{"id":"https://openalex.org/W4310881753","doi":"https://doi.org/10.1145/3563357.3567407","title":"Utilizing autoencoders to improve transfer learning when sensor data is sparse","display_name":"Utilizing autoencoders to improve transfer learning when sensor data is sparse","publication_year":2022,"publication_date":"2022-11-09","ids":{"openalex":"https://openalex.org/W4310881753","doi":"https://doi.org/10.1145/3563357.3567407"},"language":"en","primary_location":{"id":"doi:10.1145/3563357.3567407","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3563357.3567407","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","raw_type":"proceedings-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/A5032891017","display_name":"Maira Alvi","orcid":"https://orcid.org/0000-0003-1840-5789"},"institutions":[{"id":"https://openalex.org/I177877127","display_name":"University of Western Australia","ror":"https://ror.org/047272k79","country_code":"AU","type":"education","lineage":["https://openalex.org/I177877127"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Maira Alvi","raw_affiliation_strings":["The University of Western Australia, Perth, Australia"],"affiliations":[{"raw_affiliation_string":"The University of Western Australia, Perth, Australia","institution_ids":["https://openalex.org/I177877127"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037889355","display_name":"Rachel Cardell\u2010Oliver","orcid":"https://orcid.org/0000-0003-0590-1003"},"institutions":[{"id":"https://openalex.org/I177877127","display_name":"University of Western Australia","ror":"https://ror.org/047272k79","country_code":"AU","type":"education","lineage":["https://openalex.org/I177877127"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Rachel Cardell-Oliver","raw_affiliation_strings":["The University of Western Australia, Perth, Australia"],"affiliations":[{"raw_affiliation_string":"The University of Western Australia, Perth, Australia","institution_ids":["https://openalex.org/I177877127"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5030763134","display_name":"Tim French","orcid":"https://orcid.org/0000-0002-0748-8040"},"institutions":[{"id":"https://openalex.org/I177877127","display_name":"University of Western Australia","ror":"https://ror.org/047272k79","country_code":"AU","type":"education","lineage":["https://openalex.org/I177877127"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Tim French","raw_affiliation_strings":["The University of Western Australia, Perth, Australia"],"affiliations":[{"raw_affiliation_string":"The University of Western Australia, Perth, Australia","institution_ids":["https://openalex.org/I177877127"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5032891017"],"corresponding_institution_ids":["https://openalex.org/I177877127"],"apc_list":null,"apc_paid":null,"fwci":0.4585,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.60109313,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"500","last_page":"503"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11220","display_name":"Water Systems and Optimization","score":0.9950000047683716,"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"}},"topics":[{"id":"https://openalex.org/T11220","display_name":"Water Systems and Optimization","score":0.9950000047683716,"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"}},{"id":"https://openalex.org/T12697","display_name":"Water Quality Monitoring Technologies","score":0.9915000200271606,"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"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9890000224113464,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.8988458514213562},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.855513334274292},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7779906392097473},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7136678695678711},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.6567152738571167},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6278097033500671},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5233639478683472},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.5135996341705322},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5099805593490601},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4800761342048645},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.4604334235191345},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4526301920413971},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4454091489315033},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3706095218658447},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.13181722164154053},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07444238662719727}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8988458514213562},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.855513334274292},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7779906392097473},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7136678695678711},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.6567152738571167},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6278097033500671},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5233639478683472},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.5135996341705322},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5099805593490601},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4800761342048645},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4604334235191345},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4526301920413971},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4454091489315033},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3706095218658447},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.13181722164154053},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07444238662719727},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3563357.3567407","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3563357.3567407","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","raw_type":"proceedings-article"},{"id":"pmh:oai:pure.atira.dk:publications/5cb06a96-bbd5-4ff7-b422-7fc24f7835fa","is_oa":false,"landing_page_url":"https://research-repository.uwa.edu.au/en/publications/5cb06a96-bbd5-4ff7-b422-7fc24f7835fa","pdf_url":null,"source":{"id":"https://openalex.org/S4306402523","display_name":"UWA Profiles and Research Repository (University of Western Australia)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I177877127","host_organization_name":"The University of Western Australia","host_organization_lineage":["https://openalex.org/I177877127"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Alvi, M, Cardell-Oliver, R & French, T 2022, Utilizing autoencoders to improve transfer learning when sensor data is sparse. in BuildSys 2022 - Proceedings of the 2022 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation. Association for Computing Machinery (ACM), pp. 500-503, 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, BuildSys 2022, Boston, United States, 9/11/22. https://doi.org/10.1145/3563357.3567407","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Clean water and sanitation","id":"https://metadata.un.org/sdg/6","score":0.6100000143051147}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W2053059515","https://openalex.org/W2887131022","https://openalex.org/W3041133507","https://openalex.org/W3094733926","https://openalex.org/W3163271609","https://openalex.org/W4284699011","https://openalex.org/W4285178158"],"related_works":["https://openalex.org/W2786391746","https://openalex.org/W3171384686","https://openalex.org/W2991483587","https://openalex.org/W4381430104","https://openalex.org/W2995102745","https://openalex.org/W4226059458","https://openalex.org/W2914559142","https://openalex.org/W1990237101","https://openalex.org/W4285322112","https://openalex.org/W3196471634"],"abstract_inverted_index":{"Accurate":[0],"deep":[1],"predictive":[2,20,43],"models":[3,21,44],"of":[4,25,78,98,158],"wastewater":[5,34,79],"processing":[6],"plants":[7],"are":[8,14],"important":[9],"to":[10,30,60,94,105,113,122],"ensure":[11],"operational":[12],"parameters":[13],"safe":[15],"and":[16,51,74],"sustainable.":[17],"Training":[18],"such":[19,136],"requires":[22],"large":[23,96],"volumes":[24,97],"data":[26,46,100,135,153],"that":[27,129,137],"is":[28],"hard":[29],"find":[31],"in":[32],"the":[33,39,55,61,66,75,110,119,132,144,151,156,159],"domain.":[35,57],"Transfer":[36],"learning":[37,73,82,116],"addresses":[38],"problem,":[40],"by":[41],"training":[42,99],"using":[45],"from":[47,109],"an":[48,86,127],"adopted":[49,68],"domain,":[50,112],"fine-tuning":[52],"it":[53,140],"on":[54,150],"target":[56,76,111,133,145],"However,":[58],"due":[59],"significant":[62],"distributional":[63],"shift":[64],"between":[65],"commonly":[67],"source":[69],"domains":[70],"for":[71,118],"transfer":[72,81,115],"domain":[77,134,146],"processes,":[80],"rarely":[83],"performs":[84],"at":[85],"acceptable":[87],"level.":[88],"This":[89],"paper":[90],"proposes":[91],"a":[92,102,106],"method":[93],"generate":[95],"with":[101],"similar":[103],"distribution":[104],"sample":[107],"taken":[108],"boost":[114],"performance":[117],"task,":[120],"referred":[121],"as":[123],"AETL.":[124],"It":[125],"leverages":[126],"autoencoder":[128],"systematically":[130],"augments":[131],"synthetic":[138],"samples":[139],"generates":[141],"closely":[142],"follow":[143],"distribution.":[147],"The":[148],"results":[149],"real-world":[152],"set":[154],"establish":[155],"efficacy":[157],"proposed":[160],"framework.":[161]},"counts_by_year":[{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
