{"id":"https://openalex.org/W4401870888","doi":"https://doi.org/10.1109/jiot.2024.3445965","title":"Lightweight Deep Learning for Missing Data Imputation in Wastewater Treatment With Variational Residual Auto-Encoder","display_name":"Lightweight Deep Learning for Missing Data Imputation in Wastewater Treatment With Variational Residual Auto-Encoder","publication_year":2024,"publication_date":"2024-08-20","ids":{"openalex":"https://openalex.org/W4401870888","doi":"https://doi.org/10.1109/jiot.2024.3445965"},"language":"en","primary_location":{"id":"doi:10.1109/jiot.2024.3445965","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2024.3445965","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"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 Internet of Things Journal","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/A5100444393","display_name":"Wen Zhang","orcid":"https://orcid.org/0000-0002-6216-2262"},"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":"Wen Zhang","raw_affiliation_strings":["School of Economics and Management, Beijing University of Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-6216-2262","affiliations":[{"raw_affiliation_string":"School of Economics and Management, Beijing University of Technology, Beijing, China","institution_ids":["https://openalex.org/I37796252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004764605","display_name":"Rui Li","orcid":"https://orcid.org/0000-0002-1305-2139"},"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":"Rui Li","raw_affiliation_strings":["School of Economics and Management, Beijing University of Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Economics and Management, Beijing University of Technology, Beijing, China","institution_ids":["https://openalex.org/I37796252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100414014","display_name":"Pei Quan","orcid":"https://orcid.org/0000-0001-6005-5714"},"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":"Pei Quan","raw_affiliation_strings":["School of Economics and Management, Beijing University of Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Economics and Management, Beijing University of Technology, Beijing, China","institution_ids":["https://openalex.org/I37796252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033780788","display_name":"Jiang Chang","orcid":"https://orcid.org/0000-0001-7064-9321"},"institutions":[{"id":"https://openalex.org/I4210145902","display_name":"Beijing Drainage Group (China)","ror":"https://ror.org/04pfhpy42","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210145902"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiang Chang","raw_affiliation_strings":["Technology Research and Development Center of Beijing Drainage Group, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technology Research and Development Center of Beijing Drainage Group, Beijing, China","institution_ids":["https://openalex.org/I4210145902"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100578724","display_name":"Yongsheng Bai","orcid":null},"institutions":[{"id":"https://openalex.org/I4210145902","display_name":"Beijing Drainage Group (China)","ror":"https://ror.org/04pfhpy42","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210145902"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongsheng Bai","raw_affiliation_strings":["Technology Research and Development Center of Beijing Drainage Group, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technology Research and Development Center of Beijing Drainage Group, Beijing, China","institution_ids":["https://openalex.org/I4210145902"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5024482023","display_name":"Bojun Su","orcid":null},"institutions":[{"id":"https://openalex.org/I4210145902","display_name":"Beijing Drainage Group (China)","ror":"https://ror.org/04pfhpy42","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210145902"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bojun Su","raw_affiliation_strings":["Technology Research and Development Center of Beijing Drainage Group, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technology Research and Development Center of Beijing Drainage Group, Beijing, China","institution_ids":["https://openalex.org/I4210145902"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.8188,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.703379,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"11","issue":"23","first_page":"38312","last_page":"38326"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12697","display_name":"Water Quality Monitoring Technologies","score":0.949999988079071,"subfield":{"id":"https://openalex.org/subfields/2312","display_name":"Water Science and Technology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12697","display_name":"Water Quality Monitoring Technologies","score":0.949999988079071,"subfield":{"id":"https://openalex.org/subfields/2312","display_name":"Water Science and Technology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/imputation","display_name":"Imputation (statistics)","score":0.8681854009628296},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.8506444692611694},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.7837188243865967},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7364831566810608},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5677281022071838},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5579200983047485},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.5536754131317139},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5055139064788818},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4704393744468689},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.39795807003974915},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37642163038253784},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3359428942203522},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2570849359035492},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.08073076605796814}],"concepts":[{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.8681854009628296},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.8506444692611694},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.7837188243865967},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7364831566810608},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5677281022071838},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5579200983047485},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.5536754131317139},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5055139064788818},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4704393744468689},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39795807003974915},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37642163038253784},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3359428942203522},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2570849359035492},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.08073076605796814},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/jiot.2024.3445965","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2024.3445965","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"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 Internet of Things Journal","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/6","score":0.8500000238418579,"display_name":"Clean water and sanitation"}],"awards":[{"id":"https://openalex.org/G152187440","display_name":"\u57fa\u4e8e\u7f51\u7edc\u751f\u6001\u7684\u667a\u6167\u4f9b\u5e94\u94fe\u91d1\u878d\u6a21\u5f0f\u7814\u7a76","funder_award_id":"71932002","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5388351832","display_name":null,"funder_award_id":"72174018","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320323068","display_name":"Beijing Postdoctoral Science Foundation","ror":"https://ror.org/0426zh255"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W2110828928","https://openalex.org/W2196662288","https://openalex.org/W2247991820","https://openalex.org/W2279098554","https://openalex.org/W2618530766","https://openalex.org/W2791741309","https://openalex.org/W2886851211","https://openalex.org/W2903722086","https://openalex.org/W2908619578","https://openalex.org/W2919115771","https://openalex.org/W3003689553","https://openalex.org/W3004543888","https://openalex.org/W3008742172","https://openalex.org/W3011554625","https://openalex.org/W3034368386","https://openalex.org/W3034870355","https://openalex.org/W3092242892","https://openalex.org/W3097488018","https://openalex.org/W3098718496","https://openalex.org/W3120617816","https://openalex.org/W3133744039","https://openalex.org/W3138154797","https://openalex.org/W3172129612","https://openalex.org/W3189386762","https://openalex.org/W3209249023","https://openalex.org/W3215698464","https://openalex.org/W3216741772","https://openalex.org/W4200456600","https://openalex.org/W4224282325","https://openalex.org/W4296311523","https://openalex.org/W4308086663","https://openalex.org/W4362653662","https://openalex.org/W4376618150","https://openalex.org/W6695314431","https://openalex.org/W6755207826","https://openalex.org/W6778883912","https://openalex.org/W6783948878"],"related_works":["https://openalex.org/W2181530120","https://openalex.org/W4211215373","https://openalex.org/W2024529227","https://openalex.org/W2055961818","https://openalex.org/W1574575415","https://openalex.org/W3144172081","https://openalex.org/W3179858851","https://openalex.org/W3028371478","https://openalex.org/W2081476516","https://openalex.org/W2581984549"],"abstract_inverted_index":{"Deep":[0],"variational":[1],"residual":[2],"auto-encoder":[3],"(ResNet-VAE)":[4],"has":[5],"shown":[6],"promising":[7],"outcomes":[8],"in":[9,64,179,198],"missing":[10,39,201],"imputation":[11,202],"of":[12,27,89,106,124,158,171,181,200],"wastewater":[13,32,204],"quality":[14,149,205],"data.":[15],"Nevertheless,":[16],"with":[17,167,189],"its":[18],"large":[19],"storage":[20,141,184],"size":[21,105,123,157],"and":[22,61,97,139,174,186,213],"computation":[23,138,182],"overhead,":[24],"it":[25],"is":[26,117],"great":[28],"difficulty":[29],"to":[30,52,75,84,102,111,119,164,208],"deploy":[31],"treatment":[33],"plant":[34],"(WWTP)":[35],"sensors":[36],"for":[37,203],"real-time":[38],"imputation.":[40],"To":[41],"address":[42],"this":[43,65],"problem,":[44],"we":[45,68,93],"propose":[46],"a":[47,70,168,191],"novel":[48],"approach":[49],"called":[50],"lightweight-ResNet-VAE":[51,154],"compress":[53,103,121],"classical":[54,126,160],"ResNet-VAE":[55,127,161],"by":[56,80,128],"network":[57,72],"pruning,":[58],"weight":[59,78,95,108],"quantization,":[60],"relative":[62,115],"indexing":[63,116],"article.":[66],"First,":[67],"develop":[69,94],"three-step":[71],"pruning":[73,209],"method":[74],"sparsify":[76],"the":[77,86,104,114,122,125,136,145,156,159,176,215],"matrices":[79],"removing":[81],"insignificant":[82],"weights":[83,101,212],"reduce":[85],"time":[87],"cost":[88],"model":[90,137],"inference.":[91],"Second,":[92],"quantization":[96],"use":[98],"eight":[99],"shared":[100],"each":[107],"from":[109,162],"32-bit":[110],"3-bit.":[112],"Finally,":[113],"adopted":[118],"further":[120],"compressed":[129],"sparse":[130],"row":[131],"(CSR),":[132],"which":[133],"greatly":[134],"accelerates":[135],"saves":[140],"size.":[142],"Experiments":[143],"on":[144,194],"Beipai":[146],"IoT":[147],"influent":[148],"data":[150,206],"set":[151],"demonstrate":[152],"that":[153],"compresses":[155],"301.88":[163],"26.24":[165],"kB":[166],"compression":[169],"rate":[170],"11.50":[172],"times,":[173],"outperforms":[175],"baseline":[177],"methods":[178],"terms":[180],"acceleration,":[183],"saving":[185],"energy":[187],"consumption":[188],"only":[190],"slight":[192],"decrease":[193],"accuracy":[195],"as":[196],"2.74%":[197],"MAPE":[199],"due":[207],"less":[210],"significant":[211],"quantizing":[214],"remaining":[216],"weights.":[217]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1}],"updated_date":"2025-12-26T23:08:49.675405","created_date":"2025-10-10T00:00:00"}