Alpha spectral analysis via artificial neural networks

An artificial neural network system that assigns quality factors to alpha particle energy spectra is discussed. The alpha energy spectra are used to detect plutonium contamination in the work environment. The quality factors represent the levels of spectral degradation caused by miscalibration and f...

Full description

Saved in:
Bibliographic Details
Main Authors: Kangas, L.J., Troyer, G.L., Keller, P.E., Hashem, S., Kouzes, R.T.
Format: Conference Proceeding
Language:English
Subjects:
Air cleaners
Alpha particles
Artificial neural networks
Degradation
Filters
Instruments
Laboratories
Performance evaluation
Q factor
Spectral analysis
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page 421 vol.1
container_issue
container_start_page 418
container_title
container_volume 1
creator Kangas, L.J.
Troyer, G.L.
Keller, P.E.
Hashem, S.
Kouzes, R.T.
description An artificial neural network system that assigns quality factors to alpha particle energy spectra is discussed. The alpha energy spectra are used to detect plutonium contamination in the work environment. The quality factors represent the levels of spectral degradation caused by miscalibration and foreign matter affecting the instruments. A set of spectra was labeled with a quality factor by an expert and used in training the artificial neural network expert system. Our investigation shows that the expert knowledge of alpha spectra quality factors can be transferred to an ANN system.< >
doi_str_mv 10.1109/NSSMIC.1994.474348
format conference_proceeding
fullrecord <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_474348</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>474348</ieee_id><sourcerecordid>474348</sourcerecordid><originalsourceid>FETCH-LOGICAL-i148t-87b98b7330310a4032d7ae21a1e9133447c3979575763671338cecc50292356e3</originalsourceid><addsrcrecordid>eNotj81KA0EQhAdEUOK-QE77ArvObPf8HcPiTyDRQ_QcOmMHR9e4zKxK3t7BWBQUfIeiSoi5kq1S0l8_bDbrZd8q77FFi4DuTFTeOlkMnUb0F6LK-U0WodaozKUwi2F8pTqPHKZEQ00HGo455vo7Uk1pivsYYuEH_kp_Mf18pvd8Jc73NGSu_nMmnm9vnvr7ZvV4t-wXqyYqdFPj7M67nQWQoCRhWfFiiTtFir0CQLQBvPXaamvA2IJc4BC07HwH2jDMxPzUG5l5O6b4Qem4PZ2DXzxeQ2Y</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Alpha spectral analysis via artificial neural networks</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Kangas, L.J. ; Troyer, G.L. ; Keller, P.E. ; Hashem, S. ; Kouzes, R.T.</creator><creatorcontrib>Kangas, L.J. ; Troyer, G.L. ; Keller, P.E. ; Hashem, S. ; Kouzes, R.T.</creatorcontrib><description>An artificial neural network system that assigns quality factors to alpha particle energy spectra is discussed. The alpha energy spectra are used to detect plutonium contamination in the work environment. The quality factors represent the levels of spectral degradation caused by miscalibration and foreign matter affecting the instruments. A set of spectra was labeled with a quality factor by an expert and used in training the artificial neural network expert system. Our investigation shows that the expert knowledge of alpha spectra quality factors can be transferred to an ANN system.&lt; &gt;</description><identifier>ISBN: 9780780325449</identifier><identifier>ISBN: 0780325443</identifier><identifier>DOI: 10.1109/NSSMIC.1994.474348</identifier><language>eng</language><publisher>IEEE</publisher><subject>Air cleaners ; Alpha particles ; Artificial neural networks ; Degradation ; Filters ; Instruments ; Laboratories ; Performance evaluation ; Q factor ; Spectral analysis</subject><ispartof>Proceedings of 1994 IEEE Nuclear Science Symposium - NSS'94, 1994, Vol.1, p.418-421 vol.1</ispartof><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/474348$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,4050,4051,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/474348$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Kangas, L.J.</creatorcontrib><creatorcontrib>Troyer, G.L.</creatorcontrib><creatorcontrib>Keller, P.E.</creatorcontrib><creatorcontrib>Hashem, S.</creatorcontrib><creatorcontrib>Kouzes, R.T.</creatorcontrib><title>Alpha spectral analysis via artificial neural networks</title><title>Proceedings of 1994 IEEE Nuclear Science Symposium - NSS'94</title><addtitle>NSSMIC</addtitle><description>An artificial neural network system that assigns quality factors to alpha particle energy spectra is discussed. The alpha energy spectra are used to detect plutonium contamination in the work environment. The quality factors represent the levels of spectral degradation caused by miscalibration and foreign matter affecting the instruments. A set of spectra was labeled with a quality factor by an expert and used in training the artificial neural network expert system. Our investigation shows that the expert knowledge of alpha spectra quality factors can be transferred to an ANN system.&lt; &gt;</description><subject>Air cleaners</subject><subject>Alpha particles</subject><subject>Artificial neural networks</subject><subject>Degradation</subject><subject>Filters</subject><subject>Instruments</subject><subject>Laboratories</subject><subject>Performance evaluation</subject><subject>Q factor</subject><subject>Spectral analysis</subject><isbn>9780780325449</isbn><isbn>0780325443</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>1994</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotj81KA0EQhAdEUOK-QE77ArvObPf8HcPiTyDRQ_QcOmMHR9e4zKxK3t7BWBQUfIeiSoi5kq1S0l8_bDbrZd8q77FFi4DuTFTeOlkMnUb0F6LK-U0WodaozKUwi2F8pTqPHKZEQ00HGo455vo7Uk1pivsYYuEH_kp_Mf18pvd8Jc73NGSu_nMmnm9vnvr7ZvV4t-wXqyYqdFPj7M67nQWQoCRhWfFiiTtFir0CQLQBvPXaamvA2IJc4BC07HwH2jDMxPzUG5l5O6b4Qem4PZ2DXzxeQ2Y</recordid><startdate>1994</startdate><enddate>1994</enddate><creator>Kangas, L.J.</creator><creator>Troyer, G.L.</creator><creator>Keller, P.E.</creator><creator>Hashem, S.</creator><creator>Kouzes, R.T.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>1994</creationdate><title>Alpha spectral analysis via artificial neural networks</title><author>Kangas, L.J. ; Troyer, G.L. ; Keller, P.E. ; Hashem, S. ; Kouzes, R.T.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i148t-87b98b7330310a4032d7ae21a1e9133447c3979575763671338cecc50292356e3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>1994</creationdate><topic>Air cleaners</topic><topic>Alpha particles</topic><topic>Artificial neural networks</topic><topic>Degradation</topic><topic>Filters</topic><topic>Instruments</topic><topic>Laboratories</topic><topic>Performance evaluation</topic><topic>Q factor</topic><topic>Spectral analysis</topic><toplevel>online_resources</toplevel><creatorcontrib>Kangas, L.J.</creatorcontrib><creatorcontrib>Troyer, G.L.</creatorcontrib><creatorcontrib>Keller, P.E.</creatorcontrib><creatorcontrib>Hashem, S.</creatorcontrib><creatorcontrib>Kouzes, R.T.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE/IET Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Kangas, L.J.</au><au>Troyer, G.L.</au><au>Keller, P.E.</au><au>Hashem, S.</au><au>Kouzes, R.T.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Alpha spectral analysis via artificial neural networks</atitle><btitle>Proceedings of 1994 IEEE Nuclear Science Symposium - NSS'94</btitle><stitle>NSSMIC</stitle><date>1994</date><risdate>1994</risdate><volume>1</volume><spage>418</spage><epage>421 vol.1</epage><pages>418-421 vol.1</pages><isbn>9780780325449</isbn><isbn>0780325443</isbn><abstract>An artificial neural network system that assigns quality factors to alpha particle energy spectra is discussed. The alpha energy spectra are used to detect plutonium contamination in the work environment. The quality factors represent the levels of spectral degradation caused by miscalibration and foreign matter affecting the instruments. A set of spectra was labeled with a quality factor by an expert and used in training the artificial neural network expert system. Our investigation shows that the expert knowledge of alpha spectra quality factors can be transferred to an ANN system.&lt; &gt;</abstract><pub>IEEE</pub><doi>10.1109/NSSMIC.1994.474348</doi><oa>free_for_read</oa></addata></record>
fulltext fulltext_linktorsrc
identifier ISBN: 9780780325449
ispartof Proceedings of 1994 IEEE Nuclear Science Symposium - NSS'94, 1994, Vol.1, p.418-421 vol.1
issn
language eng
recordid cdi_ieee_primary_474348
source IEEE Electronic Library (IEL) Conference Proceedings
subjects Air cleaners
Alpha particles
Artificial neural networks
Degradation
Filters
Instruments
Laboratories
Performance evaluation
Q factor
Spectral analysis
title Alpha spectral analysis via artificial neural networks
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-26T02%3A15%3A49IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Alpha%20spectral%20analysis%20via%20artificial%20neural%20networks&rft.btitle=Proceedings%20of%201994%20IEEE%20Nuclear%20Science%20Symposium%20-%20NSS'94&rft.au=Kangas,%20L.J.&rft.date=1994&rft.volume=1&rft.spage=418&rft.epage=421%20vol.1&rft.pages=418-421%20vol.1&rft.isbn=9780780325449&rft.isbn_list=0780325443&rft_id=info:doi/10.1109/NSSMIC.1994.474348&rft_dat=%3Cieee_6IE%3E474348%3C/ieee_6IE%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-i148t-87b98b7330310a4032d7ae21a1e9133447c3979575763671338cecc50292356e3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=474348&rfr_iscdi=true