{"id":"https://openalex.org/W4377968407","doi":"https://doi.org/10.1109/icps58381.2023.10128044","title":"An Input Module of Deep Learning for the Analysis of Time Series with Unequal Length","display_name":"An Input Module of Deep Learning for the Analysis of Time Series with Unequal Length","publication_year":2023,"publication_date":"2023-05-08","ids":{"openalex":"https://openalex.org/W4377968407","doi":"https://doi.org/10.1109/icps58381.2023.10128044"},"language":"en","primary_location":{"id":"doi:10.1109/icps58381.2023.10128044","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icps58381.2023.10128044","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS)","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/A5032132088","display_name":"Hewei Gao","orcid":"https://orcid.org/0000-0002-1626-4401"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hewei Gao","raw_affiliation_strings":["Harbin Institute of Technology,Control and Simulation Center,Harbin,China","Control and Simulation Center, Harbin Institute of Technology, Harbin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Harbin Institute of Technology,Control and Simulation Center,Harbin,China","institution_ids":["https://openalex.org/I204983213"]},{"raw_affiliation_string":"Control and Simulation Center, Harbin Institute of Technology, Harbin, China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103042152","display_name":"Xin Huo","orcid":"https://orcid.org/0000-0003-4945-6271"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Huo","raw_affiliation_strings":["Harbin Institute of Technology,Control and Simulation Center,Harbin,China","Control and Simulation Center, Harbin Institute of Technology, Harbin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Harbin Institute of Technology,Control and Simulation Center,Harbin,China","institution_ids":["https://openalex.org/I204983213"]},{"raw_affiliation_string":"Control and Simulation Center, Harbin Institute of Technology, Harbin, China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5113893865","display_name":"Chao Zhu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210153682","display_name":"Intelligent Health (United Kingdom)","ror":"https://ror.org/0576zak10","country_code":"GB","type":"company","lineage":["https://openalex.org/I4210153682"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Chao Zhu","raw_affiliation_strings":["Intelligent Research Institute, Shanghai Rising Digital Co.,Ltd,Shanghai,China","Intelligent Research Institute, Shanghai Rising Digital Co.,Ltd, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Intelligent Research Institute, Shanghai Rising Digital Co.,Ltd,Shanghai,China","institution_ids":["https://openalex.org/I4210153682"]},{"raw_affiliation_string":"Intelligent Research Institute, Shanghai Rising Digital Co.,Ltd, Shanghai, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2417,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.38238051,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9998999834060669,"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":0.9998999834060669,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.995199978351593,"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/T11309","display_name":"Music and Audio Processing","score":0.9165999889373779,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/dynamic-time-warping","display_name":"Dynamic time warping","score":0.9077947735786438},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6736704707145691},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6611355543136597},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.653373122215271},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6037532091140747},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5917524695396423},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5687718391418457},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5589895844459534},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.48929187655448914},{"id":"https://openalex.org/keywords/image-warping","display_name":"Image warping","score":0.47831228375434875},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.4683123528957367},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.4586394727230072},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4154205322265625},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.32187414169311523},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.21336838603019714},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.18419015407562256}],"concepts":[{"id":"https://openalex.org/C88516994","wikidata":"https://www.wikidata.org/wiki/Q1268863","display_name":"Dynamic time warping","level":2,"score":0.9077947735786438},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6736704707145691},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6611355543136597},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.653373122215271},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6037532091140747},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5917524695396423},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5687718391418457},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5589895844459534},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.48929187655448914},{"id":"https://openalex.org/C157202957","wikidata":"https://www.wikidata.org/wiki/Q1659609","display_name":"Image warping","level":2,"score":0.47831228375434875},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.4683123528957367},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.4586394727230072},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4154205322265625},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32187414169311523},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.21336838603019714},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.18419015407562256},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icps58381.2023.10128044","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icps58381.2023.10128044","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1597494499","display_name":null,"funder_award_id":"HIT.NSRIF202242","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G8442416970","display_name":null,"funder_award_id":"LH2021F025","funder_id":"https://openalex.org/F4320323085","funder_display_name":"Natural Science Foundation of Heilongjiang Province"}],"funders":[{"id":"https://openalex.org/F4320323085","display_name":"Natural Science Foundation of Heilongjiang Province","ror":null},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W2042714950","https://openalex.org/W2807510164","https://openalex.org/W2928165649","https://openalex.org/W3088306914","https://openalex.org/W3154823968","https://openalex.org/W3158446466","https://openalex.org/W3163085940","https://openalex.org/W3168997536","https://openalex.org/W4206020427","https://openalex.org/W4285224065","https://openalex.org/W4285257534","https://openalex.org/W4285814268","https://openalex.org/W4292601103","https://openalex.org/W4292972665","https://openalex.org/W4293088171","https://openalex.org/W4296916681"],"related_works":["https://openalex.org/W2347413598","https://openalex.org/W2330863229","https://openalex.org/W71572444","https://openalex.org/W1997383766","https://openalex.org/W2154472250","https://openalex.org/W2350336482","https://openalex.org/W2609942398","https://openalex.org/W2764033112","https://openalex.org/W4380451100","https://openalex.org/W2772616816"],"abstract_inverted_index":{"Deep":[0],"learning,":[1,58],"particularly":[2],"deep":[3,16,57],"neural":[4,71,122],"networks,":[5],"has":[6],"received":[7],"increasing":[8],"interest":[9],"in":[10,47],"time":[11,31,39,60,90,145],"series":[12,32,40,61,146],"classification,":[13],"and":[14],"several":[15],"learning":[17,142],"methods":[18],"have":[19],"been":[20],"proposed":[21,115],"recently.":[22],"However,":[23],"most":[24],"of":[25,38,56,89,105,113],"these":[26],"algorithms":[27],"are":[28,124],"designed":[29],"for":[30,73],"with":[33,41,62,119,147],"equal":[34,109],"length,":[35],"while":[36],"clustering":[37],"unequal":[42,63,148],"length":[43,64,149],"is":[44,79,97],"often":[45],"encountered":[46],"real-world":[48],"problems.":[49],"This":[50],"paper":[51],"proposes":[52],"an":[53],"input":[54,116,137],"module":[55,117,138],"transforming":[59],"into":[65],"a":[66],"warping":[67,77,103],"matrix":[68,78,104],"processed":[69],"by":[70,81],"network":[72],"training.":[74],"The":[75,92,111],"trajectory":[76],"generated":[80],"DTW":[82],"algorithm":[83,96],"according":[84],"to":[85,99,108,143],"the":[86,102,114,128,132,136,140],"similarity":[87],"difference":[88],"series.":[91],"Gaussian":[93],"blur":[94],"iterative":[95],"introduced":[98],"converted":[100],"from":[101],"any":[106],"size":[107],"dimension.":[110],"effectiveness":[112],"combined":[118],"some":[120],"advanced":[121],"networks":[123],"assessed":[125],"based":[126],"on":[127],"CWRU":[129],"dataset.":[130],"Overall,":[131],"analysis":[133],"shows":[134],"that":[135],"assists":[139],"depth":[141],"classify":[144],"accurately.":[150]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
