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SPIN accepts an input specification with an integrated system model using a language called PROMELA.
The language is designed to support dynamic monitoring of temporal properties at simulation runtime, as well as to provide the input specification for formal property checking.
The user interface to OMEGA separates input specification from output display, relies on pointing rather than typing, and exploits interaction in semantic analysis to detect many errors as soon as they are made.
In this paper, we address the automated tuning of input specification for supervised inductive learning and develop combinatorial optimization solutions for two such tuning problems.
The input specification of the control system includes (i) the constraints on the location of its components, which are imposed by the plant, (ii) the communication requirements among the components, and (iii) an estimation of the real-time constraints for the correct behavior of the algorithms implementing the control law.
We design a generic fitness function for validation of input specification, then use it to develop two genetic algorithm wrappers: one for the variable selection problem for decision tree inducers and one for the variable ordering problem for Bayesian network structure learning.
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Usually, structures are designed according to input specifications.
The input specifications for optimization are: maximum useable energy (35 kW h), rotational speed (15,000 rpm), height, and inner radius.
Experiments suggest that the outcomes of NETCARBENCH are satisfactory in terms of their closeness to the input specifications.
Unlike the economic chart designs, most input specifications required by the design of an ¯¯¯X&S M-chart can be easily determined by the practitioners.
The role of the engineer who interacts with the model is to provide the input specifications such as component geometry, material, and production machine.
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