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If the data must be digital, the receiver will consist of a frequency-to-digital converter.
This is especially advantageous when a large number of channels are required: the remote VFCs can be used to provide "converter-per-channel" data acquisition.
A data acquisition system for remote sensor using the proposed ADC technique is shown in Figure 1.
Each conversion method offers a different combination of performance and price that makes it suitable for specific set of data acquisition applications.
[10] Data Acquisition and Control Hand Book (Keithley, USA 2001).

Meanwhile, MOFAT simulator was selected to carry out numerical simulation research, and simulator accuracy was analyzed by comparing to physical simulation data.
Numerical modeling was an important means to simulate[6-8]and predict PHCs transport in subsurface, so MOFAT simulator was introduced to PHCs transport research, and used physical simulation experimental data to analysis the accuracy of the MOFAT simulator.
Numerical simulation of benzene transport （a） (b) （c） (d) Fig. 5 Benzene transport simulations after PHCs leaking at 12h(a), 24(b), 36h(c) and 48h(d) Table 2 Accuracy analysis of MOFAT simulator Simulation time RMSE (mg/g） RRMSE (%) 12h 3.643 329.0 24h 3.427 73.8 36h 4.395 184.0 48h 3.099 109.7 Average 3.641 174.1 (a) (b) Fig. 6 Benzene transport prediction after PHCs leaking at 72h(a), 96h(b) Benzene contents changed in sandbox were gotten from output data of MOFAT numerical simulator.
Compared to physical simulation experimental data, the average RMSE and RRMSE of numerical simulation were 3.641 mg/g and 174.1% respectively(Table 2).
PHCs transport prediction in saturated zone was also carry out by MOFAT simulator(fig.6), results showed that benzene contents tent to steady with reduction of concentration gradient.

Data from pot experiment revealed that activated rocks with higher bioavailability promoted the growth and K accumulation of gain amaranth.
The size reduction particle of the ground rocks appeared to prevail from 10 minutes to 120 minutes.
This indicated that a reduction in the particle size down to 1.64 m2/g was simply attained and the increased fine particle could be caused by prolonged grinding due to mechanical fracturing.
A perusal of the data (Figure 3-a) showed significantly higher amount of K release from various potassium-feldspar rocks during the initial stages of leaching.
Data revealed that the release rates of K from the rocks in the acid solution followed a two stage process (Table 2).

The Ge NCs presence was confirmed by HREM data.
The phonon dispersion for volume Ge was taken from low energy neutron scattering data [4].
The NCs presence was confirmed by HRTEM data (figure 5).
As one can see, the average NCs size obtained from Raman data is in good correlation with direct HRTEM data.
As one can see, for NCs diameter 2.6 nm (from Raman data), the calculated optical gap for finite barriers is in very good agreement with experimental PL data.

is less than the pre-set error;take the reconstructed surface data and the original point cloud data for comparison, more than 94% of the total data points is in the range of permissible, prove that the fitting surface accuracy is qualified.
Analysis of the error sources in reverse engineering In reverse engineering,the error is inevitable,in order to explore the factors which affect the accuracy of the surface reconstruction,it is necessary to analysis the causes of the error.The main sources of error are as follows:(1)the original model error: in the manufacturing process of the entity sample, it existed manufacturing error and the original design dimensions,worse still, for the used sample model,there are wear error and surface roughness error in it,thus can affect the measurement accuracy.(2)measurement error:it related to the methods of measurement,the equipment itself,the skill of operators, the external environment,such as ambient light,vibration,will also cause some impact.(3)data processing error: in the data processing,we process the point cloud data with deletion,hole filling and data reduction, also cause data errors compared to the original data [5].(4)surface reconstruction error and manufacturing error:in surface
Measurement data for Mirrors using the dimensional laser scanner,to obtion the point cloud data, then use Geomagic software for data fitting process, get the reconstructed model and is shown in Figure 1.
Since the point cloud data scanned is placed at random,and the fitting surface models are not in the same coordinate system,so when the two data import Geomagic qualify software together,the data needs to be aligned.By using Best-fit alignment to the completion of alignment,then using 3D Compare command to obtain the comparison of three-dimensional model and as is shown in Figure 2.
Table 2 Error Analysis of the XY plane >=Min data points in the range of 0.312 ~ -0.312 total number of data points,proved that the data points of XY cross section fitted in line with requirements

The method first stores the training data in memory and then finds relevant data to answer a particular query.
The method first stores the training data in memory and then finds relevant data to answer a particular query.
Nearby data points are assigned high relevance (or weights) and conversely distant data are assigned low relevance.
As can be seen from equation (3), in order to calculate the output value, every data point in the training data set is used.
Compared with the best linear model, the learning approach achieved a 21% reduction in error for an average patient (range: 10% - 42%).

Foundation settlements and rotations derived from the reduction of the footprint under the piers threaten masonry arch bridges integrity more than any gravity load.
Some conditions are to be met for obtaining a sufficient quantity of data to perform a Single Value Decomposition (SVD) on the Hankel matrix.
The aim of SVD is to reconstruct (3) from redundant data.
When ambient excitation is considered, the input is unmeasured and equation (4) becomes: {uk+1}=[A]{uk}+{ek} k = 0,1,2,… (5) A Stochastic Subspace Identification (SSI) starts by building large block Hankel matrices from the output sequence, divided up in 'past' and 'future' data matrices [5].
Pappa: An eigensystem realisation algorithm (ERA) for modal parameter identification and modal reduction, NASA/JPL Workshop on Identification and Control of Flexible Space Structures (1984)

The present work focuses on the evaluation of formability of Ti-6Al-4V using the hot single point incremental forming (SPIF) process which seems appropriate to produce small batches of parts due to its flexibility as it allows the reduction of costs and lead times.
In this sense, Ti-6Al-4V is widely used in the aeronautical industry in the manufacturing of high strength lightweight parts, which lead to a reduction of both the fuel consumption and the associated pollution.
Interaction between the forming temperature and the tool step down The feed rate parameter, it could not be included on the ANOVA analysis because of the lack of enough data to analyze its influence.
As it can be observed in Fig. 14, there is a slight decrease of material strength compared to base material data at RT shown in Fig. 3 because of the SPIF operation.
Tensile test data, part P2: strength relief values along tool step direction at 0º (top left) and 90º to RD (top right), elongation along tool step direction at 0º to RD (bottom left), strength relief values along feed rate direction (bottom right) Conclusions The present study demonstrates the potential of Global Hot Single Point Incremental Forming process (using an electric furnace) to deform Ti-6Al-4V.

Obtained outcomes highlighted the remarkable capabilities of the implemented procedure in terms of reliability of temperature predictions and of drastic reduction of the computational time with respect to classic computational models.
Indeed, during the process, the initial heating of the material leads to a reduction of the resin viscosity, allowing excess resin to squeeze out from the reinforcing layers.
The learning and recognizing patterns in large data sets is the key ability of neural networks to achieve learning and memory [21-23].
Physical properties of each constituent, resin reaction kinetics models and parameters are defined according to data reported in [3,5].
The proposed model is tested versus unknown data related to the autoclave temperature heating over time.

The selectivity of propylene depended strongly on the phosphorus content in the zeolites; The enhancement of propylene selectivity with increasing phosphorous content was attributed to reduction of strong acid sites on the H-ZSM-5.
Table 1 data also showed that SBET and VP did not change synchronously with the increase of phosphorous content.
Table 1 Pore structure parameters of ZSM-5 zeolites with different phosphorus content Samples S0 S1 S2 S3 S4 S5 S6 SBET/(m2×g-1) 331.8 315.0 301.9 286.2 253.6 212.5 161.9 VP/(cm3×g-1) 0.173 0.170 0.165 0.158 0.150 0.145 0.132 SBET—BET specific surface area;VP—Total pore volume Combining the N2 adsorption-desorption isotherms with XRD data, we knew that impregnated phosphorus entered the channels of ZSM-5 zeolite, reacted with its framework atoms, and caused the shrinkage of zeolite channels, which made the BET surface area and pore volume of phosphorus modified samples decrease.
Table 2 Catalytic Performance of catalyst smaples Sample Name S0 S1 S2 S3 S4 S5 S6 Conversion/% 100 100 100 100 100 100 100 Product Distributions /wt% CO+CO2+CH4 3.73 3.61 2.15 2.49 2.25 3.26 3.02 Total C2~C4 paraffins 7.02 3.83 3.64 3.48 3.02 2.81 2.27 Ethylene 9.07 15.82 15.21 11.23 10.09 12.17 15.64 Propylene 34.38 34.45 38.12 42.38 44.57 45.97 47.01 Butylenes 15.08 14.27 16.56 20.13 24.16 25.11 24.33 C5+ 30.72 28.02 24.32 20.29 15.91 10.68 7.73 Data obtained at 60 minutes on stream time.
The reduction of the concentration of acid sites which are responsible for the hydrogen transfer reaction, decreases the conversion of olefins to paraffins, thus selectivities to propane and butane decreased with increasing phosphorous content in ZSM-5 zeolites.