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The Wavelet Noise Reduction is used to deal with the original vibration signals that obtained by the collection system.
Picking-up Characteristics After noise reduction, the vibration signals are decomposed to several sequences by the Wavelet Packet method.
After the Wavelet Noise Reduction, the veracity of state recognition will be improved.
Example with the inlet valve abrasion of diesel engines, the state data can correspond with different running states, such as the normal state, the slight abrasion state, the moderate abrasion state and the serious abrasion state.
After the wavelet noise reduction and picking-up characteristics, thirty groups of vectors on every state are acquired as the training swatches.

The impedance data was studied with reference to changes in ac conductivity, relative permittivity and bulk resistance.
Energy dispersive X-ray analysis data was collected and analyzed with the INCA Energy 350 software by OXFORD Instruments.
A WIN9400 series version 2.00 software by the Micrometrics Instruments Corporation allowed analysis of data downloaded from the Micrometrics Interface Controllers.
Examination of the data showed that the imaginary impedance was a minimum at a frequency of 32 kHz.
Impedance data from the second and third week after mixing indicated a further increase in resistance following by a leveling of the impedance.

The peptide hydrogel containing ciprofloxacin and tobramycin resulted in 7.68 ± 0.33 and 6.13 ± 0.40 log reduction, respectively.
These data could provide a better rationale for antibiotic clinical applications to eradicate planktonic bacteria and biofilm infections found in chronic wounds.
Antibiotic killing was reported as a log reduction.
Log reduction states that incorporation of 10 μg/mL of ciprofloxacin or 10 μg/mL of tobramycin inhibited microbial growth.
The peptide hydrogel containing ciprofloxacin and tobramycin resulted in 7.68 ± 0.33 and 6.13 ± 0.40 log reduction, respectively (Fig. 2A).

Then propose testing method of the market pull and the coefficients assessing diffusion rates, and use historical data of twenty countries to conduct empirical studies.
The study makes the theoretical hypotheses according to distinguish features of PV industry and technical innovation theory, and builds the testing model and indicators of PV market pull mechanism, and uses the historical data of twenty countries to test hypotheses, so as to find the principles based on evidences for policy designing.
Data for Statistical modeling Schmookler [7], Scherer [10] and Xuxia [11] used the purchase volume or sales revenue of products to represent changing market demand scale in the empirical study.
The study collects patent and installation data of twenty countries from various organizations and literatures.
The volume of patent granted in the USA and in Japan is from CambridgeIP database, and the data of other countries is from the database of EPO.

Algorithm steps are as follows: 1)Data selection and network initialization.
Data normalization process all the data into a number [0,1], which aims to cancel an order of magnitude difference between the dimensional data input and output data to avoid because of the larger magnitude differences caused by network prediction errors.
Hidden layer is a layer using BP neural network, according to data dimension.
Subsequently, the sorter training data for training, the discharge end of the training of the test to identify the type of test data, test results are shown in Table 2.
Research on Data Dimensionality Reduction Algorithms Based on Manifold Learning[D].

Foam stability was investigated through observation of the foam bubble size and the reduction of foam height inside the observation tube.
Solution with 4000 ppm of nanosilica generated the most stable foam with only 5% volume reduction [4,11].
Unfortunately the concentration was not enough for the foam to stabilize for a long period of time and also from the observation and data tabulated in Fig. 3, it shows that the foam was reduced up to 61% in 120 minutes.
CO2 foam reduction different nanosilica concentration Effect of brine concentration.
The results and data produced which were plotted in Fig. 7 demonstrate clearly that the addition of surfactant to the nanosilica solution has improved the probability of CO2 foam generation [4].

To investigate the impact of the model support design on the measured data, four fundamental designs were investigated and combined by a full factorial design process.
Introduction Supersonic wind tunnels are frequently used to get testing data of high speed aircraft and spacecraft vehicles.
The force acting on the model support can be reduced through this factor by 4.53 N (=2.26 N 2.27 N), but the factor interaction of BD is reducing this effect, leading to an absolute pressure force reduction of 3.37 N.
A chamfer at the leading edge of the model support causes a reduction in pressure force of 1.27 N.
The influence of factor A with a pressure force reduction of 0.73 N for the thin model support is increased by the factor interaction of AD with an additional reduction of 0.81 N.

After fractional release pretreatment, the sulfur reduction of Chenghe coal could be reached to 53.10%.
As the temperature exceeded 60℃, sulfur reduction presented a slight decline.
As to Hancheng coal, the data was 92.41% and 18.46%.
There was a slight reduction of thiophene.
There was a slight reduction of thiophene.

Introduction In recent years, the crystalline quality of 4H-SiC wafers has been significantly improved with respect to reduction of micropipes [1].
The set-up can acquire data in a single wavelength mode (m-PL intensity mapping mode) or in a full spectral scanning mode (PL spectroscopy mode), with the spectral resolution of 1 nm.
The intensity reduction at the TSD is also slightly larger than that of TED.
The PL intensity is reduced in the irradiated region, being consistent with the reduction of carrier lifetime, as shown in Fig. 2(c).
Our data indicate that the carrier lifetimes longer than 0.5 ms are crucial for the nondestructive visualization of dislocations in 4H-SiC epilayers by m-PL mapping.

As a measure of plasticity, the value of a reduction in the area is assumed.
Data in Figure 2 indicate that the zero plasticity temperature of the examined steel is about 1420 °C.
Based on data shown in Figure 3 it can be assumed that plasticity recovery temperature for steel S355J2G3 is about 1375 °C.
Measurement points in Figure 4 refer to the average value of a reduction in the area.
The curve of a reduction in the area as a function of deformation temperature as determined in simulation experiments Data shown in Figure 4 indicate that the examined steel exhibits two temperature ranges of reduced plasticity, namely in the range from 1000 to 12000 °C with a minimum at a temperature of about 1100 °C, and in the range from 700 to 950 °C with a minimum at about 800 °C.