{"id":"https://openalex.org/W7125965918","doi":"https://doi.org/10.1109/smc58881.2025.11342911","title":"MFDS-GFNet: A Multi-Feature Dual-Stream Gate Fusion Network for Distributed Optical Fiber Temperature Event Recognition","display_name":"MFDS-GFNet: A Multi-Feature Dual-Stream Gate Fusion Network for Distributed Optical Fiber Temperature Event Recognition","publication_year":2025,"publication_date":"2025-10-05","ids":{"openalex":"https://openalex.org/W7125965918","doi":"https://doi.org/10.1109/smc58881.2025.11342911"},"language":null,"primary_location":{"id":"doi:10.1109/smc58881.2025.11342911","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc58881.2025.11342911","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","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/A5124090351","display_name":"Wanchang Jiang","orcid":null},"institutions":[{"id":"https://openalex.org/I179060312","display_name":"Northeast Electric Power University","ror":"https://ror.org/00zqaxa34","country_code":"CN","type":"education","lineage":["https://openalex.org/I179060312"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wanchang Jiang","raw_affiliation_strings":["Northeast Electric Power University,School of Computer Science,Jilin,China"],"affiliations":[{"raw_affiliation_string":"Northeast Electric Power University,School of Computer Science,Jilin,China","institution_ids":["https://openalex.org/I179060312"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5124118723","display_name":"Rihao Tang","orcid":null},"institutions":[{"id":"https://openalex.org/I179060312","display_name":"Northeast Electric Power University","ror":"https://ror.org/00zqaxa34","country_code":"CN","type":"education","lineage":["https://openalex.org/I179060312"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rihao Tang","raw_affiliation_strings":["Northeast Electric Power University,School of Computer Science,Jilin,China"],"affiliations":[{"raw_affiliation_string":"Northeast Electric Power University,School of Computer Science,Jilin,China","institution_ids":["https://openalex.org/I179060312"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5124090351"],"corresponding_institution_ids":["https://openalex.org/I179060312"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.69087361,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2079","last_page":"2086"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.21899999678134918,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.21899999678134918,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.1720000058412552,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.03620000183582306,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.5964000225067139},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5939000248908997},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.5554999709129333},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5472999811172485},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5152000188827515},{"id":"https://openalex.org/keywords/raw-data","display_name":"Raw data","score":0.4496999979019165},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4399999976158142},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.41609999537467957}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.649399995803833},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.5964000225067139},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5939000248908997},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5769000053405762},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5554999709129333},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5472999811172485},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5152000188827515},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4674000144004822},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.4496999979019165},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4399999976158142},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.41609999537467957},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.3747999966144562},{"id":"https://openalex.org/C72293138","wikidata":"https://www.wikidata.org/wiki/Q909741","display_name":"Temperature measurement","level":2,"score":0.37380000948905945},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.36649999022483826},{"id":"https://openalex.org/C175291020","wikidata":"https://www.wikidata.org/wiki/Q1156822","display_name":"Offset (computer science)","level":2,"score":0.36309999227523804},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.3361999988555908},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.3325999975204468},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3314000070095062},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.32919999957084656},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.28949999809265137},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.28380000591278076},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.2833000123500824},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.28119999170303345},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.271699994802475},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.26190000772476196}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/smc58881.2025.11342911","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc58881.2025.11342911","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320327282","display_name":"Department of Science and Technology of Jilin Province","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W2900129548","https://openalex.org/W2966209250","https://openalex.org/W2979700798","https://openalex.org/W3021142045","https://openalex.org/W3092376199","https://openalex.org/W3192285679","https://openalex.org/W4308567597","https://openalex.org/W4377090146","https://openalex.org/W4378802028","https://openalex.org/W4385062852","https://openalex.org/W4388210958","https://openalex.org/W4388440524","https://openalex.org/W4393139737","https://openalex.org/W4399258932","https://openalex.org/W4400490113","https://openalex.org/W4401991078"],"related_works":[],"abstract_inverted_index":{"Distributed":[0],"temperature":[1,12,41,125],"data":[2,65,78],"collected":[3],"by":[4,136,140],"BOTDR":[5],"systems":[6],"exhibit":[7],"significant":[8],"spatiotemporal":[9],"patterns,":[10],"where":[11,76],"events":[13],"often":[14],"share":[15],"similar":[16],"magnitudes":[17],"but":[18],"differ":[19],"in":[20],"structural":[21],"characteristics,":[22],"making":[23],"accurate":[24],"classification":[25],"challenging.":[26],"To":[27],"address":[28],"this,":[29],"we":[30],"propose":[31],"MFDS-GFNet,":[32],"a":[33,48,97],"Multi-Feature":[34],"Dual-Stream":[35],"Gate":[36],"Fusion":[37],"Network":[38],"for":[39,111],"multi-class":[40],"event":[42],"recognition.":[43],"The":[44,142],"method":[45],"begins":[46],"with":[47,148],"multi-dimensional":[49],"feature":[50,91,105],"extraction":[51],"framework":[52],"that":[53,117],"derives":[54],"temporal":[55],"gradients,":[56,58],"spatial":[57],"and":[59,79,93,138],"local":[60],"statistical":[61],"features":[62,81],"from":[63],"raw":[64,77],"to":[66,88,128],"capture":[67],"event-specific":[68],"patterns.":[69],"A":[70],"dual-stream":[71],"architecture":[72],"is":[73],"then":[74],"employed,":[75],"physical":[80],"are":[82],"processed":[83],"separately":[84],"using":[85],"residual":[86],"networks":[87],"enhance":[89],"deep":[90],"learning":[92],"training":[94],"stability.":[95],"Finally,":[96],"gate":[98],"fusion":[99],"module":[100],"adaptively":[101],"integrates":[102],"the":[103,134],"two":[104],"streams,":[106],"automatically":[107],"weighting":[108],"their":[109],"contributions":[110],"optimal":[112],"classification.":[113],"Experimental":[114],"results":[115],"show":[116],"MFDS-GFNet":[118],"achieves":[119],"99.74%":[120],"accuracy":[121],"across":[122],"five":[123,129],"representative":[124],"events.":[126],"Compared":[127],"baseline":[130],"models,":[131],"it":[132],"outperforms":[133],"Transformer":[135],"1.07%":[137],"GoogLeNet":[139],"1.53%.":[141],"model":[143],"also":[144],"supports":[145],"real-time":[146],"deployment":[147],"an":[149],"inference":[150],"latency":[151],"of":[152],"0.40":[153],"ms":[154],"per":[155],"sample.":[156]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2026-01-29T00:00:00"}