{"id":"https://openalex.org/W4410636591","doi":"https://doi.org/10.1145/3701716.3715229","title":"Global Feature Enhancing and Fusion Framework for Strain Gauge Status Recognition","display_name":"Global Feature Enhancing and Fusion Framework for Strain Gauge Status Recognition","publication_year":2025,"publication_date":"2025-05-08","ids":{"openalex":"https://openalex.org/W4410636591","doi":"https://doi.org/10.1145/3701716.3715229"},"language":"en","primary_location":{"id":"doi:10.1145/3701716.3715229","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3701716.3715229","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3701716.3715229","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion Proceedings of the ACM on Web Conference 2025","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3701716.3715229","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Xu Zhang","orcid":"https://orcid.org/0009-0006-5317-2422"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xu Zhang","raw_affiliation_strings":["Shanghai Key Laboratory of Data Science, School of Computer Science and Technology, Fudan University, Shang Hai, China"],"raw_orcid":"https://orcid.org/0009-0006-5317-2422","affiliations":[{"raw_affiliation_string":"Shanghai Key Laboratory of Data Science, School of Computer Science and Technology, Fudan University, Shang Hai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100396080","display_name":"Peng Wang","orcid":"https://orcid.org/0000-0002-8136-9621"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Wang","raw_affiliation_strings":["Shanghai Key Laboratory of Data Science, School of Computer Science and Technology, Fudan University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-8136-9621","affiliations":[{"raw_affiliation_string":"Shanghai Key Laboratory of Data Science, School of Computer Science and Technology, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100337653","display_name":"Chen Wang","orcid":"https://orcid.org/0000-0003-1698-8992"},"institutions":[{"id":"https://openalex.org/I4210096250","display_name":"Beijing Institute of Big Data Research","ror":"https://ror.org/00s1sz824","country_code":"CN","type":"facility","lineage":["https://openalex.org/I20231570","https://openalex.org/I37796252","https://openalex.org/I4210096250"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chen Wang","raw_affiliation_strings":["National Engineering Research Center for Big Data Software, Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-1698-8992","affiliations":[{"raw_affiliation_string":"National Engineering Research Center for Big Data Software, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I4210096250","https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Zhe Xu","orcid":"https://orcid.org/0009-0005-3188-2258"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhe Xu","raw_affiliation_strings":["School of Software, Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0005-3188-2258","affiliations":[{"raw_affiliation_string":"School of Software, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Xiaohua Nie","orcid":"https://orcid.org/0009-0008-7277-9574"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiaohua Nie","raw_affiliation_strings":["National Key Laboratory of Strength and Structural Integrity, Aircraft Strength Research Institute of China, Xi'an, Shaanxi, China"],"raw_orcid":"https://orcid.org/0009-0008-7277-9574","affiliations":[{"raw_affiliation_string":"National Key Laboratory of Strength and Structural Integrity, Aircraft Strength Research Institute of China, Xi'an, Shaanxi, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100392156","display_name":"Wei Wang","orcid":"https://orcid.org/0000-0003-0264-788X"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Wang","raw_affiliation_strings":["Shanghai Key Laboratory of Data Science, School of Computer Science and Technology, Fudan University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0003-0264-788X","affiliations":[{"raw_affiliation_string":"Shanghai Key Laboratory of Data Science, School of Computer Science and Technology, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I24943067"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.04796476,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"611","last_page":"620"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.991599977016449,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.991599977016449,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T12549","display_name":"Image and Object Detection Techniques","score":0.9846000075340271,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9811000227928162,"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/feature","display_name":"Feature (linguistics)","score":0.5462727546691895},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.5392531156539917},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5191608667373657},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5102238655090332},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47841864824295044},{"id":"https://openalex.org/keywords/strain-gauge","display_name":"Strain gauge","score":0.4329894185066223},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.13531279563903809},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.07107660174369812}],"concepts":[{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5462727546691895},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.5392531156539917},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5191608667373657},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5102238655090332},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47841864824295044},{"id":"https://openalex.org/C60584519","wikidata":"https://www.wikidata.org/wiki/Q610723","display_name":"Strain gauge","level":2,"score":0.4329894185066223},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.13531279563903809},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.07107660174369812},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/3701716.3715229","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3701716.3715229","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3701716.3715229","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion Proceedings of the ACM on Web Conference 2025","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2511.11629","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2511.11629","pdf_url":"https://arxiv.org/pdf/2511.11629","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:doi:10.48550/arxiv.2511.11629","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"}],"best_oa_location":{"id":"doi:10.1145/3701716.3715229","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3701716.3715229","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3701716.3715229","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion Proceedings of the ACM on Web Conference 2025","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4410636591.pdf","grobid_xml":"https://content.openalex.org/works/W4410636591.grobid-xml"},"referenced_works_count":29,"referenced_works":["https://openalex.org/W2056716515","https://openalex.org/W2154851992","https://openalex.org/W2164998314","https://openalex.org/W2475334473","https://openalex.org/W2892880750","https://openalex.org/W2932012013","https://openalex.org/W2962858109","https://openalex.org/W2966720510","https://openalex.org/W2967352563","https://openalex.org/W2967988901","https://openalex.org/W2972810968","https://openalex.org/W2990908872","https://openalex.org/W3034222740","https://openalex.org/W3044983307","https://openalex.org/W3083891030","https://openalex.org/W3093327960","https://openalex.org/W3103520796","https://openalex.org/W3104097132","https://openalex.org/W3106006733","https://openalex.org/W3190664711","https://openalex.org/W3202364339","https://openalex.org/W4212824777","https://openalex.org/W4285240208","https://openalex.org/W4309119517","https://openalex.org/W4315645290","https://openalex.org/W4362579348","https://openalex.org/W4367047139","https://openalex.org/W4387848690","https://openalex.org/W4400114450"],"related_works":["https://openalex.org/W2278574869","https://openalex.org/W4205398706","https://openalex.org/W4385658569","https://openalex.org/W1492000841","https://openalex.org/W4206704705","https://openalex.org/W1968336058","https://openalex.org/W2088454087","https://openalex.org/W4243330325","https://openalex.org/W2033914206","https://openalex.org/W2042327336"],"abstract_inverted_index":{"Strain":[0],"Gauge":[1],"Status":[2],"(SGS)":[3],"recognition":[4,204],"is":[5],"crucial":[6],"in":[7,62,115,125,224],"the":[8,15,63,74,78,85,94,99,123,131,144,196],"field":[9],"of":[10,17,25,112,133,198],"intelligent":[11],"manufacturing":[12],"based":[13],"on":[14,211],"Internet":[16],"Things,":[18],"as":[19,66],"accurate":[20],"identification":[21],"helps":[22],"timely":[23],"detection":[24],"failed":[26],"mechanical":[27],"components,":[28],"avoiding":[29],"accidents.":[30],"The":[31],"loading":[32],"and":[33,172,182,187,214],"unloading":[34],"sequences":[35],"generated":[36],"by":[37],"strain":[38],"gauges":[39],"can":[40,68],"be":[41,90],"identified":[42],"through":[43,156],"time":[44,79,95,104,146,200],"series":[45,105],"classification":[46],"(TSC)":[47],"algorithms.":[48],"Recently,":[49],"deep":[50],"learning":[51,181],"models,":[52],"e.g.,":[53,109],"convolutional":[54],"neural":[55],"networks":[56],"(CNNs)":[57],"have":[58],"shown":[59],"remarkable":[60],"success":[61],"TSC":[64],"task,":[65],"they":[67],"extract":[69],"discriminative":[70],"local":[71,86,100,164],"features":[72,87,128,139,155,165,190],"from":[73,122],"subsequences":[75],"to":[76,130,140,166,194],"identify":[77],"series.":[80],"However,":[81],"we":[82,148,175],"observe":[83],"only":[84],"may":[88],"not":[89],"sufficient":[91],"for":[92,191,221],"expressing":[93],"series,":[96,147,201],"especially":[97],"when":[98],"sub-sequences":[101],"between":[102,163],"different":[103],"are":[106,209],"very":[107],"similar,":[108],"SGS":[110,145,199,213,225],"data":[111,223],"aircraft":[113],"wings":[114],"static":[116],"strength":[117],"experiments.":[118],"Nevertheless,":[119],"CNNs":[120],"suffer":[121],"limitation":[124],"extracting":[126,137],"global":[127,138,154,168,179,189],"due":[129],"nature":[132],"convolution":[134],"operations.":[135],"For":[136],"more":[141],"comprehensively":[142],"represent":[143],"propose":[149,176],"two":[150],"insights:":[151],"(i)":[152],"Constructing":[153],"feature":[157,180],"engineering.":[158],"(ii)":[159],"Learning":[160],"high-order":[161],"relationships":[162],"capture":[167],"features.":[169],"To":[170],"realize":[171],"utilize":[173],"them,":[174],"a":[177],"hypergraph-based":[178],"fusion":[183],"framework,":[184],"which":[185],"learns":[186],"fuses":[188],"semantic":[192],"consistency":[193],"enhance":[195],"representation":[197],"thereby":[202],"improving":[203],"accuracy.":[205],"Our":[206],"method":[207],"designs":[208],"validated":[210],"industrial":[212],"public":[215],"UCR":[216],"datasets,":[217],"showing":[218],"better":[219],"generalization":[220],"unseen":[222],"recognition.":[226]},"counts_by_year":[],"updated_date":"2026-06-02T09:04:35.204637","created_date":"2025-05-24T00:00:00"}