{"id":"https://openalex.org/W4399251263","doi":"https://doi.org/10.1145/3653804.3656268","title":"Infrared Spectral Deconvolution Algorithm Based on Masked Pre-training Transformer","display_name":"Infrared Spectral Deconvolution Algorithm Based on Masked Pre-training Transformer","publication_year":2024,"publication_date":"2024-01-19","ids":{"openalex":"https://openalex.org/W4399251263","doi":"https://doi.org/10.1145/3653804.3656268"},"language":"en","primary_location":{"id":"doi:10.1145/3653804.3656268","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3653804.3656268","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Conference on Computer Vision and Deep Learning","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":null,"display_name":"Lei Gao","orcid":"https://orcid.org/0009-0001-3399-2953"},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Lei Gao","raw_affiliation_strings":["College of Electronic and Optical Engineering & College of Flexible Electronics (Future Technology), Nanjing University of Posts and Telecommunications, China"],"affiliations":[{"raw_affiliation_string":"College of Electronic and Optical Engineering & College of Flexible Electronics (Future Technology), Nanjing University of Posts and Telecommunications, China","institution_ids":["https://openalex.org/I41198531"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100767155","display_name":"Xiaohong Yan","orcid":"https://orcid.org/0000-0002-9700-9540"},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaohong Yan","raw_affiliation_strings":["College of Electronic and Optical Engineering & College of Flexible Electronics (Future Technology), Nanjing University of Posts and Telecommunications, China"],"affiliations":[{"raw_affiliation_string":"College of Electronic and Optical Engineering & College of Flexible Electronics (Future Technology), Nanjing University of Posts and Telecommunications, China","institution_ids":["https://openalex.org/I41198531"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5052986170","display_name":"Hu Zhu","orcid":"https://orcid.org/0000-0002-5528-8721"},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hu Zhu","raw_affiliation_strings":["School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, China"],"affiliations":[{"raw_affiliation_string":"School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, China","institution_ids":["https://openalex.org/I41198531"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I41198531"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.09758509,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"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/T12389","display_name":"Infrared Target Detection Methodologies","score":0.9969000220298767,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/T12389","display_name":"Infrared Target Detection Methodologies","score":0.9969000220298767,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9934999942779541,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"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/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.9889000058174133,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/deconvolution","display_name":"Deconvolution","score":0.7162947654724121},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6348446607589722},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5400091409683228},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.5133132338523865},{"id":"https://openalex.org/keywords/infrared","display_name":"Infrared","score":0.49021846055984497},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.4777454733848572},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.46253255009651184},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4588371813297272},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.35840725898742676},{"id":"https://openalex.org/keywords/electronic-engineering","display_name":"Electronic engineering","score":0.3204178810119629},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.15118354558944702},{"id":"https://openalex.org/keywords/optics","display_name":"Optics","score":0.11790397763252258}],"concepts":[{"id":"https://openalex.org/C174576160","wikidata":"https://www.wikidata.org/wiki/Q1183700","display_name":"Deconvolution","level":2,"score":0.7162947654724121},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6348446607589722},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5400091409683228},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.5133132338523865},{"id":"https://openalex.org/C158355884","wikidata":"https://www.wikidata.org/wiki/Q11388","display_name":"Infrared","level":2,"score":0.49021846055984497},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.4777454733848572},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.46253255009651184},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4588371813297272},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.35840725898742676},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.3204178810119629},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.15118354558944702},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.11790397763252258},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3653804.3656268","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3653804.3656268","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Conference on Computer Vision and Deep Learning","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.6700000166893005}],"awards":[{"id":"https://openalex.org/G5477581308","display_name":null,"funder_award_id":"62072256","funder_id":"https://openalex.org/F4320323817","funder_display_name":"Universitas Brawijaya"}],"funders":[{"id":"https://openalex.org/F4320323817","display_name":"Universitas Brawijaya","ror":"https://ror.org/01wk3d929"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1956818252","https://openalex.org/W2739748921","https://openalex.org/W2790149307","https://openalex.org/W2790941332","https://openalex.org/W2891432604","https://openalex.org/W2996008575","https://openalex.org/W3017541653","https://openalex.org/W3092404985","https://openalex.org/W3133696297","https://openalex.org/W3194637726","https://openalex.org/W4285794683","https://openalex.org/W4293428643","https://openalex.org/W4367848139","https://openalex.org/W4377143267","https://openalex.org/W4382073739","https://openalex.org/W4390872582","https://openalex.org/W6790690058"],"related_works":["https://openalex.org/W3090782779","https://openalex.org/W4251527294","https://openalex.org/W4250106855","https://openalex.org/W2037261263","https://openalex.org/W3149087629","https://openalex.org/W4231036715","https://openalex.org/W2032074591","https://openalex.org/W1986156575","https://openalex.org/W2751689993","https://openalex.org/W1966302070"],"abstract_inverted_index":{"Nowadays,":[0],"infrared":[1,16,30,165],"spectral":[2,17,48,88,105,127],"analysis":[3],"technology":[4],"has":[5,150],"been":[6],"widely":[7],"used":[8],"in":[9,12,68,79],"various":[10],"fields":[11],"real":[13],"life,":[14],"however,":[15],"signals":[18],"are":[19,53,134],"susceptible":[20],"to":[21,26,40,76,157],"degradation":[22],"and":[23,35,44,51,92,142,154],"contamination":[24],"due":[25],"the":[27,42,73,82,86,93,96,103,108,116,123,137,143,148,160,164],"aging":[28],"of":[29,46,163],"spectrometer":[31],"equipment,":[32],"bandwidth":[33],"overlap,":[34],"random":[36],"noise.":[37],"In":[38],"order":[39],"improve":[41],"quality":[43],"reliability":[45],"IR":[47,80,87,126],"signals,":[49],"denoising":[50],"reconstruction":[52],"important":[54],"preprocessing":[55],"steps.":[56],"To":[57,71],"address":[58],"these":[59],"challenges,":[60],"a":[61],"masked":[62,97],"pre-trained":[63],"Transformer":[64],"model":[65],"is":[66,155],"proposed":[67],"this":[69],"paper.":[70],"train":[72],"model's":[74],"ability":[75],"suppress":[77],"noise":[78],"spectra,":[81],"encoder":[83],"randomly":[84],"masks":[85],"sequences":[89,128],"during":[90],"pre-training,":[91],"decoder":[94],"reconstructs":[95],"inputs,":[98],"which":[99],"can":[100],"greatly":[101],"enhance":[102],"downstream":[104],"predictor":[106],"with":[107,136],"learnt":[109],"hidden":[110],"representations.":[111],"The":[112,132],"masking":[113],"strategy":[114],"makes":[115],"learned":[117],"representation":[118],"more":[119],"robust":[120],"by":[121],"capturing":[122],"dependencies":[124],"between":[125],"for":[129],"feature":[130],"extraction.":[131],"experiments":[133],"compared":[135],"current":[138],"state-of-the-art":[139],"deconvolution":[140,152],"technique,":[141],"experimental":[144],"results":[145],"show":[146],"that":[147],"method":[149],"excellent":[151],"performance":[153],"able":[156],"effectively":[158],"recover":[159],"texture":[161],"details":[162],"spectrum.":[166]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-10T00:00:00"}