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192 Effect of Strong Neutron Absorption on Texture and Diffraction Data Analysis H.M.
The impact of strong neutron absorption on data analysis and the comparison of two different absorption corrections for neutron diffraction data implemented in the GSAS Rietveld code are the foci of this work.
Again, all rolling directions of the foils were held coincident during the data collection.
Therefore, it is logical that the linear model would be more appropriate for fitting these data from foils.
Rows represent data from the same sample with thickness shown at left.

And at the same time, in the routing decisions this paper chooses fragmentary active routing decisions, further energy consumption and transmission delay reduction.
Store the paths in the routing table according to the priority classification, and then when the further data comes; direct transmit the data according to different choices of the routing table of the data in the right path.
When sending data, the energy consumption model is as follow:
In route choice, due to the different data, the routing requirements are not the same, some data require for real-time, some data require for reliability.
When first transmit data, the source node find all feasible paths to meet the conditions for data transmission based on MMSPEED agreement.

The experimental facility is divided into 3 main components, namely the piping system, battery components, and data acquisition system.
The NTC thermistor sensors were connected to the Arduino board, whilst the thermocouple sensors were connected to the NI-DAQ 6008 data acquisition system.
Battery temperature data was collected in each variation, including the discharge rate and liquid flow rate.
The data was then processed to estimate the effective convective heat transfer coefficient (h) of the liquid cold plate.
The corresponding pressure drop (∆P) data for different flow rate variations are detailed in Table 2.

In many cases, damage data are often incomplete, which make actual measurement of structural response data ambiguous.
Rough set theory is based on the classification mechanism, andregards knowledge as data classification.
It can actually describe and deal with the problem of uncertainty without providing prior information other than data sets need to be processed.
Dimension of massive data should be reduced to effectively deal with uncertain fuzzy data from the structural health monitoring system, rough set with data fusion technologies for damage identification has been made great progress in recent years [18, 19].
Damage detection based on improved particle swarm optimization using vibration data.

An experimental evaluation is completed to evaluate the natural frequency and damping response of mild steel sandwich shaft disc system using Lab VIEW 8.5 software and National Instrument’s data acquisition (DAQ) system.
Fig. 2 Mild steel sandwich shaft disc system Experimental setup An experimental setup was established with National Instrument’s data acquisition (DAQ) system and Lab VIEW 8.5 software as shown in Fig. 3.
The disc kept nearer the end fallout in reduction of frequency values.
“On the dynamic analysis of rotors using modal reduction”, Finite Elements in Analysis and Design, Vol. 26, pp. 41-55, 1997

The lighting audit for the faculty buildings consists of a walk-through audit, lighting desktop work, field data measurement, and lighting analysis.
Lighting data measurement in an active area during operating hours and ensure space is occupied.
Tables 1-3 consist of lighting data and operational information gathered for all three levels.
It hinders the reduction of light usage.
Review of data-driven energy modelling techniques for building retrofit.

Mass reduction After figured out the amplitude and phase of unbalanced couple, mass reduction process will be applied to the rotor.
The location of mass reduction should be symmetric about the rotor's center, and the mass and location can be calculated by experimental data.
The mass reduction methods including drill, grinding, chemical etch, laser irradiation and so on.
Precision Analysis of Dynamic Balance This accuracy of the dynamic balance method is depend on static balance precision before, the resolution of displacement sensors, flatness and surface roughness of testing plane, and also the precision of mass reduction.

The material data that is required as model input, such as tension and shear properties, can either be obtained by experimental or virtual tests.
Thus, computational models, which are set up with pure material input data without any couplings (shear, tension, bending), fail in predicting the correct forming behaviour.
Nguyen et al. examined compaction tests on a mesoscale model of a woven fabric to achieve the resulting compacted geometry and data about the pressure needed for compaction [5].
Additionally, a forming geometry has to be developed, where the improved input data results in a more accurate simulation of the drapability.
Numerical drape simulations of this preforming process have to be performed with and without the improved material input data to proof the eligibility of these examinations.

Performing the button cells by means of I-V testing at 600, 700 and 800°C, the data of maximum power density (Pm) depicted the order LN75 < BSCF < LN15 < LN30< LN50 regardless of temperatures.
The data measured from the testing of electrical conductivity for various pellets in the range from 400 to 800 °C were recorded and plotted in Fig. 3(a).
The data of conductivity for the pellet come from pure BSCF decreased from 20.9 to 18.5 S/cm with increasing temperature from 500 to 800 °C.
For instance, at 700 ° C, the data of Pm (mW/cm2) increased in the order 4.5 (LN75) < 12.7 (LN15) < 17.2 (LN30) < 19.3 (BSCF) < 24.2 (LN50).
(a) Data of the electric conductivity as a function of temperatures in air for the pellets come from mixed powders LN15, LN30, LN50 and LN75 compared with that of pure BSCF powder.

The whole Test Rig is equipped with devices for on-line data acquisition of main operating parameters such as temperatures, pressures and flow rates.
Measured values are recorded and processed by a data collection system including a multi-channel data logger and a personal computer for on line data acquisition and processing.
The experimental data are linked with the evaporator configuration.
During the test runs two series of performance data were derived: when using both R422A and R22.
The data derived by the experimental tests performed with R22 and R422A for wound coaxial coil evaporator having a rated duty of 25 kW (by Catalogue data), when working with R22, confirm that the rated cooling power reported in the catalogues is right for the case of R22, while, when using the evaporator with R422A, there is a leak of performance which is significant.