Development of machine learning techniques and evaluation of analysis results

Stefanos Kollias; Andreas Stafylopatis; Georgios Leontidis; Georgios Alexandridis; Tatiana Tabouratzis; Aiden Durrant

Development of machine learning techniques and evaluation of analysis results

Stefanos Kollias, Andreas Stafylopatis, Georgios Leontidis, Georgios Alexandridis, Tatiana Tabouratzis, Aiden Durrant

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Abstract

This document outlines and describes the development of deep neural network architectures and other machine learning techniques for the unfolding of reactor transfer functions from in-core and ex-core neutron detectors, developed in CORTEX Workpackage 3, mainly in Task 3.3. The techniques developed utilise simulated modelling of the induced neutron flux of perturbations to classify and localise perturbation types and their sources.

Original language	English
Publisher	European Commission
Number of pages	42
Publication status	Published - 12 Aug 2019

Bibliographical note

CORTEX - Research and Innovation Action (RIA)
This project has received funding from the European
Union's Horizon 2020 research and innovation programme
under grant agreement No 754316.

Keywords

nuclear reactors
Machine learning

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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CORTEX_D3_4_Development_of_machine_learning_techniques_and_evaluation_of_analysis_results_V1Final published version, 1.37 MBLicence: Unspecified

Cite this

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title = "Development of machine learning techniques and evaluation of analysis results",

abstract = "This document outlines and describes the development of deep neural network architectures and other machine learning techniques for the unfolding of reactor transfer functions from in-core and ex-core neutron detectors, developed in CORTEX Workpackage 3, mainly in Task 3.3. The techniques developed utilise simulated modelling of the induced neutron flux of perturbations to classify and localise perturbation types and their sources.",

keywords = "nuclear reactors, Machine learning",

author = "Stefanos Kollias and Andreas Stafylopatis and Georgios Leontidis and Georgios Alexandridis and Tatiana Tabouratzis and Aiden Durrant",

note = "CORTEX - Research and Innovation Action (RIA) This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 754316.",

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day = "12",

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T1 - Development of machine learning techniques and evaluation of analysis results

AU - Kollias, Stefanos

AU - Stafylopatis, Andreas

AU - Leontidis, Georgios

AU - Alexandridis, Georgios

AU - Tabouratzis, Tatiana

AU - Durrant, Aiden

N1 - CORTEX - Research and Innovation Action (RIA) This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 754316.

PY - 2019/8/12

Y1 - 2019/8/12

N2 - This document outlines and describes the development of deep neural network architectures and other machine learning techniques for the unfolding of reactor transfer functions from in-core and ex-core neutron detectors, developed in CORTEX Workpackage 3, mainly in Task 3.3. The techniques developed utilise simulated modelling of the induced neutron flux of perturbations to classify and localise perturbation types and their sources.

AB - This document outlines and describes the development of deep neural network architectures and other machine learning techniques for the unfolding of reactor transfer functions from in-core and ex-core neutron detectors, developed in CORTEX Workpackage 3, mainly in Task 3.3. The techniques developed utilise simulated modelling of the induced neutron flux of perturbations to classify and localise perturbation types and their sources.

KW - nuclear reactors

KW - Machine learning

UR - https://cortex-h2020.eu/wp-content/uploads/2020/04/CORTEX_D3_4_Development_of_machine_learning_techniques_and_evaluation_of_analysis_results_V1.pdf

M3 - Other contribution

PB - European Commission

ER -

Development of machine learning techniques and evaluation of analysis results

Abstract

Bibliographical note

Keywords

UN SDGs

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