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We present a code, developed with the Geant4 Monte Carlo (MC) toolkit, to model automatically complex-shaped multi-leaf collimators (MLC) by means of geometrical parameters provided by the vendor.
Robots were perfectly capable of automatically manufacturing complex things decades ago.
More specifically, it aids in automatically extracting complex data representations from large volumes of unsupervised data.
Deep learning algorithms use a huge amount of unsupervised data to automatically extract complex representation.
It automatically identifies complex solutions such as trigeneration demonstrating the power of the suggested optimization framework.
This paper proposes a novel methodology that can automatically fill complex polygonal holes with a piecewise manner.
In recent years, there has been a growing interest in the adoption of ontologies and ontological reasoning to automatically recognize complex context data such as human activities.
With the capacity of automatically learning complex features of input data, deep learning architectures have great potential to overcome drawbacks of traditional intelligent fault diagnosis.
It automatically creates complex process dynamic models from a simple graphical interface, where system components can be dragged from a component data base and interconnected so as to produce the overall system, following the Piping and Instrumentation Diagram.
Typical wings nonetheless need to perform semi automatically– the complex movements and cyclic changes in attitude and shape associated with flapping flight, and these necessities strongly influence their structural design.
The goal is for an algorithm to automatically learn complex functions that map inputs to outputs, without using hand-crafted features or rules (Bengio, 2009).
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