Three generic gas turbines, whose main parameters have been taken from off the shelf machines, are proposed as representative of certain classes of gas turbines.
This exploits the fact that most off-the-shelf machine vision frame grabber hardware is already equipped to handle and de-multiplex multi-tap video [47].
These vectors can then be plugged into off-the-shelf machine learning methods to derive functional insights about genes or proteins.
With the ever-increasing need for processing vast amounts of data, researchers often rely on off-the-shelf machine learning solutions to tackle their problems using so-called black-boxes.
However, online learning in real-time applications – as required in control – cannot be realized by straightforward usage of off-the-shelf machine learning methods such as Gaussian process regression or support vector regression.
Currently available comorbidity indices are largely inaccurate to predict adverse events other than death, as well as off-the-shelf machine learning models do not typically account for the temporal sequence of events to enable predictive analytics.
AQT has modified off-the-shelf machines used to make computer hard drives to create CIGS cells using a proprietary process.
Akamai engineers in Cambridge, Massachusetts are constantly preparing newer, faster servers the size of pizza boxes cheap off-the-shelf machines embedded with special Akamai DNA and deploying them to the farthest edges of the network.
The main drawback of this implementation is that big networks cannot be entirely loaded into primary memory using off-the-shelf machines.
Our approach based on local implementations of network-based algorithms and on novel secondary memory-based technologies provides a solution to the large main memory requirements induced by large multi-species protein networks, thus making possible the analysis of big networks using off-the-shelf machines.
The use of smart strategies for caching the portions of graph needed for computation [ 19], the minimization of the number of accesses to secondary memory [ 20], and the usage of compressed data structures for maintaining the graph in primary memory [ 21] are the main challenges for making the management of large graph networks in off-the-shelf machines comparable to distributed approaches.
