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As new information is received, the state of the machine changes.
In classical machine tool studies, tool point FRF is obtained experimentally or analytically for the idle state of the machine.
Analogously to human communication, a way is needed to communicate the state of the machine to an observer.
The on- and offline training systems visualize the state of the machine at a certain step of the procedure using structural SysML models.
This 'virtual twin' is populated with data streaming in from the physical machines to represent a near real-time as-is state of the machine in cyberspace.
The basic idea of a FSM is to store a sequence of different unique states and transition between them depending on the values of the inputs and the current state of the machine.
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The AZOrange package is intended to aid scientist with limited knowledge in machine learning to accurately use state-of-the-art machine learning algorithms.
To this end, we used state-of-the-art machine learning approaches to build support vector machine models that can accurately predict binding specificity.
Support vector machines (SVMs) [55] are among the state-of-the-art machine learning methods for linear and non-linear classification.
AZOrange complements already available machine learning packages by interfacing and customizing several state-of-the-art machine learning algorithms.
Our experiments demonstrate that the discourse aware model outperforms state-of-the-art machine comprehension systems.
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