{"id":"https://openalex.org/W2909677757","doi":"https://doi.org/10.1109/sibgrapi.2018.00062","title":"Image-Based State Recognition for Disconnect Switches in Electric Power Distribution Substations","display_name":"Image-Based State Recognition for Disconnect Switches in Electric Power Distribution Substations","publication_year":2018,"publication_date":"2018-10-01","ids":{"openalex":"https://openalex.org/W2909677757","doi":"https://doi.org/10.1109/sibgrapi.2018.00062","mag":"2909677757"},"language":"en","primary_location":{"id":"doi:10.1109/sibgrapi.2018.00062","doi":"https://doi.org/10.1109/sibgrapi.2018.00062","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sibgrapi.2018.00062","pdf_url":null,"source":{"id":"https://openalex.org/S7407086070","display_name":"SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5001239191","display_name":"Bogdan Tomoyuki Nassu","orcid":"https://orcid.org/0000-0001-6441-8543","observed_orcids":["https://orcid.org/0000-0001-6441-8543"]},"institutions":[{"id":"https://openalex.org/I1283613182","display_name":"Universidade Tecnol\u00f3gica Federal do Paran\u00e1","ror":"https://ror.org/002v2kq79","country_code":"BR","type":"education","lineage":["https://openalex.org/I1283613182"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Bogdan Tomoyuki Nassu","raw_affiliation_strings":["Federal University of Technology, Parana, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Federal University of Technology, Parana, Brazil","institution_ids":["https://openalex.org/I1283613182"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003053157","display_name":"Lourival Lippmann","orcid":"https://orcid.org/0000-0001-7369-5550","observed_orcids":["https://orcid.org/0000-0001-7369-5550"]},"institutions":[{"id":"https://openalex.org/I4210160554","display_name":"Institutos Lactec","ror":"https://ror.org/05j2csz34","country_code":"BR","type":"facility","lineage":["https://openalex.org/I4210160554"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Lourival Lippmann","raw_affiliation_strings":["Amanda Canestraro and Rafael Wagner, Institutos Lactec, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Amanda Canestraro and Rafael Wagner, Institutos Lactec, Brazil","institution_ids":["https://openalex.org/I4210160554"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041125404","display_name":"Bruno Marchesi","orcid":"https://orcid.org/0000-0002-6366-036X","observed_orcids":["https://orcid.org/0000-0002-6366-036X"]},"institutions":[{"id":"https://openalex.org/I4210160554","display_name":"Institutos Lactec","ror":"https://ror.org/05j2csz34","country_code":"BR","type":"facility","lineage":["https://openalex.org/I4210160554"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Bruno Marchesi","raw_affiliation_strings":["Amanda Canestraro and Rafael Wagner, Institutos Lactec, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Amanda Canestraro and Rafael Wagner, Institutos Lactec, Brazil","institution_ids":["https://openalex.org/I4210160554"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075584040","display_name":"Amanda Canestraro","orcid":"https://orcid.org/0000-0001-7627-8701","observed_orcids":["https://orcid.org/0000-0001-7627-8701"]},"institutions":[{"id":"https://openalex.org/I4210160554","display_name":"Institutos Lactec","ror":"https://ror.org/05j2csz34","country_code":"BR","type":"facility","lineage":["https://openalex.org/I4210160554"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Amanda Canestraro","raw_affiliation_strings":["Inst. Lactec, Curitiba, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Inst. Lactec, Curitiba, Brazil","institution_ids":["https://openalex.org/I4210160554"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028543615","display_name":"Rafael Wagner","orcid":"https://orcid.org/0000-0002-9462-7108","observed_orcids":["https://orcid.org/0000-0002-9462-7108"]},"institutions":[{"id":"https://openalex.org/I4210160554","display_name":"Institutos Lactec","ror":"https://ror.org/05j2csz34","country_code":"BR","type":"facility","lineage":["https://openalex.org/I4210160554"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Rafael Wagner","raw_affiliation_strings":["Inst. Lactec, Curitiba, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Inst. Lactec, Curitiba, Brazil","institution_ids":["https://openalex.org/I4210160554"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014569336","display_name":"Vanderlei Zarnicinski","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vanderlei Zarnicinski","raw_affiliation_strings":["Companhia Paranaense de, Energia, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Companhia Paranaense de, Energia, Brazil","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.6606,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":{"value":0.80696329,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":98},"biblio":{"volume":"2018","issue":null,"first_page":"432","last_page":"439"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.3668999969959259,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.3668999969959259,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.31619998812675476,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.06080000102519989,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/distribution-substations","display_name":"distribution substations","score":0.9039999842643738},{"id":"https://openalex.org/keywords/surveillance-cameras","display_name":"surveillance cameras","score":0.718999981880188},{"id":"https://openalex.org/keywords/sift-descriptors","display_name":"SIFT descriptors","score":0.8230000138282776},{"id":"https://openalex.org/keywords/convolutional-neural-networks","display_name":"convolutional neural networks","score":0.8100000023841858},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"support vector machine","score":0.9549999833106995},{"id":"https://openalex.org/keywords/illumination-robustness","display_name":"illumination robustness","score":0.7149999737739563},{"id":"https://openalex.org/keywords/viewpoint-variation","display_name":"viewpoint variation","score":0.7089999914169312}],"concepts":[{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.5757862329483032},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.5225633978843689},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5128824710845947},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.45451897382736206},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.44416093826293945},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2679018974304199},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.1027238667011261},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.06633999943733215},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/sibgrapi.2018.00062","doi":"https://doi.org/10.1109/sibgrapi.2018.00062","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sibgrapi.2018.00062","pdf_url":null,"source":{"id":"https://openalex.org/S7407086070","display_name":"SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)","raw_type":"proceedings-article"},{"id":"mag:3178707766","doi":null,"is_oa":false,"landing_page_url":"https://jglobal.jst.go.jp/detail?JGLOBAL_ID=201902265261447836","pdf_url":null,"source":{"id":"https://openalex.org/S4306512817","display_name":"IEEE Conference Proceedings","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"IEEE Conference Proceedings","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.6773999929428101}],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320309480","display_name":"Nvidia","ror":"https://ror.org/03jdj4y14"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W139646307","https://openalex.org/W1522301498","https://openalex.org/W2000962974","https://openalex.org/W2067189215","https://openalex.org/W2085141030","https://openalex.org/W2117539524","https://openalex.org/W2119447687","https://openalex.org/W2121647436","https://openalex.org/W2151103935","https://openalex.org/W2153635508","https://openalex.org/W2158453800","https://openalex.org/W2161969291","https://openalex.org/W2587466160","https://openalex.org/W2770784248","https://openalex.org/W2919115771","https://openalex.org/W2964121744","https://openalex.org/W4239510810"],"related_works":["https://openalex.org/W3155592053","https://openalex.org/W2587466160","https://openalex.org/W4388807080","https://openalex.org/W4313289590","https://openalex.org/W3016442028","https://openalex.org/W3173982282","https://openalex.org/W2807028072","https://openalex.org/W2896426773","https://openalex.org/W2909225380","https://openalex.org/W2313379517"],"abstract_inverted_index":{"Knowing":[0],"the":[1,4,36,57,140,170,179],"state":[2,58],"of":[3,35,46,59],"disconnect":[5],"switches":[6],"in":[7,62,105,117,167],"a":[8,63,88,93,128],"power":[9],"distribution":[10],"substation":[11,130],"is":[12,24],"important":[13],"to":[14,114,139,152],"avoid":[15],"accidents,":[16],"damaged":[17],"equipment,":[18],"and":[19,85,92,155],"service":[20],"interruptions.":[21],"This":[22],"information":[23,103],"usually":[25],"provided":[26],"by":[27,69],"human":[28],"operators,":[29],"who":[30],"can":[31,147,162],"commit":[32],"errors":[33],"because":[34],"cluttered":[37],"environment,":[38],"bad":[39],"weather":[40],"or":[41,44,176],"lighting":[42,153],"conditions,":[43],"lack":[45],"attention.":[47],"In":[48],"this":[49],"paper,":[50],"we":[51],"introduce":[52],"an":[53,106],"approach":[54,76,120],"for":[55],"determining":[56],"each":[60],"switch":[61],"substation,":[64],"based":[65],"on":[66,124],"images":[67],"captured":[68],"regular":[70],"pan-tilt-zoom":[71],"surveillance":[72],"cameras.":[73],"The":[74],"proposed":[75],"includes":[77],"noise":[78],"reduction,":[79],"image":[80,111,144],"registration":[81,145],"using":[82,87],"phase":[83,142],"correlation,":[84],"classification":[86],"convolutional":[89],"neural":[90],"network":[91],"support":[94],"vector":[95],"machine":[96],"fed":[97],"with":[98,110],"gradient-based":[99],"descriptors.":[100],"By":[101],"combining":[102],"given":[104],"initial":[107],"labeling":[108],"stage":[109],"processing":[112],"techniques":[113],"reduce":[115],"variations":[116],"viewpoint,":[118],"our":[119],"achieved":[121],"100%":[122],"accuracy":[123],"experiments":[125],"performed":[126],"at":[127],"real":[129],"over":[131],"multiple":[132],"days.":[133],"We":[134],"also":[135],"show":[136],"how":[137,156],"modifications":[138],"standard":[141],"correlation":[143],"algorithm":[146],"make":[148],"it":[149],"more":[150,165],"robust":[151,166],"variations,":[154],"SIFT":[157],"(Scale-Invariant":[158],"Feature":[159],"Transform)":[160],"descriptors":[161],"be":[163,174],"made":[164],"scenarios":[168],"where":[169],"relevant":[171],"objects":[172],"may":[173],"brighter":[175],"darker":[177],"than":[178],"background.":[180]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":6}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2019-01-25T00:00:00"}