Entropy-based Inhomogeneity Detection in Fiber Materials
We study a change-point problem for random fields based on a univariate detection of outliers via the 3 σ -rule in order to recognize inhomogeneities in glass fiber reinforced polymers (GFRP). In particular, we focus on GFRP modeled by stochastic fiber processes with high fiber intensity and search...
Gespeichert in:
| Veröffentlicht in: | Methodology and computing in applied probability 2018-12, Vol.20 (4), p.1223-1239 |
|---|---|
| Hauptverfasser: | , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 1239 |
|---|---|
| container_issue | 4 |
| container_start_page | 1223 |
| container_title | Methodology and computing in applied probability |
| container_volume | 20 |
| creator | Alonso Ruiz, Patricia Spodarev, Evgeny |
| description | We study a change-point problem for random fields based on a univariate detection of outliers via the 3
σ
-rule in order to recognize inhomogeneities in glass fiber reinforced polymers (GFRP). In particular, we focus on GFRP modeled by stochastic fiber processes with high fiber intensity and search for abrupt changes in the direction of the fibers. As a measure of change, the entropy of the directional distribution is locally estimated within a window that scans the region to be analyzed. |
| doi_str_mv | 10.1007/s11009-017-9603-2 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_1968661694</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1968661694</sourcerecordid><originalsourceid>FETCH-LOGICAL-c316t-6626e8604441b0ec956cee74068b1526a5d6082c3604173bd4d1346fdbe00c393</originalsourceid><addsrcrecordid>eNp1kDFPwzAQhS0EEqXwA9giMRvuYuecjKi0UKmIBWYrca4lFY2LnQ7997gKAwvTu-F776RPiFuEewQwDxFTVBLQyIpAyfxMTLAwShqD6jzdqjSyKDVeiqsYtwA5FkpPRDnvh-D3R9nUkdts2X_6nd9wz91wzJ54YDd0vs-6Plt0DYfstR44dPVXvBYX6xR885tT8bGYv89e5OrteTl7XEmnkAZJlBOXBFprbIBdVZBjNhqobLDIqS5agjJ3KiFoVNPqFpWmddswgFOVmoq7cXcf_PeB42C3_hD69NJiRSURUqUThSPlgo8x8NruQ7erw9Ei2JMgOwqySZA9CbJ56uRjJya233D4s_xv6Qf_4WZh</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1968661694</pqid></control><display><type>article</type><title>Entropy-based Inhomogeneity Detection in Fiber Materials</title><source>Business Source Complete</source><source>SpringerNature Journals</source><creator>Alonso Ruiz, Patricia ; Spodarev, Evgeny</creator><creatorcontrib>Alonso Ruiz, Patricia ; Spodarev, Evgeny</creatorcontrib><description>We study a change-point problem for random fields based on a univariate detection of outliers via the 3
σ
-rule in order to recognize inhomogeneities in glass fiber reinforced polymers (GFRP). In particular, we focus on GFRP modeled by stochastic fiber processes with high fiber intensity and search for abrupt changes in the direction of the fibers. As a measure of change, the entropy of the directional distribution is locally estimated within a window that scans the region to be analyzed.</description><identifier>ISSN: 1387-5841</identifier><identifier>EISSN: 1573-7713</identifier><identifier>DOI: 10.1007/s11009-017-9603-2</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Business and Management ; Data analysis ; Economics ; Electrical Engineering ; Entropy ; Fiber reinforced polymers ; Glass fiber reinforced plastics ; Inhomogeneity ; Life Sciences ; Mathematics and Statistics ; Outliers (statistics) ; Statistics ; Stochastic processes</subject><ispartof>Methodology and computing in applied probability, 2018-12, Vol.20 (4), p.1223-1239</ispartof><rights>Springer Science+Business Media, LLC, part of Springer Nature 2017</rights><rights>Methodology and Computing in Applied Probability is a copyright of Springer, (2017). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c316t-6626e8604441b0ec956cee74068b1526a5d6082c3604173bd4d1346fdbe00c393</citedby><cites>FETCH-LOGICAL-c316t-6626e8604441b0ec956cee74068b1526a5d6082c3604173bd4d1346fdbe00c393</cites><orcidid>0000-0003-0618-6603</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11009-017-9603-2$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11009-017-9603-2$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>315,781,785,27929,27930,41493,42562,51324</link.rule.ids></links><search><creatorcontrib>Alonso Ruiz, Patricia</creatorcontrib><creatorcontrib>Spodarev, Evgeny</creatorcontrib><title>Entropy-based Inhomogeneity Detection in Fiber Materials</title><title>Methodology and computing in applied probability</title><addtitle>Methodol Comput Appl Probab</addtitle><description>We study a change-point problem for random fields based on a univariate detection of outliers via the 3
σ
-rule in order to recognize inhomogeneities in glass fiber reinforced polymers (GFRP). In particular, we focus on GFRP modeled by stochastic fiber processes with high fiber intensity and search for abrupt changes in the direction of the fibers. As a measure of change, the entropy of the directional distribution is locally estimated within a window that scans the region to be analyzed.</description><subject>Business and Management</subject><subject>Data analysis</subject><subject>Economics</subject><subject>Electrical Engineering</subject><subject>Entropy</subject><subject>Fiber reinforced polymers</subject><subject>Glass fiber reinforced plastics</subject><subject>Inhomogeneity</subject><subject>Life Sciences</subject><subject>Mathematics and Statistics</subject><subject>Outliers (statistics)</subject><subject>Statistics</subject><subject>Stochastic processes</subject><issn>1387-5841</issn><issn>1573-7713</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp1kDFPwzAQhS0EEqXwA9giMRvuYuecjKi0UKmIBWYrca4lFY2LnQ7997gKAwvTu-F776RPiFuEewQwDxFTVBLQyIpAyfxMTLAwShqD6jzdqjSyKDVeiqsYtwA5FkpPRDnvh-D3R9nUkdts2X_6nd9wz91wzJ54YDd0vs-6Plt0DYfstR44dPVXvBYX6xR885tT8bGYv89e5OrteTl7XEmnkAZJlBOXBFprbIBdVZBjNhqobLDIqS5agjJ3KiFoVNPqFpWmddswgFOVmoq7cXcf_PeB42C3_hD69NJiRSURUqUThSPlgo8x8NruQ7erw9Ei2JMgOwqySZA9CbJ56uRjJya233D4s_xv6Qf_4WZh</recordid><startdate>20181201</startdate><enddate>20181201</enddate><creator>Alonso Ruiz, Patricia</creator><creator>Spodarev, Evgeny</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7WY</scope><scope>7WZ</scope><scope>7XB</scope><scope>87Z</scope><scope>88I</scope><scope>8AO</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>8FL</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FRNLG</scope><scope>F~G</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K60</scope><scope>K6~</scope><scope>K7-</scope><scope>L.-</scope><scope>L6V</scope><scope>M0C</scope><scope>M2P</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYYUZ</scope><scope>Q9U</scope><orcidid>https://orcid.org/0000-0003-0618-6603</orcidid></search><sort><creationdate>20181201</creationdate><title>Entropy-based Inhomogeneity Detection in Fiber Materials</title><author>Alonso Ruiz, Patricia ; Spodarev, Evgeny</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c316t-6626e8604441b0ec956cee74068b1526a5d6082c3604173bd4d1346fdbe00c393</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Business and Management</topic><topic>Data analysis</topic><topic>Economics</topic><topic>Electrical Engineering</topic><topic>Entropy</topic><topic>Fiber reinforced polymers</topic><topic>Glass fiber reinforced plastics</topic><topic>Inhomogeneity</topic><topic>Life Sciences</topic><topic>Mathematics and Statistics</topic><topic>Outliers (statistics)</topic><topic>Statistics</topic><topic>Stochastic processes</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Alonso Ruiz, Patricia</creatorcontrib><creatorcontrib>Spodarev, Evgeny</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Access via ABI/INFORM (ProQuest)</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Global (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Business Premium Collection (Alumni)</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>Computer Science Database</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ProQuest Engineering Collection</collection><collection>ABI/INFORM Global</collection><collection>Science Database</collection><collection>Engineering Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Business</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>ABI/INFORM Collection China</collection><collection>ProQuest Central Basic</collection><jtitle>Methodology and computing in applied probability</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Alonso Ruiz, Patricia</au><au>Spodarev, Evgeny</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Entropy-based Inhomogeneity Detection in Fiber Materials</atitle><jtitle>Methodology and computing in applied probability</jtitle><stitle>Methodol Comput Appl Probab</stitle><date>2018-12-01</date><risdate>2018</risdate><volume>20</volume><issue>4</issue><spage>1223</spage><epage>1239</epage><pages>1223-1239</pages><issn>1387-5841</issn><eissn>1573-7713</eissn><abstract>We study a change-point problem for random fields based on a univariate detection of outliers via the 3
σ
-rule in order to recognize inhomogeneities in glass fiber reinforced polymers (GFRP). In particular, we focus on GFRP modeled by stochastic fiber processes with high fiber intensity and search for abrupt changes in the direction of the fibers. As a measure of change, the entropy of the directional distribution is locally estimated within a window that scans the region to be analyzed.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s11009-017-9603-2</doi><tpages>17</tpages><orcidid>https://orcid.org/0000-0003-0618-6603</orcidid></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1387-5841 |
| ispartof | Methodology and computing in applied probability, 2018-12, Vol.20 (4), p.1223-1239 |
| issn | 1387-5841 1573-7713 |
| language | eng |
| recordid | cdi_proquest_journals_1968661694 |
| source | Business Source Complete; SpringerNature Journals |
| subjects | Business and Management Data analysis Economics Electrical Engineering Entropy Fiber reinforced polymers Glass fiber reinforced plastics Inhomogeneity Life Sciences Mathematics and Statistics Outliers (statistics) Statistics Stochastic processes |
| title | Entropy-based Inhomogeneity Detection in Fiber Materials |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-12T18%3A45%3A04IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Entropy-based%20Inhomogeneity%20Detection%20in%20Fiber%20Materials&rft.jtitle=Methodology%20and%20computing%20in%20applied%20probability&rft.au=Alonso%20Ruiz,%20Patricia&rft.date=2018-12-01&rft.volume=20&rft.issue=4&rft.spage=1223&rft.epage=1239&rft.pages=1223-1239&rft.issn=1387-5841&rft.eissn=1573-7713&rft_id=info:doi/10.1007/s11009-017-9603-2&rft_dat=%3Cproquest_cross%3E1968661694%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1968661694&rft_id=info:pmid/&rfr_iscdi=true |