{"id":"https://openalex.org/W2126289448","doi":null,"title":"Distributed active learning with application to battery health management","display_name":"Distributed active learning with application to battery health management","publication_year":2011,"publication_date":"2011-07-05","ids":{"openalex":"https://openalex.org/W2126289448","mag":"2126289448"},"language":"en","primary_location":{"id":"pmh:oai:CiteSeerX.psu:10.1.1.929.3628","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.929.3628","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"https://works.bepress.com/huimin_chen/16/download/","raw_type":"text"},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"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/A5100356337","display_name":"Huimin Chen","orcid":"https://orcid.org/0000-0002-4244-107X","observed_orcids":["https://orcid.org/0000-0002-4244-107X"]},"institutions":[{"id":"https://openalex.org/I192396691","display_name":"LSU New Orleans","ror":"https://ror.org/034mtvk83","country_code":"US","type":"education","lineage":["https://openalex.org/I192396691"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Huimin Chen","raw_affiliation_strings":["Department of Electrical Engineering, University of New Orleans, New Orleans, LA, 70148, U. S. A"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, University of New Orleans, New Orleans, LA, 70148, U. S. A","institution_ids":["https://openalex.org/I192396691"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066692075","display_name":"X. Rong Li","orcid":"https://orcid.org/0000-0001-6594-5919","observed_orcids":["https://orcid.org/0000-0001-6594-5919","https://orcid.org/0000-0001-9081-817X"]},"institutions":[{"id":"https://openalex.org/I192396691","display_name":"LSU New Orleans","ror":"https://ror.org/034mtvk83","country_code":"US","type":"education","lineage":["https://openalex.org/I192396691"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"X. Rong Li","raw_affiliation_strings":["Department of Electrical Engineering, University of New Orleans, New Orleans, LA, 70148, U. S. A"],"raw_orcid":"https://orcid.org/0000-0001-6594-5919","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, University of New Orleans, New Orleans, LA, 70148, U. S. A","institution_ids":["https://openalex.org/I192396691"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I192396691"],"apc_list":null,"apc_paid":null,"fwci":1.0489,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.79417584,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9800000190734863,"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"}},"topics":[{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9800000190734863,"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"}},{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.004900000058114529,"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"}},{"id":"https://openalex.org/T10780","display_name":"Reliability and Maintenance Optimization","score":0.004100000020116568,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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/battery-health-management","display_name":"battery health management","score":0.7139999866485596},{"id":"https://openalex.org/keywords/remaining-useful-life-prediction","display_name":"remaining useful life prediction","score":0.9890000224113464},{"id":"https://openalex.org/keywords/prognostics-and-health-management","display_name":"prognostics and health management","score":0.9190000295639038},{"id":"https://openalex.org/keywords/importance-sampling","display_name":"importance sampling","score":0.722000002861023},{"id":"https://openalex.org/keywords/boosting","display_name":"boosting","score":0.5199999809265137},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"reinforcement learning","score":0.45899999141693115},{"id":"https://openalex.org/keywords/limited-training-data","display_name":"limited training data","score":0.46700000762939453},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"semi-supervised learning","score":0.8370000123977661}],"concepts":[{"id":"https://openalex.org/C129364497","wikidata":"https://www.wikidata.org/wiki/Q3042561","display_name":"Prognostics","level":2,"score":0.7439128756523132},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7408401370048523},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.6213127374649048},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.6051523089408875},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.589673638343811},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5742247700691223},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.48477286100387573},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.43231287598609924},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.4190279543399811},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3336944580078125},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.15502876043319702}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:CiteSeerX.psu:10.1.1.929.3628","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.929.3628","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"https://works.bepress.com/huimin_chen/16/download/","raw_type":"text"},{"id":"mag:2126289448","doi":null,"is_oa":false,"landing_page_url":"http://ieeexplore.ieee.org/document/5977535/","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"International Conference on Information Fusion","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1600192391","https://openalex.org/W1949231392","https://openalex.org/W1976009969","https://openalex.org/W1988387263","https://openalex.org/W1988790447","https://openalex.org/W1990334093","https://openalex.org/W2018770010","https://openalex.org/W2068871408","https://openalex.org/W2077902449","https://openalex.org/W2115233498","https://openalex.org/W2115404432","https://openalex.org/W2117756453","https://openalex.org/W2121863487","https://openalex.org/W2123959253","https://openalex.org/W2127342270","https://openalex.org/W2133496267","https://openalex.org/W2167595980","https://openalex.org/W2182412433","https://openalex.org/W2399495046"],"related_works":["https://openalex.org/W2594608700","https://openalex.org/W2804837173","https://openalex.org/W2006484294","https://openalex.org/W2965753329","https://openalex.org/W3217016149","https://openalex.org/W2107003417","https://openalex.org/W2964074580","https://openalex.org/W3215013410","https://openalex.org/W2579857932","https://openalex.org/W7007813152"],"abstract_inverted_index":{"Abstract\u2014This":[0],"paper":[1],"focuses":[2],"on":[3],"distributed":[4,89,153],"implementation":[5,90],"of":[6,13,44,97,152,166],"active":[7,84,93,154],"learning":[8,50,59,85,144],"with":[9,51,91,141,162],"a":[10,26,45,57,73,106,163,172],"limited":[11,52],"number":[12,165],"queries.":[14],"In":[15],"the":[16,22,36,62,68,138,150],"prognos-tics":[17],"and":[18,86,117,159],"health":[19,132],"management":[20,133],"domain,":[21],"cost":[23],"to":[24,66,88,115,136,170],"obtain":[25],"training":[27,167],"sample":[28],"can":[29],"be":[30],"fairly":[31],"high,":[32],"especially":[33],"when":[34],"studying":[35],"aging":[37],"process":[38],"for":[39,83,130],"remaining":[40,126],"useful":[41,127],"life":[42,128],"prediction":[43,129,160],"mission":[46],"critical":[47],"component.":[48],"Active":[49],"resource":[53],"is":[54,81,103,113,134],"formulated":[55],"as":[56,105,120,122],"reinforcement":[58],"problem,":[60],"where":[61],"sampling":[63,78],"strategy":[64],"has":[65],"minimize":[67],"expected":[69],"generalization":[70],"error":[71],"within":[72],"finite":[74],"horizon.":[75],"An":[76],"importance":[77,98],"based":[79],"method":[80,140],"adopted":[82],"extended":[87],"multiple":[92,101],"learners.":[94],"The":[95,110,125],"fusion":[96,151],"weights":[99],"from":[100],"learners":[102,155],"interpreted":[104],"special":[107],"boosting":[108],"strategy.":[109],"proposed":[111,139],"framework":[112],"applicable":[114],"classification":[116,158],"regression":[118],"problems":[119],"well":[121],"semi-supervised":[123],"learning.":[124],"battery":[131],"used":[135],"compare":[137],"conventional":[142],"passive":[143],"methods.":[145],"Empirical":[146],"study":[147],"shows":[148],"that":[149],"achieves":[156],"better":[157],"accuracy":[161],"reduced":[164],"samples":[168],"needed":[169],"have":[171],"complete":[173],"run-to-failure":[174],"profile.":[175]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":1},{"year":2012,"cited_by_count":2}],"updated_date":"2026-11-05T12:25:35.769452","created_date":"2016-06-24T00:00:00"}
