{"id":"https://openalex.org/W2966818808","doi":"https://doi.org/10.1109/wocc.2019.8770552","title":"Extreme Learning Machine Ensemble for CSI based Device-free Indoor Localization","display_name":"Extreme Learning Machine Ensemble for CSI based Device-free Indoor Localization","publication_year":2019,"publication_date":"2019-05-01","ids":{"openalex":"https://openalex.org/W2966818808","doi":"https://doi.org/10.1109/wocc.2019.8770552","mag":"2966818808"},"language":"en","primary_location":{"id":"doi:10.1109/wocc.2019.8770552","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wocc.2019.8770552","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 28th Wireless and Optical Communications Conference (WOCC)","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/A5064211964","display_name":"Ruofei Gao","orcid":"https://orcid.org/0009-0001-6750-1550"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ruofei Gao","raw_affiliation_strings":["Beijing Engineering Research Center of Industrial Spectrum Imaging, Beijing, 100083, China","Beijing Engineering Research Center of Industrial Spectrum Imaging, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Engineering Research Center of Industrial Spectrum Imaging, Beijing, 100083, China","institution_ids":[]},{"raw_affiliation_string":"Beijing Engineering Research Center of Industrial Spectrum Imaging, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006957309","display_name":"Jianqiang Xue","orcid":"https://orcid.org/0000-0001-6932-4028"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jianqiang Xue","raw_affiliation_strings":["Beijing Engineering Research Center of Industrial Spectrum Imaging, Beijing, 100083, China","Beijing Engineering Research Center of Industrial Spectrum Imaging, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Engineering Research Center of Industrial Spectrum Imaging, Beijing, 100083, China","institution_ids":[]},{"raw_affiliation_string":"Beijing Engineering Research Center of Industrial Spectrum Imaging, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025288570","display_name":"Wendong Xiao","orcid":"https://orcid.org/0000-0002-2270-7889"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wendong Xiao","raw_affiliation_strings":["Beijing Engineering Research Center of Industrial Spectrum Imaging, Beijing, 100083, China","Beijing Engineering Research Center of Industrial Spectrum Imaging, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Engineering Research Center of Industrial Spectrum Imaging, Beijing, 100083, China","institution_ids":[]},{"raw_affiliation_string":"Beijing Engineering Research Center of Industrial Spectrum Imaging, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100949604","display_name":"Baoyong Zhao","orcid":null},"institutions":[{"id":"https://openalex.org/I78675632","display_name":"Beijing Information Science & Technology University","ror":"https://ror.org/04xnqep60","country_code":"CN","type":"education","lineage":["https://openalex.org/I78675632"]},{"id":"https://openalex.org/I92403157","display_name":"University of Science and Technology Beijing","ror":"https://ror.org/02egmk993","country_code":"CN","type":"education","lineage":["https://openalex.org/I92403157"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Baoyong Zhao","raw_affiliation_strings":["School of Automation &#x0026; Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, China","School of Automation & Electrical Engineering, University of Science and Technology Beijing, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automation &#x0026; Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, China","institution_ids":["https://openalex.org/I78675632","https://openalex.org/I92403157"]},{"raw_affiliation_string":"School of Automation & Electrical Engineering, University of Science and Technology Beijing, Beijing, China","institution_ids":["https://openalex.org/I92403157"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100378893","display_name":"Sen Zhang","orcid":"https://orcid.org/0000-0002-8010-6045"},"institutions":[{"id":"https://openalex.org/I78675632","display_name":"Beijing Information Science & Technology University","ror":"https://ror.org/04xnqep60","country_code":"CN","type":"education","lineage":["https://openalex.org/I78675632"]},{"id":"https://openalex.org/I92403157","display_name":"University of Science and Technology Beijing","ror":"https://ror.org/02egmk993","country_code":"CN","type":"education","lineage":["https://openalex.org/I92403157"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sen Zhang","raw_affiliation_strings":["School of Automation &#x0026; Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, China","School of Automation & Electrical Engineering, University of Science and Technology Beijing, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automation &#x0026; Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, China","institution_ids":["https://openalex.org/I78675632","https://openalex.org/I92403157"]},{"raw_affiliation_string":"School of Automation & Electrical Engineering, University of Science and Technology Beijing, Beijing, China","institution_ids":["https://openalex.org/I92403157"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"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/T12676","display_name":"Machine Learning and ELM","score":0.9991999864578247,"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/T12676","display_name":"Machine Learning and ELM","score":0.9991999864578247,"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/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.9991000294685364,"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"}},{"id":"https://openalex.org/T11192","display_name":"Underwater Vehicles and Communication Systems","score":0.9768000245094299,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/extreme-learning-machine","display_name":"Extreme learning machine","score":0.8008972406387329},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.76715087890625},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.7363004684448242},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.6433515548706055},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5799883008003235},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.548311173915863},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4592573046684265},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.4374512732028961},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4221721589565277},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.32892340421676636},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.18436720967292786}],"concepts":[{"id":"https://openalex.org/C2780150128","wikidata":"https://www.wikidata.org/wiki/Q21948731","display_name":"Extreme learning machine","level":3,"score":0.8008972406387329},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.76715087890625},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7363004684448242},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.6433515548706055},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5799883008003235},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.548311173915863},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4592573046684265},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.4374512732028961},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4221721589565277},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32892340421676636},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.18436720967292786}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wocc.2019.8770552","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wocc.2019.8770552","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 28th Wireless and Optical Communications Conference (WOCC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.6200000047683716,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1930517239","https://openalex.org/W1983301216","https://openalex.org/W1998497571","https://openalex.org/W2026131661","https://openalex.org/W2032306167","https://openalex.org/W2035026907","https://openalex.org/W2044846595","https://openalex.org/W2051376734","https://openalex.org/W2089695767","https://openalex.org/W2100989187","https://openalex.org/W2111072639","https://openalex.org/W2143228105","https://openalex.org/W2170102584","https://openalex.org/W2240192984","https://openalex.org/W2345276999","https://openalex.org/W2471935249","https://openalex.org/W2509382177","https://openalex.org/W2736051620","https://openalex.org/W2810466531","https://openalex.org/W2888967269","https://openalex.org/W4235628085","https://openalex.org/W7062057604"],"related_works":["https://openalex.org/W2067443264","https://openalex.org/W31566076","https://openalex.org/W4297902562","https://openalex.org/W2741186499","https://openalex.org/W2804652951","https://openalex.org/W2556335056","https://openalex.org/W2002678693","https://openalex.org/W1584764049","https://openalex.org/W2743832667","https://openalex.org/W4386889652"],"abstract_inverted_index":{"A":[0],"bstract-Device-free":[1],"localization":[2,40,97,120],"is":[3,35,53,79,144,175],"a":[4,36,54,95,123,129,185],"new":[5],"and":[6,42,56,135,193,202],"developing":[7],"technology":[8],"which":[9,66,105],"estimates":[10],"an":[11,100,163,176],"object's":[12],"locations":[13],"without":[14],"requiring":[15],"it":[16,60],"to":[17,80,85,132,147,167,189],"equip":[18],"any":[19],"devices.":[20],"Channel":[21],"State":[22],"Information":[23],"(CSI),":[24],"containing":[25],"more":[26],"fine-grained":[27],"information":[28],"than":[29],"Received":[30],"Signal":[31],"Strength":[32],"Indication":[33],"(RSSI),":[34],"natural":[37],"candidate":[38],"for":[39],"application":[41],"has":[43,61,108],"been":[44],"studied":[45],"in":[46,104,184],"many":[47],"works.":[48],"Extreme":[49],"Learning":[50],"Machine":[51],"(ELM)":[52],"fast":[55],"robust":[57],"algorithm,":[58],"but":[59],"only":[62],"one":[63],"hidden":[64,113],"layer,":[65],"limits":[67],"its":[68,191],"capacity.":[69],"One":[70],"of":[71,76,102,112,151,165,179,199,203],"the":[72,109,119,149,152,157,194,197],"most":[73],"popular":[74],"ways":[75],"improving":[77],"accuracy":[78],"use":[81],"multiple":[82],"different":[83],"models":[84,118],"obtain":[86],"better":[87],"predictive":[88],"performance.":[89],"In":[90],"this":[91],"paper,":[92],"we":[93,127],"propose":[94],"device-free":[96],"approach":[98,117,201],"using":[99],"ensemble":[101,164],"ELMs":[103,166],"each":[106],"ELM":[107],"same":[110],"number":[111],"nodes.":[114],"The":[115,139,172],"proposed":[116],"task":[121],"as":[122],"regression":[124],"problem.":[125],"First,":[126],"leverage":[128],"modified":[130],"driver":[131],"collect":[133],"CSI":[134],"extract":[136],"phase":[137,153],"information.":[138],"Principal":[140],"Component":[141],"Analysis":[142],"(PCA)":[143],"then":[145],"applied":[146],"reduce":[148],"dimensionality":[150],"features.":[154],"After":[155],"that,":[156],"processed":[158],"features":[159],"are":[160],"fed":[161],"into":[162],"output":[168],"their":[169],"respective":[170],"predictions.":[171],"final":[173],"prediction":[174],"average":[177],"combination":[178],"them.":[180],"We":[181],"conducted":[182],"experiments":[183],"typical":[186],"indoor":[187],"environment":[188],"verify":[190],"performance,":[192],"results":[195],"demonstrated":[196],"effectiveness":[198],"our":[200],"CSI.":[204]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":1}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}
