Sensor Network Design based on the Observability and Precision degree

Autores
Rodriguez Aguilar, Leandro Pedro Faustino; Pantano, Maria Nadia; Scaglia, Gustavo Juan Eduardo; Sanchez, Mabel Cristina
Año de publicación
2023
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The Unscented Kalman Filter is a state estimation method used in nonlinear dynamic systems to estimate the mean and covariance of a random variable undergoing a nonlinear transformation, knowing the process model and the measurements. Therefore, an adequate choice of the measured variables improves the performance of the filter technique. In this context, the sensor network design problem allows selecting a set of variables that minimizes the global estimation error when the instrumentation budget is limited. This is solved using a level traversal tree search algorithm, whose computation time is reduced by evaluating the design criteria sequentially. In this work, it is proposed to address the effect of the circumstantial loss of measurements on the system observability and the estimates precision. The success of the sensor network design methodology is demonstrated for the copolymerization process of Methyl Methacrylate and Vinyl Acetate, widely studied in the literature.
Fil: Rodriguez Aguilar, Leandro Pedro Faustino. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina
Fil: Pantano, Maria Nadia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina
Fil: Scaglia, Gustavo Juan Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina
Fil: Sanchez, Mabel Cristina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Planta Piloto de Ingeniería Química (I). Grupo Vinculado al Plapiqui - Investigación y Desarrollo en Tecnología Química; Argentina
Materia
COPOLYMERIZATION PROCESS
LEVEL TRAVERSAL SEARCH
OBSERVABILITY AND PRECISION DEGREE
SENSOR NETWORK DESIGN
UNSCENTED KALMAN FILTER
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/225050
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Sensor Network Design based on the Observability and Precision degreeRodriguez Aguilar, Leandro Pedro FaustinoPantano, Maria NadiaScaglia, Gustavo Juan EduardoSanchez, Mabel CristinaCOPOLYMERIZATION PROCESSLEVEL TRAVERSAL SEARCHOBSERVABILITY AND PRECISION DEGREESENSOR NETWORK DESIGNUNSCENTED KALMAN FILTERhttps://purl.org/becyt/ford/2.4https://purl.org/becyt/ford/2The Unscented Kalman Filter is a state estimation method used in nonlinear dynamic systems to estimate the mean and covariance of a random variable undergoing a nonlinear transformation, knowing the process model and the measurements. Therefore, an adequate choice of the measured variables improves the performance of the filter technique. In this context, the sensor network design problem allows selecting a set of variables that minimizes the global estimation error when the instrumentation budget is limited. This is solved using a level traversal tree search algorithm, whose computation time is reduced by evaluating the design criteria sequentially. In this work, it is proposed to address the effect of the circumstantial loss of measurements on the system observability and the estimates precision. The success of the sensor network design methodology is demonstrated for the copolymerization process of Methyl Methacrylate and Vinyl Acetate, widely studied in the literature.Fil: Rodriguez Aguilar, Leandro Pedro Faustino. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; ArgentinaFil: Pantano, Maria Nadia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; ArgentinaFil: Scaglia, Gustavo Juan Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; ArgentinaFil: Sanchez, Mabel Cristina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Planta Piloto de Ingeniería Química (I). Grupo Vinculado al Plapiqui - Investigación y Desarrollo en Tecnología Química; ArgentinaInstitute of Electrical and Electronics Engineers2023-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/225050Rodriguez Aguilar, Leandro Pedro Faustino; Pantano, Maria Nadia; Scaglia, Gustavo Juan Eduardo; Sanchez, Mabel Cristina; Sensor Network Design based on the Observability and Precision degree; Institute of Electrical and Electronics Engineers; IEEE Latin America Transactions; 21; 4; 4-2023; 588-5941548-0992CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://latamt.ieeer9.org/index.php/transactions/article/view/7137info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:27:20Zoai:ri.conicet.gov.ar:11336/225050instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:27:21.142CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Sensor Network Design based on the Observability and Precision degree
title Sensor Network Design based on the Observability and Precision degree
spellingShingle Sensor Network Design based on the Observability and Precision degree
Rodriguez Aguilar, Leandro Pedro Faustino
COPOLYMERIZATION PROCESS
LEVEL TRAVERSAL SEARCH
OBSERVABILITY AND PRECISION DEGREE
SENSOR NETWORK DESIGN
UNSCENTED KALMAN FILTER
title_short Sensor Network Design based on the Observability and Precision degree
title_full Sensor Network Design based on the Observability and Precision degree
title_fullStr Sensor Network Design based on the Observability and Precision degree
title_full_unstemmed Sensor Network Design based on the Observability and Precision degree
title_sort Sensor Network Design based on the Observability and Precision degree
dc.creator.none.fl_str_mv Rodriguez Aguilar, Leandro Pedro Faustino
Pantano, Maria Nadia
Scaglia, Gustavo Juan Eduardo
Sanchez, Mabel Cristina
author Rodriguez Aguilar, Leandro Pedro Faustino
author_facet Rodriguez Aguilar, Leandro Pedro Faustino
Pantano, Maria Nadia
Scaglia, Gustavo Juan Eduardo
Sanchez, Mabel Cristina
author_role author
author2 Pantano, Maria Nadia
Scaglia, Gustavo Juan Eduardo
Sanchez, Mabel Cristina
author2_role author
author
author
dc.subject.none.fl_str_mv COPOLYMERIZATION PROCESS
LEVEL TRAVERSAL SEARCH
OBSERVABILITY AND PRECISION DEGREE
SENSOR NETWORK DESIGN
UNSCENTED KALMAN FILTER
topic COPOLYMERIZATION PROCESS
LEVEL TRAVERSAL SEARCH
OBSERVABILITY AND PRECISION DEGREE
SENSOR NETWORK DESIGN
UNSCENTED KALMAN FILTER
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.4
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv The Unscented Kalman Filter is a state estimation method used in nonlinear dynamic systems to estimate the mean and covariance of a random variable undergoing a nonlinear transformation, knowing the process model and the measurements. Therefore, an adequate choice of the measured variables improves the performance of the filter technique. In this context, the sensor network design problem allows selecting a set of variables that minimizes the global estimation error when the instrumentation budget is limited. This is solved using a level traversal tree search algorithm, whose computation time is reduced by evaluating the design criteria sequentially. In this work, it is proposed to address the effect of the circumstantial loss of measurements on the system observability and the estimates precision. The success of the sensor network design methodology is demonstrated for the copolymerization process of Methyl Methacrylate and Vinyl Acetate, widely studied in the literature.
Fil: Rodriguez Aguilar, Leandro Pedro Faustino. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina
Fil: Pantano, Maria Nadia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina
Fil: Scaglia, Gustavo Juan Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina
Fil: Sanchez, Mabel Cristina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Planta Piloto de Ingeniería Química (I). Grupo Vinculado al Plapiqui - Investigación y Desarrollo en Tecnología Química; Argentina
description The Unscented Kalman Filter is a state estimation method used in nonlinear dynamic systems to estimate the mean and covariance of a random variable undergoing a nonlinear transformation, knowing the process model and the measurements. Therefore, an adequate choice of the measured variables improves the performance of the filter technique. In this context, the sensor network design problem allows selecting a set of variables that minimizes the global estimation error when the instrumentation budget is limited. This is solved using a level traversal tree search algorithm, whose computation time is reduced by evaluating the design criteria sequentially. In this work, it is proposed to address the effect of the circumstantial loss of measurements on the system observability and the estimates precision. The success of the sensor network design methodology is demonstrated for the copolymerization process of Methyl Methacrylate and Vinyl Acetate, widely studied in the literature.
publishDate 2023
dc.date.none.fl_str_mv 2023-04
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/225050
Rodriguez Aguilar, Leandro Pedro Faustino; Pantano, Maria Nadia; Scaglia, Gustavo Juan Eduardo; Sanchez, Mabel Cristina; Sensor Network Design based on the Observability and Precision degree; Institute of Electrical and Electronics Engineers; IEEE Latin America Transactions; 21; 4; 4-2023; 588-594
1548-0992
CONICET Digital
CONICET
url http://hdl.handle.net/11336/225050
identifier_str_mv Rodriguez Aguilar, Leandro Pedro Faustino; Pantano, Maria Nadia; Scaglia, Gustavo Juan Eduardo; Sanchez, Mabel Cristina; Sensor Network Design based on the Observability and Precision degree; Institute of Electrical and Electronics Engineers; IEEE Latin America Transactions; 21; 4; 4-2023; 588-594
1548-0992
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://latamt.ieeer9.org/index.php/transactions/article/view/7137
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.069144

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