Search results

Coded and Actual Operating Conditions of the Factors Factor Name Units Type Minimum Maximum Coded Low Coded High A MT hr Numeric 5 10 -1 5 ↔ +1 ↔ 10 B ST oC Numeric 800 900 -1 800 ↔ +1 ↔ 900 The analysis of variance (ANOVA) was performed to assess the experimental outcomes and statistical analysis data.
The analysis of the densification process during the sintering operation depends on the sintering data gathered from the SPS method. 2.4 Density and porosity analyses of the fabricated HEAs To analyse the densification of the sintered materials, their relative densities were determined with reference to the computed theoretical densities and densities obtained from experiment.
A number of 5 indentations was performed on each sample to ensure accuracy of the hardness data collected. 3.
This is supported by increase in the necking between the particles of the grains, which results in inter-particle bonding and appreciable reduction in the porosity of the material.
Jamal, A New Useful Exponential Model with Applications to Quality Control and Actuarial Data, Comput.

The data shown are the result of only 100millisecs (20millisecs being the actual "event") of data collected during hard but not abusive door slams.
Without such vibrations a 30 second integration time gives data of acceptable quality.
The stress intensity factor was successfully determined from the thermoelastic data for each notch using the method developed by Tomlinson et al [7] and it is intended to fatigue test the blade to destruction in order to compare the reduction in fatigue strength with these thermoelastic results.
If a SPATE system were to record data of similar resolution then it would take several hours and it would be likely that he crack would grow or the blade fail during data collection.
Fig. 9 Thermoelastic data around a notch on the leading edge of the compressor blade.

Asphalt pavement cross - section and thermocouple locations Data Collection.
The content of data collection includesthe surface temperature of pavement，the temperature of different depths in pavement，air temperature.
Pavement temperature data collection started in July and lasted for two months.
Thus according to these data collected from bridge deck in XinJiang province, a model could simulate the variation of temperature in bridge deck which suits to the region of XinJiang province is developed.
Analyzed these temperature data of bridge deck using a linear regression method, obtain the relationship between surface of bridge deck and 0.04m and 0.09m below surface，shown in Figure 4, Figure 5.

According to the element analysis data, the least soluble fraction had the highest molybdenum content (Table 1).
Based on the element analysis data, the relative molecular weight of the elementary unit of this fraction was calculated.
The respective data are shown in Table 2.
This fragment size was calculated from the data of X-ray structural analysis of molybdenyl bis(acetylacetonate) [9].
As seen from the element analysis data, the compositions of these fractions did not virtually change after purification (Table 4).

This data are of important to the model.
Normally, this data was explored and collected by various organizations.
In this paper, the data collected by the Office of Nation Resources and Environmental Policy and Planning of Thailand is utilized and can be shown in Table 2 [10].
Table 3 The input data to the model Legend Tree Absorption CO2 (Ton per year) Expense ($) Trading price ($) Area Si (m2) L (MTonCO2 ~) a Nutmeg 98.62 600.00 11.00 16 16.05 b Bermese 68.04 588.43 c Bengal Almond 89.162 595.34 d Copper pod 107.39 564.67 e Iron Wood 1 83.25 608.50 f Cananga 166.50 614.00 g Afzelia xylocarpa 126.15 598.44 h Iron wood 2 138.75 615.10 i Lagerstroemia loudonii 101.51 598.84 j Bullet wood 121.34 598.50 k Mesawa 91.39 621.34 l Alexandrian Laurel 90.01 600.34 m Java Plum 77.26 615.07 n Kayu 88.32 591.77 Result Using data in Table 3, the optimal solution is obtained and shown in Table 4.
Prombanpong(2010).”The Research Framework for a Reduction of Global Warming Through Efficient Afforestation and Reforestation in Thailand”, 11th Asia Pacific Industrial Engineering and Management Systems Conference 2010, 6-9 December, 2010, pp. 115-119

Fig. 1 Fuselage panel model Generally, thin-walled components such as frames are fixed based on the ‘N-2-1’ locating principle [11] that the number of locators required is N (N > 3), 2 and 1 on the primary datum, secondary datum and tertiary datum, respectively.
Given the sample matrix of is , where n is the number of sample data, then Δ can be decomposed by PCA as follow: (2) where Br×r is the eigenvector matrix of the geometrical covariance matrix cov(Δ) whose eigenvalue matrix is V.
Supposing that all frame positioning errors follow Gaussian distribution ～N(0.5mm, 1.0), the sample data Δ10000×42 and its covariance matrix B can be obtained by 10000-cycle Monte Carlo simulations, then the procedures of the optimum selection are as follows: 1.
Through Eq. 8, all pose parameters are obtained and their error distributions are plotted in Fig. 4 (there just plots the error data of Frames Ⅰ, Ⅲ and Ⅶ).
Contrarily, with the reductions of measurement point number on other frames, both μρ and σρ also become smaller.

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.

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.

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.

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.