{"id":"https://openalex.org/W4376852348","doi":"https://doi.org/10.1145/3573942.3573960","title":"Neural Network Prediction Model Based on Differential Localization","display_name":"Neural Network Prediction Model Based on Differential Localization","publication_year":2022,"publication_date":"2022-09-23","ids":{"openalex":"https://openalex.org/W4376852348","doi":"https://doi.org/10.1145/3573942.3573960"},"language":"en","primary_location":{"id":"doi:10.1145/3573942.3573960","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1145/3573942.3573960","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","raw_type":"proceedings-article"},"type":"article","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/A5038927324","display_name":"Yuanhua Liu","orcid":"https://orcid.org/0000-0002-8330-5250"},"institutions":[{"id":"https://openalex.org/I4210136859","display_name":"Xi\u2019an University of Posts and Telecommunications","ror":"https://ror.org/04jn0td46","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136859"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yuanhua Liu","raw_affiliation_strings":["Xi'an University of Posts & Telecommunications, China"],"raw_orcid":"https://orcid.org/0000-0002-8330-5250","affiliations":[{"raw_affiliation_string":"Xi'an University of Posts & Telecommunications, China","institution_ids":["https://openalex.org/I4210136859"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007031932","display_name":"Ruini Li","orcid":"https://orcid.org/0000-0002-8369-8090"},"institutions":[{"id":"https://openalex.org/I4210136859","display_name":"Xi\u2019an University of Posts and Telecommunications","ror":"https://ror.org/04jn0td46","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136859"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ruini Li","raw_affiliation_strings":["Xi'an University of Posts & Telecommunications, China"],"raw_orcid":"https://orcid.org/0000-0002-8369-8090","affiliations":[{"raw_affiliation_string":"Xi'an University of Posts & Telecommunications, China","institution_ids":["https://openalex.org/I4210136859"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101867642","display_name":"Xinliang Niu","orcid":"https://orcid.org/0000-0001-7253-2098"},"institutions":[{"id":"https://openalex.org/I194716290","display_name":"China Academy of Space Technology","ror":"https://ror.org/025397a59","country_code":"CN","type":"government","lineage":["https://openalex.org/I194716290","https://openalex.org/I2802615301"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinliang Niu","raw_affiliation_strings":["Xi'an Branch of China Academy of Space Technology, China"],"raw_orcid":"https://orcid.org/0000-0001-7253-2098","affiliations":[{"raw_affiliation_string":"Xi'an Branch of China Academy of Space Technology, China","institution_ids":["https://openalex.org/I194716290"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5038927324"],"corresponding_institution_ids":["https://openalex.org/I4210136859"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.31715927,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"1","issue":null,"first_page":"115","last_page":"120"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10655","display_name":"GNSS positioning and interference","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/T10655","display_name":"GNSS positioning and interference","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/T11405","display_name":"Geophysics and Gravity Measurements","score":0.9814000129699707,"subfield":{"id":"https://openalex.org/subfields/1910","display_name":"Oceanography"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13734","display_name":"Advanced Computational Techniques and Applications","score":0.9707000255584717,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/recurrent-neural-network","display_name":"Recurrent neural network","score":0.8871920108795166},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.765916109085083},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.689734935760498},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.658207893371582},{"id":"https://openalex.org/keywords/mean-absolute-percentage-error","display_name":"Mean absolute percentage error","score":0.5061275362968445},{"id":"https://openalex.org/keywords/gnss-applications","display_name":"GNSS applications","score":0.5037960410118103},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4793001711368561},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3303777575492859},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.32296520471572876},{"id":"https://openalex.org/keywords/global-positioning-system","display_name":"Global Positioning System","score":0.21321427822113037},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.21156471967697144},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.17867371439933777},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.05834534764289856}],"concepts":[{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.8871920108795166},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.765916109085083},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.689734935760498},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.658207893371582},{"id":"https://openalex.org/C150217764","wikidata":"https://www.wikidata.org/wiki/Q6803607","display_name":"Mean absolute percentage error","level":3,"score":0.5061275362968445},{"id":"https://openalex.org/C14279187","wikidata":"https://www.wikidata.org/wiki/Q5514012","display_name":"GNSS applications","level":3,"score":0.5037960410118103},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4793001711368561},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3303777575492859},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32296520471572876},{"id":"https://openalex.org/C60229501","wikidata":"https://www.wikidata.org/wiki/Q18822","display_name":"Global Positioning System","level":2,"score":0.21321427822113037},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.21156471967697144},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17867371439933777},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.05834534764289856}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3573942.3573960","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1145/3573942.3573960","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3022769117","display_name":null,"funder_award_id":"Grant Nos.61971348","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W2086878282","https://openalex.org/W2154851817","https://openalex.org/W2161500186","https://openalex.org/W2246250604","https://openalex.org/W2619982747","https://openalex.org/W2805646944","https://openalex.org/W3000495313","https://openalex.org/W3085218782","https://openalex.org/W3099282531","https://openalex.org/W3114327555","https://openalex.org/W3159011438","https://openalex.org/W3217017713","https://openalex.org/W4207064822","https://openalex.org/W6646505299","https://openalex.org/W6666761814"],"related_works":["https://openalex.org/W4318676890","https://openalex.org/W2039947585","https://openalex.org/W4385195237","https://openalex.org/W3178576217","https://openalex.org/W4285102093","https://openalex.org/W4210644201","https://openalex.org/W3111532652","https://openalex.org/W2510451507","https://openalex.org/W4283367183","https://openalex.org/W4381189085"],"abstract_inverted_index":{"The":[0,20,80,108],"Global":[1],"Navigation":[2],"Satellite":[3],"System-Reflectometry":[4],"(GNSS-R)":[5],"is":[6,23,49],"affected":[7],"by":[8,135,155],"buildings,":[9],"trees,":[10],"etc.":[11],"during":[12],"the":[13,31,53,61,64,69,77,114,117,130,142,145,150,163,169],"transmission":[14],"process,":[15],"which":[16,51,160],"generates":[17],"large":[18],"errors.":[19],"traditional":[21],"method":[22],"to":[24,27,33,67,101],"use":[25],"differential":[26,47,54],"eliminate":[28],"most":[29],"of":[30,63,129,149,168],"errors":[32],"improve":[34],"positioning":[35],"accuracy.":[36],"In":[37],"this":[38],"paper,":[39],"a":[40],"neural":[41,65],"network":[42,66],"prediction":[43],"model":[44,132,152],"based":[45],"on":[46],"results":[48,55,109],"proposed,":[50],"uses":[52,82],"X,":[56],"Y":[57],"and":[58,72,91,105,123,137,147,157,166],"Z":[59],"as":[60],"inputs":[62],"predict":[68],"satellite":[70],"position,":[71],"finally":[73],"compare":[74],"it":[75],"with":[76,113,141],"real":[78],"value.":[79],"paper":[81],"Artificial":[83],"Neural":[84,88,96],"Network":[85,89,97],"(ANN),":[86],"Recurrent":[87],"(RNN)":[90],"Long":[92],"Short":[93],"Term":[94],"Memory-Recurrent":[95],"(LSTM-RNN)":[98],"are":[99,133,153,171],"used":[100],"establish":[102],"training":[103,164],"models":[104],"make":[106],"predictions.":[107],"show":[110],"that":[111,162],"compared":[112,140],"ANN":[115],"model,":[116,144],"Mean":[118,125],"Absolute":[119],"Percentage":[120],"Error":[121,127],"(MAPE)":[122],"Root":[124],"Squared":[126],"(RMSE)":[128],"RNN":[131,143],"reduced":[134,154],"1.54%":[136],"3.59%,":[138],"respectively;":[139],"MAPE":[146],"RMSE":[148],"LSTM-RNN":[151,170],"21.16%":[156],"14.81%,":[158],"respectively,":[159],"proves":[161],"accuracy":[165],"fit":[167],"better.":[172]},"counts_by_year":[],"updated_date":"2025-12-21T23:12:01.093139","created_date":"2025-10-10T00:00:00"}