乐胖代购免代理版

{"id":"https://openalex.org/W2076907444","doi":"https://doi.org/10.1109/ats.2012.49","title":"Board-Level Functional Fault Diagnosis Using Learning Based on Incremental Support-Vector Machines","display_name":"Board-Level Functional Fault Diagnosis Using Learning Based on Incremental Support-Vector Machines","publication_year":2012,"publication_date":"2012-11-01","ids":{"openalex":"https://openalex.org/W2076907444","doi":"https://doi.org/10.1109/ats.2012.49","mag":"2076907444"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/ats.2012.49","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-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/A5018889229","display_name":"Fangming Ye","orcid":null},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"funder","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fangming Ye","raw_affiliation_strings":["ECE Dept., Duke Univ., Durham, UK"],"affiliations":[{"raw_affiliation_string":"ECE Dept., Duke Univ., Durham, UK","institution_ids":["https://openalex.org/I170897317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101914136","display_name":"Zhaobo Zhang","orcid":"https://orcid.org/0000-0002-8883-3191"},"institutions":[{"id":"https://openalex.org/I4210146936","display_name":"Huawei Technologies (United States)","ror":"https://ror.org/03jyqk712","country_code":"US","type":"company","lineage":["https://openalex.org/I2250955327","https://openalex.org/I4210146936"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhaobo Zhang","raw_affiliation_strings":["Huawei Technol. Co. Ltd., San Jose, CA, USA"],"affiliations":[{"raw_affiliation_string":"Huawei Technol. Co. Ltd., San Jose, CA, USA","institution_ids":["https://openalex.org/I4210146936"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033880864","display_name":"Krishnendu Chakrabarty","orcid":"https://orcid.org/0000-0003-4475-6435"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"funder","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Krishnendu Chakrabarty","raw_affiliation_strings":["ECE Dept., Duke Univ., Durham, UK"],"affiliations":[{"raw_affiliation_string":"ECE Dept., Duke Univ., Durham, UK","institution_ids":["https://openalex.org/I170897317"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014106133","display_name":"Xinli Gu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210146936","display_name":"Huawei Technologies (United States)","ror":"https://ror.org/03jyqk712","country_code":"US","type":"company","lineage":["https://openalex.org/I2250955327","https://openalex.org/I4210146936"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xinli Gu","raw_affiliation_strings":["Huawei Technol. Co. Ltd., San Jose, CA, USA"],"affiliations":[{"raw_affiliation_string":"Huawei Technol. Co. Ltd., San Jose, CA, USA","institution_ids":["https://openalex.org/I4210146936"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":5.297,"has_fulltext":true,"fulltext_origin":"ngrams","cited_by_count":12,"citation_normalized_percentile":{"value":0.719472,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":86,"max":87},"biblio":{"volume":null,"issue":null,"first_page":"208","last_page":"213"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9979,"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"}},"topics":[{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9979,"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9977,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9812,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/incremental-learning","display_name":"Incremental Learning","score":0.51044476},{"id":"https://openalex.org/keywords/relevance-vector-machine","display_name":"Relevance vector machine","score":0.44937897},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.43132734}],"concepts":[{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.8170398},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.70347285},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6248714},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6210347},{"id":"https://openalex.org/C2780735816","wikidata":"https://www.wikidata.org/wiki/Q28324931","display_name":"Incremental learning","level":2,"score":0.51044476},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.50201654},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4702075},{"id":"https://openalex.org/C14948415","wikidata":"https://www.wikidata.org/wiki/Q7310972","display_name":"Relevance vector machine","level":3,"score":0.44937897},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.43132734},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34229046},{"id":"https://openalex.org/C165205528","wikidata":"https://www.wikidata.org/wiki/Q83371","display_name":"Seismology","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/ats.2012.49","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.53,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"grants":[],"datasets":[],"versions":[],"referenced_works_count":19,"referenced_works":["https://openalex.org/W1497704817","https://openalex.org/W1544582518","https://openalex.org/W1562027785","https://openalex.org/W1593490557","https://openalex.org/W1986146602","https://openalex.org/W2013568003","https://openalex.org/W2024681686","https://openalex.org/W2049088930","https://openalex.org/W2080963611","https://openalex.org/W2096751652","https://openalex.org/W2097898932","https://openalex.org/W2108271008","https://openalex.org/W2114656739","https://openalex.org/W2119821739","https://openalex.org/W2119884533","https://openalex.org/W2154749389","https://openalex.org/W2169610","https://openalex.org/W4239510810","https://openalex.org/W4240038564"],"related_works":["https://openalex.org/W3147035969","https://openalex.org/W2565312173","https://openalex.org/W2383027800","https://openalex.org/W2359936972","https://openalex.org/W2335152656","https://openalex.org/W2206547991","https://openalex.org/W2186666570","https://openalex.org/W2104936869","https://openalex.org/W2066952721","https://openalex.org/W2010005619"],"abstract_inverted_index":{"Advanced":[0],"machine":[1],"learning":[2,48,64],"techniques":[3],"offer":[4],"an":[5],"unprecedented":[6],"opportunity":[7],"to":[8,36,69,72,103],"increase":[9],"the":[10,19,27,37,41,51,66,90,105],"accuracy":[11,116],"of":[12,22,40,84,93,111],"board-level":[13],"functional":[14],"fault":[15,80],"diagnosis":[16,33,52,67,107,115,121],"based":[17,123],"on":[18,124],"historical":[20,42],"data":[21,87],"successfully":[23],"repaired":[24],"boards.":[25],"However,":[26],"training":[28,112],"complexity":[29],"increases":[30],"significantly":[31],"in":[32,50,98,109],"systems":[34],"due":[35],"increasing":[38],"amount":[39],"data.":[43],"We":[44],"propose":[45],"a":[46,118],"smart":[47],"method":[49],"system":[53,68,122],"using":[54,62],"incremental":[55,63],"support-vector":[56,126],"machines":[57],"(SVMs).":[58],"The":[59],"SVMs":[60],"updated":[61],"allow":[65],"quickly":[70],"adapt":[71],"new":[73],"error":[74],"observations":[75],"and":[76,114],"provide":[77],"more":[78],"accurate":[79],"diagnosis.":[81],"Two":[82],"sets":[83],"large-scale":[85],"synthetic":[86],"generated":[88],"from":[89],"log":[91],"information":[92],"two":[94],"complex":[95],"industrial":[96],"boards,":[97],"volume":[99],"production,":[100],"are":[101],"used":[102],"validate":[104],"proposed":[106,120],"approach":[108],"terms":[110],"time":[113],"over":[117],"previously":[119],"simple":[125],"machines.":[127]},"abstract_inverted_index_v3":null,"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W2076907444","counts_by_year":[{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":3},{"year":2014,"cited_by_count":3},{"year":2013,"cited_by_count":4}],"updated_date":"2025-03-15T18:16:50.581418","created_date":"2016-06-24"}