{"id":"https://openalex.org/W6926168946","doi":"https://doi.org/10.21227/8nnk-kd35","title":"Fluid concentration estimation","display_name":"Fluid concentration estimation","publication_year":2022,"publication_date":"2022-03-19","ids":{"openalex":"https://openalex.org/W6926168946","doi":"https://doi.org/10.21227/8nnk-kd35"},"language":"en","primary_location":{"id":"doi:10.21227/8nnk-kd35","is_oa":true,"landing_page_url":"https://doi.org/10.21227/8nnk-kd35","pdf_url":null,"source":{"id":"https://openalex.org/S7407051695","display_name":"IEEE DataPort","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Dataset"},"type":"dataset","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.21227/8nnk-kd35","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Thati, Venu Babu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Thati, Venu Babu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Zaaroura, Ibrahim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zaaroura, Ibrahim","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Toubal, Malika","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Toubal, Malika","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Smagin, Nikolay","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Smagin, Nikolay","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Harmand, Souad","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Harmand, Souad","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Carlier, Julien","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Carlier, Julien","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Alouani, Ihsen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alouani, Ihsen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":null,"topics":[],"keywords":[{"id":"https://openalex.org/keywords/reflection-coefficient","display_name":"Reflection coefficient","score":0.6485999822616577},{"id":"https://openalex.org/keywords/reflection","display_name":"Reflection (computer programming)","score":0.632099986076355},{"id":"https://openalex.org/keywords/correlation-coefficient","display_name":"Correlation coefficient","score":0.5708000063896179},{"id":"https://openalex.org/keywords/sensitivity","display_name":"Sensitivity (control systems)","score":0.515500009059906},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.48649999499320984},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4690999984741211}],"concepts":[{"id":"https://openalex.org/C41700454","wikidata":"https://www.wikidata.org/wiki/Q1852282","display_name":"Reflection coefficient","level":2,"score":0.6485999822616577},{"id":"https://openalex.org/C65682993","wikidata":"https://www.wikidata.org/wiki/Q1056451","display_name":"Reflection (computer programming)","level":2,"score":0.632099986076355},{"id":"https://openalex.org/C2780092901","wikidata":"https://www.wikidata.org/wiki/Q3433612","display_name":"Correlation coefficient","level":2,"score":0.5708000063896179},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.515500009059906},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.48649999499320984},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4690999984741211},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.42419999837875366},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.42089998722076416},{"id":"https://openalex.org/C24890656","wikidata":"https://www.wikidata.org/wiki/Q82811","display_name":"Acoustics","level":1,"score":0.41530001163482666},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.3246000111103058},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.2928999960422516},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.2782000005245209},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.25679999589920044},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.2515999972820282},{"id":"https://openalex.org/C108597893","wikidata":"https://www.wikidata.org/wiki/Q663650","display_name":"Reflectivity","level":2,"score":0.2502000033855438}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.21227/8nnk-kd35","is_oa":true,"landing_page_url":"https://doi.org/10.21227/8nnk-kd35","pdf_url":null,"source":{"id":"https://openalex.org/S7407051695","display_name":"IEEE DataPort","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Dataset"}],"best_oa_location":{"id":"doi:10.21227/8nnk-kd35","is_oa":true,"landing_page_url":"https://doi.org/10.21227/8nnk-kd35","pdf_url":null,"source":{"id":"https://openalex.org/S7407051695","display_name":"IEEE DataPort","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Dataset"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Using":[0],"acoustic":[1,106,133],"waves":[2],"to":[3,12,51,82,97,123],"estimate":[4,83,124],"fluid":[5,84,125],"concentration":[6,126],"is":[7,121],"a":[8,71],"promising":[9,110],"technology":[10],"due":[11],"its":[13],"practicality":[14],"and":[15,35],"non-intrusive":[16],"aspect,":[17],"especially":[18],"for":[19],"medical":[20,66],"applications.":[21,67],"The":[22],"existing":[23],"approaches":[24],"are":[25,41],"exclusively":[26],"based":[27,101],"on":[28,102],"the":[29,32,36,44,48,90,103,114],"correlation":[30],"between":[31],"reflection":[33,49,91],"coefficient":[34,50],"concentration.":[37,85],"However,":[38],"these":[39],"techniques":[40],"limited":[42],"by":[43],"high":[45],"sensitivity":[46],"of":[47,87],"environmental":[52],"conditions":[53],"changes,":[54],"even":[55],"slight":[56],"ones.":[57],"This":[58,68],"introduces":[59],"inaccuracies":[60],"that":[61,112,120],"cannot":[62],"be":[63],"tolerated":[64],"in":[65],"paper":[69],"proposed":[70],"deep":[72],"learning":[73],"model,":[74],"Fluid":[75],"Concentration":[76],"Estimation":[77],"Convolutional":[78],"Neural":[79],"Network":[80],"(FCE-CNN),":[81],"Instead":[86],"using":[88,130],"only":[89],"coefficient,":[92],"we":[93],"train":[94],"our":[95,118],"model":[96,119],"detect":[98],"concentration-related":[99],"patterns":[100],"whole":[104],"received":[105],"signal.":[107],"FCE-CNN":[108],"shows":[109],"results":[111],"overcome":[113],"state-of-the-art":[115],"limitations.":[116],"Specifically,":[117],"able":[122],"with":[127],"$98.5\\%$":[128],"accuracy":[129],"ultra":[131],"high-frequency":[132],"waves.":[134]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2025-10-10T00:00:00"}