{"id":"https://openalex.org/W3094747624","doi":"https://doi.org/10.1109/icnsc48988.2020.9238105","title":"A Deep Learning Approach to Large-Scale Light Curve Prediction and Real-Time Anomaly Detection with Grubbs Criterion","display_name":"A Deep Learning Approach to Large-Scale Light Curve Prediction and Real-Time Anomaly Detection with Grubbs Criterion","publication_year":2020,"publication_date":"2020-10-30","ids":{"openalex":"https://openalex.org/W3094747624","doi":"https://doi.org/10.1109/icnsc48988.2020.9238105","mag":"3094747624"},"language":"en","primary_location":{"id":"doi:10.1109/icnsc48988.2020.9238105","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icnsc48988.2020.9238105","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5016410509","display_name":"Xiaodong Huang","orcid":"https://orcid.org/0000-0002-3573-718X"},"institutions":[{"id":"https://openalex.org/I4210162215","display_name":"Naval Aeronautical and Astronautical University","ror":"https://ror.org/02j2yhq26","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210162215"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaodong Huang","raw_affiliation_strings":["Naval Aeronautical University, Shandong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Naval Aeronautical University, Shandong, China","institution_ids":["https://openalex.org/I4210162215"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101695827","display_name":"Lei Peng","orcid":"https://orcid.org/0000-0003-1467-6960"},"institutions":[{"id":"https://openalex.org/I37796252","display_name":"Beijing University of Technology","ror":"https://ror.org/037b1pp87","country_code":"CN","type":"education","lineage":["https://openalex.org/I37796252"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Peng","raw_affiliation_strings":["Faculty of Information Technology, Beijing University of Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Information Technology, Beijing University of Technology, Beijing, China","institution_ids":["https://openalex.org/I37796252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024288211","display_name":"Cheng Lu","orcid":"https://orcid.org/0000-0002-7651-3924"},"institutions":[{"id":"https://openalex.org/I37796252","display_name":"Beijing University of Technology","ror":"https://ror.org/037b1pp87","country_code":"CN","type":"education","lineage":["https://openalex.org/I37796252"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cheng Lu","raw_affiliation_strings":["Faculty of Information Technology, Beijing University of Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Information Technology, Beijing University of Technology, Beijing, China","institution_ids":["https://openalex.org/I37796252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057935426","display_name":"Jing Bi","orcid":"https://orcid.org/0000-0002-4610-0141"},"institutions":[{"id":"https://openalex.org/I37796252","display_name":"Beijing University of Technology","ror":"https://ror.org/037b1pp87","country_code":"CN","type":"education","lineage":["https://openalex.org/I37796252"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jing Bi","raw_affiliation_strings":["Faculty of Information Technology, Beijing University of Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Information Technology, Beijing University of Technology, Beijing, China","institution_ids":["https://openalex.org/I37796252"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070546858","display_name":"Haitao Yuan","orcid":"https://orcid.org/0000-0001-8475-419X"},"institutions":[{"id":"https://openalex.org/I118118575","display_name":"New Jersey Institute of Technology","ror":"https://ror.org/05e74xb87","country_code":"US","type":"education","lineage":["https://openalex.org/I118118575"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Haitao Yuan","raw_affiliation_strings":["New Jersey Institute of Technology, Newark, NJ, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"New Jersey Institute of Technology, Newark, NJ, USA","institution_ids":["https://openalex.org/I118118575"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"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":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9905999898910522,"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":0.9905999898910522,"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/T12389","display_name":"Infrared Target Detection Methodologies","score":0.9803000092506409,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9754999876022339,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7853562831878662},{"id":"https://openalex.org/keywords/dropout","display_name":"Dropout (neural networks)","score":0.7338096499443054},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6583220362663269},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5944156050682068},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.5313582420349121},{"id":"https://openalex.org/keywords/brightness","display_name":"Brightness","score":0.5116961002349854},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.4597248435020447},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4591393768787384},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.43868914246559143},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.37591812014579773},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3223297595977783},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.286162793636322}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7853562831878662},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.7338096499443054},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6583220362663269},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5944156050682068},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.5313582420349121},{"id":"https://openalex.org/C125245961","wikidata":"https://www.wikidata.org/wiki/Q221656","display_name":"Brightness","level":2,"score":0.5116961002349854},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.4597248435020447},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4591393768787384},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.43868914246559143},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37591812014579773},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3223297595977783},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.286162793636322},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icnsc48988.2020.9238105","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icnsc48988.2020.9238105","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6536760645","display_name":null,"funder_award_id":"41401020401,41401050102","funder_id":"https://openalex.org/F4320325551","funder_display_name":"National Defense Pre-Research Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320325551","display_name":"National Defense Pre-Research Foundation of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1563599472","https://openalex.org/W1597587295","https://openalex.org/W1906995411","https://openalex.org/W2056972503","https://openalex.org/W2064675550","https://openalex.org/W2074795523","https://openalex.org/W2095705004","https://openalex.org/W2102148524","https://openalex.org/W2146651617","https://openalex.org/W2165956633","https://openalex.org/W2509641632","https://openalex.org/W2572575327","https://openalex.org/W2791247133","https://openalex.org/W2906170845","https://openalex.org/W2917746835","https://openalex.org/W2938313730","https://openalex.org/W2955791536","https://openalex.org/W2964121744","https://openalex.org/W3101373212","https://openalex.org/W6631190155","https://openalex.org/W6674330103","https://openalex.org/W6732065150"],"related_works":["https://openalex.org/W3082178636","https://openalex.org/W2782041652","https://openalex.org/W2612657834","https://openalex.org/W2387055199","https://openalex.org/W4298287631","https://openalex.org/W2953061907","https://openalex.org/W2392157706","https://openalex.org/W2599192953","https://openalex.org/W2952088488","https://openalex.org/W3008584592"],"abstract_inverted_index":{"In":[0],"light":[1,84],"curves":[2],"(LCs),":[3],"the":[4,91,108,112,116],"brightness":[5,139],"of":[6,29,53,83,111,134,137],"stars":[7],"is":[8,14],"associated":[9],"with":[10,99,121],"time,":[11],"and":[12,26,68],"so":[13],"its":[15],"image.":[16],"The":[17],"traditional":[18],"data":[19,28],"processing":[20],"methods":[21,131],"cannot":[22],"effectively":[23],"handle":[24],"real-time":[25],"large-volume":[27],"various":[30],"LCs.":[31,143],"To":[32],"address":[33],"this":[34,36,88],"issue,":[35],"work":[37,89],"develops":[38],"a":[39,96,122],"deep":[40],"neural":[41],"network":[42,109],"named":[43],"Dropout-based":[44],"Recurrent":[45],"Neural":[46],"Networks":[47],"(DRNN).":[48],"It":[49],"extracts":[50],"complicated":[51],"features":[52],"all":[54],"images":[55],"captured":[56],"by":[57,94],"Mini":[58],"Ground-based":[59],"Wide-Angle":[60],"Camera":[61],"array":[62],"(Mini-GWAC)":[63],"for":[64,80],"point":[65],"source":[66],"extraction":[67],"cross-certification":[69],"through":[70],"Long":[71],"Short-Term":[72],"Memory":[73],"units.":[74],"DRNN":[75,127],"can":[76],"also":[77],"produce":[78],"warnings":[79],"abnormal":[81],"values":[82],"change":[85],"curves.":[86],"Furthermore,":[87],"optimizes":[90],"training":[92],"model":[93],"combining":[95],"dropout":[97],"method":[98],"an":[100],"adaptive":[101],"moment":[102],"estimation":[103],"algorithm":[104],"to":[105],"iteratively":[106],"update":[107],"weight":[110],"RNN":[113],"based":[114],"on":[115],"LCs":[117],"data.":[118],"Extensive":[119],"experiments":[120],"Mini-GWAC":[123],"dataset":[124],"demonstrate":[125],"that":[126],"outperforms":[128],"several":[129],"typical":[130],"in":[132,140],"terms":[133],"prediction":[135],"performance":[136],"star":[138],"large-scale":[141],"astronomical":[142]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}