{"id":"https://openalex.org/W4312606004","doi":"https://doi.org/10.1109/access.2022.3217238","title":"Research on Improved Residual Network Classification Method for Defect Recognition of Thermal Battery","display_name":"Research on Improved Residual Network Classification Method for Defect Recognition of Thermal Battery","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4312606004","doi":"https://doi.org/10.1109/access.2022.3217238"},"language":"en","primary_location":{"id":"doi:10.1109/access.2022.3217238","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3217238","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9668973/09930520.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/9668973/09930520.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5072708687","display_name":"Wenchao Xu","orcid":"https://orcid.org/0000-0002-7655-3061"},"institutions":[{"id":"https://openalex.org/I184843921","display_name":"Hebei University of Technology","ror":"https://ror.org/018hded08","country_code":"CN","type":"education","lineage":["https://openalex.org/I184843921"]},{"id":"https://openalex.org/I110109458","display_name":"Tianjin University of Commerce","ror":"https://ror.org/02b6amy98","country_code":"CN","type":"education","lineage":["https://openalex.org/I110109458"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wenchao Xu","raw_affiliation_strings":["College of Mechanical Engineering, Hebei University of Technology, Tianjin, China","College of Information Engineering, Tianjin University of Commerce, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"College of Mechanical Engineering, Hebei University of Technology, Tianjin, China","institution_ids":["https://openalex.org/I184843921"]},{"raw_affiliation_string":"College of Information Engineering, Tianjin University of Commerce, Tianjin, China","institution_ids":["https://openalex.org/I110109458"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032681672","display_name":"Sixiang Zhang","orcid":"https://orcid.org/0000-0001-6320-9248"},"institutions":[{"id":"https://openalex.org/I184843921","display_name":"Hebei University of Technology","ror":"https://ror.org/018hded08","country_code":"CN","type":"education","lineage":["https://openalex.org/I184843921"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sixiang Zhang","raw_affiliation_strings":["College of Mechanical Engineering, Hebei University of Technology, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"College of Mechanical Engineering, Hebei University of Technology, Tianjin, China","institution_ids":["https://openalex.org/I184843921"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046277831","display_name":"Fang Bai","orcid":"https://orcid.org/0000-0003-0639-8104"},"institutions":[{"id":"https://openalex.org/I110109458","display_name":"Tianjin University of Commerce","ror":"https://ror.org/02b6amy98","country_code":"CN","type":"education","lineage":["https://openalex.org/I110109458"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fang Bai","raw_affiliation_strings":["College of Information Engineering, Tianjin University of Commerce, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"College of Information Engineering, Tianjin University of Commerce, Tianjin, China","institution_ids":["https://openalex.org/I110109458"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004960174","display_name":"Tao Zhao","orcid":"https://orcid.org/0000-0002-4448-6730"},"institutions":[{"id":"https://openalex.org/I184843921","display_name":"Hebei University of Technology","ror":"https://ror.org/018hded08","country_code":"CN","type":"education","lineage":["https://openalex.org/I184843921"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tao Zhao","raw_affiliation_strings":["College of Mechanical Engineering, Hebei University of Technology, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"College of Mechanical Engineering, Hebei University of Technology, Tianjin, China","institution_ids":["https://openalex.org/I184843921"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5072708687"],"corresponding_institution_ids":["https://openalex.org/I110109458","https://openalex.org/I184843921"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":1.0673,"has_fulltext":true,"cited_by_count":9,"citation_normalized_percentile":{"value":0.80603849,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"10","issue":null,"first_page":"113234","last_page":"113248"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9977999925613403,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9977999925613403,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/T11856","display_name":"Thermography and Photoacoustic Techniques","score":0.9664000272750854,"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/T12282","display_name":"Mineral Processing and Grinding","score":0.9617999792098999,"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/overfitting","display_name":"Overfitting","score":0.8702315092086792},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7196341753005981},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.6574123501777649},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6573137640953064},{"id":"https://openalex.org/keywords/battery","display_name":"Battery (electricity)","score":0.5993637442588806},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.5627326965332031},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5163558721542358},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.49405959248542786},{"id":"https://openalex.org/keywords/dropout","display_name":"Dropout (neural networks)","score":0.45987480878829956},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.42543062567710876},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.42494022846221924},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3044642210006714},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.2949730157852173},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.14497700333595276}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.8702315092086792},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7196341753005981},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.6574123501777649},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6573137640953064},{"id":"https://openalex.org/C555008776","wikidata":"https://www.wikidata.org/wiki/Q267298","display_name":"Battery (electricity)","level":3,"score":0.5993637442588806},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.5627326965332031},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5163558721542358},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.49405959248542786},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.45987480878829956},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.42543062567710876},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.42494022846221924},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3044642210006714},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.2949730157852173},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.14497700333595276},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","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/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2022.3217238","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3217238","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9668973/09930520.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:8a15b47c99334adc957a7fd1d933db7b","is_oa":true,"landing_page_url":"https://doaj.org/article/8a15b47c99334adc957a7fd1d933db7b","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 10, Pp 113234-113248 (2022)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2022.3217238","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3217238","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9668973/09930520.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4312606004.pdf","grobid_xml":"https://content.openalex.org/works/W4312606004.grobid-xml"},"referenced_works_count":40,"referenced_works":["https://openalex.org/W138557408","https://openalex.org/W344203161","https://openalex.org/W639708223","https://openalex.org/W1970576880","https://openalex.org/W1989123369","https://openalex.org/W2009434431","https://openalex.org/W2010560725","https://openalex.org/W2011537729","https://openalex.org/W2026445526","https://openalex.org/W2040730201","https://openalex.org/W2044465660","https://openalex.org/W2061171222","https://openalex.org/W2067637630","https://openalex.org/W2076844371","https://openalex.org/W2102605133","https://openalex.org/W2105055468","https://openalex.org/W2112796928","https://openalex.org/W2133059825","https://openalex.org/W2133236494","https://openalex.org/W2152441963","https://openalex.org/W2155903085","https://openalex.org/W2158061095","https://openalex.org/W2166221155","https://openalex.org/W2169099300","https://openalex.org/W2346804162","https://openalex.org/W2772386856","https://openalex.org/W2801077816","https://openalex.org/W2809788830","https://openalex.org/W2893356526","https://openalex.org/W2899280016","https://openalex.org/W2924873663","https://openalex.org/W2963037989","https://openalex.org/W3007098591","https://openalex.org/W3009260410","https://openalex.org/W3091128192","https://openalex.org/W3093024097","https://openalex.org/W3094482290","https://openalex.org/W3134639409","https://openalex.org/W4250174831","https://openalex.org/W6784735426"],"related_works":["https://openalex.org/W4298017035","https://openalex.org/W3110700750","https://openalex.org/W2792147139","https://openalex.org/W4226354336","https://openalex.org/W2998675825","https://openalex.org/W3128220493","https://openalex.org/W2736804899","https://openalex.org/W2897443685","https://openalex.org/W4307654087","https://openalex.org/W2951100320"],"abstract_inverted_index":{"Thermal":[0],"battery":[1,49,53,188,280,314],"is":[2,57,74,91,100,126,173,293,315],"an":[3],"ideal":[4],"power":[5],"supply":[6],"for":[7,147,305],"military":[8],"applications":[9],"such":[10],"as":[11],"artillery":[12],"and":[13,43,93,116,121,133,149,154,179,182,199,202,205,219,236,252,260,284,286,301],"ship":[14],"equipment.":[15],"Due":[16],"to":[17,105,159,175],"the":[18,22,31,38,64,71,80,84,87,94,108,113,117,129,135,143,150,161,165,169,176,186,209,229,237,263,275,298],"sheet-type":[19],"process":[20],"of":[21,33,40,47,79,164,185,214,233,240,250,312],"thermal":[23,34,48,52,187,279,313],"battery,":[24],"various":[25],"installation":[26],"error":[27],"defects":[28,281],"occur":[29],"in":[30,103,112,308],"assembly":[32],"battery.":[35],"Aiming":[36],"at":[37],"problems":[39],"low":[41,44],"efficiency":[42],"defect-recognition":[45],"rate":[46,267],"detection,":[50],"a":[51,302],"defect":[54,109,184,289,306],"detection":[55,307],"model":[56,131,145,171,276],"proposed":[58],"based":[59,69],"on":[60,70],"residual":[61,77,81,97],"network.":[62],"First,":[63],"squeeze-and-excitation":[65],"networks":[66],"(SENet)":[67],"structure":[68],"attention":[72],"mechanism":[73],"introduced":[75,127,158],"into":[76,128,142],"block":[78],"neural":[82],"network,":[83],"connection":[85],"between":[86],"feature":[88],"extraction":[89],"channels":[90],"established,":[92],"improved":[95],"deep":[96],"network":[98,212],"I-ResNet50":[99,146,166,172],"obtained;":[101],"Second,":[102],"order":[104],"prevent":[106],"overfitting,":[107],"images":[110,235,242],"processed":[111],"production":[114],"line":[115],"laboratory":[118],"are":[119,140,157,189,193,221],"data-enhanced":[120],"labeled.":[122],"Transfer":[123],"learning":[124,203],"strategy":[125],"recognition":[130,144,170,230,265],"I-ResNet50,":[132],"then":[134],"training":[136],"set":[137,178],"data":[138,226],"samples":[139],"input":[141],"training,":[148],"activation":[151],"function":[152],"LReLu":[153],"Dropout":[155],"skills":[156],"improve":[160],"classification":[162],"ability":[163],"model;":[167],"Finally,":[168],"applied":[174],"test":[177,225,272],"validation":[180],"set,":[181],"each":[183],"output.":[190],"Comparison":[191],"experiments":[192,207],"tested":[194],"under":[195],"different":[196,200],"migration":[197],"strategies":[198],"optimizers":[201],"rates,":[204],"comparison":[206],"with":[208],"five":[210],"classic":[211],"structures":[213],"ResNet50,":[215],"YOLOV3,":[216],"MobileNetV2,":[217],"VGG16,":[218],"YOLOV4":[220],"also":[222],"tested.":[223],"The":[224,271],"show":[227],"that":[228],"accuracy":[231,266],"rates":[232],"qualified":[234],"three":[238],"types":[239],"defective":[241],"(Qualified":[243],"Assembly,":[244],"Missing":[245],"Current":[246],"Plate,":[247],"Wrong":[248],"Number":[249],"Stacks,":[251],"Reverse":[253],"Stack)":[254],"can":[255,268,277],"reach":[256,269],"99.64%,":[257],"98.17%,":[258],"99.11%,":[259],"95.40%,":[261],"respectively,":[262],"overall":[264],"98.10%.":[270],"results":[273],"illustrate":[274],"detect":[278],"more":[282],"accurately":[283],"quickly,":[285],"has":[287],"good":[288],"diagnosis":[290],"ability,":[291],"which":[292],"nearly":[294],"5%":[295],"higher":[296],"than":[297],"traditional":[299],"method,":[300],"new":[303],"solution":[304],"practical":[309],"industrial":[310],"scenarios":[311],"provided.":[316]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
