{"id":"https://openalex.org/W4404608897","doi":"https://doi.org/10.1109/icmlca63499.2024.10754508","title":"High-speed Maglev Train Suspension Gap Prediction Based on CNN-AGCRN","display_name":"High-speed Maglev Train Suspension Gap Prediction Based on CNN-AGCRN","publication_year":2024,"publication_date":"2024-10-18","ids":{"openalex":"https://openalex.org/W4404608897","doi":"https://doi.org/10.1109/icmlca63499.2024.10754508"},"language":"en","primary_location":{"id":"doi:10.1109/icmlca63499.2024.10754508","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icmlca63499.2024.10754508","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 5th International Conference on Machine Learning and Computer Application (ICMLCA)","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/A5100425366","display_name":"Mingwei Li","orcid":"https://orcid.org/0000-0003-4695-0717"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mingwei Li","raw_affiliation_strings":["CRRC QINGDAO SIFANG CO., LTD,State Key Laboratory of High-speed Maglev Transportation Technology,Qingdao City,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CRRC QINGDAO SIFANG CO., LTD,State Key Laboratory of High-speed Maglev Transportation Technology,Qingdao City,China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111321124","display_name":"Gengchen Lv","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gengchen Lv","raw_affiliation_strings":["CRRC QINGDAO SIFANG CO., LTD,State Key Laboratory of High-speed Maglev Transportation Technology,Qingdao City,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CRRC QINGDAO SIFANG CO., LTD,State Key Laboratory of High-speed Maglev Transportation Technology,Qingdao City,China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101804452","display_name":"Xindong Wang","orcid":"https://orcid.org/0000-0002-7805-5779"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xindong Wang","raw_affiliation_strings":["CRRC QINGDAO SIFANG CO., LTD,State Key Laboratory of High-speed Maglev Transportation Technology,Qingdao City,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CRRC QINGDAO SIFANG CO., LTD,State Key Laboratory of High-speed Maglev Transportation Technology,Qingdao City,China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008834412","display_name":"Guirui Yu","orcid":"https://orcid.org/0000-0002-1859-8966"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tianxiang Yue","raw_affiliation_strings":["Tongkong Institute (Anhui) Ltd"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tongkong Institute (Anhui) Ltd","institution_ids":[]}]}],"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":"425","last_page":"430"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11602","display_name":"Magnetic Bearings and Levitation Dynamics","score":0.9891999959945679,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T11602","display_name":"Magnetic Bearings and Levitation Dynamics","score":0.9891999959945679,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T14446","display_name":"Civil and Geotechnical Engineering Research","score":0.9050999879837036,"subfield":{"id":"https://openalex.org/subfields/2200","display_name":"General 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/maglev","display_name":"Maglev","score":0.9920451641082764},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6660915613174438},{"id":"https://openalex.org/keywords/suspension","display_name":"Suspension (topology)","score":0.6362103223800659},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.38505303859710693},{"id":"https://openalex.org/keywords/automotive-engineering","display_name":"Automotive engineering","score":0.3761550784111023},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.21775373816490173},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.12836533784866333},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09698283672332764}],"concepts":[{"id":"https://openalex.org/C196781063","wikidata":"https://www.wikidata.org/wiki/Q160047","display_name":"Maglev","level":2,"score":0.9920451641082764},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6660915613174438},{"id":"https://openalex.org/C105341887","wikidata":"https://www.wikidata.org/wiki/Q1307987","display_name":"Suspension (topology)","level":3,"score":0.6362103223800659},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38505303859710693},{"id":"https://openalex.org/C171146098","wikidata":"https://www.wikidata.org/wiki/Q124192","display_name":"Automotive engineering","level":1,"score":0.3761550784111023},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.21775373816490173},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.12836533784866333},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09698283672332764},{"id":"https://openalex.org/C5961521","wikidata":"https://www.wikidata.org/wiki/Q746083","display_name":"Homotopy","level":2,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icmlca63499.2024.10754508","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icmlca63499.2024.10754508","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 5th International Conference on Machine Learning and Computer Application (ICMLCA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.550000011920929}],"awards":[{"id":"https://openalex.org/G212936622","display_name":null,"funder_award_id":"2023YFB4302506","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2067051746","https://openalex.org/W4361864837","https://openalex.org/W2349467427","https://openalex.org/W2376546541","https://openalex.org/W3169368101","https://openalex.org/W2138208077","https://openalex.org/W1975648247"],"abstract_inverted_index":{"Suspension":[0],"gap":[1,47,143,167],"is":[2,31,90],"a":[3,36,54],"critical":[4],"parameter":[5],"for":[6,43],"the":[7,16,23,45,69,81,100,105,110,116,123,140,146,151,165],"stability":[8],"control":[9],"of":[10,22,48,75,99,109,120,139,168],"high-speed":[11,24,49,169],"maglev":[12,25,50,170],"trains.":[13,51,171],"Due":[14],"to":[15,67,92,131],"nonlinearity":[17],"and":[18,61,72,79,96,107,128,133,137],"strong":[19],"coupling":[20],"characteristics":[21],"suspension":[26,46,101,142,166],"systems,":[27],"predicting":[28,44,164],"their":[29],"gaps":[30],"challenging.":[32],"This":[33],"paper":[34],"proposes":[35],"joint":[37,117],"prediction":[38,82,118,124],"model":[39,55,119,159],"based":[40],"on":[41],"CNN-AGCRN":[42,121],"It":[52],"establishes":[53],"integrating":[56],"Gated":[57],"Recurrent":[58],"Units":[59],"(GRU)":[60],"Adaptive":[62],"Graph":[63],"Convolutional":[64],"Neural":[65],"Networks":[66],"extract":[68],"time":[70,77],"dependencies":[71],"variable":[73],"correlations":[74],"multivariate":[76],"series,":[78],"models":[80],"results":[83,113,162],"with":[84],"Gaussian":[85],"noise.":[86],"The":[87,112],"loss":[88],"function":[89],"improved":[91],"generate":[93],"point":[94],"estimates":[95,98],"interval":[97],"gap,":[102],"thereby":[103],"enhancing":[104],"accuracy":[106,125],"robustness":[108],"predictions.":[111],"show":[114],"that":[115,157],"improves":[122],"by":[126],"21.8%":[127],"5.7%":[129],"compared":[130],"GRU":[132],"LSTM":[134],"models,":[135],"respectively,":[136],"97.08%":[138],"actual":[141],"values":[144],"in":[145,163],"test":[147],"set":[148],"fall":[149],"within":[150],"predicted":[152],"95%":[153],"confidence":[154],"interval,":[155],"demonstrating":[156],"this":[158],"achieves":[160],"excellent":[161]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}