{"id":"https://openalex.org/W4399768872","doi":"https://doi.org/10.1109/isoen61239.2024.10556309","title":"Gas Distribution Mapping With Radius-Based, Bi-directional Graph Neural Networks (RABI-GNN)","display_name":"Gas Distribution Mapping With Radius-Based, Bi-directional Graph Neural Networks (RABI-GNN)","publication_year":2024,"publication_date":"2024-05-12","ids":{"openalex":"https://openalex.org/W4399768872","doi":"https://doi.org/10.1109/isoen61239.2024.10556309"},"language":"en","primary_location":{"id":"doi:10.1109/isoen61239.2024.10556309","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isoen61239.2024.10556309","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN)","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/A5082512377","display_name":"Nicolas P. Winkler","orcid":"https://orcid.org/0000-0002-3804-432X"},"institutions":[{"id":"https://openalex.org/I1330165540","display_name":"Federal Institute For Materials Research and Testing","ror":"https://ror.org/03x516a66","country_code":"DE","type":"facility","lineage":["https://openalex.org/I1330165540"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Nicolas P. Winkler","raw_affiliation_strings":["Bundesanstalt für Materialforschung und -prüfung (BAM),Berlin,Germany"],"affiliations":[{"raw_affiliation_string":"Bundesanstalt für Materialforschung und -prüfung (BAM),Berlin,Germany","institution_ids":["https://openalex.org/I1330165540"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089752284","display_name":"Patrick P. Neumann","orcid":"https://orcid.org/0000-0002-3835-3421"},"institutions":[{"id":"https://openalex.org/I1330165540","display_name":"Federal Institute For Materials Research and Testing","ror":"https://ror.org/03x516a66","country_code":"DE","type":"facility","lineage":["https://openalex.org/I1330165540"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Patrick P. Neumann","raw_affiliation_strings":["Bundesanstalt für Materialforschung und -prüfung (BAM),Berlin,Germany"],"affiliations":[{"raw_affiliation_string":"Bundesanstalt für Materialforschung und -prüfung (BAM),Berlin,Germany","institution_ids":["https://openalex.org/I1330165540"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024984249","display_name":"Erik Schaffernicht","orcid":"https://orcid.org/0000-0002-0804-8637"},"institutions":[{"id":"https://openalex.org/I26437253","display_name":"\u00d6rebro University","ror":"https://ror.org/05kytsw45","country_code":"SE","type":"education","lineage":["https://openalex.org/I26437253"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Erik Schaffernicht","raw_affiliation_strings":["Örebro University,Örebro,Sweden"],"affiliations":[{"raw_affiliation_string":"Örebro University,Örebro,Sweden","institution_ids":["https://openalex.org/I26437253"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5088586617","display_name":"Achim J. Lilienthal","orcid":"https://orcid.org/0000-0003-0217-9326"},"institutions":[{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Achim J. Lilienthal","raw_affiliation_strings":["Technische Universität München (TUM),Munich,Germany"],"affiliations":[{"raw_affiliation_string":"Technische Universität München (TUM),Munich,Germany","institution_ids":["https://openalex.org/I62916508"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5082512377"],"corresponding_institution_ids":["https://openalex.org/I1330165540"],"apc_list":null,"apc_paid":null,"fwci":0.196,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.45598055,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"3"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.9980999827384949,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.9980999827384949,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/T11111","display_name":"Spectroscopy and Laser Applications","score":0.9865000247955322,"subfield":{"id":"https://openalex.org/subfields/1607","display_name":"Spectroscopy"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10683","display_name":"Mass Spectrometry Techniques and Applications","score":0.9815999865531921,"subfield":{"id":"https://openalex.org/subfields/1607","display_name":"Spectroscopy"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5733509063720703},{"id":"https://openalex.org/keywords/radius","display_name":"RADIUS","score":0.4987368583679199},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4965866208076477},{"id":"https://openalex.org/keywords/distribution","display_name":"Distribution (mathematics)","score":0.4781278669834137},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.3283596932888031},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.232427179813385},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2192842960357666},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.12411588430404663},{"id":"https://openalex.org/keywords/mathematical-analysis","display_name":"Mathematical analysis","score":0.10374364256858826}],"concepts":[{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5733509063720703},{"id":"https://openalex.org/C178635117","wikidata":"https://www.wikidata.org/wiki/Q747499","display_name":"RADIUS","level":2,"score":0.4987368583679199},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4965866208076477},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.4781278669834137},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.3283596932888031},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.232427179813385},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2192842960357666},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.12411588430404663},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.10374364256858826}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isoen61239.2024.10556309","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isoen61239.2024.10556309","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320338440","display_name":"HORIZON EUROPE Health","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W2653160509","https://openalex.org/W3037343259","https://openalex.org/W3087257704","https://openalex.org/W3158866108","https://openalex.org/W3174686248","https://openalex.org/W3210210168","https://openalex.org/W4286795917","https://openalex.org/W4365796113","https://openalex.org/W4379882551","https://openalex.org/W4383066454","https://openalex.org/W4383200183","https://openalex.org/W6797464607"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2935759653","https://openalex.org/W3105167352","https://openalex.org/W54078636","https://openalex.org/W2954470139","https://openalex.org/W1501425562","https://openalex.org/W2902782467","https://openalex.org/W3084825885","https://openalex.org/W2298861036"],"abstract_inverted_index":{"Gas":[0],"Distribution":[1],"Mapping":[2],"(GDM)":[3],"is":[4],"essential":[5],"in":[6,28,54,116],"monitoring":[7],"hazardous":[8],"environments,":[9],"where":[10],"uneven":[11],"sampling":[12],"and":[13,30,49,72],"spatial":[14],"sparsity":[15],"of":[16,107],"data":[17,114],"present":[18],"significant":[19],"challenges.":[20],"Traditional":[21],"methods":[22],"for":[23,67,124],"GDM":[24,69,127],"often":[25],"fall":[26],"short":[27],"accuracy":[29],"expressiveness.":[31],"Modern":[32],"learning-based":[33,68],"approaches":[34],"employing":[35],"Convolutional":[36],"Neural":[37,64],"Networks":[38,65],"(CNNs)":[39],"require":[40],"regular-sized":[41],"input":[42],"data,":[43],"limiting":[44],"their":[45],"adaptability":[46],"to":[47,112],"irregular":[48,71],"sparse":[50,74],"datasets":[51],"typically":[52],"encountered":[53],"GDM.":[55],"This":[56],"study":[57],"addresses":[58],"these":[59],"shortcomings":[60],"by":[61,109],"showcasing":[62],"Graph":[63],"(GNNs)":[66],"on":[70,85,91],"spatially":[73],"sensor":[75],"data.":[76],"Our":[77],"Radius-Based,":[78],"Bi-Directionally":[79],"connected":[80],"GNN":[81],"(RABI-GNN)":[82],"was":[83],"trained":[84],"a":[86],"synthetic":[87],"gas":[88],"distribution":[89],"dataset":[90],"which":[92],"it":[93,111],"outperforms":[94],"our":[95],"previous":[96],"CNN-based":[97],"model":[98],"while":[99],"overcoming":[100],"its":[101],"constraints.":[102],"We":[103],"demonstrate":[104],"the":[105],"flexibility":[106],"RABI-GNN":[108],"applying":[110],"real-world":[113],"obtained":[115],"an":[117],"industrial":[118],"steel":[119],"factory,":[120],"highlighting":[121],"promising":[122],"opportunities":[123],"more":[125],"accurate":[126],"models.":[128]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}