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  • Signal interpretation in two-phase fluid dynamics through machine learning and evolutionary computing
    Filipič, Bogdan ; Žun, Iztok ; Perpar, Matjaž, strojnik
    The paper shows how techniques of machine learning and evolutionary computing can assist in making human expertise in sensor data interpretation explicit and suitable for computer execution. The ... study refers to a specific task in two-phase fluid dznamics, i. e. the interpretation of probe signals detected in gas-liquid flow. Given a raw probe signal, the corresponding two-state signal needs to be constructed which denotes the presence of the two phases. Due to the lack of knowledgeabout the processes on a micro scale, no exact procedure exists for accomplishing this task. However, operators are capable of interpreting visually presented probe signals through experience and intuition. To imitate their performance, a prototipe signal interpretation procedure was designed manually, and its parameters tuned with genetic algorithms. In an alternative approach, skill acquisition was performed automatically, using inductive machine learning. The induced signal interpretation procedures were tested successsfully on air-water pipe flow under laboratory conditions.
    Vir: Industrial and engineering application of artificial intelligence and expert systems = IEA/AIE 96 : Proceedings of the Ninth International Conference, Fukuoka, Japan, June 4-7, 1996 (str. 461-466)
    Vrsta gradiva - prispevek na konferenci
    Leto - 1996
    Jezik - angleški
    COBISS.SI-ID - 1999387

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