{"id":"https://openalex.org/W3209374526","doi":"https://doi.org/10.3390/s21217139","title":"Feasibility of a Neural Network-Based Virtual Sensor for Vehicle Unsprung Mass Relative Velocity Estimation","display_name":"Feasibility of a Neural Network-Based Virtual Sensor for Vehicle Unsprung Mass Relative Velocity Estimation","publication_year":2021,"publication_date":"2021-10-27","ids":{"openalex":"https://openalex.org/W3209374526","doi":"https://doi.org/10.3390/s21217139","mag":"3209374526","pmid":"https://pubmed.ncbi.nlm.nih.gov/34770447"},"language":"en","primary_location":{"id":"doi:10.3390/s21217139","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s21217139","pdf_url":"https://www.mdpi.com/1424-8220/21/21/7139/pdf?version=1635472954","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1424-8220/21/21/7139/pdf?version=1635472954","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5001406123","display_name":"Eldar \u0160abanovi\u010d","orcid":"https://orcid.org/0000-0002-4724-0787"},"institutions":[{"id":"https://openalex.org/I167420956","display_name":"Vilnius Gediminas Technical University","ror":"https://ror.org/02x3e4q36","country_code":"LT","type":"education","lineage":["https://openalex.org/I167420956"]}],"countries":["LT"],"is_corresponding":true,"raw_author_name":"Eldar \u0160abanovi\u010d","raw_affiliation_strings":["Transport and Logistics Competence Centre, Transport Engineering Faculty, Vilnius Gediminas Technical University, 10223 Vilnius, Lithuania"],"raw_orcid":"https://orcid.org/0000-0002-4724-0787","affiliations":[{"raw_affiliation_string":"Transport and Logistics Competence Centre, Transport Engineering Faculty, Vilnius Gediminas Technical University, 10223 Vilnius, Lithuania","institution_ids":["https://openalex.org/I167420956"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002113178","display_name":"Paulius Kojis","orcid":"https://orcid.org/0000-0003-0566-559X"},"institutions":[{"id":"https://openalex.org/I167420956","display_name":"Vilnius Gediminas Technical University","ror":"https://ror.org/02x3e4q36","country_code":"LT","type":"education","lineage":["https://openalex.org/I167420956"]}],"countries":["LT"],"is_corresponding":false,"raw_author_name":"Paulius Kojis","raw_affiliation_strings":["Transport and Logistics Competence Centre, Transport Engineering Faculty, Vilnius Gediminas Technical University, 10223 Vilnius, Lithuania"],"raw_orcid":"https://orcid.org/0000-0003-0566-559X","affiliations":[{"raw_affiliation_string":"Transport and Logistics Competence Centre, Transport Engineering Faculty, Vilnius Gediminas Technical University, 10223 Vilnius, Lithuania","institution_ids":["https://openalex.org/I167420956"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065647030","display_name":"\u0160ar\u016bnas \u0160ukevi\u010dius","orcid":"https://orcid.org/0000-0002-1393-1731"},"institutions":[{"id":"https://openalex.org/I167420956","display_name":"Vilnius Gediminas Technical University","ror":"https://ror.org/02x3e4q36","country_code":"LT","type":"education","lineage":["https://openalex.org/I167420956"]}],"countries":["LT"],"is_corresponding":false,"raw_author_name":"\u0160ar\u016bnas \u0160ukevi\u010dius","raw_affiliation_strings":["Department of Mobile Machinery and Railway Transport, Transport Engineering Faculty, Vilnius Gediminas Technical University, 08101 Vilnius, Lithuania"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mobile Machinery and Railway Transport, Transport Engineering Faculty, Vilnius Gediminas Technical University, 08101 Vilnius, Lithuania","institution_ids":["https://openalex.org/I167420956"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087543232","display_name":"Barys Shyrokau","orcid":"https://orcid.org/0000-0003-4530-8853"},"institutions":[{"id":"https://openalex.org/I98358874","display_name":"Delft University of Technology","ror":"https://ror.org/02e2c7k09","country_code":"NL","type":"education","lineage":["https://openalex.org/I98358874"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Barys Shyrokau","raw_affiliation_strings":["Department of Cognitive Robotics, Delft University of Technology, 2628 CD Delft, The Netherlands"],"raw_orcid":"https://orcid.org/0000-0003-4530-8853","affiliations":[{"raw_affiliation_string":"Department of Cognitive Robotics, Delft University of Technology, 2628 CD Delft, The Netherlands","institution_ids":["https://openalex.org/I98358874"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082684102","display_name":"Valentin Ivanov","orcid":"https://orcid.org/0000-0001-7252-7184"},"institutions":[{"id":"https://openalex.org/I119449181","display_name":"Technische Universit\u00e4t Ilmenau","ror":"https://ror.org/01weqhp73","country_code":"DE","type":"education","lineage":["https://openalex.org/I119449181"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Valentin Ivanov","raw_affiliation_strings":["Automotive Engineering Group, Technische Universit\u00e4t Ilmenau, 98693 Ilmenau, Germany"],"raw_orcid":"https://orcid.org/0000-0001-7252-7184","affiliations":[{"raw_affiliation_string":"Automotive Engineering Group, Technische Universit\u00e4t Ilmenau, 98693 Ilmenau, Germany","institution_ids":["https://openalex.org/I119449181"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083052483","display_name":"Miguel Dhaens","orcid":"https://orcid.org/0000-0003-3644-3340"},"institutions":[{"id":"https://openalex.org/I4210095085","display_name":"Tenneco (Belgium)","ror":"https://ror.org/00p3e1n56","country_code":"BE","type":"company","lineage":["https://openalex.org/I4210095085","https://openalex.org/I68109076"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Miguel Dhaens","raw_affiliation_strings":["Tenneco Automotive Europe, 3800 Sint-Truiden, Belgium"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tenneco Automotive Europe, 3800 Sint-Truiden, Belgium","institution_ids":["https://openalex.org/I4210095085"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000256560","display_name":"Viktor Skrickij","orcid":"https://orcid.org/0000-0002-8080-875X"},"institutions":[{"id":"https://openalex.org/I167420956","display_name":"Vilnius Gediminas Technical University","ror":"https://ror.org/02x3e4q36","country_code":"LT","type":"education","lineage":["https://openalex.org/I167420956"]}],"countries":["LT"],"is_corresponding":false,"raw_author_name":"Viktor Skrickij","raw_affiliation_strings":["Transport and Logistics Competence Centre, Transport Engineering Faculty, Vilnius Gediminas Technical University, 10223 Vilnius, Lithuania"],"raw_orcid":"https://orcid.org/0000-0002-8080-875X","affiliations":[{"raw_affiliation_string":"Transport and Logistics Competence Centre, Transport Engineering Faculty, Vilnius Gediminas Technical University, 10223 Vilnius, Lithuania","institution_ids":["https://openalex.org/I167420956"]}]}],"institutions":[],"countries_distinct_count":4,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5001406123"],"corresponding_institution_ids":["https://openalex.org/I167420956"],"apc_list":{"value":2400,"currency":"CHF","value_usd":2598},"apc_paid":{"value":2400,"currency":"CHF","value_usd":2598},"fwci":1.7586,"has_fulltext":true,"cited_by_count":17,"citation_normalized_percentile":{"value":0.83324644,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"21","issue":"21","first_page":"7139","last_page":"7139"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10534","display_name":"Structural Health Monitoring Techniques","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10534","display_name":"Structural Health Monitoring Techniques","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12564","display_name":"Sensor Technology and Measurement Systems","score":0.996399998664856,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T14304","display_name":"Transport Systems and Technology","score":0.9955999851226807,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.7221545577049255},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6008513569831848},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5384019017219543},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.510917603969574},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.4668712317943573},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4398340582847595},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.4310188293457031},{"id":"https://openalex.org/keywords/automotive-industry","display_name":"Automotive industry","score":0.4246682822704315},{"id":"https://openalex.org/keywords/soft-sensor","display_name":"Soft sensor","score":0.4131982624530792},{"id":"https://openalex.org/keywords/approximation-error","display_name":"Approximation error","score":0.4114108383655548},{"id":"https://openalex.org/keywords/simulation","display_name":"Simulation","score":0.37171420454978943},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3553503155708313},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.3431891202926636},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.275091290473938},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.1803402602672577},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09423452615737915},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.08760383725166321}],"concepts":[{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.7221545577049255},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6008513569831848},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5384019017219543},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.510917603969574},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.4668712317943573},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4398340582847595},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.4310188293457031},{"id":"https://openalex.org/C526921623","wikidata":"https://www.wikidata.org/wiki/Q190117","display_name":"Automotive industry","level":2,"score":0.4246682822704315},{"id":"https://openalex.org/C115575686","wikidata":"https://www.wikidata.org/wiki/Q18822403","display_name":"Soft sensor","level":3,"score":0.4131982624530792},{"id":"https://openalex.org/C122383733","wikidata":"https://www.wikidata.org/wiki/Q865920","display_name":"Approximation error","level":2,"score":0.4114108383655548},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.37171420454978943},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3553503155708313},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3431891202926636},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.275091290473938},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.1803402602672577},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09423452615737915},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.08760383725166321},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[{"descriptor_ui":"D001334","descriptor_name":"Automobile Driving","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D001334","descriptor_name":"Automobile Driving","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D001334","descriptor_name":"Automobile Driving","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D001334","descriptor_name":"Automobile Driving","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D001499","descriptor_name":"Bayes Theorem","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001499","descriptor_name":"Bayes Theorem","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001499","descriptor_name":"Bayes Theorem","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001499","descriptor_name":"Bayes Theorem","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D005240","descriptor_name":"Feasibility Studies","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D005240","descriptor_name":"Feasibility Studies","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D005240","descriptor_name":"Feasibility Studies","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D005240","descriptor_name":"Feasibility Studies","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D057567","descriptor_name":"Memory, Long-Term","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D057567","descriptor_name":"Memory, Long-Term","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":7,"locations":[{"id":"doi:10.3390/s21217139","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s21217139","pdf_url":"https://www.mdpi.com/1424-8220/21/21/7139/pdf?version=1635472954","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},{"id":"pmid:34770447","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/34770447","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:doaj.org/article:457fd2d432384d908f258e36fc565764","is_oa":true,"landing_page_url":"https://doaj.org/article/457fd2d432384d908f258e36fc565764","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors, Vol 21, Iss 21, p 7139 (2021)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/1424-8220/21/21/7139/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/s21217139","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors; Volume 21; Issue 21; Pages: 7139","raw_type":"Text"},{"id":"pmh:oai:pubmedcentral.nih.gov:8587321","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/8587321","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors (Basel)","raw_type":"Text"},{"id":"pmh:oai:tudelft.nl:uuid:7a893499-fa70-4744-83bd-0449d60842a5","is_oa":false,"landing_page_url":"http://resolver.tudelft.nl/uuid:7a893499-fa70-4744-83bd-0449d60842a5","pdf_url":null,"source":{"id":"https://openalex.org/S4306400906","display_name":"Research Repository (Delft University of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I98358874","host_organization_name":"Delft University of Technology","host_organization_lineage":["https://openalex.org/I98358874"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"journal article"},{"id":"pmh:oai:www.db-thueringen.de:dbt_mods_00050381","is_oa":true,"landing_page_url":"http://uri.gbv.de/document/gvk:ppn:1777402646","pdf_url":null,"source":{"id":"https://openalex.org/S4306400120","display_name":"Common Library Network (Der Gemeinsame Bibliotheksverbund)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210149878","host_organization_name":"Verbundzentrale des GBV","host_organization_lineage":["https://openalex.org/I4210149878"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/s21217139","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s21217139","pdf_url":"https://www.mdpi.com/1424-8220/21/21/7139/pdf?version=1635472954","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.6100000143051147,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[{"id":"https://openalex.org/G5540518814","display_name":"Benchmarking of Wheel Corner Concepts Towards Optimal Comfort by Automated Driving","funder_award_id":"872907","funder_id":"https://openalex.org/F4320338337","funder_display_name":"H2020 Marie Sk\u0142odowska-Curie Actions"},{"id":"https://openalex.org/G6925045906","display_name":"Benchmarking of Wheel Corner Concepts Towards Optimal Comfort by Automated Driving","funder_award_id":"872907","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"},{"id":"https://openalex.org/G7955706205","display_name":null,"funder_award_id":"Horizon 2020","funder_id":"https://openalex.org/F4320338337","funder_display_name":"H2020 Marie Sk\u0142odowska-Curie Actions"}],"funders":[{"id":"https://openalex.org/F4320320300","display_name":"European Commission","ror":"https://ror.org/00k4n6c32"},{"id":"https://openalex.org/F4320338337","display_name":"H2020 Marie Sk\u0142odowska-Curie Actions","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3209374526.pdf","grobid_xml":"https://content.openalex.org/works/W3209374526.grobid-xml"},"referenced_works_count":30,"referenced_works":["https://openalex.org/W1498289133","https://openalex.org/W1533819203","https://openalex.org/W2042052970","https://openalex.org/W2062009191","https://openalex.org/W2112831478","https://openalex.org/W2296744026","https://openalex.org/W2519926870","https://openalex.org/W2550838689","https://openalex.org/W2564533742","https://openalex.org/W2598009994","https://openalex.org/W2739799219","https://openalex.org/W2754816854","https://openalex.org/W2767985321","https://openalex.org/W2889708793","https://openalex.org/W2896872581","https://openalex.org/W2901849646","https://openalex.org/W2908305534","https://openalex.org/W2914625278","https://openalex.org/W2914952844","https://openalex.org/W2948736204","https://openalex.org/W2948853306","https://openalex.org/W2971245478","https://openalex.org/W2976534600","https://openalex.org/W3006253770","https://openalex.org/W3084293035","https://openalex.org/W3102838398","https://openalex.org/W3128538548","https://openalex.org/W3136952091","https://openalex.org/W3187255836","https://openalex.org/W4243100843"],"related_works":["https://openalex.org/W2097933059","https://openalex.org/W2361682757","https://openalex.org/W4387072315","https://openalex.org/W1571161833","https://openalex.org/W1769418564","https://openalex.org/W2180861079","https://openalex.org/W3213242382","https://openalex.org/W2102605209","https://openalex.org/W2889840829","https://openalex.org/W135180227"],"abstract_inverted_index":{"With":[0],"the":[1,44,59,68,72,99,145,179,215,220,228],"automotive":[2],"industry":[3],"moving":[4],"towards":[5],"automated":[6],"driving,":[7],"sensing":[8,55],"is":[9,32,56,79,97,195],"increasingly":[10],"important":[11],"in":[12,39,43,140],"enabling":[13],"technology.":[14],"The":[15,81,95,128,156,172,192,225],"virtual":[16,54,115,234],"sensors":[17,24,235],"allow":[18],"data":[19,147],"fusion":[20],"from":[21],"various":[22],"vehicle":[23,62,77,138,238],"and":[25,120,169,205,211],"provide":[26],"a":[27,88,114,124,190],"prediction":[28],"for":[29,58,75,151],"measurement":[30],"that":[31,98],"hard":[33],"or":[34,42],"too":[35],"expensive":[36],"to":[37,165,177,219,236],"measure":[38],"another":[40],"way":[41],"case":[45,60],"of":[46,61,71,102,197,214,231],"demand":[47],"on":[48],"continuous":[49],"detection.":[50],"In":[51,111],"this":[52,112],"paper,":[53,113],"discussed":[57],"suspension":[63],"control,":[64],"where":[65],"information":[66],"about":[67],"relative":[69,241],"velocity":[70],"unsprung":[73,239],"mass":[74,240],"each":[76],"corner":[78],"required.":[80],"corresponding":[82],"goal":[83],"can":[84,108],"be":[85],"identified":[86],"as":[87,189],"regression":[89],"task":[90],"with":[91],"multi-input":[92],"sequence":[93],"input.":[94],"hypothesis":[96],"state-of-art":[100],"method":[101],"Bidirectional":[103],"Long-Short":[104],"Term":[105],"Memory":[106],"(BiLSTM)":[107],"solve":[109],"it.":[110],"sensor":[116],"has":[117,162],"been":[118,131,163],"proposed":[119],"developed":[121],"by":[122],"training":[123],"neural":[125,181,232],"network":[126,182,194],"model.":[127],"simulations":[129],"have":[130],"performed":[132],"using":[133,184],"an":[134],"experimentally":[135],"validated":[136],"full":[137],"model":[139],"IPG":[141],"Carmaker.":[142],"Simulations":[143],"provided":[144],"reference":[146,221],"which":[148],"were":[149],"used":[150,164,176],"Neural":[152],"Network":[153],"(NN)":[154],"training.":[155],"extensive":[157],"dataset":[158],"covering":[159],"26":[160],"scenarios":[161],"obtain":[166],"training,":[167],"validation":[168],"testing":[170],"data.":[171],"Bayesian":[173],"Search":[174],"was":[175],"select":[178],"best":[180,193],"structure":[183],"root":[185],"mean":[186],"square":[187],"error":[188],"metric.":[191],"made":[196],"167":[198],"BiLSTM,":[199],"256":[200],"fully":[201],"connected":[202],"hidden":[203],"units":[204],"4":[206],"output":[207],"units.":[208],"Error":[209],"histograms":[210],"spectral":[212],"analysis":[213],"predicted":[216],"signal":[217,222],"compared":[218],"are":[223],"presented.":[224],"results":[226],"demonstrate":[227],"good":[229],"applicability":[230],"network-based":[233],"estimate":[237],"velocity.":[242]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":2}],"updated_date":"2026-06-22T08:00:12.763002","created_date":"2025-10-10T00:00:00"}