{"id":"https://openalex.org/W2018243080","doi":"https://doi.org/10.1109/sii.2012.6427328","title":"Noise-resistant vascular parameter identification for artery testing","display_name":"Noise-resistant vascular parameter identification for artery testing","publication_year":2012,"publication_date":"2012-12-01","ids":{"openalex":"https://openalex.org/W2018243080","doi":"https://doi.org/10.1109/sii.2012.6427328","mag":"2018243080"},"language":"en","primary_location":{"id":"doi:10.1109/sii.2012.6427328","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sii.2012.6427328","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE/SICE International Symposium on System Integration (SII)","raw_type":"proceedings-article"},"type":"conference-paper","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":"https://openalex.org/A5088978814","display_name":"Hayato Koba","orcid":"https://orcid.org/0000-0002-7443-5432"},"institutions":[{"id":"https://openalex.org/I146399215","display_name":"University of Tsukuba","ror":"https://ror.org/02956yf07","country_code":"JP","type":"education","lineage":["https://openalex.org/I146399215"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hayato Koba","raw_affiliation_strings":["Cybernics Laboratory, Systems and Information Engineering, University of Tsukuba, Ibaraki, Japan","Cybernics Laboratory, Systems and Information Engineering, University of Tsukuba, Ibaraki 3050006 Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Cybernics Laboratory, Systems and Information Engineering, University of Tsukuba, Ibaraki, Japan","institution_ids":["https://openalex.org/I146399215"]},{"raw_affiliation_string":"Cybernics Laboratory, Systems and Information Engineering, University of Tsukuba, Ibaraki 3050006 Japan","institution_ids":["https://openalex.org/I146399215"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110469844","display_name":"Kin\u2010ichi Nakata","orcid":null},"institutions":[{"id":"https://openalex.org/I104946051","display_name":"Nihon University","ror":"https://ror.org/05jk51a88","country_code":"JP","type":"education","lineage":["https://openalex.org/I104946051"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kinichi Nakata","raw_affiliation_strings":["Department of Cardiovascular Surgery, Nihon University School of Medicine, Itabashi, Tokyo, Japan","Department of Cardiovascular Surgery, Nihon University School of Medicine, Itabashi, Tokyo 1738610 Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Cardiovascular Surgery, Nihon University School of Medicine, Itabashi, Tokyo, Japan","institution_ids":["https://openalex.org/I104946051"]},{"raw_affiliation_string":"Department of Cardiovascular Surgery, Nihon University School of Medicine, Itabashi, Tokyo 1738610 Japan","institution_ids":["https://openalex.org/I104946051"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039658736","display_name":"Yoshiyuki Sankai","orcid":"https://orcid.org/0000-0002-6345-1541"},"institutions":[{"id":"https://openalex.org/I146399215","display_name":"University of Tsukuba","ror":"https://ror.org/02956yf07","country_code":"JP","type":"education","lineage":["https://openalex.org/I146399215"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yoshiyuki Sankai","raw_affiliation_strings":["Cybernics Laboratory, Systems and Information Engineering, University of Tsukuba, Ibaraki, Japan","Cybernics Laboratory, Systems and Information Engineering, University of Tsukuba, Ibaraki 3050006 Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Cybernics Laboratory, Systems and Information Engineering, University of Tsukuba, Ibaraki, Japan","institution_ids":["https://openalex.org/I146399215"]},{"raw_affiliation_string":"Cybernics Laboratory, Systems and Information Engineering, University of Tsukuba, Ibaraki 3050006 Japan","institution_ids":["https://openalex.org/I146399215"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.12853629,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"49","issue":null,"first_page":"498","last_page":"503"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10924","display_name":"Cardiovascular Health and Disease Prevention","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T10924","display_name":"Cardiovascular Health and Disease Prevention","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10821","display_name":"Cardiovascular Function and Risk Factors","score":0.9973999857902527,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10372","display_name":"Cardiac Imaging and Diagnostics","score":0.9950000047683716,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.7033323645591736},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6177396774291992},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.5507012605667114},{"id":"https://openalex.org/keywords/estimation-theory","display_name":"Estimation theory","score":0.531093955039978},{"id":"https://openalex.org/keywords/white-noise","display_name":"White noise","score":0.5231431722640991},{"id":"https://openalex.org/keywords/system-identification","display_name":"System identification","score":0.413156658411026},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.36068806052207947},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.17492085695266724},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.14849570393562317}],"concepts":[{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.7033323645591736},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6177396774291992},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5507012605667114},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.531093955039978},{"id":"https://openalex.org/C112633086","wikidata":"https://www.wikidata.org/wiki/Q381287","display_name":"White noise","level":2,"score":0.5231431722640991},{"id":"https://openalex.org/C119247159","wikidata":"https://www.wikidata.org/wiki/Q1366192","display_name":"System identification","level":3,"score":0.413156658411026},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.36068806052207947},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.17492085695266724},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.14849570393562317},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","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/sii.2012.6427328","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sii.2012.6427328","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE/SICE International Symposium on System Integration (SII)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.800000011920929,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W1507701463","https://openalex.org/W1989627885","https://openalex.org/W1990653032","https://openalex.org/W2043665507","https://openalex.org/W2046861835","https://openalex.org/W2057779615","https://openalex.org/W2138831096","https://openalex.org/W2152276855","https://openalex.org/W2155182607","https://openalex.org/W2168172844","https://openalex.org/W2168284231","https://openalex.org/W2505595318","https://openalex.org/W3147678084","https://openalex.org/W6684831433"],"related_works":["https://openalex.org/W2009454197","https://openalex.org/W2030139799","https://openalex.org/W2752753867","https://openalex.org/W1576477388","https://openalex.org/W2281804591","https://openalex.org/W2093353624","https://openalex.org/W1909999440","https://openalex.org/W1576040274","https://openalex.org/W2094241856","https://openalex.org/W2101377286"],"abstract_inverted_index":{"Artery":[0],"testing":[1,44,97],"prevents":[2],"us":[3],"from":[4],"atherosclerotic":[5],"disease":[6],"and":[7,30,64,86,115,179,195,202,220],"several":[8,160],"methods":[9,19],"to":[10,84,130,215,221],"evaluate":[11],"physiological":[12,21,36,110,148],"change":[13],"are":[14,26,33],"performed.":[15],"However,":[16],"comprehensive":[17],"estimation":[18],"of":[20,23,47,53,67,75,80,91,105,112,134,190,218,228],"characteristics":[22,52,74],"blood":[24,54,76],"vessel":[25],"not":[27],"well":[28],"established":[29],"measured":[31],"data":[32,165,176,198],"influenced":[34],"by":[35],"conditions.":[37],"Vascular":[38],"system":[39,62,114],"model":[40,49,63,111,149],"can":[41,71,99,125],"improve":[42],"artery":[43,96],"because":[45],"parameters":[46,225],"the":[48,51,68,73,101,132,135,223],"indicate":[50],"vessel.":[55,77],"Our":[56],"laboratory":[57],"has":[58],"been":[59],"developing":[60],"vascular":[61,92,113,224],"identification":[65,94,117,120,136],"method":[66,90,121,212],"model,":[69],"which":[70],"estimate":[72],"The":[78,119],"purpose":[79],"this":[81],"study":[82],"is":[83],"construct":[85],"verify":[87,131],"a":[88,109,141,147,151],"noise-resistant":[89],"parameter":[93,102,116,162],"for":[95,197],"that":[98,124,166,187,210],"identify":[100,222],"accurately":[103,226],"regardless":[104,227],"noise.":[106,127,170,183,229],"We":[107,145,171],"developed":[108],"method.":[118],"includes":[122],"filter":[123],"eliminate":[126],"In":[128],"order":[129],"performance":[133],"method,":[137],"we":[138],"carried":[139],"out":[140],"computer":[142,152],"simulation":[143],"experiments.":[144],"constructed":[146],"on":[150],"with":[153,164,177,199],"preset":[154],"parameters.":[155],"Simulations":[156],"were":[157,192],"performed":[158,173],"in":[159],"different":[161],"settings":[163],"contain":[167],"10%":[168,201],"white":[169,182],"also":[172],"simulations":[174],"using":[175],"5%":[178],"20":[180],"%":[181],"Identification":[184],"results":[185,208],"showed":[186,209],"maximum":[188],"values":[189],"errors":[191],"0.7200%,":[193],"4.947%":[194],"11.56%":[196],"5%,":[200],"20%":[203],"noise":[204,219],"intensity,":[205],"respectively.":[206],"Those":[207],"our":[211],"was":[213],"able":[214],"reduce":[216],"influence":[217]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
