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Questionnaire design data collection.
The data collection was divided into two parts: (1) Primary data: survey of the LED industry
Questionnaire design and data analysis.
This study employed SPSS17.0 statistical software programs for data analysis, data reliability and descriptive statistical analysis.
Data Analysis Analysis of questionnaire data.

In this paper, we use the well-known model proposed by [3] which has a linear decay and is given by (1) c determines the white phase noise floor of the oscillator. a gives the phase noise level near the center frequency up to . b is the steepness of noise reduction with increasing frequency distance up to where the noise floor becomes dominant.
The whole steps are shown below: Step1: Coarse data estimate 1) LS estimation at pilot subcarriers to obtain pilot frequency responses. 2) Linear interpolation at time domain between adjacent symbols. 3) Linear interpolation at frequency domain to get all frequency responses. 4) Coarse data estimation utilizes the frequency response coefficients.
Step2: ICI cancellation 1) Construct ICI channel matrix by Taylor’s expansion. 2) Substract the ICI coefficient of received data. 3) And then update the received data.
Step3: Fine data estimation and decision feedback 1) LS estimation and linear interpolation at both the frequency and time domain from the update received data to get ICI free data. 2) Using the decision feedback procedure with ICI free data, get the feedback data.
Step4: Update the phase noise vector by relation between feedback and received data of present time.

In this paper, the design of smart state machine and various data processing module.
Constantly from the frame, on the other hand, the cache to retrieve data, is in accordance with the prescribed format output.
The FPGA as the logic control chip, responsible for identifying the source signal, the image data according to certain format writing frame cache, while at a fixed rate of reading data from the frame buffer, and generate the corresponding sync signal, sent to the DAC together; Block and EEPROM used in active serial mode of the FPGA configuration on electricity.
The image processing system design based on FPGA Image processing system should be implemented based on FPGA and external to communicate with other systems, or receive the output image data, cache the image data, and meet the requirement of algorithm, and thus to hardware algorithm processing of images, and have certain ability of the advanced treatment of image.
After the completion of the system configuration, video acquisition device for video images, each frame by the analog-to-digital conversion generated image data into preprocessing module, after preprocessing the image data into SDRAM memory, the subsequent processing of the image by the Nios II processor and control, after processing of the image by digital to analog conversion on the monitor real-time display.

The data obtained from the experiment is shown in Table 1.
The diagram of curves Fig. 3 shows the relationship between the processing speed and roughness of workpiece surface refereed to the experiment data.
The data obtained from the experiment is shown in Table 2.
The diagram of curves Fig. 4 shows the relationship between the magnetic field intensity and roughness of workpiece surface refereed to the experiment data.
Conclusion The experimental data from Table 1 to Table 3 are measured by 80 orders for abrasive particle size.

According to the simulation experience and related data, the maximum number of iterations is 20, water content tolerance is 0.0001, pressure head tolerance is 0.1, upper optimal iterative range is 7, the lower optimal iterative range is 3, upper time step multiplication factor is 0.33, the lower time step multiplication factor is 1.3.
Water flow parameters.According to the Van Genvchten-Mualem model and software unique experience of soil parameters database, combined with the experimental measured data, set up the moisture migration parameters are shown in table 1.
In combination with the determination of the actual test data at the same time, compared with the simulation results, found the simulated data and experimental data have the same change trend, as shown in figure 1 ~ figure 3, the simulation accords with actual situation, software reliability is verified.
Fig.1 Ammonia nitrogen simulation data and test data comparison chart Fig.2 Nitrite-nitrogen simulation data and test data comparison chart Fig.3 Nitrate-nitrogen simulation data and test comparison chart Analysis.
Initial interactions with two autotrophic nitrification occurs under the action of bacterial nitrification, ammonia nitrogen under the action of nitrite bacteria is converted into nitrite-nitrogen, nitrite nitrogen under the action of nitrate bacteria is converted into nitrate-nitrogen, this will cause the reduction of ammonia nitrogen, nitrate on the rise.

The most common one is represented by the reduction of the limit tensions used during the design phase; reductions of up to 70% of the initial value of the allowables.
The amplitude of the delamination zone, obtained from the numerical analysis, was compared with the experimental data; also this comparison showed a correct interpretation of the real phenomenon.
In Tables 2, for the case at 10 J, the comparison between the numerical and experimental data, for the various support fixtures, in terms of peak value of the impact contact force, is shown.
In particular, the correlation of numerical and experimental data were shown in Table 3.
Comparison between numerical and experimental data for the 10 J impact test.

Using again the data of sil54d2 engine for ExtractionMain03 Fig. 4.
The generalization here depends also on the use of more experiment - oriented data concerning the described group under study.
One must interpret these new indices in term of machine damage and operational data.
The whole idea was illustrated by data processing taken from real diagnostic measurements on some Diesel engines.
At the end of the paper, it is postulated that some generalization of SVD is needed, in order to include some other data and matrices concerning the external and internal condition of machine operation.

Fig. 1 Experiments setup It is noteworthy that UAT resulted in a considerable reduction of both cutting force components and surface roughness compared with conventional turning as could be envisaged.
The reduction of cutting force can easily be evidenced from Fig. 2.
As mentioned earlier different experimental data have been used for training and testing the networks.
It can be evidenced from Fig. 4 that a good agreement exists between the networks prediction and the experimental data.
Fig. 4 Comparison between the results of the trained networks and the experimental data, a) for cutting force, b) for surface roughness Conclusions Neural networks have been developed in the present study for prediction of cutting force and surface roughness in ultrasonic-vibration assisted turning of 1.1191 steel.

To observe the performance of flood control, a proper hydrology-hydraulics model of reservoir routing should be applied by taking into account the latest condition data, both physical reservoirs including the change in storage characteristics and inflow flood hydrograph.
Reservoir characteristic data was obtained from echo sounding survey in 2005 in which sediment storage reservoir volume at the controlled water level (CWL) of +135.30 m MSL was 11×106 m3.
The rainfall data and catchment characteristics including the current land use and vegetation cover were analyzed to obtain the updated design flood hydrograph of 60 years return period (standard flood), 500 years return period (extraordinary flood), and PMF.
The data of the reservoir storage characteristics are the result of echo sounding measurement in the year 2008.
Acknowledgment Our gratitude’s are delivered to Jasa Tirta I Public Corporation and Bengawan Solo River Basin Organization for the permission to conduct a study in the Wonogiri Reservoir, as well as the data provision and intensive discussions related to the sedimentation issues and the development of the reservoir operating rule for flood control purpose.

Data Reduction.
Before calculation, smoothing of the contour date was extremely important to decrease data fluctuation.
It can be seen from Fig. 3 that a lot of excessive stresses exist in the center because of the unsmoothed data.
The data for Al 7075 was used instead of Al 7050 in the reference, and the data was also replaced in the verification model.
Altan, Prediction of residual stresses in quenched aluminum blocks and their reduction through cold working processes, Journal of Materials Processing Technology. 174(1) (2006): 342.