{"id":"https://openalex.org/W3217356707","doi":"https://doi.org/10.1155/2021/3577541","title":"Neural Network for Intelligent and Efficient Volleyball Passing Training","display_name":"Neural Network for Intelligent and Efficient Volleyball Passing Training","publication_year":2021,"publication_date":"2021-11-22","ids":{"openalex":"https://openalex.org/W3217356707","doi":"https://doi.org/10.1155/2021/3577541","mag":"3217356707"},"language":"en","primary_location":{"id":"doi:10.1155/2021/3577541","is_oa":true,"landing_page_url":"https://doi.org/10.1155/2021/3577541","pdf_url":"https://downloads.hindawi.com/journals/misy/2021/3577541.pdf","source":{"id":"https://openalex.org/S152111507","display_name":"Mobile Information Systems","issn_l":"1574-017X","issn":["1574-017X","1875-905X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Mobile Information Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://downloads.hindawi.com/journals/misy/2021/3577541.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100461668","display_name":"Bo Liu","orcid":"https://orcid.org/0000-0002-6037-4536"},"institutions":[{"id":"https://openalex.org/I4210150411","display_name":"Shandong Youth University of Political Science","ror":"https://ror.org/04bwp4t29","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210150411"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Liu","raw_affiliation_strings":["Shandong Youth University of Political Science, Jinan 250103, China"],"affiliations":[{"raw_affiliation_string":"Shandong Youth University of Political Science, Jinan 250103, China","institution_ids":["https://openalex.org/I4210150411"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100686916","display_name":"Ning Yang","orcid":"https://orcid.org/0000-0003-4715-1247"},"institutions":[{"id":"https://openalex.org/I4210150411","display_name":"Shandong Youth University of Political Science","ror":"https://ror.org/04bwp4t29","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210150411"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ning Yang","raw_affiliation_strings":["Shandong Youth University of Political Science, Jinan 250103, China"],"affiliations":[{"raw_affiliation_string":"Shandong Youth University of Political Science, Jinan 250103, China","institution_ids":["https://openalex.org/I4210150411"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100578768","display_name":"Xiangwei Han","orcid":null},"institutions":[{"id":"https://openalex.org/I4210150411","display_name":"Shandong Youth University of Political Science","ror":"https://ror.org/04bwp4t29","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210150411"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangwei Han","raw_affiliation_strings":["Shandong Youth University of Political Science, Jinan 250103, China"],"affiliations":[{"raw_affiliation_string":"Shandong Youth University of Political Science, Jinan 250103, China","institution_ids":["https://openalex.org/I4210150411"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100322200","display_name":"Chen Liu","orcid":"https://orcid.org/0000-0003-1558-6836"},"institutions":[{"id":"https://openalex.org/I4210150411","display_name":"Shandong Youth University of Political Science","ror":"https://ror.org/04bwp4t29","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210150411"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chen Liu","raw_affiliation_strings":["Shandong Youth University of Political Science, Jinan 250103, China"],"affiliations":[{"raw_affiliation_string":"Shandong Youth University of Political Science, Jinan 250103, China","institution_ids":["https://openalex.org/I4210150411"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100686916"],"corresponding_institution_ids":["https://openalex.org/I4210150411"],"apc_list":{"value":2100,"currency":"USD","value_usd":2100},"apc_paid":{"value":2100,"currency":"USD","value_usd":2100},"fwci":0.9518,"has_fulltext":true,"cited_by_count":7,"citation_normalized_percentile":{"value":0.8070446,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"2021","issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9959999918937683,"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"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9959999918937683,"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"}},{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9753999710083008,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11674","display_name":"Sports Analytics and Performance","score":0.9753999710083008,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8998989462852478},{"id":"https://openalex.org/keywords/softmax-function","display_name":"Softmax function","score":0.7578681707382202},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6219670176506042},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5706077814102173},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5317771434783936},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5106425881385803},{"id":"https://openalex.org/keywords/grasp","display_name":"GRASP","score":0.4992544651031494},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.44857558608055115},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.4105128049850464},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39927101135253906}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8998989462852478},{"id":"https://openalex.org/C188441871","wikidata":"https://www.wikidata.org/wiki/Q7554146","display_name":"Softmax function","level":3,"score":0.7578681707382202},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6219670176506042},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5706077814102173},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5317771434783936},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5106425881385803},{"id":"https://openalex.org/C171268870","wikidata":"https://www.wikidata.org/wiki/Q1486676","display_name":"GRASP","level":2,"score":0.4992544651031494},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.44857558608055115},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.4105128049850464},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39927101135253906},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1155/2021/3577541","is_oa":true,"landing_page_url":"https://doi.org/10.1155/2021/3577541","pdf_url":"https://downloads.hindawi.com/journals/misy/2021/3577541.pdf","source":{"id":"https://openalex.org/S152111507","display_name":"Mobile Information Systems","issn_l":"1574-017X","issn":["1574-017X","1875-905X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Mobile Information Systems","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:def3d8f60834424a94efca10b246ffd5","is_oa":true,"landing_page_url":"https://doaj.org/article/def3d8f60834424a94efca10b246ffd5","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-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Mobile Information Systems, Vol 2021 (2021)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1155/2021/3577541","is_oa":true,"landing_page_url":"https://doi.org/10.1155/2021/3577541","pdf_url":"https://downloads.hindawi.com/journals/misy/2021/3577541.pdf","source":{"id":"https://openalex.org/S152111507","display_name":"Mobile Information Systems","issn_l":"1574-017X","issn":["1574-017X","1875-905X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Mobile Information Systems","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8895254542","display_name":null,"funder_award_id":"21CTYJ18","funder_id":"https://openalex.org/F4320336624","funder_display_name":"Social Science Planning Project of Shandong Province"}],"funders":[{"id":"https://openalex.org/F4320336624","display_name":"Social Science Planning Project of Shandong Province","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3217356707.pdf","grobid_xml":"https://content.openalex.org/works/W3217356707.grobid-xml"},"referenced_works_count":30,"referenced_works":["https://openalex.org/W1836465849","https://openalex.org/W2293003696","https://openalex.org/W2408279554","https://openalex.org/W2540481276","https://openalex.org/W2560342169","https://openalex.org/W2565639579","https://openalex.org/W2612445135","https://openalex.org/W2619082050","https://openalex.org/W2734591310","https://openalex.org/W2736637215","https://openalex.org/W2752782242","https://openalex.org/W2798472916","https://openalex.org/W2884585870","https://openalex.org/W2886904239","https://openalex.org/W2925359305","https://openalex.org/W2960737790","https://openalex.org/W2962971773","https://openalex.org/W2963111876","https://openalex.org/W2963125010","https://openalex.org/W2963524571","https://openalex.org/W2963795951","https://openalex.org/W2964241181","https://openalex.org/W2982770724","https://openalex.org/W2987284154","https://openalex.org/W2998228095","https://openalex.org/W2999597864","https://openalex.org/W3000881061","https://openalex.org/W3016705886","https://openalex.org/W4297775537","https://openalex.org/W6730903564"],"related_works":["https://openalex.org/W3107204728","https://openalex.org/W4287591324","https://openalex.org/W3108503355","https://openalex.org/W3090555870","https://openalex.org/W4226420367","https://openalex.org/W2962876041","https://openalex.org/W3022820045","https://openalex.org/W2801655600","https://openalex.org/W2899027234","https://openalex.org/W3120400911"],"abstract_inverted_index":{"Passing":[0],"is":[1,110,121,153],"a":[2,18,237],"relatively":[3],"basic":[4],"technique":[5,16],"in":[6,131,172],"volleyball.":[7],"In":[8,49],"volleyball":[9,58,87,173,232],"passing":[10,15,59,97,168,233],"teaching,":[11],"training":[12,98],"the":[13,30,33,37,44,47,53,62,74,82,90,106,117,128,132,141,145,149,158,163,177,185,191,194,201,208,211,228],"correct":[14,23],"plays":[17],"very":[19],"important":[20],"role.":[21],"The":[22,103,134,221],"pass":[24],"can":[25],"not":[26],"only":[27],"accurately":[28,126],"grasp":[29],"direction":[31],"of":[32,57,65,81,105,144,179,196,207,230],"ball":[34],"point":[35,39],"and":[36,46,55,72,77,96,116,125,148,155,199,204,210,219,227],"drop":[38],"but":[40],"also":[41],"effectively":[42,124],"connect":[43],"defense":[45],"offense.":[48],"order":[50],"to":[51,123,157,162],"improve":[52,61,73],"efficiency":[54],"quality":[56],"training,":[60],"precise":[63],"extraction":[64],"sport":[66],"targets,":[67],"reduce":[68],"redundant":[69,197],"feature":[70,151],"information,":[71],"generalization":[75,217],"performance":[76,218],"nonlinear":[78],"fitting":[79,222],"capabilities":[80],"algorithm,":[83,209],"this":[84],"paper":[85],"studies":[86],"based":[88],"on":[89,176],"nested":[91,135],"convolutional":[92,107,213],"neural":[93,108,214],"network":[94,109,215],"model":[95,120],"wrong":[99,169],"movement":[100],"detection":[101,171,178,229],"method.":[102],"structure":[104],"improved":[111,212],"by":[112],"nesting":[113],"mlpconv":[114,137],"layers,":[115],"Gaussian":[118],"mixture":[119],"used":[122],"extract":[127],"foreground":[129,146],"objects":[130],"video.":[133],"multilayer":[136],"layer":[138,166],"automatically":[139],"learns":[140],"deep-level":[142],"features":[143],"target,":[147],"generated":[150],"map":[152],"vectorized":[154],"input":[156],"Softmax":[159],"classifier":[160],"connected":[161,165],"fully":[164],"for":[167],"behavior":[170],"training.":[174],"Based":[175],"nearly":[180],"1,000":[181],"athletes\u2019":[182],"action":[183],"datasets,":[184],"simulation":[186],"experiment":[187],"results":[188],"show":[189],"that":[190],"algorithm":[192],"reduces":[193],"acquisition":[195],"information":[198],"shortens":[200],"calculation":[202],"time":[203,206],"learning":[205],"has":[216,224,235],"nonlinearity.":[220],"ability":[223],"been":[225],"improved,":[226],"abnormal":[231],"behaviors":[234],"achieved":[236],"higher":[238],"accuracy":[239],"rate.":[240]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
