{"id":"https://openalex.org/W2803086989","doi":"https://doi.org/10.1109/tii.2018.2828811","title":"An Estimation Method of Defect Size From MFL Image Using Visual Transformation Convolutional Neural Network","display_name":"An Estimation Method of Defect Size From MFL Image Using Visual Transformation Convolutional Neural Network","publication_year":2018,"publication_date":"2018-04-20","ids":{"openalex":"https://openalex.org/W2803086989","doi":"https://doi.org/10.1109/tii.2018.2828811","mag":"2803086989"},"language":"en","primary_location":{"id":"doi:10.1109/tii.2018.2828811","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tii.2018.2828811","pdf_url":null,"source":{"id":"https://openalex.org/S184777250","display_name":"IEEE Transactions on Industrial Informatics","issn_l":"1551-3203","issn":["1551-3203","1941-0050"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Industrial Informatics","raw_type":"journal-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/A5085638054","display_name":"Senxiang Lu","orcid":"https://orcid.org/0000-0002-7155-441X"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Senxiang Lu","raw_affiliation_strings":["School of Information Science and Eng-ineering, Northeastern University, Shenyang, China"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Eng-ineering, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010565829","display_name":"Jian Feng","orcid":"https://orcid.org/0000-0001-6813-6754"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jian Feng","raw_affiliation_strings":["School of Information Science and Eng-ineering, Northeastern University, Shenyang, China"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Eng-ineering, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100625792","display_name":"Huaguang Zhang","orcid":"https://orcid.org/0000-0002-2375-9824"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huaguang Zhang","raw_affiliation_strings":["School of Information Science and Eng-ineering, Northeastern University, Shenyang, China"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Eng-ineering, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100729536","display_name":"Jinhai Liu","orcid":"https://orcid.org/0000-0002-1256-1337"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinhai Liu","raw_affiliation_strings":["School of Information Science and Eng-ineering, Northeastern University, Shenyang, China"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Eng-ineering, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101962189","display_name":"Zhenning Wu","orcid":"https://orcid.org/0000-0002-8742-904X"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenning Wu","raw_affiliation_strings":["School of Information Science and Eng-ineering, Northeastern University, Shenyang, China"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Eng-ineering, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5085638054"],"corresponding_institution_ids":["https://openalex.org/I9224756"],"apc_list":null,"apc_paid":null,"fwci":8.8624,"has_fulltext":false,"cited_by_count":157,"citation_normalized_percentile":{"value":0.98624838,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"15","issue":"1","first_page":"213","last_page":"224"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12169","display_name":"Non-Destructive Testing Techniques","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical 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/T12169","display_name":"Non-Destructive Testing Techniques","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical 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/T10662","display_name":"Ultrasonics and Acoustic Wave Propagation","score":0.9968000054359436,"subfield":{"id":"https://openalex.org/subfields/2211","display_name":"Mechanics of Materials"},"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/T12086","display_name":"Structural Integrity and Reliability Analysis","score":0.9968000054359436,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical 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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7868525981903076},{"id":"https://openalex.org/keywords/magnetic-flux-leakage","display_name":"Magnetic flux leakage","score":0.7308963537216187},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6979600787162781},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.679688036441803},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6514122486114502},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.639395534992218},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5145596861839294},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4989631175994873},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4612511396408081},{"id":"https://openalex.org/keywords/transformation","display_name":"Transformation (genetics)","score":0.45332494378089905},{"id":"https://openalex.org/keywords/nondestructive-testing","display_name":"Nondestructive testing","score":0.4485345482826233},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.23211818933486938},{"id":"https://openalex.org/keywords/electromagnetic-coil","display_name":"Electromagnetic coil","score":0.09140601754188538}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7868525981903076},{"id":"https://openalex.org/C20892748","wikidata":"https://www.wikidata.org/wiki/Q4390394","display_name":"Magnetic flux leakage","level":3,"score":0.7308963537216187},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6979600787162781},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.679688036441803},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6514122486114502},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.639395534992218},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5145596861839294},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4989631175994873},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4612511396408081},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.45332494378089905},{"id":"https://openalex.org/C56529433","wikidata":"https://www.wikidata.org/wiki/Q626700","display_name":"Nondestructive testing","level":2,"score":0.4485345482826233},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.23211818933486938},{"id":"https://openalex.org/C30403606","wikidata":"https://www.wikidata.org/wiki/Q2981904","display_name":"Electromagnetic coil","level":2,"score":0.09140601754188538},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"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/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tii.2018.2828811","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tii.2018.2828811","pdf_url":null,"source":{"id":"https://openalex.org/S184777250","display_name":"IEEE Transactions on Industrial Informatics","issn_l":"1551-3203","issn":["1551-3203","1941-0050"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Industrial Informatics","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6399999856948853,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[{"id":"https://openalex.org/G1654361107","display_name":null,"funder_award_id":"61673093","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2036897366","display_name":null,"funder_award_id":"61627809","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3939043258","display_name":null,"funder_award_id":"61703087","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6770382808","display_name":null,"funder_award_id":"61473069","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W1665214252","https://openalex.org/W1974735879","https://openalex.org/W1975096300","https://openalex.org/W1995562189","https://openalex.org/W2011382171","https://openalex.org/W2024730700","https://openalex.org/W2041053548","https://openalex.org/W2046629134","https://openalex.org/W2047876835","https://openalex.org/W2060304859","https://openalex.org/W2071988844","https://openalex.org/W2079315848","https://openalex.org/W2108321865","https://openalex.org/W2112796928","https://openalex.org/W2113811448","https://openalex.org/W2117731089","https://openalex.org/W2132603731","https://openalex.org/W2154579312","https://openalex.org/W2156387975","https://openalex.org/W2160975985","https://openalex.org/W2168117308","https://openalex.org/W2189709317","https://openalex.org/W2304309600","https://openalex.org/W2342724584","https://openalex.org/W2379093513","https://openalex.org/W2518500747","https://openalex.org/W2553446265","https://openalex.org/W2554156454","https://openalex.org/W2565387298","https://openalex.org/W2566875744","https://openalex.org/W2590288147","https://openalex.org/W2593809585","https://openalex.org/W2595141258","https://openalex.org/W2595750449","https://openalex.org/W2599566251","https://openalex.org/W2605049737","https://openalex.org/W2763316552","https://openalex.org/W2919115771","https://openalex.org/W6637242042","https://openalex.org/W6682889407"],"related_works":["https://openalex.org/W4383333701","https://openalex.org/W2043230455","https://openalex.org/W2902977491","https://openalex.org/W2101676717","https://openalex.org/W4226493464","https://openalex.org/W4241743597","https://openalex.org/W3133861977","https://openalex.org/W2165594630","https://openalex.org/W2951211570","https://openalex.org/W3103566983"],"abstract_inverted_index":{"In":[0],"most":[1],"current":[2],"nondestructive":[3],"testing":[4],"systems,":[5],"a":[6,41,74,93,99,160],"magnetic":[7,163],"flux":[8],"leakage":[9],"(MFL)":[10],"method":[11,91,127,230],"is":[12,25,40,80,128,166],"widely":[13],"used":[14,145],"in":[15,34,82,92,221,231],"various":[16],"industry":[17],"fields,":[18],"where":[19],"the":[20,37,61,67,87,105,108,111,115,125,132,148,152,187,194,197,213,225,228],"structural":[21],"integrity":[22],"of":[23,26,31,63,69,89,107,154,178,189,196,227],"specimens":[24],"vital":[27],"importance.":[28],"The":[29,46,216],"estimation":[30],"defect":[32,116],"size":[33],"specimen":[35],"from":[36],"MFL":[38,109,176],"measurements":[39],"key":[42],"and":[43,60,192],"difficult":[44],"problem.":[45],"traditional":[47,90],"methods":[48],"have":[49],"low":[50],"precision":[51],"because":[52],"feature":[53,94,117],"extraction":[54,95],"procedure":[55],"relies":[56],"on":[57,131,205],"prior":[58],"knowledge":[59],"ability":[62],"designer.":[64],"Inspired":[65],"by":[66,182],"idea":[68],"convolutional":[70],"neural":[71],"network":[72],"(CNN),":[73],"novel":[75,161],"visual":[76,100],"transformation":[77,101],"CNN":[78],"(VT-CNN)":[79],"proposed":[81,214,229],"this":[83,170,183],"paper":[84],"to":[85,104,146,151,168,200,211],"overcome":[86],"limitation":[88],"procedure.":[96],"By":[97],"adding":[98],"layer":[102],"according":[103],"characteristics":[106],"measurements,":[110],"VT-CNN":[112,126],"can":[113,185],"distinguish":[114],"with":[118,140],"different":[119],"sizes":[120],"more":[121,136],"accurately.":[122],"Moreover,":[123],"since":[124],"designed":[129,167],"based":[130],"deep":[133],"learning":[134],"theory,":[135],"industrial":[137,157,171,207,232],"big":[138,158],"data":[139],"accurate":[141],"label":[142],"should":[143],"be":[144],"train":[147],"network.":[149,198],"Due":[150],"difficulty":[153],"making":[155],"real":[156,206],"data,":[159],"mesher":[162],"dipole":[164],"model":[165,184],"simulate":[169],"process.":[172],"A":[173],"large":[174],"simulated":[175],"measurement":[177],"irregular":[179],"defects":[180,204],"produced":[181],"increase":[186],"number":[188],"training":[190],"samples":[191],"improve":[193],"robustness":[195],"Experiments":[199],"estimate":[201],"natural":[202],"corrosion":[203],"pipelines":[208],"are":[209,219],"performed":[210],"validate":[212],"framework.":[215],"experimental":[217],"results":[218],"illustrated":[220],"detail,":[222],"which":[223],"highlights":[224],"superiority":[226],"applications.":[233]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":23},{"year":2024,"cited_by_count":19},{"year":2023,"cited_by_count":28},{"year":2022,"cited_by_count":30},{"year":2021,"cited_by_count":26},{"year":2020,"cited_by_count":18},{"year":2019,"cited_by_count":8}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}