{"id":"https://openalex.org/W7171554053","doi":"https://doi.org/10.48550/arxiv.2607.24525","title":"A Modern ConvNet for Solar Filament Detection","display_name":"A Modern ConvNet for Solar Filament Detection","publication_year":2026,"publication_date":"2026-07-27","ids":{"openalex":"https://openalex.org/W7171554053","doi":"https://doi.org/10.48550/arxiv.2607.24525"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.24525","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.24525","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2607.24525","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5141185551","display_name":"J. R. Hu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, J. R.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135818957","display_name":"Q. Hao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hao, Q.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107917413","display_name":"Z. Zheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zheng, Z.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5143812922","display_name":"P. F. Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, P. F.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5143778203","display_name":"C. K. Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, C.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5143777126","display_name":"Y. H. Meng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Meng, Y.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11276","display_name":"Solar Radiation and Photovoltaics","score":0.41839998960494995,"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/T11276","display_name":"Solar Radiation and Photovoltaics","score":0.41839998960494995,"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/T10468","display_name":"Photovoltaic System Optimization Techniques","score":0.12919999659061432,"subfield":{"id":"https://openalex.org/subfields/2105","display_name":"Renewable Energy, Sustainability and the Environment"},"field":{"id":"https://openalex.org/fields/21","display_name":"Energy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10251","display_name":"Solar and Space Plasma Dynamics","score":0.07890000194311142,"subfield":{"id":"https://openalex.org/subfields/3103","display_name":"Astronomy and Astrophysics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6772000193595886},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5081999897956848},{"id":"https://openalex.org/keywords/conditional-random-field","display_name":"Conditional random field","score":0.48089998960494995},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.45840001106262207},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.45329999923706055},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.444599986076355},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4415999948978424},{"id":"https://openalex.org/keywords/protein-filament","display_name":"Protein filament","score":0.4221999943256378},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.41909998655319214}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.697700023651123},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6772000193595886},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5821999907493591},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5081999897956848},{"id":"https://openalex.org/C152565575","wikidata":"https://www.wikidata.org/wiki/Q1124538","display_name":"Conditional random field","level":2,"score":0.48089998960494995},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.45840001106262207},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.45329999923706055},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.444599986076355},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4415999948978424},{"id":"https://openalex.org/C14228908","wikidata":"https://www.wikidata.org/wiki/Q2920483","display_name":"Protein filament","level":2,"score":0.4221999943256378},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.41909998655319214},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4101000130176544},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.39800000190734863},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.3880000114440918},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3864000141620636},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.3522000014781952},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.33869999647140503},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.32850000262260437},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32839998602867126},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.3138999938964844},{"id":"https://openalex.org/C46576248","wikidata":"https://www.wikidata.org/wiki/Q1114630","display_name":"DBSCAN","level":5,"score":0.31200000643730164},{"id":"https://openalex.org/C59519942","wikidata":"https://www.wikidata.org/wiki/Q650665","display_name":"Drone","level":2,"score":0.31189998984336853},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.29660001397132874},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.2766000032424927},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.2687999904155731},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.26809999346733093},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.2535000145435333},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.25099998712539673},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.250900000333786}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.24525","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.24525","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2607.24525","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.24525","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Automated":[0],"solar":[1,13,56,69,137,197],"filament":[2,57,70,138,198],"detection":[3,58],"using":[4],"deep":[5,193],"learning":[6,51,194],"faces":[7],"several":[8,135],"challenges.":[9],"Semantic":[10],"segmentation":[11,89,140],"of":[12,49,109,160,166,192],"filaments":[14],"is":[15],"a":[16,26,47,55,67,87,123,186],"complicated":[17],"multiscale":[18,93],"feature":[19,94],"extraction":[20],"task":[21],"with":[22,111,142],"long-tail":[23],"distribution.":[24],"Furthermore,":[25],"large-scale,":[27,124],"highly":[28],"complete,":[29],"and":[30,105,163,168],"finely":[31],"detailed":[32],"dataset":[33,71,126],"has":[34,146],"become":[35],"mandatory":[36],"for":[37,115,188,196],"providing":[38],"abundant":[39],"information.":[40],"To":[41],"address":[42],"these":[43],"challenges,":[44],"we":[45,64,79,121],"present":[46],"series":[48],"machine":[50],"approaches":[52,172],"to":[53,149],"develop":[54],"workflow":[59],"that":[60,132,170],"performs":[61],"superbly.":[62],"First,":[63],"manually":[65],"annotated":[66],"small-scale":[68],"based":[72],"on":[73,92],"H$\u03b1$":[74],"spectra":[75],"called":[76,127],"MHAS.":[77],"Next,":[78],"developed":[80],"the":[81,99,118,158,164,190],"Multiscale":[82],"ORiented":[83],"DENdritic":[84],"(MORDEN)":[85],"model,":[86],"semantic":[88,139],"model":[90],"focusing":[91],"extraction.":[95],"We":[96,155],"also":[97,156],"introduced":[98],"Dense":[100],"Conditional":[101],"Random":[102],"Field":[103],"(DenseCRF)":[104],"Density-Based":[106],"Spatial":[107],"Clustering":[108],"Applications":[110],"Noise":[112],"(DBSCAN)":[113],"methods":[114],"post-processing.":[116],"Using":[117],"proposed":[119],"workflow,":[120],"generated":[122],"high-quality":[125],"AHAS.":[128],"Experimental":[129],"results":[130,178],"demonstrate":[131],"MORDEN":[133],"outperforms":[134],"existing":[136],"models":[141,195],"open":[143],"access.":[144],"DenseCRF":[145],"been":[147],"demonstrated":[148],"effectively":[150],"capture":[151],"fine":[152],"edge":[153],"details.":[154],"evaluated":[157],"effects":[159],"data":[161],"scaling":[162],"reliability":[165],"DBSCAN":[167],"found":[169],"both":[171],"yield":[173],"satisfactory":[174],"performance.":[175],"Multiple":[176],"visualization":[177],"substantiate":[179],"our":[180],"quantitative":[181],"findings.":[182],"Our":[183],"work":[184],"provides":[185],"foundation":[187],"maximizing":[189],"potential":[191],"detection.":[199]},"counts_by_year":[],"updated_date":"2026-07-29T09:40:50.615796","created_date":"2026-07-29T00:00:00"}