{"id":"https://openalex.org/W3080624510","doi":"https://doi.org/10.1109/tii.2020.3018496","title":"Image Formation, Deep Learning, and Physical Implication of Multiple Time-Series One-Dimensional Signals: Method and Application","display_name":"Image Formation, Deep Learning, and Physical Implication of Multiple Time-Series One-Dimensional Signals: Method and Application","publication_year":2020,"publication_date":"2020-08-21","ids":{"openalex":"https://openalex.org/W3080624510","doi":"https://doi.org/10.1109/tii.2020.3018496","mag":"3080624510"},"language":"en","primary_location":{"id":"doi:10.1109/tii.2020.3018496","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tii.2020.3018496","pdf_url":null,"source":{"id":"https://openalex.org/S184777250","display_name":"IEEE Transactions on Industrial Informatics","issn_l":"1551-3203","issn":["1551-3203","1941-0050"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Industrial Informatics","raw_type":"journal-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/A5100412617","display_name":"Guangyu Liu","orcid":"https://orcid.org/0000-0001-7393-5497"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guangyu Liu","raw_affiliation_strings":["School of Automation Engineering, Hangzhou Dianzi University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automation Engineering, Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057762953","display_name":"Ling Zhu","orcid":"https://orcid.org/0000-0002-1815-5831"},"institutions":[{"id":"https://openalex.org/I90727586","display_name":"Zhejiang University of Finance and Economics","ror":"https://ror.org/055vj5234","country_code":"CN","type":"education","lineage":["https://openalex.org/I90727586"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ling Zhu","raw_affiliation_strings":["School of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou, China","institution_ids":["https://openalex.org/I90727586"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101923162","display_name":"Weijie Yu","orcid":"https://orcid.org/0000-0001-9158-8134"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weijie Yu","raw_affiliation_strings":["School of Automation Engineering, Hangzhou Dianzi University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automation Engineering, Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5046740710","display_name":"Wujia Yu","orcid":"https://orcid.org/0000-0003-4449-1203"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wujia Yu","raw_affiliation_strings":["School of Automation Engineering, Hangzhou Dianzi University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automation Engineering, Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.054,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":{"value":0.81616735,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":98},"biblio":{"volume":"17","issue":"7","first_page":"4566","last_page":"4574"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9951000213623047,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9951000213623047,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/T11856","display_name":"Thermography and Photoacoustic Techniques","score":0.9876000285148621,"subfield":{"id":"https://openalex.org/subfields/2211","display_name":"Mechanics of Materials"},"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/T10534","display_name":"Structural Health Monitoring Techniques","score":0.9850000143051147,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7098355293273926},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6873595118522644},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6690646409988403},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6254971027374268},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6075581312179565},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5430421829223633},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5084551572799683},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5066731572151184},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.4780586063861847},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.45076096057891846},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4357791244983673},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4295646846294403},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.33226388692855835},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2645384669303894}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7098355293273926},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6873595118522644},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6690646409988403},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6254971027374268},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6075581312179565},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5430421829223633},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5084551572799683},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5066731572151184},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.4780586063861847},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.45076096057891846},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4357791244983673},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4295646846294403},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.33226388692855835},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2645384669303894},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tii.2020.3018496","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tii.2020.3018496","pdf_url":null,"source":{"id":"https://openalex.org/S184777250","display_name":"IEEE Transactions on Industrial Informatics","issn_l":"1551-3203","issn":["1551-3203","1941-0050"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Industrial Informatics","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.44999998807907104,"id":"https://metadata.un.org/sdg/9"}],"awards":[{"id":"https://openalex.org/G2344559318","display_name":null,"funder_award_id":"61174074","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3245251595","display_name":null,"funder_award_id":"61427808","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4213929939","display_name":null,"funder_award_id":"LR14F030001","funder_id":"https://openalex.org/F4320338464","funder_display_name":"Natural Science Foundation of Zhejiang Province"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320338464","display_name":"Natural Science Foundation of Zhejiang Province","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1515333057","https://openalex.org/W1569123050","https://openalex.org/W1999393241","https://openalex.org/W2007576168","https://openalex.org/W2024476387","https://openalex.org/W2064883310","https://openalex.org/W2071004693","https://openalex.org/W2072857564","https://openalex.org/W2112796928","https://openalex.org/W2163605009","https://openalex.org/W2289846183","https://openalex.org/W2562762876","https://openalex.org/W2618530766","https://openalex.org/W2734981343","https://openalex.org/W2763128055","https://openalex.org/W2763277974","https://openalex.org/W2768753204","https://openalex.org/W2789811186","https://openalex.org/W2791957585","https://openalex.org/W2796210775","https://openalex.org/W2884780389","https://openalex.org/W2888226690","https://openalex.org/W2897606695","https://openalex.org/W2906401394","https://openalex.org/W2950517140","https://openalex.org/W2950613864","https://openalex.org/W2990442018","https://openalex.org/W3044334322"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4375867731","https://openalex.org/W2611989081","https://openalex.org/W4313906399","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W3167935049","https://openalex.org/W3029198973"],"abstract_inverted_index":{"Time-series":[0],"1-D":[1,25,44,85],"signals":[2,45,86],"are":[3,87,106,150,167,182],"ubiquitous":[4],"in":[5,30,78,122,169],"industrial":[6,186],"applications":[7],"for":[8,56,72,184],"monitoring":[9],"and":[10,49,59],"control.":[11],"However,":[12],"it":[13],"is":[14,39,69,162],"lacking":[15],"of":[16,36,52,61,64,75,92],"efficient":[17],"tools":[18,181],"to":[19,46,89,95,141,152],"deal":[20],"with":[21,134,158],"simultaneously":[22],"multiple":[23,43],"time-series":[24],"signals.":[26],"To":[27],"this":[28,31],"end,":[29],"article,":[32],"a":[33,62,90],"novel":[34],"theory":[35],"image":[37,101,148],"formation":[38],"proposed":[40,171,180],"that":[41,119,131],"converts":[42],"2-D":[47,93,123,132],"images":[48,94,124,133],"takes":[50],"advantages":[51,166],"convolutional":[53],"neural":[54],"network":[55],"feature":[57],"extraction":[58],"classification":[60,74,128],"sequence":[63,91],"images.":[65],"A":[66],"case":[67],"study":[68],"carried":[70],"out":[71],"the":[73,109,127,135,170,175,179],"working":[76,115],"conditions":[77],"photovoltaic":[79],"power":[80],"systems.":[81],"In":[82],"total,":[83],"23":[84],"mapped":[88],"derive":[96],"six":[97],"different":[98],"models":[99],"through":[100,108],"formation-based":[102],"deep":[103,176],"learning.":[104],"They":[105],"tested":[107],"outdoor":[110],"experiments":[111],"under":[112],"time":[113],"varying":[114],"conditions.":[116],"We":[117],"discover":[118],"physical":[120,165],"implication":[121],"affects":[125],"significantly":[126],"performance":[129,157],"such":[130],"clustered":[136],"currents":[137],"or":[138],"voltages":[139],"tend":[140],"create":[142],"better":[143],"results":[144],"while":[145],"randomly":[146],"arranged":[147],"patterns":[149],"prone":[151],"generate":[153],"worse":[154],"results.":[155],"Excellent":[156],"an":[159],"accuracy":[160],"96.09%":[161],"guaranteed":[163],"when":[164],"incorporated":[168],"tools.":[172],"Driven":[173],"by":[174],"learning":[177],"approaches,":[178],"promising":[183],"complicated":[185],"applications.":[187]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":4},{"year":2021,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
