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EDA gives the energy of data aggregation.
Open image in new window Fig. 8 The scree plot of PCA results; the first 8 components comprise 99.9% of the total energy of data.
Open image in new window Fig. 8 The scree plot of PCA results; the first 8 components comprise 99.9% of the total energy of data .
Dissipated energy by a laminate is determined using a second order polynomial regression based on the relationship between energy dissipation and impact energy of data points before penetration.
Open image in new window Fig. 8 The scree plot of PCA results; the first 8 components comprise 99.9% of the total energy of data 2.3 The reduced data are then normalized using the min max operator (Eq. 3) to lie between 0 and 1.
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Obviously, the cluster formed by this method, and not only the residual energy but also the energy attenuation of data transmission are taken into account.
This paper addresses the issue of energy performance of data centers by closely examining energy use of two data centers in commercial office buildings.
Some efforts have been made to control and lower energy consumption of data centers such as, proportional energy consuming hardware, dynamic provisioning, and virtualization machine techniques.
With the prevalence of data-centric computing, the key to achieving energy efficiency is to reduce the latency and energy cost of data movement.
In this case, multi-leader only receives data from their neighbors, energy consumption considered includes two parts: energy of sending data and energy of receiving data.
The energy consumption of data transmission is highly dependent on the traffic pattern, and we argue that designing energy efficient data transmissions starts by energy awareness.
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