Loading…
Decomposition of permittivity contributions from reflectance using mechanism models
In this paper, we investigate the properties of a complex nonmagnetic material through the reflectance, where the permittivity is described by a mechanism model in which an unknown probability measure is placed on the model parameters. Specifically, we consider whether or not this unknown probabilit...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Conference Proceeding |
| Language: | English |
| Subjects: | |
| Online Access: | Request full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| cited_by | |
|---|---|
| cites | |
| container_end_page | 372 |
| container_issue | |
| container_start_page | 367 |
| container_title | |
| container_volume | |
| creator | Banks, H. T. Catenacci, Jared Shuhua Hu Kenz, Zackary R. |
| description | In this paper, we investigate the properties of a complex nonmagnetic material through the reflectance, where the permittivity is described by a mechanism model in which an unknown probability measure is placed on the model parameters. Specifically, we consider whether or not this unknown probability measure can be determined from the reflectance or the derivatives of the reflectance, and we also investigate the effect of measurement noise on the estimation. The numerical results demonstrate that if only the reflectance can be observed, then the distribution form cannot be recovered even in the case where the measurement noise level is small. However, if both the reflectance and the derivative of the reflectance can be observed, then the estimated distribution is reasonably close to the true one even in the case where the measurement noise level is relatively high. |
| doi_str_mv | 10.1109/ACC.2014.6858639 |
| format | conference_proceeding |
| fullrecord | <record><control><sourceid>ieee_CHZPO</sourceid><recordid>TN_cdi_ieee_primary_6858639</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>6858639</ieee_id><sourcerecordid>6858639</sourcerecordid><originalsourceid>FETCH-LOGICAL-i217t-b9a1040400fd86a82e4275f216b37728fb38819b8f61baa0dad6dd1ea80efcdb3</originalsourceid><addsrcrecordid>eNpFkEtLAzEUheMLbKt7wU3-wNTcZJrHsoxPKLhQwV1JJjca6UyGSSr031uxIGdxFh98HA4hV8DmAMzcLJtmzhnUc6kXWgpzRKZQK2MEV_B-TCZcKF3tCZz8A65PyYSpWlQgwZyTac5fjIExkk3Iyy22qRtSjiWmnqZABxy7WEr8jmVH29SXMbrtL8w0jKmjI4YNtsX2LdJtjv0H7bD9tH3MHe2Sx02-IGfBbjJeHnpG3u7vXpvHavX88NQsV1XkoErljAVW78OC19JqjjVXi8BBOqH2q4MTWoNxOkhw1jJvvfQe0GqGofVOzMj1nzci4noYY2fH3frwjPgBeMRV0Q</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Decomposition of permittivity contributions from reflectance using mechanism models</title><source>IEEE Xplore All Conference Series</source><creator>Banks, H. T. ; Catenacci, Jared ; Shuhua Hu ; Kenz, Zackary R.</creator><creatorcontrib>Banks, H. T. ; Catenacci, Jared ; Shuhua Hu ; Kenz, Zackary R.</creatorcontrib><description>In this paper, we investigate the properties of a complex nonmagnetic material through the reflectance, where the permittivity is described by a mechanism model in which an unknown probability measure is placed on the model parameters. Specifically, we consider whether or not this unknown probability measure can be determined from the reflectance or the derivatives of the reflectance, and we also investigate the effect of measurement noise on the estimation. The numerical results demonstrate that if only the reflectance can be observed, then the distribution form cannot be recovered even in the case where the measurement noise level is small. However, if both the reflectance and the derivative of the reflectance can be observed, then the estimated distribution is reasonably close to the true one even in the case where the measurement noise level is relatively high.</description><identifier>ISSN: 0743-1619</identifier><identifier>ISBN: 1479932728</identifier><identifier>ISBN: 9781479932726</identifier><identifier>EISSN: 2378-5861</identifier><identifier>EISBN: 147993271X</identifier><identifier>EISBN: 9781479932740</identifier><identifier>EISBN: 9781479932719</identifier><identifier>EISBN: 1479932744</identifier><identifier>DOI: 10.1109/ACC.2014.6858639</identifier><language>eng</language><publisher>American Automatic Control Council</publisher><subject>Computational methods ; Data models ; Distributed parameter systems ; Estimation ; Least squares approximations ; Materials ; Mathematical model ; Noise level ; Permittivity</subject><ispartof>2014 American Control Conference, 2014, p.367-372</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/6858639$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54555,54920,54932</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6858639$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Banks, H. T.</creatorcontrib><creatorcontrib>Catenacci, Jared</creatorcontrib><creatorcontrib>Shuhua Hu</creatorcontrib><creatorcontrib>Kenz, Zackary R.</creatorcontrib><title>Decomposition of permittivity contributions from reflectance using mechanism models</title><title>2014 American Control Conference</title><addtitle>ACC</addtitle><description>In this paper, we investigate the properties of a complex nonmagnetic material through the reflectance, where the permittivity is described by a mechanism model in which an unknown probability measure is placed on the model parameters. Specifically, we consider whether or not this unknown probability measure can be determined from the reflectance or the derivatives of the reflectance, and we also investigate the effect of measurement noise on the estimation. The numerical results demonstrate that if only the reflectance can be observed, then the distribution form cannot be recovered even in the case where the measurement noise level is small. However, if both the reflectance and the derivative of the reflectance can be observed, then the estimated distribution is reasonably close to the true one even in the case where the measurement noise level is relatively high.</description><subject>Computational methods</subject><subject>Data models</subject><subject>Distributed parameter systems</subject><subject>Estimation</subject><subject>Least squares approximations</subject><subject>Materials</subject><subject>Mathematical model</subject><subject>Noise level</subject><subject>Permittivity</subject><issn>0743-1619</issn><issn>2378-5861</issn><isbn>1479932728</isbn><isbn>9781479932726</isbn><isbn>147993271X</isbn><isbn>9781479932740</isbn><isbn>9781479932719</isbn><isbn>1479932744</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2014</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNpFkEtLAzEUheMLbKt7wU3-wNTcZJrHsoxPKLhQwV1JJjca6UyGSSr031uxIGdxFh98HA4hV8DmAMzcLJtmzhnUc6kXWgpzRKZQK2MEV_B-TCZcKF3tCZz8A65PyYSpWlQgwZyTac5fjIExkk3Iyy22qRtSjiWmnqZABxy7WEr8jmVH29SXMbrtL8w0jKmjI4YNtsX2LdJtjv0H7bD9tH3MHe2Sx02-IGfBbjJeHnpG3u7vXpvHavX88NQsV1XkoErljAVW78OC19JqjjVXi8BBOqH2q4MTWoNxOkhw1jJvvfQe0GqGofVOzMj1nzci4noYY2fH3frwjPgBeMRV0Q</recordid><startdate>20140101</startdate><enddate>20140101</enddate><creator>Banks, H. T.</creator><creator>Catenacci, Jared</creator><creator>Shuhua Hu</creator><creator>Kenz, Zackary R.</creator><general>American Automatic Control Council</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>20140101</creationdate><title>Decomposition of permittivity contributions from reflectance using mechanism models</title><author>Banks, H. T. ; Catenacci, Jared ; Shuhua Hu ; Kenz, Zackary R.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i217t-b9a1040400fd86a82e4275f216b37728fb38819b8f61baa0dad6dd1ea80efcdb3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Computational methods</topic><topic>Data models</topic><topic>Distributed parameter systems</topic><topic>Estimation</topic><topic>Least squares approximations</topic><topic>Materials</topic><topic>Mathematical model</topic><topic>Noise level</topic><topic>Permittivity</topic><toplevel>online_resources</toplevel><creatorcontrib>Banks, H. T.</creatorcontrib><creatorcontrib>Catenacci, Jared</creatorcontrib><creatorcontrib>Shuhua Hu</creatorcontrib><creatorcontrib>Kenz, Zackary R.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 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) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Banks, H. T.</au><au>Catenacci, Jared</au><au>Shuhua Hu</au><au>Kenz, Zackary R.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Decomposition of permittivity contributions from reflectance using mechanism models</atitle><btitle>2014 American Control Conference</btitle><stitle>ACC</stitle><date>2014-01-01</date><risdate>2014</risdate><spage>367</spage><epage>372</epage><pages>367-372</pages><issn>0743-1619</issn><eissn>2378-5861</eissn><isbn>1479932728</isbn><isbn>9781479932726</isbn><eisbn>147993271X</eisbn><eisbn>9781479932740</eisbn><eisbn>9781479932719</eisbn><eisbn>1479932744</eisbn><abstract>In this paper, we investigate the properties of a complex nonmagnetic material through the reflectance, where the permittivity is described by a mechanism model in which an unknown probability measure is placed on the model parameters. Specifically, we consider whether or not this unknown probability measure can be determined from the reflectance or the derivatives of the reflectance, and we also investigate the effect of measurement noise on the estimation. The numerical results demonstrate that if only the reflectance can be observed, then the distribution form cannot be recovered even in the case where the measurement noise level is small. However, if both the reflectance and the derivative of the reflectance can be observed, then the estimated distribution is reasonably close to the true one even in the case where the measurement noise level is relatively high.</abstract><pub>American Automatic Control Council</pub><doi>10.1109/ACC.2014.6858639</doi><tpages>6</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 0743-1619 |
| ispartof | 2014 American Control Conference, 2014, p.367-372 |
| issn | 0743-1619 2378-5861 |
| language | eng |
| recordid | cdi_ieee_primary_6858639 |
| source | IEEE Xplore All Conference Series |
| subjects | Computational methods Data models Distributed parameter systems Estimation Least squares approximations Materials Mathematical model Noise level Permittivity |
| title | Decomposition of permittivity contributions from reflectance using mechanism models |
| url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-01T20%3A53%3A28IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_CHZPO&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Decomposition%20of%20permittivity%20contributions%20from%20reflectance%20using%20mechanism%20models&rft.btitle=2014%20American%20Control%20Conference&rft.au=Banks,%20H.%20T.&rft.date=2014-01-01&rft.spage=367&rft.epage=372&rft.pages=367-372&rft.issn=0743-1619&rft.eissn=2378-5861&rft.isbn=1479932728&rft.isbn_list=9781479932726&rft_id=info:doi/10.1109/ACC.2014.6858639&rft.eisbn=147993271X&rft.eisbn_list=9781479932740&rft.eisbn_list=9781479932719&rft.eisbn_list=1479932744&rft_dat=%3Cieee_CHZPO%3E6858639%3C/ieee_CHZPO%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-i217t-b9a1040400fd86a82e4275f216b37728fb38819b8f61baa0dad6dd1ea80efcdb3%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=6858639&rfr_iscdi=true |