Bayesian predictive assistance system: An embedded application for resource optimization in industrial cleaning processes

Abstract

Bayesian networks (BNs) have been used in different contexts of decision support solutions such as directive, strategic, tactical and operational. These contexts differ from each other only in the realization of the decision support in terms of time. The real-time implementation of BN in an embedded system for resource optimization is very challenging because of the low computation capacity in embedded systems and, to the best of our knowledge, has not been reported yet. In this paper, we present a BN based predictive assistance system that uses real-life data to perform the real-time decision support in industrial cleaning processes.

Original document

The different versions of the original document can be found in:

• http://publica.fraunhofer.de/documents/N-374298.html

• http://xplorestaging.ieee.org/ielx7/7270516/7281697/07281718.pdf?arnumber=7281718,

http://dx.doi.org/10.1109/indin.2015.7281718

• https://dblp.uni-trier.de/db/conf/indin/indin2015.html#ShresthaLN15,

https://ieeexplore.ieee.org/document/7281718,

https://academic.microsoft.com/#/detail/1675133389