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First, road tests of the vehicle are performed, and then the obtained data is analyzed.
In the field of vehicles, on the other hand, reductions in fuel consumption and emissions are becoming necessary.
Then, the experimental data is analyzed in detail, and the multi-mode vibration power generation device is proposed.
In the experiments, the accelerometer TGM2WLA050 and the data logger NR-600 were used.
Table 1 Road test conditions (a) Vehicle Minivan (Honda Odyssey) / Light vehicle (Mitsubishi eK-Sport) (b) Road condition Winding road / Urban road / Gravel road / Stone-paved road (c) Measured axis Vertical / Longitudinal / Lateral (d) Measured point Dashboard / Trunk / Hood / Door mirror / Front fender Fig. 1 Road test for measuring vehicle vibration Analysis of Road-Test Data Comparison of Axes.

Data was obtained by Questionnaire and analyzed using GM (0,N) model of Gray Theory.
This uses a sophisticated computer toolbox to analyze the questionnaire data and produces a clear indication of the effectiveness of CALL. [5].
Pronunciation includes segmental features of vowels and consonants and supra-segmental features of reduction and linking.
As there is a substantial amount of data its analysis is assisted by deploying a computer based math lab toolbox [13].
Table 1 The Analyzed Weighting Data No.

The paper discussed how to constrain the timing of receiving data to ensure system synchronization from the clock jitter, clock phase shift, phase shift data.
In the case of the data valid window significant reduction, if it brings the clock and data the phase shift at this time, it has the possibility to receiving the data falls outside the signal valid window.
So that the data-decision must fall within the data valid window.
Differences between the data phase, it will cause the data valid window becomes smaller, so that the final decision of data, becomes difficult [1].
If all the data phase shift larger, there may even result in no public data is valid, so the transmission can not be correct.

There was no comparison data available for phosphate bonded bricks at elevated temperatures.
Manufacturers publish only cold modulus of rupture data for bricks but it can be deduced (see Table 2) that those published numbers would not lead to higher hot strengths than shown in Fig.4.
Table 2 below represents data from various publications and compares different materials based on fracture toughness, work of fracture and modulus of rupture.
All data shown was evaluated at ambient temperature.
Table 4: Furnace operation data Furnace Capacity 90 t Production Rate 180 t / day Fluxing Salt per day 14.5 t Circulation through Archways 13.6 t/min.

Therefore we can get the concentration of liquid vs time based on precipitate data as listed in Table 2.
These data show that with the creation of time, the silica concentration in liquid has also increased.
SSE=(C calculated-C data)2 (8) From the fitting data, the silica concentration from the model and its comparison with the real concentration are shown in Figure 1.
Fitting concentration model based on data Fig. 1 shows that the silica concentration in the model and the concentration data are in line with the increase over time.
Fig. 1 also shows that the model concentration is close to the concentration data, and shows that the fitting process is successful with SSE is 1.9297×10-4.

In the 1D case the distributions derived by means of the modified growth law agreed well with simulation data.
However, simulations data can be reasonably well fitted by more than one candidate distribution (at least over certain ranges).
Distributions given by Eq. 5 agree well with simulation data if β > 0.5.
It is not obvious how the value of β should be extracted from simulation data.
If sufficient time is allowed, side number distributions generated from simulation data always display a finite cutoff at 12 sides (see [14]).

The donor concentration and built-in potentials calculated from C-2V data showed large discrepancy from Hall effect measurement indicating that the junctions deviate from the ideal Schottky diode model.
By fitting the experimental data to this model, we determined the ideality factor n and barrier height φB at 300K to about 1.73 and 0.76 eV, respectively.
It may be caused by electric field dependent permittivity ε of SrTiO3: ε = b / (a + E 2) 1/2 (1) Here a and b are constants which can be deduced by fitting Eq.(1) to experimental data.[5, 8].
Ideality factor, barrier height, built-in potential and donor concentration were extracted from the experimental data.
The donor concentration and built-in potentials calculated from C-2-V data showed large discrepancy from Hall effect measurement indicating that the junctions deviate from the ideal Schottky diode model.

Based on the experimental data, the curve as the internal force of pipeline is first drawn out and discussed, in turn the characteristic of stress distribution and destruction rule of pipeline are analyzed and summarized, which may be referred for designer to take appropriate measures.
According to the data of the test, the author draws a picture in curves that shows the changes of the internal force and settlement along with the change of the pore size, then gets the regular destruction pattern of pipeline and the influence on the upper pipeline by the foundation settlement, offering references to protect the pipeline in practical projects in its regular use.
In 3 o 'clock direction and 9 o 'clock direction, the transverse stick strain gauge (018 cases) gets tension from the direction along the test pipe, and the vertical stick strain gauge (017 cases) gets tension perpendicular to the test pipe. 1.3Test Principle Strain gauge and displacement meter, which are installed in default position in the pipe, through conductor, are linked with the static resistance strain acquisition instrument that through the power line connected to the computer, then sets parameters, adjusts balance, read and maintain the data of the settlement displacement and strain through a automatic data acquisition system.
In addition, if the pipe is in 3 o 'clock and 9 o 'clock direction, the data is lesser in lateral strain gauge testing.
If soil deformation is obvious and pipe displacement is relatively limited by soil, underground pipe will suffer severe damage [8] [9]. 3 Conclusions The curve of the relationship between the settlement and the internal force and the hole forming drew based on the data in the test, the conclusions are as below: (1)As the influence from the settlement of foundation soil, the maximum value of stress in buried pipe appears the top and the bottom of the pipe in the middle of the hole, the pipe in 3 o 'clock and 9 o 'clock direction also appeared some strong stress as well, so these are the most vulnerable area to be damaged, and uneven subsidence area is vulnerable section for the pipe.

We analyze and select a group of features based on public data set and the data collected in the actual network.
This method disperse the continuous attributes of dataset by binning method and reduce the features of the data through the method of complete reduction algorithm.
In addition to [5], other methods dependent on the specific data of data sets provided by Moore in[1].Web traffic accounted for the vast majority of Moore data sets and P2P traffic accounted for less than 1%.The Moore data sets can not reflect the real situation of the network.
Data sets The experimental data sets including two parts, one is Moore data set and another data set is captured in the actual network environment.
The data set describe as Moore_set.

As the theoretical results continued to get close to the measured values, on the basis of the process above can we conclude the influencing factors and the possibility to reduce the error, thus develop a new approach for the reduction of the reverse positioning error. 1.
By comparing the modelling result with experimental data, the source of the error factor was gained. 2.
Data was measured by a Mitutoyo ID-C 513-424A Digital electronic micrometer and imported to the Yokogawa DLM 2000 oscilloscope.
Data was used to compare with the simulation result along time, the simulation could forecast the experiment results.
Conclusion Compared the simulating and experimental results, we conclude that FEA is a reasonable method in evaluation of the reverse positioning process, and able to offer data close to experimental result.