{"id":"https://openalex.org/W4318147169","doi":"https://doi.org/10.1109/bigdata55660.2022.10020673","title":"Partial Discharge Detection for Underground Transmission Lines Using Nonnegative Matrix Factorization","display_name":"Partial Discharge Detection for Underground Transmission Lines Using Nonnegative Matrix Factorization","publication_year":2022,"publication_date":"2022-12-17","ids":{"openalex":"https://openalex.org/W4318147169","doi":"https://doi.org/10.1109/bigdata55660.2022.10020673"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata55660.2022.10020673","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bigdata55660.2022.10020673","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 IEEE International Conference on Big Data (Big Data)","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":"2022 IEEE International Conference on Big Data (Big Data)","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/A5065939109","display_name":"Akihiro Tanabe","orcid":"https://orcid.org/0000-0001-5171-5530"},"institutions":[{"id":"https://openalex.org/I98285908","display_name":"The University of Osaka","ror":"https://ror.org/035t8zc32","country_code":"JP","type":"education","lineage":["https://openalex.org/I98285908"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Akihiro Tanabe","raw_affiliation_strings":["Osaka University,IST","ISIR, Osaka University, Osaka, Japan","IST, Osaka University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Osaka University,IST","institution_ids":["https://openalex.org/I98285908"]},{"raw_affiliation_string":"ISIR, Osaka University, Osaka, Japan","institution_ids":["https://openalex.org/I98285908"]},{"raw_affiliation_string":"IST, Osaka University","institution_ids":["https://openalex.org/I98285908"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005415598","display_name":"Yasuko Matsubara","orcid":"https://orcid.org/0000-0003-3566-7721"},"institutions":[{"id":"https://openalex.org/I14314212","display_name":"Osaka University of Economics","ror":"https://ror.org/04g11bp59","country_code":"JP","type":"education","lineage":["https://openalex.org/I14314212"]},{"id":"https://openalex.org/I98285908","display_name":"The University of Osaka","ror":"https://ror.org/035t8zc32","country_code":"JP","type":"education","lineage":["https://openalex.org/I98285908"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yasuko Matsubara","raw_affiliation_strings":["Osaka University,ISIR,Osaka,Japan","ISIR, Osaka University, Osaka, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Osaka University,ISIR,Osaka,Japan","institution_ids":["https://openalex.org/I14314212"]},{"raw_affiliation_string":"ISIR, Osaka University, Osaka, Japan","institution_ids":["https://openalex.org/I98285908"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089668362","display_name":"Yasushi Sakurai","orcid":"https://orcid.org/0000-0001-7258-2642"},"institutions":[{"id":"https://openalex.org/I14314212","display_name":"Osaka University of Economics","ror":"https://ror.org/04g11bp59","country_code":"JP","type":"education","lineage":["https://openalex.org/I14314212"]},{"id":"https://openalex.org/I98285908","display_name":"The University of Osaka","ror":"https://ror.org/035t8zc32","country_code":"JP","type":"education","lineage":["https://openalex.org/I98285908"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yasushi Sakurai","raw_affiliation_strings":["Osaka University,ISIR,Osaka,Japan","ISIR, Osaka University, Osaka, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Osaka University,ISIR,Osaka,Japan","institution_ids":["https://openalex.org/I14314212"]},{"raw_affiliation_string":"ISIR, Osaka University, Osaka, Japan","institution_ids":["https://openalex.org/I98285908"]}]}],"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":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"13","issue":null,"first_page":"3447","last_page":"3454"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10511","display_name":"High voltage insulation and dielectric phenomena","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10511","display_name":"High voltage insulation and dielectric phenomena","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T14502","display_name":"High-Voltage Power Transmission Systems","score":0.9850000143051147,"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/T11343","display_name":"Power Transformer Diagnostics and Insulation","score":0.9811999797821045,"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/matrix-decomposition","display_name":"Matrix decomposition","score":0.6953497529029846},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.6358674764633179},{"id":"https://openalex.org/keywords/electric-power-transmission","display_name":"Electric power transmission","score":0.6256476640701294},{"id":"https://openalex.org/keywords/partial-discharge","display_name":"Partial discharge","score":0.6150169372558594},{"id":"https://openalex.org/keywords/basis","display_name":"Basis (linear algebra)","score":0.5827484726905823},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5718410611152649},{"id":"https://openalex.org/keywords/non-negative-matrix-factorization","display_name":"Non-negative matrix factorization","score":0.5371063351631165},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.4622141718864441},{"id":"https://openalex.org/keywords/transmission-line","display_name":"Transmission line","score":0.4541250765323639},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.45170676708221436},{"id":"https://openalex.org/keywords/coefficient-matrix","display_name":"Coefficient matrix","score":0.43043631315231323},{"id":"https://openalex.org/keywords/factorization","display_name":"Factorization","score":0.4231386184692383},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.420055627822876},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.393184095621109},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.31265032291412354},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.21520674228668213},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.21374863386154175},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.19547271728515625},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.18543335795402527},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.11394745111465454},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.09521514177322388},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.09101343154907227},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.08789902925491333},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.08002755045890808}],"concepts":[{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.6953497529029846},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.6358674764633179},{"id":"https://openalex.org/C140311924","wikidata":"https://www.wikidata.org/wiki/Q200928","display_name":"Electric power transmission","level":2,"score":0.6256476640701294},{"id":"https://openalex.org/C130143024","wikidata":"https://www.wikidata.org/wiki/Q1929972","display_name":"Partial discharge","level":3,"score":0.6150169372558594},{"id":"https://openalex.org/C12426560","wikidata":"https://www.wikidata.org/wiki/Q189569","display_name":"Basis (linear algebra)","level":2,"score":0.5827484726905823},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5718410611152649},{"id":"https://openalex.org/C152671427","wikidata":"https://www.wikidata.org/wiki/Q10843505","display_name":"Non-negative matrix factorization","level":4,"score":0.5371063351631165},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.4622141718864441},{"id":"https://openalex.org/C33441834","wikidata":"https://www.wikidata.org/wiki/Q693004","display_name":"Transmission line","level":2,"score":0.4541250765323639},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.45170676708221436},{"id":"https://openalex.org/C60866291","wikidata":"https://www.wikidata.org/wiki/Q5140577","display_name":"Coefficient matrix","level":3,"score":0.43043631315231323},{"id":"https://openalex.org/C187834632","wikidata":"https://www.wikidata.org/wiki/Q188804","display_name":"Factorization","level":2,"score":0.4231386184692383},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.420055627822876},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.393184095621109},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.31265032291412354},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.21520674228668213},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.21374863386154175},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.19547271728515625},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.18543335795402527},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.11394745111465454},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.09521514177322388},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.09101343154907227},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.08789902925491333},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.08002755045890808},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","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/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.0},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata55660.2022.10020673","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bigdata55660.2022.10020673","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 IEEE International Conference on Big Data (Big Data)","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":"2022 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.41999998688697815,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[{"id":"https://openalex.org/G7090605830","display_name":"Development of advanced analytical technology for  materials science","funder_award_id":"20H00585","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G7395352350","display_name":"Smart analytics for complex time-stamped data streams","funder_award_id":"21H03446","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"}],"funders":[{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"},{"id":"https://openalex.org/F4320337495","display_name":"Technology Development","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W206759535","https://openalex.org/W1589077356","https://openalex.org/W1876219421","https://openalex.org/W1902027874","https://openalex.org/W1931803241","https://openalex.org/W1997528538","https://openalex.org/W2082250201","https://openalex.org/W2105037004","https://openalex.org/W2135029798","https://openalex.org/W2143257327","https://openalex.org/W2165112220","https://openalex.org/W2742822006","https://openalex.org/W2909400870","https://openalex.org/W2921736909","https://openalex.org/W2953928707","https://openalex.org/W2954894921","https://openalex.org/W2986282442","https://openalex.org/W3162945564","https://openalex.org/W4205845894","https://openalex.org/W6680012447"],"related_works":["https://openalex.org/W2127243424","https://openalex.org/W4390394189","https://openalex.org/W2037504162","https://openalex.org/W2539013788","https://openalex.org/W2792706544","https://openalex.org/W1568451138","https://openalex.org/W2156699640","https://openalex.org/W2045265907","https://openalex.org/W2972997031","https://openalex.org/W2075222291"],"abstract_inverted_index":{"In":[0,20,67,153],"this":[1,68],"paper,":[2,69],"we":[3,70],"describe":[4],"a":[5,24,72,82,90,115,150],"method":[6,73,110],"for":[7,74,165],"detecting":[8],"partial":[9],"discharge":[10,25,87],"(PD)":[11],"in":[12,59],"underground":[13,42,63],"transmission":[14,43,64],"lines":[15],"from":[16],"time":[17,76,102,167],"series":[18,77],"data.":[19],"general,":[21],"PD":[22,49,78,133,144,159,171],"is":[23],"phenomenon":[26],"that":[27,85,93,143,158],"occurs":[28],"when":[29],"electric":[30],"fields":[31],"concentrate":[32],"on":[33],"voids":[34],"or":[35],"protrusions":[36],"at":[37],"the":[38,60,95],"insulator":[39],"interface":[40],"of":[41,48,52,62,97,118,132],"lines.":[44],"The":[45,108],"accurate":[46],"detection":[47],"and":[50,89,126,169],"classification":[51],"its":[53],"patterns":[54,88,131,145,172],"play":[55],"an":[56],"important":[57],"role":[58],"diagnosis":[61],"line":[65],"deterioration.":[66],"propose":[71],"using":[75,139],"data":[79,134,141,160],"to":[80],"form":[81],"basis":[83,99,151],"matrix":[84,92,106],"represents":[86,94],"coefficient":[91],"contribution":[96],"each":[98],"pattern":[100],"over":[101],"by":[103,135,176],"applying":[104],"nonnegative":[105],"factorization.":[107],"proposed":[109],"(a)":[111],"does":[112],"not":[113],"require":[114],"large":[116],"amount":[117],"data,":[119],"(b)":[120],"works":[121],"without":[122],"any":[123],"supervised":[124],"information,":[125],"(c)":[127],"can":[128],"track":[129],"new":[130,170],"adding":[136],"bases.":[137],"Experiments":[138],"real":[140],"confirmed":[142],"could":[146,161,173],"be":[147,162,174],"separated":[148],"as":[149],"matrix.":[152],"addition,":[154],"it":[155],"was":[156],"suggested":[157],"extracted":[163,175],"even":[164],"unknown":[166],"series,":[168],"re-learning.":[177]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
