Search results

The results showed that the predicted data agreed well with the experimental stress-strain curves.
There have been many studies working on constitutive equation but the accuracy of the calculated data varied widely [3-6].
The data obtained from the tests were used to calculate the true stress-strain data.
The R-squares between fitted and experimental data are above 0.96.
The Figure 4a-d show the distinction between the calculated data and experimental data from the different tests at different temperatures and strain rates.

Secondly, numerical data from the behavior, in static conditions, of geogrid reinforced soil structures with and without pre-tensioned reinforcements are presented.
This reduction allows that panels could have greater dimensions and a reduction in the unitary transport costs for the finished components.
In consequence for analyze the advantages of reinforcement's pre-tension is presented numerical data from the behavior, in static conditions, of geogrid reinforced soil structures with and without pre-tensioned reinforcements.
This reduction permits a better initial behavior of the structures and in consequence a better performance of them in service.
The innovations introduced permits a reduction in the horizontal structures displacements and the installation of face panels with great variety of geometries, size, colors and surface textures.

The results indicated a significant improvement in the pest-control decision-making processes, with a significant reduction in the total areas for pest control application, including more than 68% reduction for the P. oleivora pest and over 92% reduction for the P. latus pest.
The conventional approach for monitoring and controlling these pests consists of data processing and analysis from the samples collected in the field, normally grouped into operational sub-areas.
Data Capture and Processing Field sampling is carried out according to the established sampling protocol.
The field samples records are then transferred to a related database, where a set of data validation routines are executed, including coordinate verification, pest control measurement validation, as well as other validation functions.
The L. phoenicis pest had a 28.50 % reduction in the pest control area with the proposed approach.

The final direct to DDS simulation results and the overall DDS platform oscilloscope experimental data, verify the correctness of the design of DDS with the two data.
Shown in Figure 1, its main parts: the phase accumulator (to produce the output waveform phase angle data), relative to digital converter, (above the phase data is converted to the instantaneous output amplitude data), and digital-to-analog converter (DAC) (the magnitude of data into a sampled analog data points)[5].
It contains two ROM as sine wave table ROM and square wave table ROM. data width of the two ROM are 14 bits, and the data depth is 214, the data of the wave table ROM was Provided by Memory Initialization File. .
ROM data is a 14-bit signed number, amplitude keyword is also a 14-bit signed number,。
Last retain the previous 14 bit as output result. when it was Outputed, because the digital representation of the symbolic, you can flip the highest sign bit, unified data raised to above zero and expressed as an unsigned data.

Area detectors were used for all measurements, which together with the high photon flux enabled fast data acquisition.
Data analysis.
However, only three of the strain coefficients were solved in the average grain studies since the sample was not rotated, while all six coefficients were determined from the individual grain data.
Figure 3: The residual stress state after cold rolling reduction.
From our data it looks like there is a difference in behavior among the grains when plastic yielding has started.

For the strong ability of qualitative analysis, the Rough Sets has already been used successfully in many fields such as machine learning, fault diagnosis, decision analysis, process control, pattern recognition, data mining and so on.
By the way, because of the pool intuitiveness of many concepts and operations of the theory, it normally isn’t easy to understand their essence; 2) many concepts of the Rough Sets is defined and used under the hypothesis of complete information system while in the real life it’s more often to face with incomplete information systems because of the error of measurement, the limitation of data understanding or knowledge acquisition, or some other reasons.
But like the Find-S algorithm, the Candidate Reduction algorithm is also sensitive to noise.
Song[11] proposed a redundant information reduction method in 2009, it improves the speed of reducing redundant attributes through automatically clustering based on ranked data subtraction.
Beijing: Engineering Industry Publishing House (2003) [2] Shuopin Wen, Shengyong Qiao, et al., Data and rules extracting in incomplete decision table based on decision tree, Computer applications, 23(11) (2003) 17-22 [3] Changsong Sun, Xiguo Dong, Jianpei Zhang, Algorithm based on rough set and decision tree to gain minimal classing rule set, Journal of Harbin Engineering University, 23(5) (2002) 87-91 [4] Jing Yang, Hao Wang, Xue-Gang Hu, et al.

OPC UA client has user interface which can display real-time data.
The system framework is shown in figure1: Fig 1 The framework of public building energy efficiency control simulation system Development and realization The system uses OPC UA interface to realize energy data exchange between data collection layer and data processing layer.
Two types of Variables are defined, Properties and Data Variables.
Data variables represent the content of an object.
The OPC UA server and client acquired the real-time data from the underlying equipment firstly, then analyzed the data and issued energy-saving schemes.

GC data was used to calculate conversion, selectivities and Anderson-Schultz-Flory (ASF) parameter for [4, 20].
Obviously there is a difficulty in showing the promoter effect on the conversion which will be explained when discussing the selectivity data.
FTS reactor runs data Catalyst CO conv.% α S% CO2 S% C1-5 Fe/rGO 95.4 0.30 37.9 62.1 Fe-K/rGO 96.2 0.27 34.8 38.0 Fe-Mn/rGO 82.6 0.29 37.3 44.6 The selectivity of the C1-5 range showed promising trends.
From Fig. 3(a) which shows C1-5 detailed selectivity data, it is clear that promoters severely decreased the CH4 selectivity which started at a high of about 37% Fe catalyst and fell to a low of about 20 % with Fe-K and Fe-Mn.
For complete reduction to α-Fe0, higher temperatures or more reduction time is required.

The maximum value of vibration-reduction ratio is 36.7% at the ninth floor.
The vibration-reduction ratio increases with the enhancement of seismic acceleration and vibration-reduction effect is evident.
The effect of vibration-reduction is evident.
The results indicate that the dampers have a good effect on vibration-reduction
CANNY2004 DATA-INPUT MANUAL.

Before performing pre-feasibility studies using RET Screen, the analyst should have enough technical and financial information about the proposed project, such as meteorological information in the location of the project, technical data of the power station component as well as the project's cost constitution.
Meteorological data can be gathered from the NASA Surface meteorology.
The product cost data can be collected from their products supplier.
The PV case in china The PV power plants analyzed in this paper is located in the city of Hami, at the centre of the autonomous region of XingJiang in china, Wurmqi is the nearest weather data location.
Solar resource input parameters Nearest location for weather data Urumqi Latitude of project location 42.9 °N PV array tracking mode Fixed Slope of PV array 36.0° Azimuth of PV array 0.0° Application type On grid Table 3.