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The grain growth becomes significant at high densities of ρt > 80% probably due to the reduction of pore dragging effect.
The present result is compared to the earlier SPS data reported by Bernard-Granger et al. [7].
Although the present result is almost on the same order as the earlier data, which is obtained at almost the same temperature of 1225°C, the -(σeff/Geff) relationship is inconsistent with that of their data.
Except for the difference in the densification rate, the present result is consistent well with that of the earlier data obtained by HP technique [8,16-18].

These formulas established on the basis of tests and large amounts of measurement data could be accurate and reliable only if the object of research is similar to the case from which the data is obtained.
The boundary element method has the characteristic of dimensional reduction and adapting to the infinite domain, thus becomes an effective numerical method of solving the structure-borne noise.
Establish the acoustical mesh and field point mesh Establish the structure model Compute the vibration response of the structure (displacement) Acoustic transient response analysis case Define the displacement boundary condition Data transfer analysis case Acoustic mesh preprocessing set Import the acoustical mesh and field point mesh in LMS Virtual.Lab Acoustics Import the structure mesh and the transient dynamic results Set mesh parts type Define fluid material and property Fig. 3 Calculation step of vibration and radiated noise Calculation example for elevated bridge vibration and radiated noise Calculation with the moving load model.
Table 1 Maximum values of bridge vibration response Position and case Displacement[mm] Velocity[m/s] Acceleration[m/s2] Left span Model 1 3.23 0.00462 0.23137 Model 2 3.23 0.00845 1.75564 Middle span Model 1 1.64 0.00452 0.25292 Model 2 1.65 0.00875 1.92357 Right span Model 1 3.22 0.00522 0.25454 Model 2 3.23 0.00916 1.75154 Table 2 Maximum values of noise pressure[dB] Position and case Point A Point B Point C Left span Model 1 87.36 86.22 82.43 Model 2 92.63 88.64 84.87 Middle span Model 1 78.96 76.11 72.06 Model 2 89.61 83.29 79.38 Right span Model 1 92.61 87.53 85.02 Model 2 94.63 87.89 86.69 These data indicates that the displacement response based on the two kinds of vehicle model is the same, as the velocity and acceleration response has an obvious difference, the response of bridge structure with mass-spring system model bigger than the one with the moving load.

The observed diffraction pattern was in good agreement with the PDF-2Plus data of β-AgI (No. 01-078-1614), hence the crystal phase was identified as β-AgI.
The trajectory of the plotted data follows a semicircle.
The DC resistivity was estimated from the intersection of the data in the low-frequency region and the real axis, and DC electrical conductivity s was estimated by taking the reciprocal of the resistivity.
Fig. 4(a) XRD profile of the as-prepared AgI thin film, (b) PDF-2Plus data of β-AgI (No. 01-078-1614).
That is, it is concluded that this contraction is reflected in the reduction of ionic conductivity and the increase of activation energy.

Fig. 5 Curve of vault settlement with time Fig. 6 Curve of peripheral convergence with time Figs. 5~6 show that the curves of vault settlement and peripheral convergence are basically the same; the vault settlement is always greater than peripheral convergence, both of which increase rapidly in about 300 hours (t=350h) after excavation and then turn into a gradual growth stage with basically stable deformation in line with the u-t curve law of in-situ monitoring data.
Thereinto, the vehicle load is considered as highway class I load with horizontal load reduction coefficient.
The corresponding data of vertical displacement obtained by numerical calculation of ANSYS show that the tunnel vault would produce certain additional settlement of about 3.27cm under the action of inverted arch backfilling+ vehicle load+ collapsible loess soaking; the reinforcement treatment of foundation by high-pressure rotary spraying can improve the property of soil mass to make the additional settlement decrease in a certain degree with value of about 2.27cm.
The comparison between numerical results and measured data shows that the u-t curves of vault settlement and peripheral convergence are basically the same with close data, which can validate the validity of presented models and explain the tunnel displacement characteristics reasonably providing reference for the design, construction and monitoring of loess tunnel projects in the future.

Force components were measured with KISTLER 9257B three-component, piezoelectric force measuring machine and data recording system.
Diagram f-Fc, f-Ff, f-Fp Literature data and later experiences shows, that surface roughness after burnishing depend on hard turning surface roughness.
fb Fb rv Workpiece PCD-insert Fig. 5.Schematic and photo of burnishing tool with PCD-insert Experimental results During the experiments, we measured force components by dynamometer KISTLER 9257B and by data recording system.
In tables values with index o presents the average of roughness data after hard turning.
The reduction slightly depends on feed and greatly depends on burnishing force.

- command and control system for furnace based on a digital temperature regulator that allows the implementation of different regulating laws, an programmable microcontroller and data acquisition interface; Fig. 1.
Mechanical and technological characteristics of the material are [2,6]: - characteristics of material (at t = 20 0C): density r0 = 7753.6 [Kg/m3], specific heat C0 = 475.42 [Wh/Kg 0C], thermal conductivity l0 = 44.51[W/m 0C] - mechanical characteristics: hardness = 22 [HRC]; internal stress = 920 [Mpa]; elongation =14 [%]; reduction of section = 60 [%]; charpy value = 60 [J] - critical temperatures: Ac1 = 713.20 [0 C]; Ac3= 787.24 [0 C]; Ms = 331.72 [0 C] The parameters of the heat treatments which is applied to piece determined by the program to predict the structure and mechanical properties of heat treatments piece: - normalizing: temperature 841 [0 C], heating time 5.040 [sec], maintaining time 1.048 [sec], cooling on air; - quenching: temperature 831 [0 C], heating time 5.326 [sec], time 1.065 [sec], cooling on oil; - tempering: temperature 570 [0 C], heating time 12.546 [sec], time 1.048 [sec], cooling on air.
The experimental results, taken from data acquisition board installed in the electric furnace control system for quenching and high tempering treatment temperature diagrams - diagram calculated with classical algorithms heating and actual temperature of the furnace resulting from predictive PID control algorithm shown by thermocouples placed in the furnace vault and the finite element analysis (FEM) of the evolution in the time of the temperature and heat flux quenching and tempering the piece subjected to heat treatment conducted in the furnace with thermoregulatory controlled with PID predictive.
Graph of change in hardness HB of according to the measuring points According to the data presented in specialized standards-hardness steel C45 RO1.0503 they are made of low-power turbine blades for the treatment of normalization is 235 HB, comparing the results obtained shows that when treatment is classic the average hardness of 231.5 HB less than the requirements of the standard, and if the heat treatment temperature sensitive 244.8 HB average values are predictive for thermostatic a step higher values of standard requirements.
The experimental results are taken from data acquisition board installed in the control system of electric furnace for heat-sensitive treatments.

In order to reduce instrumental errors and to achieve accurate data, a high angle peak is favorable.
The counting time was set to 120 sec for each data point in order to achieve a good resolution.
FESEM micrograph of bilayer coating Ti(0.6 μm)/TiN(2 μm) The best line is fitted through the data points and the slope of the fitted line is calculated.
The data gathered from indentation test were used to plot indentation load vs. als12.
The reason of this reduction is probably mechanical compliance effect which is effective after a critical thickness of the interlayer [15].

Step 2: Transform the available data into constraint predicate formula or follow the rule of mutual exclusion.
Step 5: Mining and extraction of the data.
Step 6: The extraction of the solution to data planning.
The idea in this paper is: in the calculation process, a local optimal solution should be constantly looked for; the reduction of the computational scale and increase of the computational efficiency can be achieved by merging the local optimal solutions.
[5] Parpinelli R S，Lopes H S，Freitas A．A Data mining with an ant colony optimization algorithm[J]．IEEE Transactions on Evolutionary Computation，2002,6(4)：321-332.

The time–frequency localization features of the wavelet transforms and their ability in improving the signal to noise ratio (SNR) make them suitable for data preprocessing [10].
Another way was estimating the standard deviation coefficient of high-frequency noise from the first layer D1 which got from the five layer wavelet decomposition as mentioned above, and then applying the soft threshold and hard threshold respectively on the signal noise reduction.
(4) Where sri is the reconstruction data in which noise have been eliminated and expressed as A5+D4+D5, soi is the original data and N is the dimension of the original data.

During the course of continuous work or driving, significant reduction in a person’s attention, concentration, sleepiness and loss of ability to sustain a required performance are some of the major concerns brought by fatigue.
The analysis system took National Instrument’s LabVIEW software as its development platform to read and analyze the EEG data.
The EEG data were saved in .txt files and processed.
Analysis results of theta data on six experiments for good and poor sleepers.
Although the research had a limited number of subjects, data showed a sign of consistency in relation to how the subject slept the night before and their current condition.