Assessing Sensor Integrity for Nuclear Waste Monitoring Using Graph Neural Networks

Pierre Hembert; Chady Ghnatios; Julien Cotton; Francisco Chinesta

doi:10.3390/s24051580

Assessing Sensor Integrity for Nuclear Waste Monitoring Using Graph Neural Networks

Pierre Hembert
, Chady Ghnatios
, Julien Cotton
, Francisco Chinesta

Laboratoire Univers et Particules de Montpellier, Université Montpellier, CNRS/IN2P3

Research output: Contribution to journal › Article › peer-review

Original language	English
Article number	1580
Journal	Sensors
Volume	24
Issue number	5
DOIs	https://doi.org/10.3390/s24051580
State	Published - Mar 2024
Externally published	Yes

UN SDGs

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

SDG 12 Responsible Consumption and Production

ASJC Scopus Subject Areas

Analytical Chemistry
Information Systems
Atomic and Molecular Physics, and Optics
Biochemistry
Instrumentation
Electrical and Electronic Engineering

Keywords

classification
graph neural network
nuclear waste monitoring
sensor network
sensor state

Access to Document

10.3390/s24051580

Cite this

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title = "Assessing Sensor Integrity for Nuclear Waste Monitoring Using Graph Neural Networks",

keywords = "classification, graph neural network, nuclear waste monitoring, sensor network, sensor state",

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doi = "10.3390/s24051580",

language = "English",

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T1 - Assessing Sensor Integrity for Nuclear Waste Monitoring Using Graph Neural Networks

AU - Hembert, Pierre

AU - Ghnatios, Chady

AU - Cotton, Julien

AU - Chinesta, Francisco

PY - 2024/3

Y1 - 2024/3

KW - classification

KW - graph neural network

KW - nuclear waste monitoring

KW - sensor network

KW - sensor state

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DO - 10.3390/s24051580

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SN - 1424-8220

VL - 24

JO - Sensors

JF - Sensors

IS - 5

M1 - 1580

ER -

Assessing Sensor Integrity for Nuclear Waste Monitoring Using Graph Neural Networks

UN SDGs

ASJC Scopus Subject Areas

Keywords

Access to Document

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Cite this