{"id":"https://openalex.org/W2972686061","doi":"https://doi.org/10.1145/3341162.3349327","title":"A deep autoencoder model for pollution map recovery with mobile sensing networks","display_name":"A deep autoencoder model for pollution map recovery with mobile sensing networks","publication_year":2019,"publication_date":"2019-09-09","ids":{"openalex":"https://openalex.org/W2972686061","doi":"https://doi.org/10.1145/3341162.3349327","mag":"2972686061"},"language":"en","primary_location":{"id":"doi:10.1145/3341162.3349327","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3341162.3349327","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers","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/A5089497452","display_name":"Rui Ma","orcid":"https://orcid.org/0000-0001-7328-2924"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Rui Ma","raw_affiliation_strings":["Tsinghua University"],"affiliations":[{"raw_affiliation_string":"Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041853363","display_name":"Ning Liu","orcid":"https://orcid.org/0000-0003-0418-4516"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ning Liu","raw_affiliation_strings":["Tsinghua University"],"affiliations":[{"raw_affiliation_string":"Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054043281","display_name":"Xiangxiang Xu","orcid":"https://orcid.org/0000-0002-4178-0934"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangxiang Xu","raw_affiliation_strings":["Tsinghua University"],"affiliations":[{"raw_affiliation_string":"Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115695512","display_name":"Yue Wang","orcid":"https://orcid.org/0000-0002-7496-8888"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yue Wang","raw_affiliation_strings":["Tsinghua University"],"affiliations":[{"raw_affiliation_string":"Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082588269","display_name":"Hae Young Noh","orcid":"https://orcid.org/0000-0002-7998-3657"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hae Young Noh","raw_affiliation_strings":["Carnegie Mellon University"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101570806","display_name":"Pei Zhang","orcid":"https://orcid.org/0000-0002-5424-8509"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Pei Zhang","raw_affiliation_strings":["Carnegie Mellon University"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100351850","display_name":"Lin Zhang","orcid":"https://orcid.org/0000-0002-4394-2685"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lin Zhang","raw_affiliation_strings":["Tsinghua University"],"affiliations":[{"raw_affiliation_string":"Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5089497452"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":0.4577,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.62899247,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"577","last_page":"583"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"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/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"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/T10190","display_name":"Air Quality and Health Impacts","score":0.9972000122070312,"subfield":{"id":"https://openalex.org/subfields/2307","display_name":"Health, Toxicology and Mutagenesis"},"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/T10075","display_name":"Atmospheric chemistry and aerosols","score":0.984499990940094,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6879504919052124},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.684196412563324},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.6838301420211792},{"id":"https://openalex.org/keywords/pollution","display_name":"Pollution","score":0.6137378811836243},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5647050142288208},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5219211578369141},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.511873185634613},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4811338484287262},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4735274314880371},{"id":"https://openalex.org/keywords/air-pollution","display_name":"Air pollution","score":0.4494211971759796},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.365726113319397},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.34737128019332886},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.18465033173561096},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.15415337681770325}],"concepts":[{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6879504919052124},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.684196412563324},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6838301420211792},{"id":"https://openalex.org/C521259446","wikidata":"https://www.wikidata.org/wiki/Q58734","display_name":"Pollution","level":2,"score":0.6137378811836243},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5647050142288208},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5219211578369141},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.511873185634613},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4811338484287262},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4735274314880371},{"id":"https://openalex.org/C559116025","wikidata":"https://www.wikidata.org/wiki/Q131123","display_name":"Air pollution","level":2,"score":0.4494211971759796},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.365726113319397},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.34737128019332886},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.18465033173561096},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.15415337681770325},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","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/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3341162.3349327","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3341162.3349327","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers","raw_type":"proceedings-article"},{"id":"mag:3177016253","is_oa":false,"landing_page_url":"https://jglobal.jst.go.jp/en/detail?JGLOBAL_ID=202002244242450039","pdf_url":null,"source":{"id":"https://openalex.org/S4306500161","display_name":"ACM Proceedings","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":"ACM Proceedings","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W178604081","https://openalex.org/W209050710","https://openalex.org/W633564929","https://openalex.org/W647875031","https://openalex.org/W1485009520","https://openalex.org/W1587086928","https://openalex.org/W1992960971","https://openalex.org/W2009331722","https://openalex.org/W2039636725","https://openalex.org/W2064675550","https://openalex.org/W2079204536","https://openalex.org/W2116261113","https://openalex.org/W2128727570","https://openalex.org/W2141039137","https://openalex.org/W2145094598","https://openalex.org/W2156840454","https://openalex.org/W2250554505","https://openalex.org/W2254039850","https://openalex.org/W2388203459","https://openalex.org/W2405774341","https://openalex.org/W2497259451","https://openalex.org/W2498307004","https://openalex.org/W2548430090","https://openalex.org/W2898267065","https://openalex.org/W2997574889"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2159052453","https://openalex.org/W2566616303","https://openalex.org/W3131327266","https://openalex.org/W2669956259","https://openalex.org/W4249005693","https://openalex.org/W3094447531","https://openalex.org/W2388613575","https://openalex.org/W2362463548","https://openalex.org/W3030116098"],"abstract_inverted_index":{"Air":[0],"pollution":[1,14,33,71,105,152],"is":[2,78,119],"a":[3,59,95,122],"global":[4],"health":[5],"threat.":[6],"Nowadays,":[7],"with":[8,82,112],"the":[9,26,41,44,51,68,104],"increasing":[10],"amount":[11],"of":[12,28,53,70],"air":[13],"monitoring":[15],"data":[16,74,125],"from":[17,47,145],"either":[18],"conventional":[19],"official":[20],"stations":[21],"or":[22],"mobile":[23,48],"sensing":[24,49],"systems,":[25],"role":[27],"deep":[29,54,60],"learning":[30,55],"methods":[31],"in":[32,88,127],"map":[34],"recovery":[35],"becomes":[36],"gradually":[37],"apparent.":[38],"To":[39],"address":[40],"challenges":[42],"including":[43],"irregular":[45,86],"sampling":[46,83],"and":[50,73,90,154],"non-interpretability":[52],"models,":[56],"we":[57,93],"proposed":[58],"autoencoder":[61],"framework":[62,77],"based":[63],"inference":[64],"algorithm.":[65],"By":[66],"separating":[67],"process":[69],"generation":[72,106],"sampling,":[75],"this":[76],"able":[79],"to":[80,102,137],"deal":[81],"under":[84],"any":[85],"intervals":[87],"time":[89],"space.":[91],"Also,":[92],"adopt":[94],"convolutional":[96],"long":[97],"short-term":[98],"memory":[99],"(ConvLSTM)":[100],"structure":[101],"model":[103],"after":[107],"revealing":[108],"its":[109,146],"internal":[110],"connections":[111],"an":[113],"atmospheric":[114],"dispersion":[115],"model.":[116],"Our":[117],"algorithm":[118],"evaluated":[120],"over":[121,141],"three-month":[123],"real-world":[124],"collection":[126],"Tianjin,":[128],"China.":[129],"Results":[130],"show":[131],"our":[132],"method":[133],"can":[134],"obtain":[135],"up":[136],"2x":[138],"performance":[139],"improvement":[140],"existing":[142],"methods,":[143],"benefited":[144],"high":[147],"robustness":[148],"against":[149],"different":[150],"background":[151],"level":[153],"accidental":[155],"sensor":[156],"errors.":[157]},"counts_by_year":[{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":3},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}
