{"id":"https://openalex.org/W2753683116","doi":"https://doi.org/10.1109/icmew.2017.8026227","title":"Convolutional neural network for classification of solar radio spectrum","display_name":"Convolutional neural network for classification of solar radio spectrum","publication_year":2017,"publication_date":"2017-07-01","ids":{"openalex":"https://openalex.org/W2753683116","doi":"https://doi.org/10.1109/icmew.2017.8026227","mag":"2753683116"},"language":"en","primary_location":{"id":"doi:10.1109/icmew.2017.8026227","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmew.2017.8026227","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)","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/A5001099754","display_name":"Sisi Chen","orcid":"https://orcid.org/0000-0003-0761-3291"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sisi Chen","raw_affiliation_strings":["College of Mathematics and Statistics, Shenzhen University, Shenzhen, China","Key Laboratory of Solar Activity, Chinese Academy of Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Mathematics and Statistics, Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]},{"raw_affiliation_string":"Key Laboratory of Solar Activity, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100647349","display_name":"Long Xu","orcid":"https://orcid.org/0000-0002-9286-2876"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Long Xu","raw_affiliation_strings":["Key Laboratory of Solar Activity, Chinese Academy of Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Laboratory of Solar Activity, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017116858","display_name":"Lin Ma","orcid":"https://orcid.org/0000-0002-7331-6132"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lin Ma","raw_affiliation_strings":["Tencent AI Lab, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tencent AI Lab, Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100692904","display_name":"Wei-Qiang Zhang","orcid":"https://orcid.org/0000-0003-3841-1959"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weiqiang Zhang","raw_affiliation_strings":["College of Mathematics and Statistics, Shenzhen University, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Mathematics and Statistics, Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100345097","display_name":"Zhuo Chen","orcid":"https://orcid.org/0000-0003-0563-1760"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Zhuo Chen","raw_affiliation_strings":["School of Computer Engineering, Nanyang Technological University, Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Engineering, Nanyang Technological University, Singapore","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068724865","display_name":"Yihua Yan","orcid":"https://orcid.org/0000-0002-7106-6029"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yihua Yan","raw_affiliation_strings":["Key Laboratory of Solar Activity, Chinese Academy of Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Laboratory of Solar Activity, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I19820366"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"198","last_page":"201"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9725000262260437,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9725000262260437,"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/T10616","display_name":"Smart Agriculture and AI","score":0.968999981880188,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T14319","display_name":"Currency Recognition and Detection","score":0.9408000111579895,"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/autoencoder","display_name":"Autoencoder","score":0.8516699075698853},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7188205122947693},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6756910085678101},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5940442085266113},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.549008846282959},{"id":"https://openalex.org/keywords/solar-radio","display_name":"Solar radio","score":0.535852313041687},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5230658650398254},{"id":"https://openalex.org/keywords/radio-spectrum","display_name":"Radio spectrum","score":0.4691276550292969},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4636697769165039},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.4394233822822571},{"id":"https://openalex.org/keywords/spectrum","display_name":"Spectrum (functional analysis)","score":0.4155275523662567},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.15933552384376526},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.10579746961593628},{"id":"https://openalex.org/keywords/astrophysics","display_name":"Astrophysics","score":0.07004565000534058}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8516699075698853},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7188205122947693},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6756910085678101},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5940442085266113},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.549008846282959},{"id":"https://openalex.org/C2992142144","wikidata":"https://www.wikidata.org/wiki/Q109032554","display_name":"Solar radio","level":2,"score":0.535852313041687},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5230658650398254},{"id":"https://openalex.org/C92545706","wikidata":"https://www.wikidata.org/wiki/Q902174","display_name":"Radio spectrum","level":2,"score":0.4691276550292969},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4636697769165039},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.4394233822822571},{"id":"https://openalex.org/C156778621","wikidata":"https://www.wikidata.org/wiki/Q1365748","display_name":"Spectrum (functional analysis)","level":2,"score":0.4155275523662567},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.15933552384376526},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.10579746961593628},{"id":"https://openalex.org/C44870925","wikidata":"https://www.wikidata.org/wiki/Q37547","display_name":"Astrophysics","level":1,"score":0.07004565000534058},{"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/icmew.2017.8026227","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmew.2017.8026227","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":0.75,"display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1538131130","https://openalex.org/W1982542902","https://openalex.org/W1993882792","https://openalex.org/W2031776089","https://openalex.org/W2072128103","https://openalex.org/W2107789863","https://openalex.org/W2110750681","https://openalex.org/W2120480077","https://openalex.org/W2137313500","https://openalex.org/W2158899491","https://openalex.org/W2243731202","https://openalex.org/W2345631583","https://openalex.org/W2952230511","https://openalex.org/W2963672682","https://openalex.org/W4231109964","https://openalex.org/W6632100814","https://openalex.org/W6636501129"],"related_works":["https://openalex.org/W4287995534","https://openalex.org/W2998168123","https://openalex.org/W2897995864","https://openalex.org/W2292254049","https://openalex.org/W3024389224","https://openalex.org/W4225850200","https://openalex.org/W2517027266","https://openalex.org/W2424871898","https://openalex.org/W2592385986","https://openalex.org/W2291847203"],"abstract_inverted_index":{"This":[0],"paper":[1],"makes":[2],"the":[3,33,38,46,83,89,103],"first":[4],"attempt":[5],"to":[6],"utilize":[7],"convolutional":[8],"neural":[9],"network":[10,64,124],"(CNN)":[11],"for":[12],"classification":[13,111],"of":[14,30,35,40,49,52,66,82,91,110,112],"solar":[15,19,113],"radio":[16,20,114],"spectrums.":[17],"The":[18,61,86],"spectrum":[21,93,115],"is":[22,80],"a":[23],"two-dimensional":[24],"gray-scale":[25],"image":[26],"with":[27],"one":[28,74],"dimension":[29],"frequency":[31],"and":[32,54,73,97,126],"other":[34],"time.":[36],"Taking":[37],"advantages":[39],"CNN,":[41],"we":[42],"can":[43,106],"efficiently":[44],"learn":[45],"distinct":[47],"characteristic":[48],"different":[50],"types":[51],"spectrum,":[53],"further":[55],"classify":[56],"them":[57],"even":[58],"more":[59,108],"accurate.":[60],"proposed":[62,104],"CNN-based":[63],"consists":[65],"four":[67,70],"convolution":[68],"layers,":[69],"pooling":[71],"layers":[72],"fully":[75],"connected":[76],"layer.":[77],"Its":[78],"input":[79],"spectrums":[81],"size":[84],"120\u00d7120.":[85],"output":[87],"gives":[88],"type":[90],"each":[92],"among":[94],"\u201cburst\u201d,":[95],"\u201cnon-burst\u201d":[96],"\u201ccalibration\u201d.":[98],"Experimental":[99],"results":[100],"demonstrate":[101],"that":[102],"CNN":[105],"achieve":[107],"accuracy":[109],"beyond":[116],"our":[117],"previous":[118],"efforts":[119],"by":[120],"employing":[121],"deep":[122],"belief":[123],"(DBN)":[125],"autoencoder":[127],"(AE).":[128]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":4}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}