{"id":"https://openalex.org/W2958261378","doi":"https://doi.org/10.1145/3332305.3332315","title":"PD Pattern Recognition in Transformers Based on Grey-scale Images and Affinity Propagation Algorithm","display_name":"PD Pattern Recognition in Transformers Based on Grey-scale Images and Affinity Propagation Algorithm","publication_year":2019,"publication_date":"2019-02-23","ids":{"openalex":"https://openalex.org/W2958261378","doi":"https://doi.org/10.1145/3332305.3332315","mag":"2958261378"},"language":"en","primary_location":{"id":"doi:10.1145/3332305.3332315","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3332305.3332315","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 3rd International Conference on Virtual and Augmented Reality Simulations","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/A5043245110","display_name":"Bengang Wei","orcid":"https://orcid.org/0000-0002-3395-5917"},"institutions":[{"id":"https://openalex.org/I4210126065","display_name":"Shanghai Electric (China)","ror":"https://ror.org/0314qy595","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210126065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bengang Wei","raw_affiliation_strings":["Electric Power Research Institute, Shanghai Electric Power Company, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electric Power Research Institute, Shanghai Electric Power Company, Shanghai, China","institution_ids":["https://openalex.org/I4210126065"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114022906","display_name":"Kaixuan Huo","orcid":null},"institutions":[{"id":"https://openalex.org/I154099455","display_name":"Shandong University","ror":"https://ror.org/0207yh398","country_code":"CN","type":"education","lineage":["https://openalex.org/I154099455"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kaixuan Huo","raw_affiliation_strings":["School of Electrical Engineering, Shandong University, Jinan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering, Shandong University, Jinan, China","institution_ids":["https://openalex.org/I154099455"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063867060","display_name":"Zhoufei Yao","orcid":null},"institutions":[{"id":"https://openalex.org/I4210126065","display_name":"Shanghai Electric (China)","ror":"https://ror.org/0314qy595","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210126065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhoufei Yao","raw_affiliation_strings":["Electric Power Research Institute, Shanghai Electric Power Company, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electric Power Research Institute, Shanghai Electric Power Company, Shanghai, China","institution_ids":["https://openalex.org/I4210126065"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042975262","display_name":"Jie Lou","orcid":"https://orcid.org/0000-0003-0124-5918"},"institutions":[{"id":"https://openalex.org/I154099455","display_name":"Shandong University","ror":"https://ror.org/0207yh398","country_code":"CN","type":"education","lineage":["https://openalex.org/I154099455"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Lou","raw_affiliation_strings":["School of Electrical Engineering, Shandong University, Jinan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering, Shandong University, Jinan, China","institution_ids":["https://openalex.org/I154099455"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086057480","display_name":"Xiang\u2010Yao Li","orcid":"https://orcid.org/0000-0002-4793-0550"},"institutions":[{"id":"https://openalex.org/I154099455","display_name":"Shandong University","ror":"https://ror.org/0207yh398","country_code":"CN","type":"education","lineage":["https://openalex.org/I154099455"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangyao Li","raw_affiliation_strings":["School of Electrical Engineering, Shandong University, Jinan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering, Shandong University, Jinan, China","institution_ids":["https://openalex.org/I154099455"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.05339227,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"46","last_page":"51"},"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":0.9998000264167786,"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":0.9998000264167786,"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/T11343","display_name":"Power Transformer Diagnostics and Insulation","score":0.9990000128746033,"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/T14249","display_name":"Water Quality Monitoring and Analysis","score":0.9577999711036682,"subfield":{"id":"https://openalex.org/subfields/2311","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/partial-discharge","display_name":"Partial discharge","score":0.7663769721984863},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7528687715530396},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6249884963035583},{"id":"https://openalex.org/keywords/particle-swarm-optimization","display_name":"Particle swarm optimization","score":0.5862582325935364},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5594956874847412},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.5594496726989746},{"id":"https://openalex.org/keywords/fractal","display_name":"Fractal","score":0.5157158970832825},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5098299384117126},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.47631558775901794},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4653516411781311},{"id":"https://openalex.org/keywords/backpropagation","display_name":"Backpropagation","score":0.46461451053619385},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.45023733377456665},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4467929005622864},{"id":"https://openalex.org/keywords/learning-vector-quantization","display_name":"Learning vector quantization","score":0.44012755155563354},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3756351172924042},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.31208473443984985},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.2848172187805176},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.22362980246543884}],"concepts":[{"id":"https://openalex.org/C130143024","wikidata":"https://www.wikidata.org/wiki/Q1929972","display_name":"Partial discharge","level":3,"score":0.7663769721984863},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7528687715530396},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6249884963035583},{"id":"https://openalex.org/C85617194","wikidata":"https://www.wikidata.org/wiki/Q2072794","display_name":"Particle swarm optimization","level":2,"score":0.5862582325935364},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5594956874847412},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.5594496726989746},{"id":"https://openalex.org/C40636538","wikidata":"https://www.wikidata.org/wiki/Q81392","display_name":"Fractal","level":2,"score":0.5157158970832825},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5098299384117126},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.47631558775901794},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4653516411781311},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.46461451053619385},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.45023733377456665},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4467929005622864},{"id":"https://openalex.org/C40567965","wikidata":"https://www.wikidata.org/wiki/Q1820283","display_name":"Learning vector quantization","level":3,"score":0.44012755155563354},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3756351172924042},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.31208473443984985},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.2848172187805176},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.22362980246543884},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","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":1,"locations":[{"id":"doi:10.1145/3332305.3332315","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3332305.3332315","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 3rd International Conference on Virtual and Augmented Reality Simulations","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.5600000023841858}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1510284666","https://openalex.org/W1577476228","https://openalex.org/W2000736742","https://openalex.org/W2025081697","https://openalex.org/W2025602610","https://openalex.org/W2044465660","https://openalex.org/W2114696929","https://openalex.org/W2118456679","https://openalex.org/W2124306316","https://openalex.org/W2132047332","https://openalex.org/W2154861246","https://openalex.org/W2165232124","https://openalex.org/W2347584729","https://openalex.org/W2390862533","https://openalex.org/W2755596298"],"related_works":["https://openalex.org/W2360214423","https://openalex.org/W1647056466","https://openalex.org/W2156017042","https://openalex.org/W2137852660","https://openalex.org/W2393409683","https://openalex.org/W2515715595","https://openalex.org/W2513378678","https://openalex.org/W2963318523","https://openalex.org/W2577708104","https://openalex.org/W1982606474"],"abstract_inverted_index":{"Partial":[0],"discharge":[1,79],"(PD)":[2],"pattern":[3,149],"recognition":[4,150],"plays":[5],"an":[6],"important":[7],"role":[8],"in":[9,28,151],"determining":[10],"insulation":[11,15],"defects":[12],"and":[13,30,50,67,105,121,172],"understanding":[14],"condition":[16],"of":[17,40,47,53,75,89,102,113,181],"transformers.":[18,152],"In":[19,82],"this":[20],"paper,":[21],"four":[22],"PD":[23,48,54,148,160],"models":[24],"are":[25,57,70,116,126,162],"set":[26],"up":[27],"laboratory":[29],"pulse":[31],"current":[32],"method":[33],"is":[34,96,145,184],"used":[35,97,127],"to":[36,84,98,128],"measure":[37],"the":[38,43,51,86,90,100,159],"amplitude":[39],"apparent":[41],"charge,":[42],"power":[44],"frequency":[45,52],"phase":[46,76],"pulses":[49],"pulses.":[55],"There":[56],"19":[58],"feature":[59,103,108,157],"parameters":[60,104,109],"which":[61,110],"include":[62],"fractal":[63],"features,":[64,66],"moment":[65],"textural":[68],"features":[69],"extracted":[71],"form":[72],"grey-scale":[73],"images":[74],"resolved":[77],"partial":[78],"(PRPD)":[80],"patterns.":[81],"order":[83],"reduce":[85,99],"computational":[87],"complexity":[88],"classifier,":[91,166],"principal":[92],"component":[93],"analysis":[94],"(PCA)":[95],"dimensions":[101],"five":[106],"new":[107,156],"explain":[111],"93.50%":[112],"total":[114],"variance":[115],"obtained.":[117],"The":[118],"kernel":[119],"function":[120],"shared":[122],"nearest":[123],"neighbors":[124],"(SNN)":[125],"improve":[129],"affinity":[130],"propagation":[131,168],"(AP)":[132],"algorithm.":[133],"A":[134],"classifier":[135,183],"based":[136],"on":[137,154],"improved":[138],"AP":[139,165,182],"with":[140],"particle":[141],"swarm":[142],"optimization":[143],"(PSO)":[144],"established":[146],"for":[147],"Based":[153],"these":[155],"parameters,":[158],"patterns":[161],"recognized":[163],"by":[164],"back":[167],"neural":[169],"networks":[170],"(BPNN)":[171],"least":[173],"squares":[174],"support":[175],"vector":[176],"machine":[177],"(LSSVM).":[178],"Recognition":[179],"rate":[180],"85%":[185]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
