{"id":"https://openalex.org/W7124167452","doi":"https://doi.org/10.1109/icpads67057.2025.11322982","title":"Large Receptive Field Network for Time Series Image Classification","display_name":"Large Receptive Field Network for Time Series Image Classification","publication_year":2025,"publication_date":"2025-12-14","ids":{"openalex":"https://openalex.org/W7124167452","doi":"https://doi.org/10.1109/icpads67057.2025.11322982"},"language":null,"primary_location":{"id":"doi:10.1109/icpads67057.2025.11322982","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpads67057.2025.11322982","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 31th International Conference on Parallel and Distributed Systems (ICPADS)","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/A5056466106","display_name":"Yanxuan Wei","orcid":"https://orcid.org/0000-0002-3364-5492"},"institutions":[{"id":"https://openalex.org/I28006308","display_name":"Shandong Normal University","ror":"https://ror.org/01wy3h363","country_code":"CN","type":"education","lineage":["https://openalex.org/I28006308"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanxuan Wei","raw_affiliation_strings":["School of Information Science and Engineering, Shandong Normal University,Jinan,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Shandong Normal University,Jinan,China","institution_ids":["https://openalex.org/I28006308"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060438482","display_name":"Yingxia Tang","orcid":"https://orcid.org/0009-0005-9574-1788"},"institutions":[{"id":"https://openalex.org/I28006308","display_name":"Shandong Normal University","ror":"https://ror.org/01wy3h363","country_code":"CN","type":"education","lineage":["https://openalex.org/I28006308"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yingxia Tang","raw_affiliation_strings":["School of Information Science and Engineering, Shandong Normal University,Jinan,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Shandong Normal University,Jinan,China","institution_ids":["https://openalex.org/I28006308"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101972728","display_name":"Yi Hu","orcid":"https://orcid.org/0000-0003-2418-5188"},"institutions":[{"id":"https://openalex.org/I154099455","display_name":"Shandong University","ror":"https://ror.org/0207yh398","country_code":"CN","type":"education","lineage":["https://openalex.org/I154099455"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yupeng Hu","raw_affiliation_strings":["School of Software, Shandong University,Jinan,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Software, Shandong University,Jinan,China","institution_ids":["https://openalex.org/I154099455"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049974611","display_name":"Xiangwei Zheng","orcid":"https://orcid.org/0000-0003-4873-4567"},"institutions":[{"id":"https://openalex.org/I28006308","display_name":"Shandong Normal University","ror":"https://ror.org/01wy3h363","country_code":"CN","type":"education","lineage":["https://openalex.org/I28006308"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangwei Zheng","raw_affiliation_strings":["School of Information Science and Engineering, Shandong Normal University,Jinan,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Shandong Normal University,Jinan,China","institution_ids":["https://openalex.org/I28006308"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066754863","display_name":"Cun Ji","orcid":"https://orcid.org/0000-0002-3326-6572"},"institutions":[{"id":"https://openalex.org/I28006308","display_name":"Shandong Normal University","ror":"https://ror.org/01wy3h363","country_code":"CN","type":"education","lineage":["https://openalex.org/I28006308"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cun Ji","raw_affiliation_strings":["School of Information Science and Engineering, Shandong Normal University,Jinan,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Shandong Normal University,Jinan,China","institution_ids":["https://openalex.org/I28006308"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"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":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9664999842643738,"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":0.9664999842643738,"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/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.0031999999191612005,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.002300000051036477,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/receptive-field","display_name":"Receptive field","score":0.6822999715805054},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.666100025177002},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6140999794006348},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.5566999912261963},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.5289999842643738},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.49869999289512634},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.4966000020503998},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.49070000648498535},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.4212000072002411}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7143999934196472},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6976000070571899},{"id":"https://openalex.org/C19071747","wikidata":"https://www.wikidata.org/wiki/Q1755207","display_name":"Receptive field","level":2,"score":0.6822999715805054},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.666100025177002},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6140999794006348},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5566999912261963},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5289999842643738},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.49869999289512634},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.4966000020503998},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.49070000648498535},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4706999957561493},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.4212000072002411},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.3828999996185303},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3702999949455261},{"id":"https://openalex.org/C2781195486","wikidata":"https://www.wikidata.org/wiki/Q289436","display_name":"Texture (cosmology)","level":3,"score":0.33559998869895935},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3343999981880188},{"id":"https://openalex.org/C2993807640","wikidata":"https://www.wikidata.org/wiki/Q103709453","display_name":"Attention network","level":2,"score":0.33410000801086426},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.3046000003814697},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3043000102043152},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.30149999260902405},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2915000021457672},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.2784999907016754},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.2644999921321869},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.2615000009536743},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.25529998540878296},{"id":"https://openalex.org/C16345878","wikidata":"https://www.wikidata.org/wiki/Q107472979","display_name":"Orientation (vector space)","level":2,"score":0.2513999938964844},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2513999938964844}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpads67057.2025.11322982","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpads67057.2025.11322982","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 31th International Conference on Parallel and Distributed Systems (ICPADS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4418676495552063,"id":"https://metadata.un.org/sdg/12","display_name":"Responsible consumption and production"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1968354112","https://openalex.org/W2106595237","https://openalex.org/W2551393996","https://openalex.org/W2786161686","https://openalex.org/W2888791883","https://openalex.org/W2903434143","https://openalex.org/W2952352769","https://openalex.org/W2963604034","https://openalex.org/W2969848393","https://openalex.org/W2972810968","https://openalex.org/W2982438846","https://openalex.org/W2988244882","https://openalex.org/W3010246639","https://openalex.org/W3034486173","https://openalex.org/W3034752215","https://openalex.org/W3041886307","https://openalex.org/W3112330479","https://openalex.org/W3131491382","https://openalex.org/W3161273030","https://openalex.org/W3177182244","https://openalex.org/W3209308485","https://openalex.org/W4200378309","https://openalex.org/W4213017200","https://openalex.org/W4226334005","https://openalex.org/W4286717572","https://openalex.org/W4291034092","https://openalex.org/W4309197050","https://openalex.org/W4385359441","https://openalex.org/W4387035631","https://openalex.org/W4387591897","https://openalex.org/W4402716415","https://openalex.org/W4407903024","https://openalex.org/W4408317688","https://openalex.org/W4413279295"],"related_works":[],"abstract_inverted_index":{"Time":[0],"Series":[1],"Classification":[2],"(TSC)":[3],"has":[4],"achieved":[5],"significant":[6],"results":[7,164],"in":[8,60,182,192],"improving":[9],"the":[10,23,84,89,108,131,160,171,178,193,215],"efficiency":[11],"and":[12,16,53,113,126,137,148],"safety":[13],"of":[14,25,92,150,173,206,217],"life":[15],"production.":[17],"This":[18],"paper":[19,68],"aims":[20],"to":[21,37,87,158,184],"enhance":[22],"accuracy":[24,172,205],"TSC":[26,61,174],"by":[27,82,99],"encoding":[28],"one-dimensional":[29],"time":[30,109],"series":[31,110],"into":[32],"two-dimensional":[33],"Recurrence":[34],"Plots":[35],"(RP)":[36],"obtain":[38,123],"richer":[39],"texture":[40],"information.":[41],"However,":[42],"existing":[43],"RPs":[44],"face":[45],"issues":[46,91],"such":[47],"as":[48,111],"multi-scale":[49,90,161],"issues,":[50],"tendency":[51],"confusion,":[52],"information":[54,81,155],"redundancy,":[55],"which":[56],"hinder":[57],"their":[58],"application":[59],"tasks.":[62,175],"To":[63],"address":[64,159],"these":[65],"problems,":[66],"this":[67],"proposes":[69],"a":[70],"Large":[71],"Receptive":[72],"Field":[73],"Network":[74],"(LRFN).":[75],"LRFN":[76,167,176],"advocates":[77],"for":[78],"extracting":[79],"global":[80,127,154],"enlarging":[83],"receptive":[85,128,151,209],"field":[86,210],"overcome":[88],"RPs.":[93],"It":[94,196],"further":[95,169,213],"addresses":[96],"other":[97],"problems":[98],"applying":[100],"Multi-scale":[101],"Signed":[102],"RP":[103],"(MSRP).":[104],"The":[105],"method":[106],"encodes":[107],"MSRP":[112],"then":[114],"constructs":[115],"multiple":[116],"Global-Local":[117],"Convolution":[118],"(GLC)":[119],"modules.":[120],"These":[121],"modules":[122],"medium,":[124],"local,":[125],"fields":[129],"through":[130],"Dilated":[132],"Residual":[133],"Module,":[134],"SelfCalibrated":[135],"Convolution,":[136],"Atrous":[138],"Spatial":[139],"Pyramid":[140],"Pooling,":[141],"respectively.":[142],"By":[143],"integrating":[144],"three":[145],"different":[146,190],"qualities":[147],"sizes":[149],"fields,":[152],"comprehensive":[153,180],"is":[156],"acquired":[157],"problem.":[162],"Experimental":[163],"show":[165],"that":[166],"can":[168],"improve":[170],"demonstrated":[177],"best":[179],"performance":[181],"comparison":[183],"13":[185],"baseline":[186],"methods":[187],"across":[188],"43":[189],"domains":[191],"UCR":[194],"dataset.":[195],"performed":[197],"optimally":[198],"on":[199],"20":[200],"datasets,":[201],"with":[202],"an":[203],"average":[204],"91.3%.":[207],"Additionally,":[208],"visualization":[211],"experiments":[212],"validate":[214],"effectiveness":[216],"LRFN.":[218]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-01-15T00:00:00"}