{"id":"https://openalex.org/W4312600726","doi":"https://doi.org/10.1109/etfa52439.2022.9921466","title":"Enhancing Vehicle State Recognition in Logistics Industrial Parks via Dynamic Hidden Markov Model","display_name":"Enhancing Vehicle State Recognition in Logistics Industrial Parks via Dynamic Hidden Markov Model","publication_year":2022,"publication_date":"2022-09-06","ids":{"openalex":"https://openalex.org/W4312600726","doi":"https://doi.org/10.1109/etfa52439.2022.9921466"},"language":"en","primary_location":{"id":"doi:10.1109/etfa52439.2022.9921466","is_oa":false,"landing_page_url":"https://doi.org/10.1109/etfa52439.2022.9921466","pdf_url":null,"source":{"id":"https://openalex.org/S4363607916","display_name":"2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA)","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/A5100356239","display_name":"Yang Liu","orcid":"https://orcid.org/0009-0007-9978-0842"},"institutions":[{"id":"https://openalex.org/I4210103986","display_name":"Jingdong (China)","ror":"https://ror.org/01dkjkq64","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103986"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yang Liu","raw_affiliation_strings":["JD Logistics,Beijing,China","JD Logistics, Beijing, China"],"affiliations":[{"raw_affiliation_string":"JD Logistics,Beijing,China","institution_ids":["https://openalex.org/I4210103986"]},{"raw_affiliation_string":"JD Logistics, Beijing, China","institution_ids":["https://openalex.org/I4210103986"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076034895","display_name":"Mingjie Guo","orcid":"https://orcid.org/0000-0001-7265-9478"},"institutions":[{"id":"https://openalex.org/I4210103986","display_name":"Jingdong (China)","ror":"https://ror.org/01dkjkq64","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103986"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingjie Guo","raw_affiliation_strings":["JD Logistics,Beijing,China","JD Logistics, Beijing, China"],"affiliations":[{"raw_affiliation_string":"JD Logistics,Beijing,China","institution_ids":["https://openalex.org/I4210103986"]},{"raw_affiliation_string":"JD Logistics, Beijing, China","institution_ids":["https://openalex.org/I4210103986"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031580282","display_name":"Shiyan Hu","orcid":"https://orcid.org/0000-0003-2512-0634"},"institutions":[{"id":"https://openalex.org/I43439940","display_name":"University of Southampton","ror":"https://ror.org/01ryk1543","country_code":"GB","type":"education","lineage":["https://openalex.org/I43439940"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Shiyan Hu","raw_affiliation_strings":["University of Southampton,Department of Electronics and Computer Science,Southampton,UK","Department of Electronics and Computer Science, University of Southampton, Southampton, UK"],"affiliations":[{"raw_affiliation_string":"University of Southampton,Department of Electronics and Computer Science,Southampton,UK","institution_ids":["https://openalex.org/I43439940"]},{"raw_affiliation_string":"Department of Electronics and Computer Science, University of Southampton, Southampton, UK","institution_ids":["https://openalex.org/I43439940"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015611759","display_name":"Wenming Zhe","orcid":"https://orcid.org/0000-0003-1753-5784"},"institutions":[{"id":"https://openalex.org/I4210103986","display_name":"Jingdong (China)","ror":"https://ror.org/01dkjkq64","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103986"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenming Zhe","raw_affiliation_strings":["JD Logistics,Beijing,China","JD Logistics, Beijing, China"],"affiliations":[{"raw_affiliation_string":"JD Logistics,Beijing,China","institution_ids":["https://openalex.org/I4210103986"]},{"raw_affiliation_string":"JD Logistics, Beijing, China","institution_ids":["https://openalex.org/I4210103986"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100356239"],"corresponding_institution_ids":["https://openalex.org/I4210103986"],"apc_list":null,"apc_paid":null,"fwci":0.719,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.72468502,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12707","display_name":"Vehicle License Plate Recognition","score":0.9829000234603882,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T12707","display_name":"Vehicle License Plate Recognition","score":0.9829000234603882,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T12406","display_name":"IoT and GPS-based Vehicle Safety Systems","score":0.9210000038146973,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical 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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9136999845504761,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive 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/viterbi-algorithm","display_name":"Viterbi algorithm","score":0.7698347568511963},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7552188634872437},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.7544049024581909},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.578090250492096},{"id":"https://openalex.org/keywords/interference","display_name":"Interference (communication)","score":0.5476499795913696},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.5225020051002502},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5195653438568115},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.5185611844062805},{"id":"https://openalex.org/keywords/markov-model","display_name":"Markov model","score":0.4662396311759949},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.43744468688964844},{"id":"https://openalex.org/keywords/work","display_name":"Work (physics)","score":0.4346506595611572},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.41468170285224915},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3753705620765686},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.37127530574798584},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.30941033363342285},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.18704333901405334},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.17255154252052307},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.16318339109420776},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.12003296613693237},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.0817331075668335}],"concepts":[{"id":"https://openalex.org/C60582962","wikidata":"https://www.wikidata.org/wiki/Q83886","display_name":"Viterbi algorithm","level":3,"score":0.7698347568511963},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7552188634872437},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.7544049024581909},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.578090250492096},{"id":"https://openalex.org/C32022120","wikidata":"https://www.wikidata.org/wiki/Q797225","display_name":"Interference (communication)","level":3,"score":0.5476499795913696},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.5225020051002502},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5195653438568115},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.5185611844062805},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.4662396311759949},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.43744468688964844},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.4346506595611572},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.41468170285224915},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3753705620765686},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.37127530574798584},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.30941033363342285},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.18704333901405334},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.17255154252052307},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.16318339109420776},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.12003296613693237},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0817331075668335},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"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/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/etfa52439.2022.9921466","is_oa":false,"landing_page_url":"https://doi.org/10.1109/etfa52439.2022.9921466","pdf_url":null,"source":{"id":"https://openalex.org/S4363607916","display_name":"2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.49000000953674316}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2136652457","https://openalex.org/W2169849734","https://openalex.org/W2160171981","https://openalex.org/W1975869217","https://openalex.org/W2236912844","https://openalex.org/W2129150969","https://openalex.org/W2401728283","https://openalex.org/W2116722627","https://openalex.org/W2379938888","https://openalex.org/W3044198794"],"abstract_inverted_index":{"Platform-based":[0],"vehicle":[1,90,127],"recognition":[2],"is":[3,42,82,100,115,133,143],"a":[4],"critical":[5],"task":[6],"in":[7,20],"logistics":[8,55],"scenarios":[9],"that":[10],"facilitates":[11],"the":[12,21,31,36,40,46,52,66,86,93,105,126,138,141],"efficient":[13],"management":[14],"of":[15,33,38,54,69,89],"resources.":[16],"Although":[17],"recent":[18],"advances":[19],"computer":[22],"vision":[23],"domain":[24],"can":[25,124],"be":[26],"conveniently":[27],"adopted":[28],"to":[29,84,103,135],"recognize":[30,125],"identities":[32],"vehicles":[34,70],"and":[35,49,73,132],"occupations":[37],"platforms,":[39],"efficacy":[41],"significantly":[43,136],"compromised":[44],"by":[45],"severe":[47],"interference":[48,142],"noise":[50],"at":[51],"platforms":[53],"industrial":[56],"parks.":[57],"This":[58],"work":[59],"tackles":[60],"these":[61],"difficulties":[62],"through":[63,120],"concentrating":[64],"on":[65],"sequential":[67],"characteristics":[68],"during":[71],"arrival":[72],"departure.":[74],"An":[75],"innovative":[76],"dynamic":[77,97],"hidden":[78],"Markov":[79],"model":[80],"(DHMM)":[81],"proposed":[83,106,113],"estimate":[85],"real":[87],"sequence":[88],"states":[91,128],"from":[92],"noisy":[94],"observations.":[95],"A":[96],"Viterbi":[98],"algorithm":[99],"also":[101],"developed":[102],"solve":[104],"DHMM":[107],"method":[108,114],"with":[109,129],"high":[110,130],"efficiency.":[111],"The":[112],"evaluated":[116],"against":[117],"multiple":[118],"baselines":[119,139],"experiments,":[121],"where":[122],"it":[123],"accuracy":[131],"demonstrated":[134],"outperform":[137],"when":[140],"strong.":[144]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}