{"id":"https://openalex.org/W4384158241","doi":"https://doi.org/10.1109/aiiot58121.2023.10174297","title":"Cross analysis by matrix geometric method for general distribution probability in discrete time","display_name":"Cross analysis by matrix geometric method for general distribution probability in discrete time","publication_year":2023,"publication_date":"2023-06-07","ids":{"openalex":"https://openalex.org/W4384158241","doi":"https://doi.org/10.1109/aiiot58121.2023.10174297"},"language":"en","primary_location":{"id":"doi:10.1109/aiiot58121.2023.10174297","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/aiiot58121.2023.10174297","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE World AI IoT Congress (AIIoT)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5051930523","display_name":"Takako Hoshiyama","orcid":"https://orcid.org/0000-0003-4163-8599"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Takako Hoshiyama","raw_affiliation_strings":["The University of Tokyo,Department of Advanced Interdisciplinary Studies,Tokyo,Japan","Department of Advanced Interdisciplinary Studies, The University of Tokyo, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Tokyo,Department of Advanced Interdisciplinary Studies,Tokyo,Japan","institution_ids":["https://openalex.org/I74801974"]},{"raw_affiliation_string":"Department of Advanced Interdisciplinary Studies, The University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5051930523"],"corresponding_institution_ids":["https://openalex.org/I74801974"],"apc_list":null,"apc_paid":null,"fwci":0.5363,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.59805313,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"0256","last_page":"0258"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10974","display_name":"Advanced Queuing Theory Analysis","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/1404","display_name":"Management Information Systems"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10974","display_name":"Advanced Queuing Theory Analysis","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/1404","display_name":"Management Information Systems"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11716","display_name":"Random Matrices and Applications","score":0.97079998254776,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13601","display_name":"Urban Transport Systems Analysis","score":0.9452999830245972,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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/queueing-theory","display_name":"Queueing theory","score":0.7818557024002075},{"id":"https://openalex.org/keywords/geometric-distribution","display_name":"Geometric distribution","score":0.7476794719696045},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.6831214427947998},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6283966898918152},{"id":"https://openalex.org/keywords/layered-queueing-network","display_name":"Layered queueing network","score":0.5873214602470398},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.5376002192497253},{"id":"https://openalex.org/keywords/probability-distribution","display_name":"Probability distribution","score":0.513918399810791},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.5011498928070068},{"id":"https://openalex.org/keywords/discrete-time-and-continuous-time","display_name":"Discrete time and continuous time","score":0.5004456043243408},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.48346859216690063},{"id":"https://openalex.org/keywords/markovian-arrival-process","display_name":"Markovian arrival process","score":0.432742178440094},{"id":"https://openalex.org/keywords/distribution","display_name":"Distribution (mathematics)","score":0.4233204424381256},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.42210352420806885},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.4086071848869324},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.29925817251205444},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1472398340702057},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.09106847643852234},{"id":"https://openalex.org/keywords/mathematical-analysis","display_name":"Mathematical analysis","score":0.09095188975334167}],"concepts":[{"id":"https://openalex.org/C22684755","wikidata":"https://www.wikidata.org/wiki/Q847526","display_name":"Queueing theory","level":2,"score":0.7818557024002075},{"id":"https://openalex.org/C39947850","wikidata":"https://www.wikidata.org/wiki/Q729523","display_name":"Geometric distribution","level":3,"score":0.7476794719696045},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.6831214427947998},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6283966898918152},{"id":"https://openalex.org/C33891772","wikidata":"https://www.wikidata.org/wiki/Q6505536","display_name":"Layered queueing network","level":3,"score":0.5873214602470398},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.5376002192497253},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.513918399810791},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.5011498928070068},{"id":"https://openalex.org/C55689738","wikidata":"https://www.wikidata.org/wiki/Q15963867","display_name":"Discrete time and continuous time","level":2,"score":0.5004456043243408},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.48346859216690063},{"id":"https://openalex.org/C192163390","wikidata":"https://www.wikidata.org/wiki/Q612061","display_name":"Markovian arrival process","level":3,"score":0.432742178440094},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.4233204424381256},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.42210352420806885},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4086071848869324},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.29925817251205444},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1472398340702057},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.09106847643852234},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.09095188975334167},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/aiiot58121.2023.10174297","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/aiiot58121.2023.10174297","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE World AI IoT Congress (AIIoT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W1665377869","https://openalex.org/W1724438738","https://openalex.org/W1990578816","https://openalex.org/W2084673526","https://openalex.org/W2154602821","https://openalex.org/W4212922449","https://openalex.org/W4366250542"],"related_works":["https://openalex.org/W2084787362","https://openalex.org/W2474880244","https://openalex.org/W2359006202","https://openalex.org/W2360475649","https://openalex.org/W635082018","https://openalex.org/W3046747209","https://openalex.org/W1970318028","https://openalex.org/W2354039126","https://openalex.org/W2911715936","https://openalex.org/W1565037240"],"abstract_inverted_index":{"Queueing":[0],"systems,":[1],"which":[2],"adapt":[3],"the":[4,29,59,68,86,89,96,100,107,111,118,122,125],"general":[5],"distribution":[6,98],"probability":[7],"(GI/G/1":[8],"in":[9,73],"Kendall":[10],"symbols)":[11],"to":[12,48,99],"processes":[13,20,30,104],"of":[14,21,88,113,124],"arrivals":[15],"with":[16,23,129],"infinite":[17],"buffers":[18],"and":[19,102,105,117,120],"services":[22],"finite":[24],"buffers,":[25],"are":[26],"known":[27],"for":[28],"that":[31,58],"cannot":[32],"be":[33,64],"completely":[34],"described":[35],"theoretically":[36,66],"by":[37],"a":[38],"Markov":[39],"chain.":[40],"Therefore,":[41],"approximate":[42,115],"formulas":[43,116],"have":[44],"generally":[45],"been":[46,56],"utilized":[47],"evaluate":[49],"this":[50,83],"queueing":[51,61,127],"system.":[52],"Recently,":[53],"it":[54],"has":[55],"reported":[57],"GI/G/1":[60,90,126],"system":[62,93,128],"can":[63],"solved":[65],"using":[67,114],"matrix":[69],"geometric":[70],"method":[71],"(MGM)":[72],"discrete":[74],"time":[75],"under":[76],"limited":[77],"conditions.":[78],"Using":[79],"its":[80],"MGM":[81,119],"method,":[82],"study":[84],"evaluates":[85],"performance":[87],"discrete-time":[91],"queuing":[92],"when":[94],"utilizing":[95],"Gamma":[97,130],"arrival":[101],"service":[103],"summarizes":[106],"results.":[108],"We":[109],"compare":[110],"results":[112],"discuss":[121],"characteristics":[123],"distribution.":[131]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}