{"id":"https://openalex.org/W3196333164","doi":"https://doi.org/10.23919/eusipco54536.2021.9616148","title":"Robust Deep Residual Shrinkage Networks for Online Fault Classification","display_name":"Robust Deep Residual Shrinkage Networks for Online Fault Classification","publication_year":2021,"publication_date":"2021-08-23","ids":{"openalex":"https://openalex.org/W3196333164","doi":"https://doi.org/10.23919/eusipco54536.2021.9616148","mag":"3196333164"},"language":"en","primary_location":{"id":"doi:10.23919/eusipco54536.2021.9616148","is_oa":false,"landing_page_url":"https://doi.org/10.23919/eusipco54536.2021.9616148","pdf_url":null,"source":{"id":"https://openalex.org/S4363607854","display_name":"2021 29th European Signal Processing Conference (EUSIPCO)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 29th European Signal Processing Conference (EUSIPCO)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://researchonline.gcu.ac.uk/en/publications/74d8f66c-c09c-45a6-bc86-57bdaedea92b","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5014653891","display_name":"Alireza Salimy","orcid":null},"institutions":[{"id":"https://openalex.org/I195939026","display_name":"Glasgow Caledonian University","ror":"https://ror.org/03dvm1235","country_code":"GB","type":"education","lineage":["https://openalex.org/I195939026"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Alireza Salimy","raw_affiliation_strings":["School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow, United Kingdom"],"affiliations":[{"raw_affiliation_string":"School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow, United Kingdom","institution_ids":["https://openalex.org/I195939026"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010827986","display_name":"Imene Mitiche","orcid":"https://orcid.org/0000-0001-9169-835X"},"institutions":[{"id":"https://openalex.org/I195939026","display_name":"Glasgow Caledonian University","ror":"https://ror.org/03dvm1235","country_code":"GB","type":"education","lineage":["https://openalex.org/I195939026"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Imene Mitiche","raw_affiliation_strings":["School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow, United Kingdom"],"affiliations":[{"raw_affiliation_string":"School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow, United Kingdom","institution_ids":["https://openalex.org/I195939026"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000142779","display_name":"Philip Boreham","orcid":"https://orcid.org/0000-0003-0071-7717"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Philip Boreham","raw_affiliation_strings":["Innovation Centre for Online Systems, Doble Engineering, Bere Regis, United Kingdom"],"affiliations":[{"raw_affiliation_string":"Innovation Centre for Online Systems, Doble Engineering, Bere Regis, United Kingdom","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085129880","display_name":"A. Nesbitt","orcid":"https://orcid.org/0000-0003-3518-7656"},"institutions":[{"id":"https://openalex.org/I195939026","display_name":"Glasgow Caledonian University","ror":"https://ror.org/03dvm1235","country_code":"GB","type":"education","lineage":["https://openalex.org/I195939026"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Alan Nesbitt","raw_affiliation_strings":["School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow, United Kingdom"],"affiliations":[{"raw_affiliation_string":"School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow, United Kingdom","institution_ids":["https://openalex.org/I195939026"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001283656","display_name":"Gordon Morison","orcid":"https://orcid.org/0000-0002-3282-5248"},"institutions":[{"id":"https://openalex.org/I195939026","display_name":"Glasgow Caledonian University","ror":"https://ror.org/03dvm1235","country_code":"GB","type":"education","lineage":["https://openalex.org/I195939026"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Gordon Morison","raw_affiliation_strings":["School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow, United Kingdom"],"affiliations":[{"raw_affiliation_string":"School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow, United Kingdom","institution_ids":["https://openalex.org/I195939026"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5014653891"],"corresponding_institution_ids":["https://openalex.org/I195939026"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.09967447,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1691","last_page":"1695"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10972","display_name":"Power Systems Fault Detection","score":0.9979000091552734,"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/T10972","display_name":"Power Systems Fault Detection","score":0.9979000091552734,"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/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9965999722480774,"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/T11343","display_name":"Power Transformer Diagnostics and Insulation","score":0.9952999949455261,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/thresholding","display_name":"Thresholding","score":0.8814576864242554},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7129607200622559},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6943148374557495},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5880440473556519},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5556460022926331},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.515556812286377},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.513701319694519},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.503415048122406},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.479025274515152},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2056381106376648},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.0879124104976654}],"concepts":[{"id":"https://openalex.org/C191178318","wikidata":"https://www.wikidata.org/wiki/Q2256906","display_name":"Thresholding","level":3,"score":0.8814576864242554},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7129607200622559},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6943148374557495},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5880440473556519},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5556460022926331},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.515556812286377},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.513701319694519},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.503415048122406},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.479025274515152},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2056381106376648},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0879124104976654}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.23919/eusipco54536.2021.9616148","is_oa":false,"landing_page_url":"https://doi.org/10.23919/eusipco54536.2021.9616148","pdf_url":null,"source":{"id":"https://openalex.org/S4363607854","display_name":"2021 29th European Signal Processing Conference (EUSIPCO)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 29th European Signal Processing Conference (EUSIPCO)","raw_type":"proceedings-article"},{"id":"pmh:oai:researchonline.gcu.ac.uk:publications/74d8f66c-c09c-45a6-bc86-57bdaedea92b","is_oa":true,"landing_page_url":"https://researchonline.gcu.ac.uk/en/publications/74d8f66c-c09c-45a6-bc86-57bdaedea92b","pdf_url":"https://researchonline.gcu.ac.uk/en/publications/74d8f66c-c09c-45a6-bc86-57bdaedea92b","source":{"id":"https://openalex.org/S4306402566","display_name":"ResearchOnline (Glasgow Caledonian University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I195939026","host_organization_name":"Glasgow Caledonian University","host_organization_lineage":["https://openalex.org/I195939026"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Salimy, A, Mitiche, I, Boreham, P, Nesbitt, A & Morison, G 2021, Robust deep residual shrinkage networks for online fault classification. in 2021 29th European Signal Processing Conference (EUSIPCO). IEEE, pp. 1691-1695, 29th European Signal Processing Conference, 23/08/21. https://doi.org/10.23919/EUSIPCO54536.2021.9616148","raw_type":"contributionToPeriodical"}],"best_oa_location":{"id":"pmh:oai:researchonline.gcu.ac.uk:publications/74d8f66c-c09c-45a6-bc86-57bdaedea92b","is_oa":true,"landing_page_url":"https://researchonline.gcu.ac.uk/en/publications/74d8f66c-c09c-45a6-bc86-57bdaedea92b","pdf_url":"https://researchonline.gcu.ac.uk/en/publications/74d8f66c-c09c-45a6-bc86-57bdaedea92b","source":{"id":"https://openalex.org/S4306402566","display_name":"ResearchOnline (Glasgow Caledonian University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I195939026","host_organization_name":"Glasgow Caledonian University","host_organization_lineage":["https://openalex.org/I195939026"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Salimy, A, Mitiche, I, Boreham, P, Nesbitt, A & Morison, G 2021, Robust deep residual shrinkage networks for online fault classification. in 2021 29th European Signal Processing Conference (EUSIPCO). IEEE, pp. 1691-1695, 29th European Signal Processing Conference, 23/08/21. https://doi.org/10.23919/EUSIPCO54536.2021.9616148","raw_type":"contributionToPeriodical"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3196333164.pdf","grobid_xml":"https://content.openalex.org/works/W3196333164.grobid-xml"},"referenced_works_count":15,"referenced_works":["https://openalex.org/W1986754283","https://openalex.org/W1989491465","https://openalex.org/W1991108537","https://openalex.org/W2082076175","https://openalex.org/W2146842127","https://openalex.org/W2163761533","https://openalex.org/W2194775991","https://openalex.org/W2513513181","https://openalex.org/W2775516437","https://openalex.org/W2954894921","https://openalex.org/W2977117446","https://openalex.org/W3086545300","https://openalex.org/W4214806317","https://openalex.org/W4249484555","https://openalex.org/W6684111346"],"related_works":["https://openalex.org/W1542224353","https://openalex.org/W1661087619","https://openalex.org/W2116854923","https://openalex.org/W2750730210","https://openalex.org/W2236974868","https://openalex.org/W4312766348","https://openalex.org/W4233939244","https://openalex.org/W2730764323","https://openalex.org/W3123806511","https://openalex.org/W1976727107"],"abstract_inverted_index":{"In":[0],"this":[1,63],"paper,":[2],"a":[3],"novel":[4,109],"approach":[5],"to":[6,81,101,138],"improve":[7],"signal":[8,50],"classification":[9,127],"in":[10,128,182],"the":[11,85,90,108,123,129,140,143,161],"presence":[12,130],"of":[13,69,92,111,131,142],"noise":[14,103],"is":[15,57],"presented.":[16],"Using":[17,77],"Stock-well":[18],"transforms":[19],"for":[20,42,49,54,89,95,99,121,126],"feature":[21],"extraction":[22],"on":[23,87],"time-series":[24],"electromagnetic":[25],"interference":[26],"data":[27,149],"and":[28,60,97,116,167],"deep":[29,78,179],"residual":[30],"neural":[31],"networks,":[32],"containing":[33],"thresholding":[34,75,93,169,177],"functions":[35,45,94,115],"(shrinkage":[36],"functions)":[37],"as":[38],"non-linear":[39],"transformation":[40],"layers":[41],"classification.":[43],"Thresholding":[44],"are":[46,135],"commonly":[47],"used":[48],"de-noising.":[51],"Setting":[52],"thresholds":[53,83],"optimal":[55],"functionality":[56],"often":[58],"complex":[59],"requires":[61],"expertise,":[62],"paper":[64,106],"will":[65],"investigate":[66],"learned":[67],"methods":[68,80],"threshold":[70,114,124],"selection":[71],"along":[72],"with":[73,145],"alternate":[74,102],"functions.":[76],"learning":[79,122],"select":[82],"reduces":[84],"dependency":[86],"experts":[88],"use":[91],"de-noising":[96],"allows":[98],"adaptation":[100],"environments.":[104],"This":[105],"proposed":[107,162],"application":[110],"two":[112],"different":[113],"introduces":[117],"an":[118],"architecture":[119],"update":[120],"parameters":[125],"noise.":[132],"Several":[133],"experiments":[134],"carried":[136],"out":[137],"compare":[139],"performance":[141,172],"systems":[144],"varying":[146],"signal-to-noise":[147,184],"ratio":[148],"sets":[150],"taken":[151],"from":[152],"real-world":[153],"operational":[154],"high-voltage":[155],"assets.":[156],"Experimental":[157],"results":[158],"show":[159],"that":[160],"approaches":[163],"using":[164],"both":[165],"Garrote":[166],"Firm":[168],"achieved":[170],"improved":[171],"increases":[173],"over":[174],"utilizing":[175],"soft":[176],"within":[178],"shrinkage":[180],"networks":[181],"low":[183],"ratios.":[185]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-03-17T17:19:04.345684","created_date":"2025-10-10T00:00:00"}
