Stopping criteria for ending autonomous, single detector radiological source searches.

While the localization of radiological sources has traditionally been handled with statistical algorithms, such a task can be augmented with advanced machine learning methodologies. The combination of deep and reinforcement learning has provided learning-based navigation to autonomous, single-detect...

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Autores principales:	Gregory R Romanchek, Shiva Abbaszadeh
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a6db2a33b7f2426d8308ebbc9ccc1f70
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spelling	oai:doaj.org-article:a6db2a33b7f2426d8308ebbc9ccc1f702021-12-02T20:10:28ZStopping criteria for ending autonomous, single detector radiological source searches.1932-620310.1371/journal.pone.0253211https://doaj.org/article/a6db2a33b7f2426d8308ebbc9ccc1f702021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0253211https://doaj.org/toc/1932-6203While the localization of radiological sources has traditionally been handled with statistical algorithms, such a task can be augmented with advanced machine learning methodologies. The combination of deep and reinforcement learning has provided learning-based navigation to autonomous, single-detector, mobile systems. However, these approaches lacked the capacity to terminate a surveying/search task without outside influence of an operator or perfect knowledge of source location (defeating the purpose of such a system). Two stopping criteria are investigated in this work for a machine learning navigated system: one based upon Bayesian and maximum likelihood estimation (MLE) strategies commonly used in source localization, and a second providing the navigational machine learning network with a "stop search" action. A convolutional neural network was trained via reinforcement learning in a 10 m × 10 m simulated environment to navigate a randomly placed detector-agent to a randomly placed source of varied strength (stopping with perfect knowledge during training). The network agent could move in one of four directions (up, down, left, right) after taking a 1 s count measurement at the current location. During testing, the stopping criteria for this navigational algorithm was based upon a Bayesian likelihood estimation technique of source presence, updating this likelihood after each step, and terminating once the confidence of the source being in a single location exceeded 0.9. A second network was trained and tested with similar architecture as the previous but which contained a fifth action: for self-stopping. The accuracy and speed of localization with set detector and source initializations were compared over 50 trials of MLE-Bayesian approach and 1000 trials of the CNN with self-stopping. The statistical stopping condition yielded a median localization error of ~1.41 m and median localization speed of 12 steps. The machine learning stopping condition yielded a median localization error of 0 m and median localization speed of 17 steps. This work demonstrated two stopping criteria available to a machine learning guided, source localization system.Gregory R RomanchekShiva AbbaszadehPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 6, p e0253211 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Gregory R Romanchek Shiva Abbaszadeh Stopping criteria for ending autonomous, single detector radiological source searches.
description	While the localization of radiological sources has traditionally been handled with statistical algorithms, such a task can be augmented with advanced machine learning methodologies. The combination of deep and reinforcement learning has provided learning-based navigation to autonomous, single-detector, mobile systems. However, these approaches lacked the capacity to terminate a surveying/search task without outside influence of an operator or perfect knowledge of source location (defeating the purpose of such a system). Two stopping criteria are investigated in this work for a machine learning navigated system: one based upon Bayesian and maximum likelihood estimation (MLE) strategies commonly used in source localization, and a second providing the navigational machine learning network with a "stop search" action. A convolutional neural network was trained via reinforcement learning in a 10 m × 10 m simulated environment to navigate a randomly placed detector-agent to a randomly placed source of varied strength (stopping with perfect knowledge during training). The network agent could move in one of four directions (up, down, left, right) after taking a 1 s count measurement at the current location. During testing, the stopping criteria for this navigational algorithm was based upon a Bayesian likelihood estimation technique of source presence, updating this likelihood after each step, and terminating once the confidence of the source being in a single location exceeded 0.9. A second network was trained and tested with similar architecture as the previous but which contained a fifth action: for self-stopping. The accuracy and speed of localization with set detector and source initializations were compared over 50 trials of MLE-Bayesian approach and 1000 trials of the CNN with self-stopping. The statistical stopping condition yielded a median localization error of ~1.41 m and median localization speed of 12 steps. The machine learning stopping condition yielded a median localization error of 0 m and median localization speed of 17 steps. This work demonstrated two stopping criteria available to a machine learning guided, source localization system.
format	article
author	Gregory R Romanchek Shiva Abbaszadeh
author_facet	Gregory R Romanchek Shiva Abbaszadeh
author_sort	Gregory R Romanchek
title	Stopping criteria for ending autonomous, single detector radiological source searches.
title_short	Stopping criteria for ending autonomous, single detector radiological source searches.
title_full	Stopping criteria for ending autonomous, single detector radiological source searches.
title_fullStr	Stopping criteria for ending autonomous, single detector radiological source searches.
title_full_unstemmed	Stopping criteria for ending autonomous, single detector radiological source searches.
title_sort	stopping criteria for ending autonomous, single detector radiological source searches.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/a6db2a33b7f2426d8308ebbc9ccc1f70
work_keys_str_mv	AT gregoryrromanchek stoppingcriteriaforendingautonomoussingledetectorradiologicalsourcesearches AT shivaabbaszadeh stoppingcriteriaforendingautonomoussingledetectorradiologicalsourcesearches
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Stopping criteria for ending autonomous, single detector radiological source searches.

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