{"id":"https://openalex.org/W7134069141","doi":"https://doi.org/10.3233/faia260016","title":"Finite Element Model Updating Method Based on Wavelet Packet Decomposition and Kriging Model","display_name":"Finite Element Model Updating Method Based on Wavelet Packet Decomposition and Kriging Model","publication_year":2026,"publication_date":"2026-03-04","ids":{"openalex":"https://openalex.org/W7134069141","doi":"https://doi.org/10.3233/faia260016"},"language":null,"primary_location":{"id":"doi:10.3233/faia260016","is_oa":true,"landing_page_url":"https://doi.org/10.3233/faia260016","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"},"type":"book-chapter","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.3233/faia260016","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5042436712","display_name":"L. Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210090460","display_name":"Nanjing Institute of Railway Technology","ror":"https://ror.org/00ctyfm67","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210090460"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Lei Wang","raw_affiliation_strings":["Nanjing Vocational Institute of Railway Technology, Nanjing, Jiangsu 210031, China"],"raw_orcid":"https://orcid.org/0009-0003-8130-6393","affiliations":[{"raw_affiliation_string":"Nanjing Vocational Institute of Railway Technology, Nanjing, Jiangsu 210031, China","institution_ids":["https://openalex.org/I4210090460"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5042436712"],"corresponding_institution_ids":["https://openalex.org/I4210090460"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.50205339,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11206","display_name":"Model Reduction and Neural Networks","score":0.5307999849319458,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11206","display_name":"Model Reduction and Neural Networks","score":0.5307999849319458,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10928","display_name":"Probabilistic and Robust Engineering Design","score":0.14300000667572021,"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/T10534","display_name":"Structural Health Monitoring Techniques","score":0.12919999659061432,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/wavelet-packet-decomposition","display_name":"Wavelet packet decomposition","score":0.7754999995231628},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.676800012588501},{"id":"https://openalex.org/keywords/kriging","display_name":"Kriging","score":0.5659999847412109},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.5196999907493591},{"id":"https://openalex.org/keywords/network-packet","display_name":"Network packet","score":0.5033000111579895},{"id":"https://openalex.org/keywords/latin-hypercube-sampling","display_name":"Latin hypercube sampling","score":0.46149998903274536},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.44130000472068787},{"id":"https://openalex.org/keywords/sensitivity","display_name":"Sensitivity (control systems)","score":0.4309999942779541}],"concepts":[{"id":"https://openalex.org/C155777637","wikidata":"https://www.wikidata.org/wiki/Q2736187","display_name":"Wavelet packet decomposition","level":4,"score":0.7754999995231628},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.676800012588501},{"id":"https://openalex.org/C81692654","wikidata":"https://www.wikidata.org/wiki/Q225926","display_name":"Kriging","level":2,"score":0.5659999847412109},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5200999975204468},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.5196999907493591},{"id":"https://openalex.org/C158379750","wikidata":"https://www.wikidata.org/wiki/Q214111","display_name":"Network packet","level":2,"score":0.5033000111579895},{"id":"https://openalex.org/C20820323","wikidata":"https://www.wikidata.org/wiki/Q6496514","display_name":"Latin hypercube sampling","level":3,"score":0.46149998903274536},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.44130000472068787},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.4309999942779541},{"id":"https://openalex.org/C8590192","wikidata":"https://www.wikidata.org/wiki/Q1054694","display_name":"Frequency response","level":2,"score":0.42489999532699585},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.41449999809265137},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.3637000024318695},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.35510000586509705},{"id":"https://openalex.org/C135628077","wikidata":"https://www.wikidata.org/wiki/Q220184","display_name":"Finite element method","level":2,"score":0.33889999985694885},{"id":"https://openalex.org/C2778258933","wikidata":"https://www.wikidata.org/wiki/Q16918986","display_name":"Decomposition method (queueing theory)","level":2,"score":0.3314000070095062},{"id":"https://openalex.org/C88829872","wikidata":"https://www.wikidata.org/wiki/Q5048176","display_name":"Cascade algorithm","level":5,"score":0.33079999685287476},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.3260999917984009},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.305400013923645},{"id":"https://openalex.org/C2777451244","wikidata":"https://www.wikidata.org/wiki/Q3352512","display_name":"Orthogonal wavelet","level":5,"score":0.28949999809265137},{"id":"https://openalex.org/C121927907","wikidata":"https://www.wikidata.org/wiki/Q1952516","display_name":"Multiresolution analysis","level":5,"score":0.2587999999523163},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.257099986076355},{"id":"https://openalex.org/C5917680","wikidata":"https://www.wikidata.org/wiki/Q2621825","display_name":"Basis function","level":2,"score":0.25619998574256897},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.25209999084472656}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3233/faia260016","is_oa":true,"landing_page_url":"https://doi.org/10.3233/faia260016","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"}],"best_oa_location":{"id":"doi:10.3233/faia260016","is_oa":true,"landing_page_url":"https://doi.org/10.3233/faia260016","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"},"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.7818726301193237,"id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"To":[0],"avoid":[1],"the":[2,17,38,50,53,60,72,96,102,106,112,124,129,132,135,140,150,153,158,166,169,178,193,202],"difficulties":[3],"of":[4,52,134,152,168],"selecting":[5],"frequency":[6,13,40,62,142,196],"points":[7],"in":[8,116],"model":[9,34,98,113,189],"updating":[10,23,183],"methods":[11],"using":[12,165],"response":[14,19,41,143,180,197],"functions,":[15],"reduce":[16],"output":[18,156],"dimensions,":[20],"and":[21,32,119,149,199],"enhance":[22],"efficiency,":[24],"a":[25],"new":[26],"approach":[27],"combining":[28],"wavelet":[29,46,146,174],"packet":[30,47,147,175],"decomposition":[31,57,108,148,176],"Kriging":[33,97,121,159],"is":[35,43,68,126],"proposed.":[36],"Initially,":[37],"calculated":[39],"function":[42,144,198],"subjected":[44],"to":[45,58,70,82,127],"decomposition,":[48],"extracting":[49],"energy":[51,103,133,151,167],"last":[54,136,154,170],"layer":[55,109,137,155,171],"after":[56,145,173],"represent":[59],"structural":[61,179],"response.":[63],"Subsequently,":[64],"Latin":[65],"hypercube":[66],"sampling":[67],"employed":[69],"design":[71],"initially":[73],"selected":[74,91],"parameters":[75,85,92],"for":[76,205],"updating,":[77],"followed":[78],"by":[79,157],"sensitivity":[80],"analysis":[81],"identify":[83],"which":[84],"should":[86],"be":[87],"updated.":[88],"Then,":[89],"these":[90],"are":[93],"fed":[94],"into":[95],"as":[99,111,177],"inputs,":[100],"while":[101],"extracted":[104,138,172],"from":[105,139],"final":[107],"serves":[110],"output,":[114],"resulting":[115],"an":[117],"accurate":[118],"efficient":[120],"model.":[122,160],"Finally,":[123],"goal":[125],"minimize":[128],"difference":[130],"between":[131],"target":[141],"Simulation":[161],"examples":[162],"show":[163],"that":[164],"achieves":[181],"higher":[182],"precision.":[184],"The":[185],"updated":[186],"finite":[187],"element":[188],"almost":[190],"coincides":[191],"with":[192],"actual":[194,203],"structure\u2019s":[195],"can":[200],"replace":[201],"structure":[204],"dynamic":[206],"analysis.":[207]},"counts_by_year":[],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2026-03-07T00:00:00"}
