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The main tasks were to appropriately mount the transducer at the designated location, acquiring vibration data using modal testing technique, analyse the acquired data and store the vibration response data using the robust Bruel & Kjaer Signal Analyser and finally deduce the resonance frequency of the designated flat plates pecimen.
The roving hammer technique with fixed transducer was used to collect the data of FRF measured.
Coherence is a function use as data quality assesment.
There appears to be a reduction in the natural frequencies of mode 1, 2, 4 and 5 from 00 to 450.

I BA =           =           −− −− ×           −−=× 100 010 001 375.025.0125.0 125.025.0375.0 25.05.025.0 201 111 021 (17) Implementation Method For reduction of complexity of calculation, in multiply operation, coefficient 0.5 is rewrite 1/2, 0.25 is 1/4 and 0.375 is 3/8(=1/4+1/8).
Fig. 1 Internal data extend procedure.
Input data is added by 4-bits(one-bit is left, three-bit is right).
Bit extension method is that: Shown as Fig. 1, original input data extends additional 4 bit.
If input data is 8 bit wide, then internal data width extends 12 bit wide.

Mean Shift Algorithm Mean shift is a procedure for locating the maxima of a density function given discrete data sampled from that function.
The mean shift clustering algorithm is a practical application of the mode finding procedure: 1) starting on the data points, run mean shift procedure to find the stationary points of the density function; 2) Prune these points by retaining only the local maxima.
Given n data points, i=1，2，…n. in the d-dimensional space,the multivariate kernel density estimator with kernel K(x), computed in the point x is expressed as the following: (4) The profile of the kernel K(x) is defined only for .The normalization constant ,which makes K(x) integrate to one .
As a result of using state transition matrix to describe the dynamic system, its have wider Application; the estimate value of Kalman filter utilized the previous and current observation data, without storing historical data which reduced computer storage requirements.
When the target is partial occlusion or complete occlusion by other objects, the Bhattacharyya coefficient of nuclear histogram in current window will be reduced; But Bhattacharyya coefficient’s reduction is not the same in different occlusions.

From a fit of the experimental data in the linear region, it was possible to determine the ideality factor n and the Schottky barrier height FB, as function of the temperature.
This plot of the experimental data is shown in Fig. 4, in which the straight line represents the ideal behavior of a Schottky barrier (n=1).
In addition, the experimental data taken from Ref. [8] are reported in the same graph as a reference.
A good agreement between the experimental data and the theoretical model is obtained with a T0 value of 40K, slightly higher than the value reported in Ref. [8] for the commercial material.
For comparison, the literature data acquired on commercial material are also reported (taken from Ref. [8]).

Orientation maps were collected using a JSM-7001F type field emission scanning electron microscope equipped with a fully automatic EBSD analysis system (TSL OIM Data Collection 5 Software).
The step sizes for EBSD data collections were chosen as 0.03 μm.
More significantly, the misorientation data (Figs.2a–f) show a tendency for a reduction of the fraction of LALB and a corresponding increase in the fraction of HALB, indicating that LALBs have gradually transformed into HALBs with increasing ECAP passes.
All data were obtained from EBSD.
Present TEM observations provide further confirmation that the fraction of HALBs increases with increasing ECAP passes and it reaches a high level after 8-pass ECAP, which is in good agreement with the EBSD data.

Fuzzy neural network-based systems make fuzzy systems with limited training data to achieve the automation of the modeling process, which has learning functions and adaptive capabilities.
Based on the production process and field sampling data analysis and operator’s experience, it can be derived: when the kiln rotation speed increases, the temperature decreased slightly in the normal production process, the kiln speed remained unchanged [4, 5].
As the structure of fuzzy inference neural network training expectations of input and output data is unknown, in order to optimize the parameters of the network training, the fuzzy neural network controller and the controlled object in series to form a closed loop negative feedback control system.
Based on the above training data of real system, more than 1000 sets data points are used for training the neural network model.
[2] Mujumdar K S,Arora A,Ranade V V: Modeling of Rotary Cement Kilns: Applications to Reduction in Energy Consumption, Ind.

This model has been used to extract temperature dependent effective mobility data for 2D grain growth simulations ‎[6] .
Considering as determined from experimental data ‎[10] Eq.
In order to compare the 3D simulation results with the experimental data and the previous 2D simulation, the EQAD was extracted from the 3D simulation by making 2D cuts through the 3D simulation data.
Reduction in pinning pressure due to dissolution of NbC precipitates has been taking into account using the concept of an effective mobility.
The simulation results are in good agreement with experimental data in bulk samples.

As noted by Barraclough et al [4] this 2:1 length to radius ratio is optimum for obtaining accurate data at high strain rates.
Torque and angular twist data was transformed to shear stress and strain via the method of critical radius (R*) proposed by Barraclough et al [4].
EBSD data was acquired using an FEI Sirion field emission gun scanning electron microscope (FEGSEM) equipped with a fully automatic HKL Technology EBSD attachment, operated at 15 kV.
All EBSD data was filtered using in-house modified Kuwahara filtering software for edge retaining orientation averaging.
Upon reversal there is significant reduction in the accumulated misorientation within the αP grains

The angular velocity matrix and the inertial tensor reduced at the disk axis with reduction system with raport ired will be: (6) The movement step in each axes will be: (7) where tX, tY, tZ are the integration times, what can be different in each axes.
Part of the block schema of the LabVIEW instrument for the assisted research of the direct kinematics problem of the satellite and the data of the driving axis Fig.7.Exemple of the calculus of the direct kinematics data and the obtained errors Fig.8.
Front panel of the virtual instrument with the results after were changed the target data.
The conclusion from this research was that the more important parameters to obtain the quickly convergence process are: the target data of the hidden layer; the step of the time delay and the position to apply them; the recurrent links and the position of the closed loops; the amplifier gain; the teaching gain.
Simulation results of the space trajectory when was changed the velocity values and the type of the simultaneously or succeed movements network was 3-8-3-3 according to the input data and direct and inverse kinematics and dynamics input and output, what was necessary to be 3.

Data are means of 5 replicates (n=5) ± SD.
Data are means of 5 replicates (n=5) ± SD.
Data are means of 5 replicates (n=5) ± SD.
Data are means of 5 replicates (n=5) ± SD.
Data are means of 5 replicates (n=5) ± SD.