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Subdiffusive Source Sensing by a Regional Detection Method

Motivated by the fact that the danger may increase if the source of pollution problem remains unknown, in this paper, we study the source sensing problem for subdiffusion processes governed by time fractional diffusion systems based on a limited number of sensor measurements. For this, we first give...

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Detalles Bibliográficos
Autores principales: Song, Weijing, Ge, Fudong, Chen, YangQuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6721240/
https://www.ncbi.nlm.nih.gov/pubmed/31405156
http://dx.doi.org/10.3390/s19163504
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author Song, Weijing
Ge, Fudong
Chen, YangQuan
author_facet Song, Weijing
Ge, Fudong
Chen, YangQuan
author_sort Song, Weijing
collection PubMed
description Motivated by the fact that the danger may increase if the source of pollution problem remains unknown, in this paper, we study the source sensing problem for subdiffusion processes governed by time fractional diffusion systems based on a limited number of sensor measurements. For this, we first give some preliminary notions such as source, detection and regional spy sensors, etc. Secondly, we investigate the characterizations of regional strategic sensors and regional spy sensors. A regional detection approach on how to solve the source sensing problem of the considered system is then presented by using the Hilbert uniqueness method (HUM). This is to identify the unknown source only in a subregion of the whole domain, which is easier to be implemented and could save a lot of energy resources. Numerical examples are finally included to test our results.
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spelling pubmed-67212402019-09-10 Subdiffusive Source Sensing by a Regional Detection Method Song, Weijing Ge, Fudong Chen, YangQuan Sensors (Basel) Article Motivated by the fact that the danger may increase if the source of pollution problem remains unknown, in this paper, we study the source sensing problem for subdiffusion processes governed by time fractional diffusion systems based on a limited number of sensor measurements. For this, we first give some preliminary notions such as source, detection and regional spy sensors, etc. Secondly, we investigate the characterizations of regional strategic sensors and regional spy sensors. A regional detection approach on how to solve the source sensing problem of the considered system is then presented by using the Hilbert uniqueness method (HUM). This is to identify the unknown source only in a subregion of the whole domain, which is easier to be implemented and could save a lot of energy resources. Numerical examples are finally included to test our results. MDPI 2019-08-10 /pmc/articles/PMC6721240/ /pubmed/31405156 http://dx.doi.org/10.3390/s19163504 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Song, Weijing
Ge, Fudong
Chen, YangQuan
Subdiffusive Source Sensing by a Regional Detection Method
title Subdiffusive Source Sensing by a Regional Detection Method
title_full Subdiffusive Source Sensing by a Regional Detection Method
title_fullStr Subdiffusive Source Sensing by a Regional Detection Method
title_full_unstemmed Subdiffusive Source Sensing by a Regional Detection Method
title_short Subdiffusive Source Sensing by a Regional Detection Method
title_sort subdiffusive source sensing by a regional detection method
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6721240/
https://www.ncbi.nlm.nih.gov/pubmed/31405156
http://dx.doi.org/10.3390/s19163504
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