{"id":"https://openalex.org/W2563709429","doi":"https://doi.org/10.1109/ntms.2016.7792429","title":"A Novel Dynamic Hidden Semi-Markov Model (D-HSMM) for Occupancy Pattern Detection from Sensor Data Stream","display_name":"A Novel Dynamic Hidden Semi-Markov Model (D-HSMM) for Occupancy Pattern Detection from Sensor Data Stream","publication_year":2016,"publication_date":"2016-11-01","ids":{"openalex":"https://openalex.org/W2563709429","doi":"https://doi.org/10.1109/ntms.2016.7792429","mag":"2563709429"},"language":"en","primary_location":{"id":"doi:10.1109/ntms.2016.7792429","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ntms.2016.7792429","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 8th IFIP International Conference on New Technologies, Mobility and Security (NTMS)","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/A5055357367","display_name":"Jose Luis Gomez Ortega","orcid":null},"institutions":[{"id":"https://openalex.org/I11983389","display_name":"Manchester Metropolitan University","ror":"https://ror.org/02hstj355","country_code":"GB","type":"education","lineage":["https://openalex.org/I11983389"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Jose Luis Gomez Ortega","raw_affiliation_strings":["Sch. of Comput. Math. & Digital Technol., Manchester Metropolitan University, Manchester, Greater Manchester, GB"],"affiliations":[{"raw_affiliation_string":"Sch. of Comput. Math. & Digital Technol., Manchester Metropolitan University, Manchester, Greater Manchester, GB","institution_ids":["https://openalex.org/I11983389"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023625466","display_name":"Liangxiu Han","orcid":"https://orcid.org/0000-0003-2491-7473"},"institutions":[{"id":"https://openalex.org/I11983389","display_name":"Manchester Metropolitan University","ror":"https://ror.org/02hstj355","country_code":"GB","type":"education","lineage":["https://openalex.org/I11983389"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Liangxiu Han","raw_affiliation_strings":["School of Computing Mathematics and Digital Technology, Manchester Metropolitan University, Manchester, UK"],"affiliations":[{"raw_affiliation_string":"School of Computing Mathematics and Digital Technology, Manchester Metropolitan University, Manchester, UK","institution_ids":["https://openalex.org/I11983389"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112354407","display_name":"Nicholas Bowring","orcid":null},"institutions":[{"id":"https://openalex.org/I11983389","display_name":"Manchester Metropolitan University","ror":"https://ror.org/02hstj355","country_code":"GB","type":"education","lineage":["https://openalex.org/I11983389"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Nicholas Bowring","raw_affiliation_strings":["School of Computing Mathematics and Digital Technology, Manchester Metropolitan University Manchester, UK"],"affiliations":[{"raw_affiliation_string":"School of Computing Mathematics and Digital Technology, Manchester Metropolitan University Manchester, UK","institution_ids":["https://openalex.org/I11983389"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5055357367"],"corresponding_institution_ids":["https://openalex.org/I11983389"],"apc_list":null,"apc_paid":null,"fwci":1.5245,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.84152702,"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":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10121","display_name":"Building Energy and Comfort Optimization","score":0.9975000023841858,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/T10121","display_name":"Building Energy and Comfort Optimization","score":0.9975000023841858,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.9958000183105469,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10603","display_name":"Smart Grid Energy Management","score":0.9908999800682068,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/hidden-markov-model","display_name":"Hidden Markov model","score":0.9060090184211731},{"id":"https://openalex.org/keywords/hidden-semi-markov-model","display_name":"Hidden semi-Markov model","score":0.8213149309158325},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6403688192367554},{"id":"https://openalex.org/keywords/occupancy","display_name":"Occupancy","score":0.599437952041626},{"id":"https://openalex.org/keywords/duration","display_name":"Duration (music)","score":0.46060022711753845},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4566146731376648},{"id":"https://openalex.org/keywords/markov-model","display_name":"Markov model","score":0.42011696100234985},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39582937955856323},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3632673919200897},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.3624531626701355},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33906400203704834},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.17632591724395752},{"id":"https://openalex.org/keywords/markov-property","display_name":"Markov property","score":0.1147204041481018}],"concepts":[{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.9060090184211731},{"id":"https://openalex.org/C64939953","wikidata":"https://www.wikidata.org/wiki/Q3859882","display_name":"Hidden semi-Markov model","level":5,"score":0.8213149309158325},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6403688192367554},{"id":"https://openalex.org/C160331591","wikidata":"https://www.wikidata.org/wiki/Q7075743","display_name":"Occupancy","level":2,"score":0.599437952041626},{"id":"https://openalex.org/C112758219","wikidata":"https://www.wikidata.org/wiki/Q16038819","display_name":"Duration (music)","level":2,"score":0.46060022711753845},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4566146731376648},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.42011696100234985},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39582937955856323},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3632673919200897},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.3624531626701355},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33906400203704834},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.17632591724395752},{"id":"https://openalex.org/C189973286","wikidata":"https://www.wikidata.org/wiki/Q176695","display_name":"Markov property","level":4,"score":0.1147204041481018},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C124952713","wikidata":"https://www.wikidata.org/wiki/Q8242","display_name":"Literature","level":1,"score":0.0},{"id":"https://openalex.org/C170154142","wikidata":"https://www.wikidata.org/wiki/Q150737","display_name":"Architectural engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ntms.2016.7792429","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ntms.2016.7792429","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 8th IFIP International Conference on New Technologies, Mobility and Security (NTMS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.8899999856948853}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1582944457","https://openalex.org/W1599494361","https://openalex.org/W1972669681","https://openalex.org/W1976526581","https://openalex.org/W2003726679","https://openalex.org/W2076618452","https://openalex.org/W2081733273","https://openalex.org/W2105773988","https://openalex.org/W2119357806","https://openalex.org/W2125838338","https://openalex.org/W2134266767","https://openalex.org/W2147242149","https://openalex.org/W2182886880","https://openalex.org/W2497114566","https://openalex.org/W2586954615","https://openalex.org/W4285719527","https://openalex.org/W6733836255"],"related_works":["https://openalex.org/W1510894296","https://openalex.org/W2379938888","https://openalex.org/W4246505579","https://openalex.org/W2134386692","https://openalex.org/W2103649304","https://openalex.org/W3044198794","https://openalex.org/W2116722627","https://openalex.org/W2176285001","https://openalex.org/W2537260108","https://openalex.org/W2799426416"],"abstract_inverted_index":{"Occupant":[0],"presence":[1,113],"and":[2,18,67,153,168],"behaviour":[3],"have":[4,146],"a":[5,40,81,105,121],"large":[6],"impact":[7],"on":[8,120],"building":[9],"energy":[10],"performance.":[11],"With":[12],"the":[13,75,89,131,140,165],"availability":[14],"of":[15,74,95,129,134,142],"low":[16],"cost":[17],"affordable":[19],"sensors,":[20],"accurate":[21],"occupancy":[22,51],"detection":[23],"by":[24],"combining":[25],"sensor":[26,54],"stream":[27,56],"data":[28,55],"with":[29,124,150,171],"machine":[30],"learning":[31],"approaches":[32,170],"becomes":[33],"possible.":[34],"In":[35,136],"this":[36,117],"paper,":[37],"we":[38,145],"propose":[39],"novel":[41],"dynamical":[42],"hidden":[43,64,68],"semi-Markov":[44,69],"model":[45,163],"(D-HSMM)":[46],"which":[47,88],"can":[48],"accurately":[49],"detect":[50],"pattern":[52],"from":[53],"in":[57,79,87,99,116],"real":[58],"time.":[59],"Our":[60],"approach":[61,77,107],"extends":[62],"traditional":[63,100,151],"Markov":[65],"(HMM)":[66],"models":[70],"(HSMM).":[71],"The":[72,156],"novelty":[73],"proposed":[76],"consists":[78],"1)":[80],"new":[82,106],"dynamic":[83],"duration":[84,90,98],"modelling":[85],"way":[86],"is":[91],"dynamically":[92],"changing,":[93],"instead":[94,128],"using":[96,130],"fixed":[97],"HSMM":[101,154],"based":[102,119],"models;":[103],"2)":[104],"to":[108,138],"state":[109],"prediction":[110],"(i.e.":[111],"occupant":[112],"or":[114],"absence":[115],"case)":[118],"weighted":[122],"function":[123],"partially":[125],"available":[126],"observations":[127],"whole":[132],"set":[133],"observations.":[135],"order":[137],"evaluate":[139],"performance":[141],"our":[143,148,161],"model,":[144],"compared":[147],"results":[149],"HMM":[152,167],"approaches.":[155],"experimental":[157],"evaluation":[158],"shows":[159],"that":[160],"D-HSMM":[162],"outperforms":[164],"conventional":[166],"HSMM-based":[169],"very":[172],"high":[173],"accuracy.":[174]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}