Clique cover of graphs

Gella, Frederick S.; Artes, Rosalio G.

doi:10.12988/ams.2014.45343

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Forward Selection For Distributed Pattern Recognition1

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2019

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“…The value of θ k is equal to the clique cover number of G k cc to ensure that the number of subsystems used for distributed pattern recognition is minimal. The clique cover number of G (i.e., the total number of subsystems used for distributed pattern recognition) is then equal to the sum of the clique cover number of its connected components …”

Section: Forward Selection For Distributed Pattern Recognitionmentioning

confidence: 99%

Optimal Feature Selection for Distributed Data-Driven Process Monitoring

Khatib

Daoutidis

2019

Ind. Eng. Chem. Res.

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Two methods are proposed in this article to address distributed data-driven process monitoring. The first method selects sensors and allocates these sensors among subsystems with the objective of optimizing the diagnostic performance of a pattern recognition method when it is implemented in a distributed configuration subject to constraints that are imposed to meet operational requirements. The second method finds the minimum number of monitoring computers required to implement the methods used for fault detection and diagnosis in a distributed configuration subject to certain operational constraints. It also finds the sensors that need to be transmitted to each of the monitors. The proposed methods rely on concepts and algorithms from graph theory and optimization, and their efficiency is shown through a case study on the benchmark Tennessee Eastman Process.

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Section: Forward Selection For Distributed Pattern Recognitionmentioning

confidence: 99%