{"id":"https://openalex.org/W4400905263","doi":"https://doi.org/10.1109/access.2024.3432801","title":"Ship Trajectory Prediction Based on CNN-MTABiGRU Model","display_name":"Ship Trajectory Prediction Based on CNN-MTABiGRU Model","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4400905263","doi":"https://doi.org/10.1109/access.2024.3432801"},"language":"en","primary_location":{"id":"doi:10.1109/access.2024.3432801","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3432801","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2024.3432801","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102688356","display_name":"Xinyu Dong","orcid":null},"institutions":[{"id":"https://openalex.org/I177739611","display_name":"Yangtze University","ror":"https://ror.org/05bhmhz54","country_code":"CN","type":"education","lineage":["https://openalex.org/I177739611"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xinyu Dong","raw_affiliation_strings":["School of Computer Science, Yangtze University, Jingzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Yangtze University, Jingzhou, China","institution_ids":["https://openalex.org/I177739611"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115602970","display_name":"S. Selvakumar Raja","orcid":"https://orcid.org/0000-0003-1030-5863"},"institutions":[{"id":"https://openalex.org/I8764889","display_name":"University of Gondar","ror":"https://ror.org/0595gz585","country_code":"ET","type":"education","lineage":["https://openalex.org/I8764889"]}],"countries":["ET"],"is_corresponding":false,"raw_author_name":"S. Selvakumar Raja","raw_affiliation_strings":["Computer Engineering Department, University of Gondar, Gonder, Ethiopia"],"affiliations":[{"raw_affiliation_string":"Computer Engineering Department, University of Gondar, Gonder, Ethiopia","institution_ids":["https://openalex.org/I8764889"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100680426","display_name":"Jian Zhang","orcid":"https://orcid.org/0000-0001-5500-2685"},"institutions":[{"id":"https://openalex.org/I177739611","display_name":"Yangtze University","ror":"https://ror.org/05bhmhz54","country_code":"CN","type":"education","lineage":["https://openalex.org/I177739611"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jian Zhang","raw_affiliation_strings":["School of Computer Science, Yangtze University, Jingzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Yangtze University, Jingzhou, China","institution_ids":["https://openalex.org/I177739611"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001617331","display_name":"Leiyu Wang","orcid":"https://orcid.org/0009-0001-2394-6937"},"institutions":[{"id":"https://openalex.org/I177739611","display_name":"Yangtze University","ror":"https://ror.org/05bhmhz54","country_code":"CN","type":"education","lineage":["https://openalex.org/I177739611"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Leiyu Wang","raw_affiliation_strings":["School of Computer Science, Yangtze University, Jingzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Yangtze University, Jingzhou, China","institution_ids":["https://openalex.org/I177739611"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5102688356"],"corresponding_institution_ids":["https://openalex.org/I177739611"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":3.1987,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.91445753,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"12","issue":null,"first_page":"115306","last_page":"115318"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11622","display_name":"Maritime Navigation and Safety","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/T11622","display_name":"Maritime Navigation and Safety","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/T11604","display_name":"Ship Hydrodynamics and Maneuverability","score":0.9412000179290771,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.919700026512146,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.8207207918167114},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7754353284835815},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.7226172685623169},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6228727102279663},{"id":"https://openalex.org/keywords/activation-function","display_name":"Activation function","score":0.5925068259239197},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.5497506260871887},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5243480801582336},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5138397812843323},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5098249316215515},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.4730847477912903},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4569040834903717},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1848328709602356},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.08472767472267151}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8207207918167114},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7754353284835815},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.7226172685623169},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6228727102279663},{"id":"https://openalex.org/C38365724","wikidata":"https://www.wikidata.org/wiki/Q4677469","display_name":"Activation function","level":3,"score":0.5925068259239197},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.5497506260871887},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5243480801582336},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5138397812843323},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5098249316215515},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.4730847477912903},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4569040834903717},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1848328709602356},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.08472767472267151},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2024.3432801","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3432801","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:caae41825fe5466d88cae353fe764f6b","is_oa":true,"landing_page_url":"https://doaj.org/article/caae41825fe5466d88cae353fe764f6b","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 12, Pp 115306-115318 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2024.3432801","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3432801","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Climate action","score":0.49000000953674316,"id":"https://metadata.un.org/sdg/13"}],"awards":[{"id":"https://openalex.org/G2011654101","display_name":null,"funder_award_id":"XT83-20-001/5242001900CQ","funder_id":"https://openalex.org/F4320329056","funder_display_name":"China Electric Power Research Institute"}],"funders":[{"id":"https://openalex.org/F4320329056","display_name":"China Electric Power Research Institute","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2112796928","https://openalex.org/W2133564696","https://openalex.org/W2890096158","https://openalex.org/W2893015365","https://openalex.org/W2965328380","https://openalex.org/W3001604145","https://openalex.org/W3135870658","https://openalex.org/W3138598836","https://openalex.org/W3156622039","https://openalex.org/W4288257146","https://openalex.org/W4320930577","https://openalex.org/W6679434410","https://openalex.org/W6684191040"],"related_works":["https://openalex.org/W4362597605","https://openalex.org/W1574414179","https://openalex.org/W4297676672","https://openalex.org/W3009056573","https://openalex.org/W2922073769","https://openalex.org/W4281702477","https://openalex.org/W2490526372","https://openalex.org/W3214206881","https://openalex.org/W2806426850","https://openalex.org/W4297795098"],"abstract_inverted_index":{"To":[0,79],"address":[1],"the":[2,16,37,85,92,107,127,138,142,162,166,173,178,183,197,212,219,231],"limitations":[3],"of":[4],"traditional":[5],"neural":[6,43],"networks":[7,44],"in":[8,72,161,190,194,204,208,230],"extracting":[9],"ship":[10,53,77,130,159,228],"trajectory":[11,54,73,131],"sequence":[12],"features":[13,51,74],"and":[14,36,104,146,154,177,192,196,206],"achieving":[15],"required":[17],"accuracy":[18,148],"for":[19,157,226],"ShenZhong":[20,163,232],"Link":[21],"navigation,":[22],"this":[23],"paper":[24],"proposes":[25],"an":[26],"improved":[27],"model,":[28,68,169],"CNN-MTABiGRU,":[29],"which":[30,170],"integrates":[31],"a":[32,57,66],"temporal":[33,70,108,174],"attention":[34,109,175],"mechanism":[35,110,176],"Mish":[38,86,179],"activation":[39,87,180],"function.":[40],"Firstly,":[41],"convolutional":[42],"(CNN)":[45],"are":[46],"used":[47],"to":[48,64,75,95,113,150,211],"extract":[49],"key":[50],"from":[52],"data.":[55],"Secondly,":[56],"BiGRU":[58],"network":[59],"is":[60,89,111],"combined":[61],"with":[62],"CNN":[63,93],"form":[65,126],"CNN-BiGRU":[67,139,213],"capturing":[69],"dependencies":[71],"predict":[76],"trajectories.":[78],"further":[80],"enhance":[81],"model":[82,140,222],"training":[83],"efficiency,":[84],"function":[88],"introduced":[90],"at":[91],"output":[94],"achieve":[96],"smoother":[97],"nonlinear":[98],"mappings,":[99],"effectively":[100],"preventing":[101],"gradient":[102],"vanishing":[103],"overfitting.":[105],"Additionally,":[106],"incorporated":[112],"highlight":[114],"feature":[115],"data":[116],"items":[117],"that":[118,137,218],"significantly":[119],"impact":[120],"prediction":[121,132,144],"accuracy.":[122],"Together,":[123],"these":[124],"elements":[125],"optimized":[128,167,220],"CNN-MTABiGRU":[129,168,221],"model.":[133,214],"Practical":[134],"results":[135],"indicate":[136],"yields":[141],"smallest":[143],"error":[145,186,200],"highest":[147],"compared":[149,210],"CNN,":[151],"BiGRU,":[152],"CNN-LSTM,":[153],"CNN-GRU":[155],"models":[156],"predicting":[158,227],"trajectories":[160,229],"Link.":[164,233],"Moreover,":[165],"incorporates":[171],"both":[172],"function,":[181],"reduces":[182],"mean":[184,198],"squared":[185],"(MSE)":[187],"by":[188,202],"17.03%":[189],"latitude":[191,205],"35.10%":[193],"longitude,":[195,209],"absolute":[199],"(MAE)":[201],"14.42%":[203],"21.47%":[207],"These":[215],"findings":[216],"demonstrate":[217],"has":[223],"significant":[224],"advantages":[225]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}