{"id":"https://openalex.org/W2395395188","doi":"https://doi.org/10.1109/tase.2016.2566579","title":"Bayesian Hierarchical Linear Modeling of Profile Data With Applications to Quality Control of Nanomanufacturing","display_name":"Bayesian Hierarchical Linear Modeling of Profile Data With Applications to Quality Control of Nanomanufacturing","publication_year":2016,"publication_date":"2016-05-25","ids":{"openalex":"https://openalex.org/W2395395188","doi":"https://doi.org/10.1109/tase.2016.2566579","mag":"2395395188"},"language":"en","primary_location":{"id":"doi:10.1109/tase.2016.2566579","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tase.2016.2566579","pdf_url":null,"source":{"id":"https://openalex.org/S34881539","display_name":"IEEE Transactions on Automation Science and Engineering","issn_l":"1545-5955","issn":["1545-5955","1558-3783"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Automation Science and Engineering","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Jianguo Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I164936912","display_name":"The University of Texas at El Paso","ror":"https://ror.org/04d5vba33","country_code":"US","type":"education","lineage":["https://openalex.org/I164936912"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Jianguo Wu","raw_affiliation_strings":["Department of Industrial, Manufacturing and Systems Engineering, University of Texas at El Paso, El Paso, TX, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Industrial, Manufacturing and Systems Engineering, University of Texas at El Paso, El Paso, TX, USA","institution_ids":["https://openalex.org/I164936912"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100420966","display_name":"Yuhang Liu","orcid":"https://orcid.org/0000-0002-8195-9349"},"institutions":[{"id":"https://openalex.org/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yuhang Liu","raw_affiliation_strings":["Department of Industrial and Systems Engineering, University of Wisconsin\u2013Madison, Madison, WI, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Industrial and Systems Engineering, University of Wisconsin\u2013Madison, Madison, WI, USA","institution_ids":["https://openalex.org/I135310074"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000499123","display_name":"Shiyu Zhou","orcid":"https://orcid.org/0000-0002-5902-8812"},"institutions":[{"id":"https://openalex.org/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shiyu Zhou","raw_affiliation_strings":["Department of Industrial and Systems Engineering, University of Wisconsin\u2013Madison, Madison, WI, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Industrial and Systems Engineering, University of Wisconsin\u2013Madison, Madison, WI, USA","institution_ids":["https://openalex.org/I135310074"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I164936912"],"apc_list":null,"apc_paid":null,"fwci":1.1187,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.81726701,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"13","issue":"3","first_page":"1355","last_page":"1366"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11798","display_name":"Optimal Experimental Design Methods","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11798","display_name":"Optimal Experimental Design Methods","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10928","display_name":"Probabilistic and Robust Engineering Design","score":0.9973000288009644,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11443","display_name":"Advanced Statistical Process Monitoring","score":0.9966999888420105,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/gibbs-sampling","display_name":"Gibbs sampling","score":0.7375072240829468},{"id":"https://openalex.org/keywords/nanomanufacturing","display_name":"Nanomanufacturing","score":0.6117174625396729},{"id":"https://openalex.org/keywords/bayesian-hierarchical-modeling","display_name":"Bayesian hierarchical modeling","score":0.5866507291793823},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.5422157049179077},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5200940370559692},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4639086127281189},{"id":"https://openalex.org/keywords/hierarchical-database-model","display_name":"Hierarchical database model","score":0.4624398648738861},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4564012587070465},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.31768298149108887},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2608987092971802},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.22130104899406433},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.09008759260177612}],"concepts":[{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.7375072240829468},{"id":"https://openalex.org/C46312889","wikidata":"https://www.wikidata.org/wiki/Q950502","display_name":"Nanomanufacturing","level":2,"score":0.6117174625396729},{"id":"https://openalex.org/C191413810","wikidata":"https://www.wikidata.org/wiki/Q17100952","display_name":"Bayesian hierarchical modeling","level":4,"score":0.5866507291793823},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.5422157049179077},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5200940370559692},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4639086127281189},{"id":"https://openalex.org/C144986985","wikidata":"https://www.wikidata.org/wiki/Q871236","display_name":"Hierarchical database model","level":2,"score":0.4624398648738861},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4564012587070465},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.31768298149108887},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2608987092971802},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.22130104899406433},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.09008759260177612},{"id":"https://openalex.org/C171250308","wikidata":"https://www.wikidata.org/wiki/Q11468","display_name":"Nanotechnology","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tase.2016.2566579","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tase.2016.2566579","pdf_url":null,"source":{"id":"https://openalex.org/S34881539","display_name":"IEEE Transactions on Automation Science and Engineering","issn_l":"1545-5955","issn":["1545-5955","1558-3783"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Automation Science and Engineering","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5434427093","display_name":null,"funder_award_id":"1335129","funder_id":"https://openalex.org/F4320337391","funder_display_name":"Division of Civil, Mechanical and Manufacturing Innovation"},{"id":"https://openalex.org/G6336281962","display_name":null,"funder_award_id":"FA9550-14-1-0384","funder_id":"https://openalex.org/F4320338279","funder_display_name":"Air Force Office of Scientific Research"}],"funders":[{"id":"https://openalex.org/F4320337391","display_name":"Division of Civil, Mechanical and Manufacturing Innovation","ror":"https://ror.org/028yd4c30"},{"id":"https://openalex.org/F4320338279","display_name":"Air Force Office of Scientific Research","ror":"https://ror.org/011e9bt93"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W21376189","https://openalex.org/W142396771","https://openalex.org/W1536497620","https://openalex.org/W1567519924","https://openalex.org/W1580673759","https://openalex.org/W1587094587","https://openalex.org/W1965627204","https://openalex.org/W1971714913","https://openalex.org/W1974315578","https://openalex.org/W1985985262","https://openalex.org/W1995443897","https://openalex.org/W2017334578","https://openalex.org/W2026487916","https://openalex.org/W2030769837","https://openalex.org/W2035332568","https://openalex.org/W2047102900","https://openalex.org/W2050399235","https://openalex.org/W2058815839","https://openalex.org/W2059006551","https://openalex.org/W2069739265","https://openalex.org/W2078542139","https://openalex.org/W2085289178","https://openalex.org/W2101122849","https://openalex.org/W2108207895","https://openalex.org/W2117213113","https://openalex.org/W2120344087","https://openalex.org/W2123736748","https://openalex.org/W2131969788","https://openalex.org/W2138928008","https://openalex.org/W2140327046","https://openalex.org/W2158672897","https://openalex.org/W2162898443","https://openalex.org/W2168175751","https://openalex.org/W2316241185","https://openalex.org/W2324392187","https://openalex.org/W2500845915","https://openalex.org/W2977620188","https://openalex.org/W3100910045","https://openalex.org/W4206439534","https://openalex.org/W4230118410","https://openalex.org/W4240135405","https://openalex.org/W4245186283","https://openalex.org/W4255582690"],"related_works":["https://openalex.org/W3006235400","https://openalex.org/W2146501959","https://openalex.org/W2057317509","https://openalex.org/W1548970483","https://openalex.org/W2266006640","https://openalex.org/W2037795434","https://openalex.org/W4213439144","https://openalex.org/W2601647274","https://openalex.org/W4372278488","https://openalex.org/W2890047383"],"abstract_inverted_index":{"In":[0,47],"this":[1],"paper,":[2],"we":[3],"propose":[4],"to":[5,19,31,59],"use":[6],"the":[7,21,33,61,70,74,88,106],"multiple":[8],"ultrasonic":[9,64,67],"attenuation":[10,65],"profiles":[11],"measured":[12],"at":[13],"different":[14],"locations":[15],"of":[16,23,73,87,105],"a":[17,28,49],"specimen":[18],"infer":[20],"microstructure":[22],"metal-matrix":[24],"nanocomposites.":[25,75],"We":[26],"present":[27],"general":[29],"framework":[30,79],"connect":[32],"profile":[34],"data":[35],"with":[36,53],"both":[37],"explanatory":[38],"variables":[39],"and":[40,69,85,100],"product":[41],"quality":[42,45],"parameters":[43,72,90],"for":[44,80],"inference.":[46],"particular,":[48],"hierarchical":[50],"linear":[51],"model":[52,60,81,83],"level-2":[54],"variance":[55],"heterogeneity":[56],"is":[57,91,109],"proposed":[58,92,107],"relationship":[62],"between":[63],"profiles,":[66],"frequency,":[68],"microstructural":[71,89],"An":[76],"integrated":[77],"Bayesian":[78],"estimation,":[82],"selection,":[84],"inference":[86],"through":[93,111],"blocked":[94],"Gibbs":[95],"sampling,":[96],"intrinsic":[97],"Bayes":[98],"factor,":[99],"importance":[101],"sampling.":[102],"The":[103],"effectiveness":[104],"approach":[108],"illustrated":[110],"case":[112],"studies.":[113]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":2},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":2}],"updated_date":"2026-04-28T14:05:53.105641","created_date":"2025-10-10T00:00:00"}