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Introduction As the pillar industry of national economy, construction industry has always faced with problems such as low efficiency, severe resource waste, how to conduct energy conservation and emissions reduction.
It is a data model that based on 3D technology and integrated with various kinds of relevant information in construction project, which is a comprehensive expression of the relevant information about the project.
There is a bidirectional data exchange in BIM model.
Which kinds of form and the model of the maturity of the interactive data between the parties also need to be clearly defined.
Therefore, absorbing advantages of the reinforcement information expressions and its information and using computer to digital processing method of standard components will adapt the graphic symbols and data correlation to meet the requirement of BIM viable path.

The industrial computer communicates with the peripheral devices by using a kinetic control card and a data acquisition card.
Industrial computer Patch board Infeed servo driver Wheel servo motor Table servo motor Length feed servo driver Kinetic control card Patch board Main motion converter Wheel frequency motor Head stock frequency motor Rotary feed converter Data acquisition card Eddy sensor Workpiece deflection Transverse grating sensor Wheel location Length grating sensor Table location Fig. 1 Block diagram of the error compensation grinding system Fig. 2 shows power supply control scheme of the four motors.
The industrial computer sends a data signal to the solid state relay through the data acquisition card and patch board.
The data signal from the patch board is a TTL electrical level and thus can be recognized by the solid state relay.
Industrial computer Patch board Solid state relay Data acquisition card AC contactor Motor controllers Fig. 2 Scheme of power supply control There are two types of solid state relay widely used in the industrial control field and they are random and zero-passage types, respectively.

Experimental data is presented in Fig. 2.
The obtained data shows that the velocity of cathode spot movement is greatly depend of cathode material.
It follows from the presented data, that the increase in the magnetic field strength results in the decrease of the registered ion current and consequently in reduction of ion emission characteristics of the plasma generator.
The data of Fig. 4 show that at certain regimes of the vacuum-arc evaporator and PF operation, the ion saturation current from plasma reaches 1.8 A.
Fig. 4 (b) presents the data on ion-emission properties of the vacuum-arc evaporator, when the magnetic field of plasma filter coincides in direction with the magnetic field on the cathode surface.

Given the published data on the kinetics of hydrodesulphurization it is possiple ti suggest the following scheme for the hydrodesulphurization of the liquid products (hydrogenate) under the low hydrogen pressure: Figure1.
The developed program “Search” allows for the calculation of kinetic data dependences for the given initial conditions and for performing an automated selection using the method of gradient of the optimal values ​​for the rate constants k1 - k7.
The kinetic data of the hydrogenate hydrodesulphurization process Figure 2 demonstrates the dependence of the sulfur content in the hydrogenate on the time of the hydrodesulphurization process.
The analysis of the kinetic data of the hydrodesulphurization process shows that the optimal process time is 15-25 minutes as the maximum yield of the above mentioned fractions takes place.
These data are necessary to improve the process parameters for the hydrotreating the liquid products and developing the recommendations for determining the optimum conditions for the process.

To carry out the overall energy consumption analysis has many variations, and analysis and measurement data are not precise enough.
If the production process is reduced to each process, data will be more precise.
According to a practical production data, energy consumption were analyzed effectively.
Steelmaking energy flow data as shown in the following table.
Residual heat and energy recovery value of S represents and the energy flow dissipation value of R represents have the greatest impact on steelmaking process in these three weeks, other energy flow data fluctuation effect is no significant change.

The main feature of remote sensing image is that they have a very high spectral resolution, so they can obtain very narrow band ranges and get a large number of spectral bands, which could result in a continuous spectrum image data that containing a wealth of information [2].
It needs to be emphasized that the remote sensing data should be treated as images instead of a mere collection of independent and identically distributed pixels [6].
The implementation procedure of the proposed method is as follows: First of all, extracting the image features from the original remote sensing data.
Meanwhile, extract the spectral features using the PCA algorithm, which is a widely used dimensionality reduction technology in data analysis [1].Also, it computes the low-dimensional representation of a high-dimensional data set, and the algorithm can most faithfully preserve its covariance structure [7].
In order to obtain , the following equation needs to be solved [9]: (2) where is a compromise fact between data reconstruction and the sparsity, satisfying .

As the criterion of channel coding in transmitting data with high speed and high quality, Turbo Code is a typical example, which has been widely applicated..
Hypothesis the interleaver length is N and input sequences can be classified into several frames, the length of frame data is equal to N.
As for Turbo encoder shown in Fig. 1, if the influence of truncated bit can be ignored, the length of output code is 3 N, which is produced by input sequences whose frame data length is N.
Under the control of FPGA logic, input bitrates and output bitrates input and output respectively by row and line to finish the data interleaving.
From Turbo code structure, it can be known that the input and output are continuous bit - stream and the interleaver deals with the code by frames so the frame buffer is needed to buff data streams.

Extraction of the mechanical properties of the tissue mainly relies on analyzing the force-deformation data obtained from an indentation test.
The QLV parameters (Young's modulus, Poisson's ratio, time constant and viscosity-related constant) of tissue can be extracted by the optimization of the force-deformation data with the QLV model from the indentation test.
A hand-held indentation probe with a US pulser/ receiver is connected to a personal computer to display and process the collected data in real time using our tailor-made indentation software [2].
In the QLV model, the force response at any discrete time (i) can be reconstructed from the indentation history u(j) by proceeding the data reduction process [9], i.e., ij ≤≤1 , ( ) ( )( ) ( ) ( )[ ] ( )( ) ( ) ( ) [ ]       ∆−+−− −+ − = ∑= ∆⋅− i j tj tejiuEjiuEjiu iuEiuEiu ah iP 1 / 2 1 0 2 1 0 2 1 2 τ κ τ α κ ν (1) where t∆ is the time interval between two adjacent data points, ( )( )iuκ can be found in the kappa's tables according to corresponding aspect ratio (a/h), Poisson's ratio (v) and indentation deformation (w/h) by interpolation and extrapolation. ( )uE e)( is the un-relaxed elastic modulus and ( )tG is the reduced relaxation function for which ( ) 10 =G . i.e. ( ) uEEuE e 10 )( += and ( ) τ αα / 1 t e tG − +−= , where 0E is the initial modulus, 1E is the non-linear factor, τ is the time constant and α is the viscosity-related constant.
Extraction of the mechanical properties of soft tissue can be obtained by solving the optimization problem about minimizing the root mean squared (RMS) error between Eq. (1) and the load-deformation data obtained experimentally [6].Genetic algorithm (GA) is proposed in the current study to tackle this optimization problem.

The surrogate models based on the input and output simulation data obtained through the solution of a complex model are valid in establishing the complicated functional relationships between the variables and the responses precisely.
Another advantage of surrogate model is that only a limited number of training data are required to create the model.
The training data can be acquired through design of experiment (DOE).
Substituting the sample data into the FEM model and proceeding the simulation computation produces the corresponding results, namely M variables vectors and M responses vectors are presented.
On the basis of the data, the establishment of the surrogate model can be launched.

The key points temperatures of the powertrain compartment were tested, and the numerical values have a good agreement with the measurement datas.
Dimensionless average velocity decreases with the reduction of the grid size.
When the mesh size is reduced to 10mm, the effect of the reduction of the dimensionless average velocity is less than 0.1%.