{"id":"https://openalex.org/W2564295794","doi":"https://doi.org/10.1109/itsc.2016.7795989","title":"Application of improved HMM model in train location unit","display_name":"Application of improved HMM model in train location unit","publication_year":2016,"publication_date":"2016-11-01","ids":{"openalex":"https://openalex.org/W2564295794","doi":"https://doi.org/10.1109/itsc.2016.7795989","mag":"2564295794"},"language":"en","primary_location":{"id":"doi:10.1109/itsc.2016.7795989","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc.2016.7795989","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)","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/A5063012966","display_name":"Yu Zang","orcid":"https://orcid.org/0000-0001-5164-9350"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yu Zang","raw_affiliation_strings":["School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, P. R. China"],"affiliations":[{"raw_affiliation_string":"School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, P. R. China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002672308","display_name":"Wei Shangguan","orcid":"https://orcid.org/0000-0003-2901-0782"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei ShangGuan","raw_affiliation_strings":["School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, P. R. China"],"affiliations":[{"raw_affiliation_string":"School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, P. R. China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040200765","display_name":"Wang Hua-shen","orcid":null},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huashen Wang","raw_affiliation_strings":["School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, P. R. China"],"affiliations":[{"raw_affiliation_string":"School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, P. R. China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100636115","display_name":"Baigen Cai","orcid":"https://orcid.org/0000-0002-6440-005X"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Baigen Cai","raw_affiliation_strings":["School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, P. R. China"],"affiliations":[{"raw_affiliation_string":"School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, P. R. China","institution_ids":["https://openalex.org/I21193070"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5063012966"],"corresponding_institution_ids":["https://openalex.org/I21193070"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.17290625,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":"31","issue":null,"first_page":"2693","last_page":"2698"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9765999913215637,"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/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9765999913215637,"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/T12707","display_name":"Vehicle License Plate Recognition","score":0.9733999967575073,"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/T13734","display_name":"Advanced Computational Techniques and Applications","score":0.9592000246047974,"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/odometer","display_name":"Odometer","score":0.7927650213241577},{"id":"https://openalex.org/keywords/train","display_name":"Train","score":0.7603296041488647},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.7503879070281982},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6141135692596436},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5279129147529602},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5239339470863342},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.5190680623054504},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5124006867408752},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3214641809463501}],"concepts":[{"id":"https://openalex.org/C93717769","wikidata":"https://www.wikidata.org/wiki/Q745105","display_name":"Odometer","level":2,"score":0.7927650213241577},{"id":"https://openalex.org/C190839683","wikidata":"https://www.wikidata.org/wiki/Q2448197","display_name":"Train","level":2,"score":0.7603296041488647},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.7503879070281982},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6141135692596436},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5279129147529602},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5239339470863342},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.5190680623054504},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5124006867408752},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3214641809463501},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/itsc.2016.7795989","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc.2016.7795989","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W1497256448","https://openalex.org/W2127690509","https://openalex.org/W2188283564","https://openalex.org/W2340199271","https://openalex.org/W2353526450","https://openalex.org/W2365165680","https://openalex.org/W2405183419"],"related_works":["https://openalex.org/W4323767814","https://openalex.org/W4387698348","https://openalex.org/W2115775154","https://openalex.org/W1964715412","https://openalex.org/W2282123131","https://openalex.org/W4301178586","https://openalex.org/W2356818805","https://openalex.org/W2789375127","https://openalex.org/W3206883612","https://openalex.org/W3032932164"],"abstract_inverted_index":{"Train":[0],"Location":[1],"Unit":[2],"plays":[3],"a":[4,46],"crucial":[5],"role":[6],"in":[7,28,112],"train":[8,25,114],"control":[9],"system,":[10],"whose":[11],"reliability":[12],"is":[13,36,56,62,172,191,217,231],"directly":[14],"related":[15],"to":[16,32,64,70,98,219,233],"the":[17,37,40,59,66,72,76,100,103,113,119,130,136,156,167,177,187,198,227],"safe":[18],"operation":[19],"of":[20,39,68,102,110,122,139,148,160,189],"trains.":[21],"There":[22,106],"are":[23,107],"many":[24],"positioning":[26],"methods":[27],"public":[29],"mainly":[30],"relates":[31],"GPS/":[33],"Odometer(ODO),":[34],"which":[35,58,180],"direction":[38],"actual":[41],"demand.":[42],"In":[43],"this":[44],"paper,":[45],"fault":[47,123,158,169,212,224],"diagnosis":[48,199,208],"method":[49,204],"based":[50],"on":[51],"improved":[52,183],"HMM(Hidden":[53],"Markov":[54],"Model)":[55],"studied":[57],"genetic":[60],"algorithm(GA)":[61],"used":[63],"obtain":[65],"parameters":[67],"HMM":[69,184],"make":[71],"training":[73,84,164],"speed":[74],"reach":[75],"steady":[77],"state":[78,124],"quickly":[79],"and":[80,151,223,226],"get":[81],"more":[82],"higher":[83],"accuracy,":[85],"rather":[86],"than":[87],"B-W":[88],"algorithm.":[89],"The":[90,141,214],"ODO":[91,161,168,190],"will":[92,143],"be":[93,144],"taken":[94],"as":[95],"an":[96],"example":[97],"verify":[99],"validity":[101],"proposed":[104,203],"method.":[105],"three":[108],"statuses":[109],"wheels":[111],"running":[115,137],"process.":[116],"To":[117],"implement":[118],"detecting":[120],"procedure":[121],"for":[125,210],"train's":[126],"wheels,":[127],"firstly":[128],"extracting":[129],"feature":[131,142],"from":[132],"Odometer":[133],"data":[134],"during":[135],"course":[138],"train.":[140],"preprocessed":[145],"with":[146,182,202],"algorithm":[147],"amplitude":[149],"normalized":[150],"scalar":[152],"quantization,":[153],"then":[154],"establishing":[155],"hidden":[157],"states":[159],"model":[162,200],"by":[163],"data.":[165],"Finally":[166],"condition":[170],"classifier":[171],"set":[173],"up.":[174],"By":[175],"inputting":[176],"observation":[178],"sequence":[179],"matches":[181],"models":[185],"classifier,":[186],"status":[188],"obtained":[192],"effectively.":[193],"Experimental":[194],"results":[195],"show":[196],"that":[197],"built":[201],"can":[205],"provide":[206],"high":[207],"performance":[209],"different":[211],"types.":[213],"diagnostic":[215,229],"accuracy":[216,230],"up":[218,232],"100%":[220],"between":[221],"normal":[222],"conditions":[225],"overall":[228],"96.67%.":[234]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}