{"id":"https://openalex.org/W2906446674","doi":"https://doi.org/10.1080/24725854.2018.1540901","title":"An adaptive fused sampling approach of high-accuracy data in the presence of low-accuracy data","display_name":"An adaptive fused sampling approach of high-accuracy data in the presence of low-accuracy data","publication_year":2018,"publication_date":"2018-12-20","ids":{"openalex":"https://openalex.org/W2906446674","doi":"https://doi.org/10.1080/24725854.2018.1540901","mag":"2906446674"},"language":"en","primary_location":{"id":"doi:10.1080/24725854.2018.1540901","is_oa":false,"landing_page_url":"https://doi.org/10.1080/24725854.2018.1540901","pdf_url":null,"source":{"id":"https://openalex.org/S4210225672","display_name":"IISE Transactions","issn_l":"2472-5854","issn":["2472-5854","2472-5862"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IISE Transactions","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://hdl.handle.net/11311/1071013","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5068140539","display_name":"Mostafa Reisi Gahrooei","orcid":"https://orcid.org/0000-0002-7633-9575"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mostafa Reisi Gahrooei","raw_affiliation_strings":["Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA;"],"affiliations":[{"raw_affiliation_string":"Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA;","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081319947","display_name":"Kamaran Paynabar","orcid":null},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Kamaran Paynabar","raw_affiliation_strings":["Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA;"],"affiliations":[{"raw_affiliation_string":"Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA;","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015596492","display_name":"Massimo Pacella","orcid":"https://orcid.org/0000-0002-3712-7932"},"institutions":[{"id":"https://openalex.org/I142910587","display_name":"University of Salento","ror":"https://ror.org/03fc1k060","country_code":"IT","type":"education","lineage":["https://openalex.org/I142910587"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Massimo Pacella","raw_affiliation_strings":["Universita del Salento, Lecce, Italy;"],"affiliations":[{"raw_affiliation_string":"Universita del Salento, Lecce, Italy;","institution_ids":["https://openalex.org/I142910587"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032398347","display_name":"Bianca Maria Colosimo","orcid":"https://orcid.org/0000-0001-6844-2030"},"institutions":[{"id":"https://openalex.org/I93860229","display_name":"Politecnico di Milano","ror":"https://ror.org/01nffqt88","country_code":"IT","type":"education","lineage":["https://openalex.org/I93860229"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Bianca Maria Colosimo","raw_affiliation_strings":["Politecnico di Milano"],"affiliations":[{"raw_affiliation_string":"Politecnico di Milano","institution_ids":["https://openalex.org/I93860229"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5081319947"],"corresponding_institution_ids":["https://openalex.org/I130701444"],"apc_list":null,"apc_paid":null,"fwci":0.8077,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.76343744,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"51","issue":"11","first_page":"1251","last_page":"1264"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10928","display_name":"Probabilistic and Robust Engineering Design","score":0.9986000061035156,"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/T11798","display_name":"Optimal Experimental Design Methods","score":0.9970999956130981,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7233585119247437},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.6315680742263794},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.5513496398925781},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5334575176239014},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.48619168996810913},{"id":"https://openalex.org/keywords/data-point","display_name":"Data point","score":0.45262688398361206},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.42699581384658813},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3631860017776489},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.22225242853164673},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1490815281867981}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7233585119247437},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6315680742263794},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.5513496398925781},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5334575176239014},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.48619168996810913},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.45262688398361206},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.42699581384658813},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3631860017776489},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.22225242853164673},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1490815281867981},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1080/24725854.2018.1540901","is_oa":false,"landing_page_url":"https://doi.org/10.1080/24725854.2018.1540901","pdf_url":null,"source":{"id":"https://openalex.org/S4210225672","display_name":"IISE Transactions","issn_l":"2472-5854","issn":["2472-5854","2472-5862"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IISE Transactions","raw_type":"journal-article"},{"id":"pmh:oai:RePEc:taf:uiiexx:v:51:y:2019:i:11:p:1251-1264","is_oa":false,"landing_page_url":"http://hdl.handle.net/10.1080/24725854.2018.1540901","pdf_url":null,"source":{"id":"https://openalex.org/S4306401271","display_name":"RePEc: Research Papers in Economics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I77793887","host_organization_name":"Federal Reserve Bank of St. Louis","host_organization_lineage":["https://openalex.org/I77793887"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},{"id":"pmh:oai:re.public.polimi.it:11311/1071013","is_oa":true,"landing_page_url":"http://hdl.handle.net/11311/1071013","pdf_url":null,"source":{"id":"https://openalex.org/S4306400312","display_name":"Virtual Community of Pathological Anatomy (University of Castilla La Mancha)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I79189158","host_organization_name":"University of Castilla-La Mancha","host_organization_lineage":["https://openalex.org/I79189158"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"pmh:oai:re.public.polimi.it:11311/1071013","is_oa":true,"landing_page_url":"http://hdl.handle.net/11311/1071013","pdf_url":null,"source":{"id":"https://openalex.org/S4306400312","display_name":"Virtual Community of Pathological Anatomy (University of Castilla La Mancha)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I79189158","host_organization_name":"University of Castilla-La Mancha","host_organization_lineage":["https://openalex.org/I79189158"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/article"},"sustainable_development_goals":[{"score":0.4099999964237213,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W1584066809","https://openalex.org/W1746819321","https://openalex.org/W1972777769","https://openalex.org/W1973333099","https://openalex.org/W1976815376","https://openalex.org/W1977046327","https://openalex.org/W1982886636","https://openalex.org/W1991413021","https://openalex.org/W1999601178","https://openalex.org/W2018044188","https://openalex.org/W2024060531","https://openalex.org/W2038669746","https://openalex.org/W2044458183","https://openalex.org/W2046790483","https://openalex.org/W2053934160","https://openalex.org/W2056145269","https://openalex.org/W2069372244","https://openalex.org/W2073401015","https://openalex.org/W2079608295","https://openalex.org/W2083837282","https://openalex.org/W2092262344","https://openalex.org/W2093347532","https://openalex.org/W2103368439","https://openalex.org/W2106390386","https://openalex.org/W2168464387","https://openalex.org/W2186850983","https://openalex.org/W2294006238","https://openalex.org/W2755155569","https://openalex.org/W4211049957","https://openalex.org/W4246916037","https://openalex.org/W4249753629"],"related_works":["https://openalex.org/W1185300216","https://openalex.org/W2954163146","https://openalex.org/W2896057011","https://openalex.org/W2899086345","https://openalex.org/W4238675884","https://openalex.org/W3033465211","https://openalex.org/W1017189767","https://openalex.org/W1971600963","https://openalex.org/W74450112","https://openalex.org/W2042102171"],"abstract_inverted_index":{"In":[0],"several":[1],"applications,":[2],"a":[3,14,29,33,45,111,150],"large":[4],"amount":[5],"of":[6,32,48,95,128,162,179],"Low-Accuracy":[7],"(LA)":[8],"data":[9,23,71,98,110,134,142,158],"can":[10],"be":[11],"acquired":[12],"at":[13],"small":[15,46],"cost.":[16],"However,":[17],"in":[18,183],"many":[19],"situations,":[20],"such":[21],"LA":[22,43,62,109,133],"is":[24,53,57,72,105,116,166],"not":[25],"sufficient":[26],"for":[27],"generating":[28,184],"higidelity":[30],"model":[31,40,115,188],"system.":[34],"To":[35],"adjust":[36],"and":[37,75,100,144,171],"improve":[38],"the":[39,61,69,87,93,108,129,138,155,163,177,180],"constructed":[41],"by":[42,132],"data,":[44,51],"sample":[47,86],"High-Accuracy":[49],"(HA)":[50],"which":[52],"expensive":[54],"to":[55,85,153,191],"obtain,":[56],"usually":[58],"fused":[59],"with":[60,107],"data.":[63,89],"Unfortunately,":[64],"current":[65],"techniques":[66],"assume":[67],"that":[68,125],"HA":[70,88,97,141,157],"already":[73],"collected":[74],"concentrate":[76],"on":[77,83],"fusion":[78],"strategies,":[79],"without":[80],"providing":[81],"guidelines":[82],"how":[84],"This":[90],"work":[91],"addresses":[92],"problem":[94],"collecting":[96],"adaptively":[99],"sequentially":[101],"so":[102],"when":[103,189],"it":[104],"integrated":[106],"more":[112],"accurate":[113,186],"surrogate":[114,187],"achieved.":[117],"For":[118],"this":[119],"purpose,":[120],"we":[121],"propose":[122],"an":[123,146,185],"approach":[124],"takes":[126],"advantage":[127],"information":[130],"provided":[131],"as":[135,137],"well":[136],"previously":[139],"selected":[140],"points":[143],"computes":[145],"improvement":[147],"criterion":[148],"over":[149],"design":[151],"space":[152],"choose":[154],"next":[156],"point.":[159],"The":[160,174],"performance":[161],"proposed":[164,181],"method":[165,182],"evaluated,":[167],"using":[168],"both":[169],"simulation":[170],"case":[172],"studies.":[173],"results":[175],"show":[176],"benefits":[178],"compared":[190],"three":[192],"other":[193],"benchmarks.":[194]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
