{"id":"https://openalex.org/W2735443201","doi":"https://doi.org/10.1109/ijcnn.2017.7966095","title":"Extreme learning machines to approximate low dimensional spaces for helicopter load signal and fatigue life estimation","display_name":"Extreme learning machines to approximate low dimensional spaces for helicopter load signal and fatigue life estimation","publication_year":2017,"publication_date":"2017-05-01","ids":{"openalex":"https://openalex.org/W2735443201","doi":"https://doi.org/10.1109/ijcnn.2017.7966095","mag":"2735443201"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2017.7966095","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2017.7966095","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 International Joint Conference on Neural Networks (IJCNN)","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/A5019572093","display_name":"Julio J. Vald\u00e9s","orcid":"https://orcid.org/0000-0003-2930-0325"},"institutions":[{"id":"https://openalex.org/I4210159778","display_name":"National Research Council Canada","ror":"https://ror.org/04mte1k06","country_code":"CA","type":"government","lineage":["https://openalex.org/I4210159778"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Julio J. Valdes","raw_affiliation_strings":["National Research Council Canada, Information and Communications Technologies, Ottawa, Ontario, Canada"],"affiliations":[{"raw_affiliation_string":"National Research Council Canada, Information and Communications Technologies, Ottawa, Ontario, Canada","institution_ids":["https://openalex.org/I4210159778"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074623356","display_name":"Catherine Cheung","orcid":"https://orcid.org/0000-0002-4647-4119"},"institutions":[{"id":"https://openalex.org/I4210159778","display_name":"National Research Council Canada","ror":"https://ror.org/04mte1k06","country_code":"CA","type":"government","lineage":["https://openalex.org/I4210159778"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Catherine Cheung","raw_affiliation_strings":["National Research Council Canada Aerospace, Ottawa, Ontario, Canada"],"affiliations":[{"raw_affiliation_string":"National Research Council Canada Aerospace, Ottawa, Ontario, Canada","institution_ids":["https://openalex.org/I4210159778"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064384322","display_name":"Alejandro Lehman Rubio","orcid":null},"institutions":[{"id":"https://openalex.org/I4210159778","display_name":"National Research Council Canada","ror":"https://ror.org/04mte1k06","country_code":"CA","type":"government","lineage":["https://openalex.org/I4210159778"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Alejandro Lehman Rubio","raw_affiliation_strings":["National Research Council Canada Aerospace, Ottawa, Ontario, Canada"],"affiliations":[{"raw_affiliation_string":"National Research Council Canada Aerospace, Ottawa, Ontario, Canada","institution_ids":["https://openalex.org/I4210159778"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5019572093"],"corresponding_institution_ids":["https://openalex.org/I4210159778"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.08322073,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1991","last_page":"1998"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.9998999834060669,"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":0.9998999834060669,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9829999804496765,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T10320","display_name":"Neural Networks and Applications","score":0.9819999933242798,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/extreme-learning-machine","display_name":"Extreme learning machine","score":0.7620557546615601},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7254412174224854},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5722094774246216},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.5463325381278992},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5211429595947266},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4540872573852539},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.4310489892959595},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4130488932132721},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3747943043708801},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.3418056070804596},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2657274901866913},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.20084208250045776}],"concepts":[{"id":"https://openalex.org/C2780150128","wikidata":"https://www.wikidata.org/wiki/Q21948731","display_name":"Extreme learning machine","level":3,"score":0.7620557546615601},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7254412174224854},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5722094774246216},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.5463325381278992},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5211429595947266},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4540872573852539},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.4310489892959595},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4130488932132721},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3747943043708801},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3418056070804596},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2657274901866913},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.20084208250045776},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","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/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/ijcnn.2017.7966095","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2017.7966095","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},{"id":"pmh:oai:cisti-icist.nrc-cnrc.ca:cistinparc:23002240","is_oa":false,"landing_page_url":"https://nrc-publications.canada.ca/eng/view/object/?id=27a61e7d-ef4d-47d9-81b8-04fe8bc15f4d","pdf_url":null,"source":{"id":"https://openalex.org/S7407055245","display_name":"NPARC","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5699999928474426,"display_name":"Responsible consumption and production","id":"https://metadata.un.org/sdg/12"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W13458803","https://openalex.org/W1520795727","https://openalex.org/W1541274513","https://openalex.org/W1990938413","https://openalex.org/W2026131661","https://openalex.org/W2049810170","https://openalex.org/W2111072639","https://openalex.org/W2113902808","https://openalex.org/W2122040390","https://openalex.org/W2137939562","https://openalex.org/W2141695047","https://openalex.org/W2157444450","https://openalex.org/W2187089797","https://openalex.org/W2267995583","https://openalex.org/W2406708863","https://openalex.org/W2554617372","https://openalex.org/W2725483183","https://openalex.org/W2735860469","https://openalex.org/W6683161245","https://openalex.org/W6740688013"],"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/W2809157142","https://openalex.org/W2037023988"],"abstract_inverted_index":{"As":[0],"aircraft":[1],"fleets":[2],"are":[3,53,57,146,155],"required":[4],"to":[5,61,106,149,180,225],"expand":[6],"their":[7,76,118],"roles":[8],"and":[9,26,36,47,56,59,75,83,102,128,142,192],"usage,":[10],"the":[11,95,134,137,140,143,168,181,186,189,201,214,217,220,226],"accurate":[12],"estimation":[13,194],"of":[14,97,100,139,188,195,211,216,219,228],"component":[15,42],"loads":[16,43,74],"in":[17,165],"a":[18,177],"helicopter":[19,72,197],"is":[20],"an":[21,158],"important":[22],"capability":[23],"for":[24,32,70,114,162,167,207],"safety":[25],"security":[27],"reasons":[28],"as":[29,31,157],"well":[30],"life":[33,37,79],"cycle":[34],"management":[35],"extension":[38],"efforts.":[39],"Although":[40],"dynamic":[41,73],"can":[44,204],"be":[45,205],"measured":[46],"monitored":[48],"directly,":[49],"these":[50],"measurement":[51],"methods":[52,122],"not":[54],"reliable":[55],"costly":[58],"difficult":[60,148],"maintain.":[62],"Computational":[63],"intelligence":[64],"techniques":[65],"have":[66,111],"been":[67,112],"successfully":[68],"used":[69,113,156,206],"estimating":[71],"resulting":[77],"fatigue":[78],"using":[80],"flight":[81],"system":[82],"control":[84],"parameters.":[85],"However,":[86],"other":[87],"approaches":[88],"work":[89],"on":[90],"low":[91,221],"dimensional":[92,222],"spaces":[93,145],"with":[94,130],"advantage":[96],"smaller":[98],"number":[99],"features":[101,138],"noise":[103],"reduction":[104],"due":[105],"information":[107],"fusion.":[108],"Nonlinear":[109],"transformations":[110],"this":[115],"purpose,":[116],"but":[117],"computation":[119,218],"via":[120],"implicit":[121,163,182,229],"becomes":[123],"more":[124,147],"complex,":[125],"time":[126],"consuming":[127],"impractical":[129],"data":[131],"growth.":[132],"Moreover,":[133],"relationships":[135],"between":[136],"original":[141],"target":[144],"uncover.":[150],"Extreme":[151],"Learning":[152],"Machines":[153],"(ELM)":[154],"explicit":[159],"functional":[160],"representation":[161],"methods,":[164],"particular":[166],"t-SNE":[169],"mapping.":[170],"It":[171],"was":[172],"found":[173],"that":[174],"ELMs":[175],"provided":[176],"good":[178],"approximation":[179],"mapping,":[183],"which":[184],"preserves":[185],"appropriateness":[187],"load":[190],"prediction":[191],"damage":[193],"critical":[196],"components.":[198],"In":[199],"addition,":[200],"ELM":[202],"model":[203],"processing":[208],"incoming":[209],"streams":[210],"data,":[212],"overcoming":[213],"limitation":[215],"mapping":[223],"inherent":[224],"use":[227],"methods.":[230]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2}],"updated_date":"2026-03-02T08:37:19.008085","created_date":"2025-10-10T00:00:00"}
