of a weight factor in the data process.

As the economic center of Hubei province, Wuhan has a large population and dense buildings, so there is no doubt that the energy saving and emission reduction are very important.
Moreover, DesignBuilder is a new kind of graphical-user-interface software on the basis of research and development of EnergyPlus, including not only the whole section of architecture structure as well as the data input of lighting system, but also all material databases from EnergyPlus.
According to the energy-saving data as well as calculation results, we concluded that: (1)From the aspect of the annual energy consumption for heating, Low-e insulating glazing has a significant impact on energy-saving, which makes the energy consumption reduced by 7.57% compared to the single clear glass.
Heat reflection membrane glass is the most electricity-saving material of which electricity reduction is 4.13%.

After transformation, the discrete degree of data has a huge reduction.
So we have 62 items of data to be the training data each time.
In the beginning, we use no.1 to no.62 data to be the training data, as the same time it will also be the test data.
We can compare the solution with the real data.
Using the model file estimates the last data.

The water table data are taken from 37 wells which located on unconfined aquifer in Pingtung plain.
Factor analysis is a statistical method that reveals variations in data and their principal causes by matrix manipulation of vast amounts of data [13].
If we have p wells and q records in each well, the water level data can be represented by a matrix function as follow: (1) Where X is the water level data matrix with q by p matrix; L is the component loading matrix with q by p matrix; F is the common factor matrix with p by p matrix; The elements (factors loading) in the loading factor matrix can be described as coefficient of relationship between the random variables and factors.
Table 1 Total variance explained by the factor analysis of water table data from 37 monitoring wells.
:Statistics and Data Analysis in Geology, 2 ed., Wiley, NY(1986), p. 527–557.

The reduction of strength with increasing tilt angle is explained by superimposed shear stresses
-200 -100 100 200 300 400 -40 -30 -20 -10 10 20 30 40 σ1 / MPa τ12 / MPa Hill Failure Criterion Experimental Data Fiber Tensile Failure Matrix Shear Failure Fiber Buckling Failure -200 -100 100 200 300 400 -40 -30 -20 -10 10 20 30 40 σ1 / MPa τ12 / MPa Hill Failure Criterion Experimental Data Fiber Tensile Failure Matrix Shear Failure Fiber Buckling Failure Figure 5.
Based on three on-axis experiments, a tensile test, a compression test, and a shear test, all necessary input data can be generated which are necessary as input data for the finite element model.
Calculated and experimental data of elastic properties (left) and strength (right) dependent on angle between loading direction and fiber orientation under compressive and tensile loading conditions.
The model can be used efficiently as all necessary input data can be derived from three mechanical tests as tensile, compressive, and shear test.

The results show that we ultimately determine the influencing factors on the removal rate of nitrogen effect model by analyzing the data through multiple linear regression method.
In conclusion, the author puts forward an idealized model, assuming that the packing layer height, temperature and TN removal rate were positively correlated, hydraulic retention time and the relationship of a quadratic polynomial, the removal rate of TN and independent among factors, through fitting of TN removal rate prediction model: TN%=-0.1HRT2+0.585HRT+0.001Z+0.008T-0.399 (4) type : TN%—TN removal rate (%); HRT—hydraulic retention time (d); Z—Bed material height(mm); T—temperature (˚C); Fig.5 TN removal prediction model residuals normal probability plot As shown in Fig.5, we compare all predicted and the measured values. , the prediction model is in line with the actual test data, the accuracy is higher.
Nutrient reduction in an in-series constructed wetland system treating landfill leachate[J].