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Fuzzy inference systems for efficient non-invasive on-line two-phase flow regime identification

Tatiana Tambouratzis ; Imre Pázsit (Institutionen för teknisk fysik, Nukleär teknik)
Lecture Notes in Computer Science: 9th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2009; Kuopio; Finland; 23 April 2009 through 25 April 2009 (03029743). Vol. 5495 LNCS (2009), p. 423-429.
[Konferensbidrag, refereegranskat]

The identification of two-phase flow regimes that occur in heated pipes is of paramount importance for monitoring nuclear installations such as boiling water reactors. A Sugeno-type fuzzy inference system is put forward for non-invasive, on-line flow regime identification. The proposed system is particularly efficient in that it employs a single directly computable input, four outputs calculated via subtractive clustering - each corresponding to one flow regime -, and four fuzzy inference rules. Despite its simplicity, the system accomplishes accurate identification of the flow regime of sequences of images from neutron radiography videos.

Denna post skapades 2018-01-08.
CPL Pubid: 254345


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Institutioner (Chalmers)

Institutionen för teknisk fysik, Nukleär teknik (2006-2015)


Teknisk fysik

Chalmers infrastruktur