{"id":"https://openalex.org/W1969781557","doi":"https://doi.org/10.1109/his.2011.6122164","title":"Automatic threshold tracking of sensor data using Expectation Maximization algorithm","display_name":"Automatic threshold tracking of sensor data using Expectation Maximization algorithm","publication_year":2011,"publication_date":"2011-12-01","ids":{"openalex":"https://openalex.org/W1969781557","doi":"https://doi.org/10.1109/his.2011.6122164","mag":"1969781557"},"language":"en","primary_location":{"id":"doi:10.1109/his.2011.6122164","is_oa":false,"landing_page_url":"https://doi.org/10.1109/his.2011.6122164","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2011 11th International Conference on Hybrid Intelligent Systems (HIS)","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/A5069022047","display_name":"Arghad Arnaout","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Arghad Arnaout","raw_affiliation_strings":["TDE GmbH, Leoben, Austria","TDE GmbH Leoben, Austria"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"TDE GmbH, Leoben, Austria","institution_ids":[]},{"raw_affiliation_string":"TDE GmbH Leoben, Austria","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084522467","display_name":"Bilal Esmael","orcid":null},"institutions":[{"id":"https://openalex.org/I182712176","display_name":"Montanuniversit\u00e4t Leoben","ror":"https://ror.org/02fhfw393","country_code":"AT","type":"education","lineage":["https://openalex.org/I182712176"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Bilal Esmael","raw_affiliation_strings":["University of Leoben, Leoben, Austria","University of Leoben,Leoben,Austria"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Leoben, Leoben, Austria","institution_ids":["https://openalex.org/I182712176"]},{"raw_affiliation_string":"University of Leoben,Leoben,Austria","institution_ids":["https://openalex.org/I182712176"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027832388","display_name":"R. Fr\u00fchwirth","orcid":"https://orcid.org/0000-0002-0054-3369"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rudolf K. Fruhwirth","raw_affiliation_strings":["TDE GmbH, Leoben, Austria","TDE GmbH Leoben, Austria"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"TDE GmbH, Leoben, Austria","institution_ids":[]},{"raw_affiliation_string":"TDE GmbH Leoben, Austria","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070604697","display_name":"Gerhard Thonhauser","orcid":null},"institutions":[{"id":"https://openalex.org/I182712176","display_name":"Montanuniversit\u00e4t Leoben","ror":"https://ror.org/02fhfw393","country_code":"AT","type":"education","lineage":["https://openalex.org/I182712176"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Gerhard Thonhauser","raw_affiliation_strings":["University of Leoben, Leoben, Austria","University of Leoben,Leoben,Austria"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Leoben, Leoben, Austria","institution_ids":["https://openalex.org/I182712176"]},{"raw_affiliation_string":"University of Leoben,Leoben,Austria","institution_ids":["https://openalex.org/I182712176"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.0172,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.86254166,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"551","last_page":"554"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11801","display_name":"Reservoir Engineering and Simulation Methods","score":0.9970999956130981,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/T11801","display_name":"Reservoir Engineering and Simulation Methods","score":0.9970999956130981,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/T10892","display_name":"Drilling and Well Engineering","score":0.9937999844551086,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/T10635","display_name":"Hydraulic Fracturing and Reservoir Analysis","score":0.9635000228881836,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/expectation\u2013maximization-algorithm","display_name":"Expectation\u2013maximization algorithm","score":0.6840094923973083},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5903175473213196},{"id":"https://openalex.org/keywords/bayes-theorem","display_name":"Bayes' theorem","score":0.5333160161972046},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.5134154558181763},{"id":"https://openalex.org/keywords/maximization","display_name":"Maximization","score":0.5132639408111572},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4956458806991577},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.4776107966899872},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4729951322078705},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.4705750346183777},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.4424087703227997},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4255625903606415},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4230496883392334},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.27460017800331116},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.22992372512817383},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.22843876481056213},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.20123440027236938},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.14939823746681213},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.10960713028907776}],"concepts":[{"id":"https://openalex.org/C182081679","wikidata":"https://www.wikidata.org/wiki/Q1275153","display_name":"Expectation\u2013maximization algorithm","level":3,"score":0.6840094923973083},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5903175473213196},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.5333160161972046},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.5134154558181763},{"id":"https://openalex.org/C2776330181","wikidata":"https://www.wikidata.org/wiki/Q18358244","display_name":"Maximization","level":2,"score":0.5132639408111572},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4956458806991577},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.4776107966899872},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4729951322078705},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.4705750346183777},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4424087703227997},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4255625903606415},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4230496883392334},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.27460017800331116},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.22992372512817383},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.22843876481056213},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.20123440027236938},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.14939823746681213},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.10960713028907776},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C19417346","wikidata":"https://www.wikidata.org/wiki/Q7922","display_name":"Pedagogy","level":1,"score":0.0},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/his.2011.6122164","is_oa":false,"landing_page_url":"https://doi.org/10.1109/his.2011.6122164","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2011 11th International Conference on Hybrid Intelligent Systems (HIS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Responsible consumption and production","score":0.44999998807907104,"id":"https://metadata.un.org/sdg/12"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W1570944911","https://openalex.org/W2049633694","https://openalex.org/W2082099670","https://openalex.org/W2340779274","https://openalex.org/W2565214501","https://openalex.org/W4288075590","https://openalex.org/W6670800121","https://openalex.org/W6704271131","https://openalex.org/W7048738093"],"related_works":["https://openalex.org/W2473373438","https://openalex.org/W2368486525","https://openalex.org/W805531662","https://openalex.org/W2077224612","https://openalex.org/W84255947","https://openalex.org/W2153481672","https://openalex.org/W2891133681","https://openalex.org/W2014842417","https://openalex.org/W3146343978","https://openalex.org/W4312864369"],"abstract_inverted_index":{"In":[0],"this":[1,147,159],"paper":[2,148],"we":[3],"present":[4],"a":[5,48,84,120,152],"novel":[6],"method":[7,94,156],"for":[8,23,157],"automatic":[9],"threshold":[10,44,126,129],"handling":[11],"and":[12,69,80,154],"tracking":[13,158],"of":[14,91,140,161],"sensor":[15,76,112,165],"data":[16,62,77,113,142,166],"at":[17],"drilling":[18,25,56],"rigs.":[19],"A":[20],"hybrid":[21],"system":[22,51],"automated":[24,55],"operation":[26],"classification":[27,58],"is":[28,67,95,103,117,133],"extended":[29],"by":[30,47],"the":[31,38,64,71,75,92,98,111,137,164],"Expectation":[32,99],"Maximization":[33,100],"algorithm":[34,101],"in":[35,53,74,83,110,146,163,176],"combination":[36],"with":[37],"Bayes'":[39,115],"theorem":[40,116],"to":[41,105,123,171],"find":[42],"automatically":[43],"values":[45,130],"required":[46],"rule":[49],"based":[50,96],"used":[52,104,118],"an":[54],"operations":[57],"system.":[59],"The":[60,88,128,144],"streaming":[61],"from":[63],"rig":[65],"site":[66],"gathered":[68],"analyzed,":[70],"main":[72],"clusters":[73],"are":[78],"identified":[79],"monitored":[81],"as":[82,119],"real":[85],"life":[86],"case.":[87],"first":[89],"part":[90,122],"suggested":[93],"on":[97,136],"which":[102],"decompose":[106],"Gaussian":[107],"mixture":[108],"models":[109],"set.":[114],"subsequent":[121],"calculate":[124],"optimal":[125],"values.":[127],"calculation":[131],"concept":[132],"heavily":[134],"depending":[135],"likelihood":[138],"probabilities":[139],"each":[141],"cluster.":[143],"work":[145],"not":[149],"only":[150],"suggests":[151],"solution":[153],"analytical":[155],"kind":[160],"thresholds":[162,175],"but":[167],"also":[168],"verifies":[169],"how":[170],"compute":[172],"such":[173],"reliable":[174],"real-time.":[177]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2013,"cited_by_count":1},{"year":2012,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}