STUDY OF FEASIBILITY AND USEFULNESS OF MACHINE LEARNING METHODS TO HELP IDENTIFYING RESIDUAL GAS COMPOSITION

This report summarizes the outcome of the collaboration between CERN and Intelligent Data Analysis Laboratory (IDAL). In this feasibility study we investigated the potential usefulness of machine-learning applications to identify residual gas compositions. The report focus on the performance of the...

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Detalles Bibliográficos
Autores principales: Jenninger, Berthold, Fernando, Mateo
Lenguaje:eng
Publicado: 2018
Materias:
Science in General
Engineering
Acceso en línea:http://cds.cern.ch/record/2740627

Internet

http://cds.cern.ch/record/2740627

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