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The results of this study provide important data that can aid in the design and optimization of SIDI CNG engines.
Based on the obtained experimental data, the predictive functions of the spray tip penetration S for the injected CNG across the combustion chamber have been calculated.
The data shows that the spray tip penetration increases as t0.58883.
Figure 8 shows the spray tip penetration as the function of ambient pressure and the data shows that the spray tip penetration decreases as Pa0.67898.
Figure 9 shows the spray tip penetration as the function of the injection pressure and the data shows that the spray tip penetration increases with Pinj0.25524.

Alongside with obvious advantages, these program complexes have a number of disadvantages such as significant time for loading of input data and calculations, complexity in learning, etc.
Input data file 2.
Windows for the data on workpiece configuration, coordinates of initial approximations and the parameters describing the process of stamping (Fig. 7) are settled down in this field.
As a result, the numerical data on values of optimized parameters, namely: the size of each cut's radius and coordinates of their centers are obtained on the screen.
The simulation results obtained have been compared to experimental data and have shown comprehensible inaccuracy.

Artificial neural network In the input layer, the input data are calculated by weights, and the calculated values are calculated with a non-linear function to output the results.
y=f(i=1nwixi+b) (1) (w: neural network weight coefficient, x: neural network Input data, b: bias) In this way, building multiple basic network structures will result in a Artificial Neural network, as shown in Figure 2.
This means that more abstract data representations can be found across each layer, thereby gaining effective machine learning performance.
Based on the experimental data, the Artificial Neural Network was used to training current density values as machining conditions change, then the simulation was conducted, and the predicted values were compared and analyzed.
As shown in Table 3, the 250 trained Artificial Neural Network(ANN) have the lowest root mean square error(RMSE) between experiment and predicted data.

Data Process and Experiment model This paper analysis on EEG analysis is mainly on the components of ERP, the steps are as follows: Step 1, Block larger drift EEG: in EEG acquisition process, such as the subjects movement, wander, outside sound effects, the EEG signal initial there will be large drift, will follow the EEG signal processing impact, so in the EEG before treatment, to remove this a part of the brain electrical signal; Step 2, Ocular artifact reduction: the EEG signal in the original, because to blink or look right and left, the impact on the eye electric signal, so before feature extraction and classification, to remove the impact of this part, this paper is mainly to remove the vertical eye film; Step 3, epoch the data, view the stimulus intervals, and generally 10%-20%, -50, is the common value of -100; the spirit of not more than one event to the principle, in this paper, the interception of data in -100~900ms.
Step 4, baseline correction: the segmented data many not at baseline, so this paper conducted a total of two times the baseline correction and a linear correction.
Step 5, artifact reject: EEG signals collected by the class, there is a part of segmented data caused by various reasons is not good, not only for data analysis useless, it will affect the analysis of the data, so to choose a certain window screening, the window is -80~80.

Table 1 shows the experiment data, H0 is the initial height of the cast specimen, D0 is the initial diameter of specimen, and H is the height of specimen after compression.
The data is obtained automatically during the controlled compression.
Next we make a multivariate linear regression analysis on the experimental data of flow stress, true strain and deformation temperature.
By making a multivariate linear regression analysis on the experimental data, the compressive deformation constitutive equation for cast AM60B Mg alloy at elevated temperatures was established.
The data indicates that the constitutive equation can describe the compressive deformation behavior of cast AM60B Mg alloy at elevated temperatures between 573 and 673K precisely.

As compared to conventional TCP data stream, streaming data transmission more easily lead to lack of resources because of its sudden, high-bandwidth, real-time, sustained, which sustained high-bandwidth occupied, and this makes the flow media can’t be friendly exist with traditional TCP flow, takes a threat to the normal flow of TCP resources.
Multimedia data will take up too much even unfair resources when transmits with TCP flows.
Window is used to control a maximum amount of data sent, in fact, whice means indirectly controlling the sending rate.
Because the TCP flow ‘s rapidly reduce the rate leads to part of the data transmission delay, but the streaming does not allow or do not want a lot of real-time data transmission to be delayed, which will affect the smooth playback of the receiver.
Meanwhile, the rate reduction will lead to a decline in the receiving end playback, which shall be avoided by streaming media.

In parallel implementation, the input data is divided into even samples and the odd samples based on their location.
This enhances the throughput as the input samples are simultaneously used to read the data from two LUTs and hence speed is increased.
Configurable logic blocks can be programmed to perform a wide variety of logical functions as well as to store data.
Input/output Blocks (IOBs) control the flow of data between the I/O pins and the internal logic of the device.
Each supports bidirectional data flow plus 3-state operation.

By introducing the got data, one managed to define parameters of a casting process, providing improvement in a level of quality of cast elements.
These gaps originate during solidification process as a result of reduction of casting volume.
Calculation of Coefficients of Heat Exchange In literature, data, concerning methodology in definition of heat exchange coefficients during precise casting of elements of nickel superalloys, are present and authors of this literarture emphasizes on initial periods of solidification.
In this item, the Kanetkar model is presented, where a substitute coefficient of heat exchange is defined by the following formula: hc/m = A + Be-Ct (2) The A, B and C constants, which are present in this formula, are defined basing on experimental data.
The primary simulations were carried out by use of data coming from literature, which concerned thermal parameters of liquid metal and ceramic moulds.

The authors proposed the optimization method based on Floyd algorithm and dynamic data structure - events list.
It also reduces the train wheel wear, improving drive dynamic properties and its noise reduction.
This strategy reduces the computational complexity from exponential to linear dependence of computational time on a range of input data.
Fig. 1 Sample section of railway track Nizbor – Roztoky u Krivoklatu Input data for solving the optimization problem are the location coordinates of geodetic points, the calendar of time-bound intervals for possible observations, specification of commuting time parameters and the length of the possible paths between geodetic control points (nodes).
Experimental results Input data is imported from text files into database with which the program is connected.

Furthermore, the data analysis showed that all the factors are significant, and the iron removal extent increases by increasing the initial pH to 4.4, sucrose content to 93.8 g/l, and spore concentration to 305.5 spores/µl, but further increase in each factor value has negative effect on the response.
Oxalic acid was found to be most promising because of its acid strength, good complexing characteristics and high reduction capacity, compared to other organic acids [6].
The results of experimental data (not shown) showed that the decrease in sugar con.
Data analysis.
[g/l] Oxalic Acid [g/l] 1 2(-1) 70(-1) 90(-1) 19.1 53.37 1.83 2 5(+1) 70(-1) 90(-1) 114.79 25.64 4.22 3 2(-1) 130(+1) 90(-1) 2.58 107.37 1.13 4 5(+1) 130(+1) 90(-1) 99.6 76.79 2.82 5 2(-1) 70(-1) 350(+1) 48.08 51.44 2.53 6 5(+1) 70(-1) 350(+1) 155.99 54.75 4.64 7 2(-1) 130(+1) 350(+1) 46.26 124.3 2.53 8 5(+1) 130(+1) 350(+1) 133.87 113.19 3.8 9 0.977(-α) 100(0) 220(0) 11.72 84.53 2.1 10 0.977(+α) 100(0) 220(0) 112.76 76.55 2.53 11 3.5(0) 49.55(-α) 220(0) 179.28 37.92 4.5 12 3.5(0) 150.45(+α) 220(0) 105.03 130.61 2.39 13 3.5(0) 100(0) 1.367(-α) 92.44 71.23 4.93 14 3.5(0) 100(0) 438.0(+α) 155.41 73.36 4.64 15 3.5(0) 100(0) 220(0) 143.77 72.88 4.22 16 3.5(0) 100(0) 220(0) 148.42 61.95 4.22 17 3.5(0) 100(0) 220(0) 134.45 63.89 5.06 18 3.5(0) 100(0) 220(0) 150.17 65.45 4.78 19 3.5(0) 100(0) 220(0) 171.13 67.23 4.22 20 3.5(0) 100(0) 220(0) 164.14 70.8 4.5 In order to quantify the curvature effects, the data from the experimental results were fited