{"id":"https://openalex.org/W4392389586","doi":"https://doi.org/10.1109/isgt59692.2024.10454226","title":"Distributed Fiber Optic Sensing for Fault Localization Caused by Fallen Tree Using Physics-informed ResNet","display_name":"Distributed Fiber Optic Sensing for Fault Localization Caused by Fallen Tree Using Physics-informed ResNet","publication_year":2024,"publication_date":"2024-02-19","ids":{"openalex":"https://openalex.org/W4392389586","doi":"https://doi.org/10.1109/isgt59692.2024.10454226"},"language":"en","primary_location":{"id":"doi:10.1109/isgt59692.2024.10454226","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isgt59692.2024.10454226","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT)","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/A5111452171","display_name":"Yangmin Ding","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Yangmin Ding","raw_affiliation_strings":["Optical Networking and Sensing NEC Labs America, Inc,Princeton,USA","Optical Networking and Sensing NEC Labs America, Inc, Princeton, USA"],"affiliations":[{"raw_affiliation_string":"Optical Networking and Sensing NEC Labs America, Inc,Princeton,USA","institution_ids":[]},{"raw_affiliation_string":"Optical Networking and Sensing NEC Labs America, Inc, Princeton, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039659774","display_name":"Sarper \u00d6zharar","orcid":"https://orcid.org/0000-0003-4190-5386"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sarper Ozharar","raw_affiliation_strings":["Optical Networking and Sensing NEC Labs America, Inc,Princeton,USA","Optical Networking and Sensing NEC Labs America, Inc, Princeton, USA"],"affiliations":[{"raw_affiliation_string":"Optical Networking and Sensing NEC Labs America, Inc,Princeton,USA","institution_ids":[]},{"raw_affiliation_string":"Optical Networking and Sensing NEC Labs America, Inc, Princeton, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070135288","display_name":"Zhuocheng Jiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhuocheng Jiang","raw_affiliation_strings":["Optical Networking and Sensing NEC Labs America, Inc,Princeton,USA","Optical Networking and Sensing NEC Labs America, Inc, Princeton, USA"],"affiliations":[{"raw_affiliation_string":"Optical Networking and Sensing NEC Labs America, Inc,Princeton,USA","institution_ids":[]},{"raw_affiliation_string":"Optical Networking and Sensing NEC Labs America, Inc, Princeton, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111139580","display_name":"Yue Tian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yue Tian","raw_affiliation_strings":["Optical Networking and Sensing NEC Labs America, Inc,Princeton,USA","Optical Networking and Sensing NEC Labs America, Inc, Princeton, USA"],"affiliations":[{"raw_affiliation_string":"Optical Networking and Sensing NEC Labs America, Inc,Princeton,USA","institution_ids":[]},{"raw_affiliation_string":"Optical Networking and Sensing NEC Labs America, Inc, Princeton, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100427914","display_name":"Ting Wang","orcid":"https://orcid.org/0000-0001-9357-4238"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ting Wang","raw_affiliation_strings":["Optical Networking and Sensing NEC Labs America, Inc,Princeton,USA","Optical Networking and Sensing NEC Labs America, Inc, Princeton, USA"],"affiliations":[{"raw_affiliation_string":"Optical Networking and Sensing NEC Labs America, Inc,Princeton,USA","institution_ids":[]},{"raw_affiliation_string":"Optical Networking and Sensing NEC Labs America, Inc, Princeton, USA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5042402520","display_name":"Junbo Zhao","orcid":"https://orcid.org/0000-0002-8498-9666"},"institutions":[{"id":"https://openalex.org/I140172145","display_name":"University of Connecticut","ror":"https://ror.org/02der9h97","country_code":"US","type":"education","lineage":["https://openalex.org/I140172145"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Junbo Zhao","raw_affiliation_strings":["University of Connecticut,Electrical and Computer Engineering,Storrs,USA","Electrical and Computer Engineering, University of Connecticut, Storrs, USA"],"affiliations":[{"raw_affiliation_string":"University of Connecticut,Electrical and Computer Engineering,Storrs,USA","institution_ids":["https://openalex.org/I140172145"]},{"raw_affiliation_string":"Electrical and Computer Engineering, University of Connecticut, Storrs, USA","institution_ids":["https://openalex.org/I140172145"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5111452171"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.7274,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.72895491,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9656999707221985,"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":0.9656999707221985,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9290000200271606,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/optical-fiber","display_name":"Optical fiber","score":0.5510337352752686},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.48495224118232727},{"id":"https://openalex.org/keywords/residual-neural-network","display_name":"Residual neural network","score":0.45569366216659546},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.4468129277229309},{"id":"https://openalex.org/keywords/fiber","display_name":"Fiber","score":0.4294034242630005},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.427035927772522},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4193047285079956},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.3202686011791229},{"id":"https://openalex.org/keywords/optics","display_name":"Optics","score":0.2453773319721222},{"id":"https://openalex.org/keywords/seismology","display_name":"Seismology","score":0.23716571927070618},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.2283325493335724},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.19838139414787292},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.16963344812393188},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12897926568984985},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.08617129921913147},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.07529547810554504}],"concepts":[{"id":"https://openalex.org/C194232370","wikidata":"https://www.wikidata.org/wiki/Q162","display_name":"Optical fiber","level":2,"score":0.5510337352752686},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.48495224118232727},{"id":"https://openalex.org/C2944601119","wikidata":"https://www.wikidata.org/wiki/Q43744058","display_name":"Residual neural network","level":3,"score":0.45569366216659546},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.4468129277229309},{"id":"https://openalex.org/C519885992","wikidata":"https://www.wikidata.org/wiki/Q161","display_name":"Fiber","level":2,"score":0.4294034242630005},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.427035927772522},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4193047285079956},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.3202686011791229},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.2453773319721222},{"id":"https://openalex.org/C165205528","wikidata":"https://www.wikidata.org/wiki/Q83371","display_name":"Seismology","level":1,"score":0.23716571927070618},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.2283325493335724},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.19838139414787292},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.16963344812393188},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12897926568984985},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.08617129921913147},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.07529547810554504},{"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/isgt59692.2024.10454226","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isgt59692.2024.10454226","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6800000071525574,"id":"https://metadata.un.org/sdg/13","display_name":"Climate action"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W2097988347","https://openalex.org/W2166310501","https://openalex.org/W2611777992","https://openalex.org/W3111453021","https://openalex.org/W4254193010","https://openalex.org/W4285815973","https://openalex.org/W4321020941","https://openalex.org/W6615821545"],"related_works":["https://openalex.org/W2599472179","https://openalex.org/W4323057981","https://openalex.org/W3178607569","https://openalex.org/W4310870954","https://openalex.org/W4308408209","https://openalex.org/W3196952692","https://openalex.org/W3177025895","https://openalex.org/W4383503462","https://openalex.org/W3034026275","https://openalex.org/W4390187226"],"abstract_inverted_index":{"Falling":[0],"trees":[1],"or":[2,11,49],"their":[3],"limbs":[4],"can":[5],"cause":[6],"power":[7,99],"lines":[8],"to":[9,56,94,115],"break":[10],"sag,":[12],"sometimes":[13],"resulting":[14],"in":[15],"devastating":[16],"wildfires.":[17],"Conventional":[18],"protections":[19],"such":[20],"as":[21],"circuit":[22,28,82],"breakers,":[23],"overcurrent":[24],"relays":[25],"and":[26,74,119,143,148],"automatic":[27],"reclosers":[29],"may":[30,40],"clear":[31],"short":[32,60,81],"circuits":[33],"caused":[34],"by":[35],"tree":[36,146],"contact,":[37],"but":[38],"they":[39],"not":[41,54,84],"detect":[42,95],"cases":[43],"where":[44,102],"the":[45,98],"conductors":[46],"remain":[47],"intact":[48],"a":[50,58,67,80],"conducting":[51],"path":[52],"is":[53,83,112],"sufficient":[55],"create":[57],"full":[59],"circuit.":[61],"In":[62],"this":[63,117],"paper,":[64],"we":[65],"introduce":[66],"novel,":[68],"non-intrusive":[69],"monitoring":[70],"technique":[71],"that":[72],"detects":[73],"locates":[75],"fallen":[76,122,145],"trees,":[77,123],"even":[78],"if":[79],"triggered.":[85],"This":[86],"method":[87],"employs":[88],"distributed":[89],"fiber":[90,104],"optic":[91],"sensing":[92],"(DFOS)":[93],"vibrations":[96],"along":[97],"distribution":[100],"line":[101],"corresponding":[103],"cables":[105],"are":[106],"installed.":[107],"A":[108],"physics-informed":[109],"ResNet":[110],"model":[111],"then":[113],"utilized":[114],"interpret":[116],"information":[118],"accurately":[120],"locate":[121],"which":[124],"sets":[125],"it":[126],"apart":[127],"from":[128],"traditional":[129],"black-box":[130],"predictions":[131],"of":[132],"machine":[133],"learning":[134],"algorithms.":[135],"Our":[136],"real-scale":[137],"lab":[138],"tests":[139],"demonstrate":[140],"highly":[141],"accurate":[142],"reliable":[144],"detection":[147],"localization.":[149]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}