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Google and Facebook, among others, use the same techniques for the same purposes: to find problems, and to create more useful and more reliable software.
In the present paper we show that by joining both techniques for the same specimens, E and ν can be deconvoluted.
We also show that the Pareto fronts corresponding to different methods can intersect indicating that more is not always better and different solution techniques for the same problem may be required in different computational regimes.
Then, physical constraints such as Time Delay (TD), reheat turbine and Generation Rate Constraint (GRC) are included in the system model and the superiority of FA is demonstrated by comparing the results over DE, Gravitational Search Algorithm GSAandand Genetic Algorithm (GA) optimization techniques for the same interconnected power system.
A direct comparison of the calculated equivalent thermal conductivity with the results obtained by two different experimental techniques for the same hollow brick block shows a satisfactory agreement, making the designed computational approach a viable alternative to the currently used methods.
This lack of precise definitions implies on tools that implement different detection techniques for the same code smell.
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The results were compared with glass powders obtained via classical melting technique for the same composition.
The method was successfully used for Ca, Mn, Fe, Ni, Cu, Zn, Se, Rb and Mo determination in wheat and chickpea and measurements were cross analyzed using inductively coupled plasma mass spectroscopy ICP-MS technique for the same samples.
The superiority of the proposed approach has been shown by comparing the results with a recently published Craziness based Particle Swarm Optimization (CPSO) technique for the same interconnected power system.
In radio fading channel, STBC require less transmission energy than SISO technique for the same Bit Error Rate and can be employed practically in Wireless Sensor Networks by using the cooperative MIMO scheme.
The average classification accuracy for an ANN classifier for the different operating conditions of the engine was around 95%. Yadav et al. [4] has proposed a spectrogram based statical feature extraction technique for the same type of test rig.
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